[BIG] pytorch-transformers => transformers

pytorch_transformers/tests/modeling_auto_test.py → transformers/tests/modeling_auto_test.py

@@ -21,15 +21,15 @@ import shutil
 import pytest
 import logging
 
-from pytorch_transformers import is_torch_available
+from transformers import is_torch_available
 
 if is_torch_available():
-    from pytorch_transformers import (AutoConfig, BertConfig,
+    from transformers import (AutoConfig, BertConfig,
                                     AutoModel, BertModel,
                                     AutoModelWithLMHead, BertForMaskedLM,
                                     AutoModelForSequenceClassification, BertForSequenceClassification,
                                     AutoModelForQuestionAnswering, BertForQuestionAnswering)
-    from pytorch_transformers.modeling_bert import BERT_PRETRAINED_MODEL_ARCHIVE_MAP
+    from transformers.modeling_bert import BERT_PRETRAINED_MODEL_ARCHIVE_MAP
 
     from .modeling_common_test import (CommonTestCases, ids_tensor)
     from .configuration_common_test import ConfigTester

pytorch_transformers/tests/modeling_bert_test.py → transformers/tests/modeling_bert_test.py

@@ -20,17 +20,17 @@ import unittest
 import shutil
 import pytest
 
-from pytorch_transformers import is_torch_available
+from transformers import is_torch_available
 
 from .modeling_common_test import (CommonTestCases, ids_tensor)
 from .configuration_common_test import ConfigTester
 
 if is_torch_available():
-    from pytorch_transformers import (BertConfig, BertModel, BertForMaskedLM,
+    from transformers import (BertConfig, BertModel, BertForMaskedLM,
                                         BertForNextSentencePrediction, BertForPreTraining,
                                         BertForQuestionAnswering, BertForSequenceClassification,
                                         BertForTokenClassification, BertForMultipleChoice)
-    from pytorch_transformers.modeling_bert import BERT_PRETRAINED_MODEL_ARCHIVE_MAP
+    from transformers.modeling_bert import BERT_PRETRAINED_MODEL_ARCHIVE_MAP
 else:
     pytestmark = pytest.mark.skip("Require Torch")
 
@@ -310,7 +310,7 @@ class BertModelTest(CommonTestCases.CommonModelTester):
 
     @pytest.mark.slow
     def test_model_from_pretrained(self):
-        cache_dir = "/tmp/pytorch_transformers_test/"
+        cache_dir = "/tmp/transformers_test/"
         for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
             model = BertModel.from_pretrained(model_name, cache_dir=cache_dir)
             shutil.rmtree(cache_dir)

pytorch_transformers/tests/modeling_common_test.py → transformers/tests/modeling_common_test.py

@@ -27,12 +27,12 @@ import unittest
 import logging
 import pytest
 
-from pytorch_transformers import is_torch_available
+from transformers import is_torch_available
 
 if is_torch_available():
     import torch
 
-    from pytorch_transformers import (PretrainedConfig, PreTrainedModel,
+    from transformers import (PretrainedConfig, PreTrainedModel,
                                     BertModel, BertConfig, BERT_PRETRAINED_MODEL_ARCHIVE_MAP,
                                     GPT2LMHeadModel, GPT2Config, GPT2_PRETRAINED_MODEL_ARCHIVE_MAP)
 else:
@@ -621,7 +621,7 @@ class CommonTestCases:
                 [[], []])
 
         def create_and_check_model_from_pretrained(self):
-            cache_dir = "/tmp/pytorch_transformers_test/"
+            cache_dir = "/tmp/transformers_test/"
             for model_name in list(self.base_model_class.pretrained_model_archive_map.keys())[:1]:
                 model = self.base_model_class.from_pretrained(model_name, cache_dir=cache_dir)
                 shutil.rmtree(cache_dir)

pytorch_transformers/tests/modeling_distilbert_test.py → transformers/tests/modeling_distilbert_test.py

@@ -19,10 +19,10 @@ from __future__ import print_function
 import unittest
 import pytest
 
-from pytorch_transformers import is_torch_available
+from transformers import is_torch_available
 
 if is_torch_available():
-    from pytorch_transformers import (DistilBertConfig, DistilBertModel, DistilBertForMaskedLM,
+    from transformers import (DistilBertConfig, DistilBertModel, DistilBertForMaskedLM,
                                     DistilBertForQuestionAnswering, DistilBertForSequenceClassification)
 else:
     pytestmark = pytest.mark.skip("Require Torch")
@@ -211,7 +211,7 @@ class DistilBertModelTest(CommonTestCases.CommonModelTester):
 
