Tests: detect lines removed from "utils/not_doctested.txt" and doctest ALL...

Tests: detect lines removed from "utils/not_doctested.txt" and doctest ALL generation files (#25763)

docs/source/en/generation_strategies.md

@@ -55,7 +55,7 @@ When you load a model explicitly, you can inspect the generation configuration t
 >>> from transformers import AutoModelForCausalLM
 
 >>> model = AutoModelForCausalLM.from_pretrained("distilgpt2")
->>> model.generation_config
+>>> model.generation_config  # doctest: +IGNORE_RESULT
 GenerationConfig {
     "_from_model_config": true,
     "bos_token_id": 50256,
@@ -77,7 +77,7 @@ producing highly repetitive results.
 You can override any `generation_config` by passing the parameters and their values directly to the [`generate`] method:
 
 ```python
->>> my_model.generate(**inputs, num_beams=4, do_sample=True)
+>>> my_model.generate(**inputs, num_beams=4, do_sample=True)  # doctest: +SKIP
 ```
 
 Even if the default decoding strategy mostly works for your task, you can still tweak a few things. Some of the
@@ -107,11 +107,11 @@ If you would like to share your fine-tuned model with a specific generation conf
 ```python
 >>> from transformers import AutoModelForCausalLM, GenerationConfig
 
->>> model = AutoModelForCausalLM.from_pretrained("my_account/my_model")
+>>> model = AutoModelForCausalLM.from_pretrained("my_account/my_model")  # doctest: +SKIP
 >>> generation_config = GenerationConfig(
 ...     max_new_tokens=50, do_sample=True, top_k=50, eos_token_id=model.config.eos_token_id
 ... )
->>> generation_config.save_pretrained("my_account/my_model", push_to_hub=True)
+>>> generation_config.save_pretrained("my_account/my_model", push_to_hub=True)  # doctest: +SKIP
 ```
 
 You can also store several generation configurations in a single directory, making use of the `config_file_name`
@@ -133,14 +133,15 @@ one for summarization with beam search). You must have the right Hub permissions
 ...     pad_token=model.config.pad_token_id,
 ... )
 
->>> translation_generation_config.save_pretrained("t5-small", "translation_generation_config.json", push_to_hub=True)
+>>> # Tip: add `push_to_hub=True` to push to the Hub
+>>> translation_generation_config.save_pretrained("/tmp", "translation_generation_config.json")
 
 >>> # You could then use the named generation config file to parameterize generation
->>> generation_config = GenerationConfig.from_pretrained("t5-small", "translation_generation_config.json")
+>>> generation_config = GenerationConfig.from_pretrained("/tmp", "translation_generation_config.json")
 >>> inputs = tokenizer("translate English to French: Configuration files are easy to use!", return_tensors="pt")
 >>> outputs = model.generate(**inputs, generation_config=generation_config)
 >>> print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
-['Les fichiers de configuration sont faciles à utiliser !']
+['Les fichiers de configuration sont faciles à utiliser!']
 ```
 
 ## Streaming
@@ -217,10 +218,9 @@ The two main parameters that enable and control the behavior of contrastive sear
 
 >>> outputs = model.generate(**inputs, penalty_alpha=0.6, top_k=4, max_new_tokens=100)
 >>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
-['Hugging Face Company is a family owned and operated business. \
-We pride ourselves on being the best in the business and our customer service is second to none.\
-\n\nIf you have any