     # @pytest.mark.slow
     # def test_model_from_pretrained(self):
-    #     cache_dir = "/tmp/pytorch_transformers_test/"
+    #     cache_dir = "/tmp/transformers_test/"
     #     for model_name in list(DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
     #         model = DistilBertModel.from_pretrained(model_name, cache_dir=cache_dir)
     #         shutil.rmtree(cache_dir)

pytorch_transformers/tests/modeling_gpt2_test.py → transformers/tests/modeling_gpt2_test.py

@@ -20,10 +20,10 @@ import unittest
 import pytest
 import shutil
 
-from pytorch_transformers import is_torch_available
+from transformers import is_torch_available
 
 if is_torch_available():
-    from pytorch_transformers import (GPT2Config, GPT2Model, GPT2_PRETRAINED_MODEL_ARCHIVE_MAP,
+    from transformers import (GPT2Config, GPT2Model, GPT2_PRETRAINED_MODEL_ARCHIVE_MAP,
                                     GPT2LMHeadModel, GPT2DoubleHeadsModel)
 else:
     pytestmark = pytest.mark.skip("Require Torch")
@@ -237,7 +237,7 @@ class GPT2ModelTest(CommonTestCases.CommonModelTester):
 
     @pytest.mark.slow
     def test_model_from_pretrained(self):
-        cache_dir = "/tmp/pytorch_transformers_test/"
+        cache_dir = "/tmp/transformers_test/"
         for model_name in list(GPT2_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
             model = GPT2Model.from_pretrained(model_name, cache_dir=cache_dir)
             shutil.rmtree(cache_dir)

pytorch_transformers/tests/modeling_openai_test.py → transformers/tests/modeling_openai_test.py

@@ -20,10 +20,10 @@ import unittest
 import pytest
 import shutil
 
-from pytorch_transformers import is_torch_available
+from transformers import is_torch_available
 
 if is_torch_available():
-    from pytorch_transformers import (OpenAIGPTConfig, OpenAIGPTModel, OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP,
+    from transformers import (OpenAIGPTConfig, OpenAIGPTModel, OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP,
                                     OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel)
 else:
     pytestmark = pytest.mark.skip("Require Torch")
@@ -205,7 +205,7 @@ class OpenAIGPTModelTest(CommonTestCases.CommonModelTester):
 
     @pytest.mark.slow
     def test_model_from_pretrained(self):
-        cache_dir = "/tmp/pytorch_transformers_test/"
+        cache_dir = "/tmp/transformers_test/"
         for model_name in list(OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
             model = OpenAIGPTModel.from_pretrained(model_name, cache_dir=cache_dir)
             shutil.rmtree(cache_dir)

pytorch_transformers/tests/modeling_roberta_test.py → transformers/tests/modeling_roberta_test.py

@@ -20,12 +20,12 @@ import unittest
 import shutil
 import pytest
 
-from pytorch_transformers import is_torch_available
+from transformers import is_torch_available
 
 if is_torch_available():
     import torch
-    from pytorch_transformers import (RobertaConfig, RobertaModel, RobertaForMaskedLM, RobertaForSequenceClassification)
-    from pytorch_transformers.modeling_roberta import ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP
+    from transformers import (RobertaConfig, RobertaModel, RobertaForMaskedLM, RobertaForSequenceClassification)
+    from transformers.modeling_roberta import ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP
 else:
     pytestmark = pytest.mark.skip("Require Torch")
 
@@ -180,7 +180,7 @@ class RobertaModelTest(CommonTestCases.CommonModelTester):
 
     @pytest.mark.slow
     def test_model_from_pretrained(self):
-        cache_dir = "/tmp/pytorch_transformers_test/"
+        cache_dir = "/tmp/transformers_test/"
         for model_name in list(ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
             model = RobertaModel.from_pretrained(model_name, cache_dir=cache_dir)
             shutil.rmtree(cache_dir)

pytorch_transformers/tests/modeling_tf_auto_test.py → transformers/tests/modeling_tf_auto_test.py

@@ -21,15 +21,15 @@ import shutil
 import pytest
 import logging
 
-from pytorch_transformers import is_tf_available
+from transformers import is_tf_available
 
 if is_tf_available():
-    from pytorch_transformers import (AutoConfig, BertConfig,
+    from transformers import (AutoConfig, BertConfig,
                                       TFAutoModel, TFBertModel,
                                       TFAutoModelWithLMHead, TFBertForMaskedLM,
                                       TFAutoModelForSequenceClassification, TFBertForSequenceClassification,
                                       TFAutoModelForQuestionAnswering, TFBertForQuestionAnswering)
-    from pytorch_transformers.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP
+    from transformers.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP
 
     from .modeling_common_test import (CommonTestCases, ids_tensor)
     from .configuration_common_test import ConfigTester

pytorch_transformers/tests/modeling_tf_bert_test.py → transformers/tests/modeling_tf_bert_test.py

@@ -24,11 +24,11 @@ import sys
 from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor)
 from .configuration_common_test import ConfigTester
 
-from pytorch_transformers import BertConfig, is_tf_available
+from transformers import BertConfig, is_tf_available
 
 if is_tf_available():
     import tensorflow as tf
-    from pytorch_transformers.modeling_tf_bert import (TFBertModel, TFBertForMaskedLM,
+    from transformers.modeling_tf_bert import (TFBertModel, TFBertForMaskedLM,
                                                        TFBertForNextSentencePrediction,
                                                        TFBertForPreTraining,
                                                        TFBertForSequenceClassification,
@@ -315,7 +315,7 @@ class TFBertModelTest(TFCommonTestCases.TFCommonModelTester):
 
     @pytest.mark.slow
     def test_model_from_pretrained(self):
-        cache_dir = "/tmp/pytorch_transformers_test/"
+        cache_dir = "/tmp/transformers_test/"
         # for model_name in list(TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
         for model_name in ['bert-base-uncased']:
             model = TFBertModel.from_pretrained(model_name, cache_dir=cache_dir)

pytorch_transformers/tests/modeling_tf_common_test.py → transformers/tests/modeling_tf_common_test.py

@@ -26,13 +26,13 @@ import uuid
 import pytest
 import sys
 
-from pytorch_transformers import is_tf_available, is_torch_available
+from transformers import is_tf_available, is_torch_available
 
 if is_tf_available():
     import tensorflow as tf
     import numpy as np
-    from pytorch_transformers import TFPreTrainedModel
-    # from pytorch_transformers.modeling_bert import BertModel, BertConfig, BERT_PRETRAINED_MODEL_ARCHIVE_MAP
+    from transformers import TFPreTrainedModel
+    # from transformers.modeling_bert import BertModel, BertConfig, BERT_PRETRAINED_MODEL_ARCHIVE_MAP
 else:
     pytestmark = pytest.mark.skip("Require TensorFlow")
 
@@ -71,19 +71,19 @@ class TFCommonTestCases:
             if not is_torch_available():
                 return
 
-            import pytorch_transformers
+            import transformers
 
             config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
 
             for model_class in self.all_model_classes:
                 pt_model_class_name = model_class.__name__[2:]  # Skip the "TF" at the beggining
-                pt_model_class = getattr(pytorch_transformers, pt_model_class_name)
+                pt_model_class = getattr(transformers, pt_model_class_name)
 
                 tf_model = model_class(config)
                 pt_model = pt_model_class(config)
 
-                tf_model = pytorch_transformers.load_pytorch_model_in_tf2_model(tf_model, pt_model, tf_inputs=inputs_dict)
-                pt_model = pytorch_transformers.load_tf2_model_in_pytorch_model(pt_model, tf_model)
+                tf_model = transformers.load_pytorch_model_in_tf2_model(tf_model, pt_model, tf_inputs=inputs_dict)
+                pt_model = transformers.load_tf2_model_in_pytorch_model(pt_model, tf_model)
 
 
         def test_keyword_and_dict_args(self):

pytorch_transformers/tests/modeling_tf_distilbert_test.py → transformers/tests/modeling_tf_distilbert_test.py

@@ -22,11 +22,11 @@ import pytest
 from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor)
 from .configuration_common_test import ConfigTester
 
-from pytorch_transformers import DistilBertConfig, is_tf_available
+from transformers import DistilBertConfig, is_tf_available
 
 if is_tf_available():
     import tensorflow as tf
-    from pytorch_transformers.modeling_tf_distilbert import (TFDistilBertModel,
+    from transformers.modeling_tf_distilbert import (TFDistilBertModel,
                                                              TFDistilBertForMaskedLM,
                                                              TFDistilBertForQuestionAnswering,
                                                              TFDistilBertForSequenceClassification)
@@ -212,7 +212,7 @@ class TFDistilBertModelTest(TFCommonTestCases.TFCommonModelTester):
 
     # @pytest.mark.slow
     # def test_model_from_pretrained(self):
-    #     cache_dir = "/tmp/pytorch_transformers_test/"
+    #     cache_dir = "/tmp/transformers_test/"
     #     for model_name in list(DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
     #         model = DistilBertModel.from_pretrained(model_name, cache_dir=cache_dir)
     #         shutil.rmtree(cache_dir)

pytorch_transformers/tests/modeling_tf_gpt2_test.py → transformers/tests/modeling_tf_gpt2_test.py

@@ -24,11 +24,11 @@ import sys
 from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor)
 from .configuration_common_test import ConfigTester
 
-from pytorch_transformers import GPT2Config, is_tf_available
+from transformers import GPT2Config, is_tf_available
 
 if is_tf_available():
     import tensorflow as tf
-    from pytorch_transformers.modeling_tf_gpt2 import (TFGPT2Model, TFGPT2LMHeadModel,
+    from transformers.modeling_tf_gpt2 import (TFGPT2Model, TFGPT2LMHeadModel,
                                                        TFGPT2DoubleHeadsModel,
                                                        TF_GPT2_PRETRAINED_MODEL_ARCHIVE_MAP)
 else:
@@ -221,7 +221,7 @@ class TFGPT2ModelTest(TFCommonTestCases.TFCommonModelTester):
 
     @pytest.mark.slow
     def test_model_from_pretrained(self):
-        cache_dir = "/tmp/pytorch_transformers_test/"
+        cache_dir = "/tmp/transformers_test/"
         for model_name in list(TF_gpt2_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
             model = TFGPT2Model.from_pretrained(model_name, cache_dir=cache_dir)
             shutil.rmtree(cache_dir)

pytorch_transformers/tests/modeling_tf_openai_gpt_test.py → transformers/tests/modeling_tf_openai_gpt_test.py

@@ -24,11 +24,11 @@ import sys
 from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor)
 from .configuration_common_test import ConfigTester
 
-from pytorch_transformers import OpenAIGPTConfig, is_tf_available
+from transformers import OpenAIGPTConfig, is_tf_available
 
 if is_tf_available():
     import tensorflow as tf
-    from pytorch_transformers.modeling_tf_openai import (TFOpenAIGPTModel, TFOpenAIGPTLMHeadModel,
+    from transformers.modeling_tf_openai import (TFOpenAIGPTModel, TFOpenAIGPTLMHeadModel,
                                                          TFOpenAIGPTDoubleHeadsModel,
                                                          TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP)
 else:
@@ -220,7 +220,7 @@ class TFOpenAIGPTModelTest(TFCommonTestCases.TFCommonModelTester):
 
     @pytest.mark.slow
     def test_model_from_pretrained(self):
-        cache_dir = "/tmp/pytorch_transformers_test/"
+        cache_dir = "/tmp/transformers_test/"
         for model_name in list(TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
             model = TFOpenAIGPTModel.from_pretrained(model_name, cache_dir=cache_dir)
             shutil.rmtree(cache_dir)

pytorch_transformers/tests/modeling_tf_roberta_test.py → transformers/tests/modeling_tf_roberta_test.py

@@ -23,12 +23,12 @@ import pytest
 from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor)
 from .configuration_common_test import ConfigTester
 
-from pytorch_transformers import RobertaConfig, is_tf_available
+from transformers import RobertaConfig, is_tf_available
 
 if is_tf_available():
     import tensorflow as tf
     import numpy
-    from pytorch_transformers.modeling_tf_roberta import (TFRobertaModel, TFRobertaForMaskedLM,
+    from transformers.modeling_tf_roberta import (TFRobertaModel, TFRobertaForMaskedLM,
                                                           TFRobertaForSequenceClassification,
                                                           TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP)
 else:
@@ -178,7 +178,7 @@ class TFRobertaModelTest(TFCommonTestCases.TFCommonModelTester):
 
     @pytest.mark.slow
     def test_model_from_pretrained(self):
-        cache_dir = "/tmp/pytorch_transformers_test/"
+        cache_dir = "/tmp/transformers_test/"
         for model_name in list(TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
             model = TFRobertaModel.from_pretrained(model_name, cache_dir=cache_dir)
             shutil.rmtree(cache_dir)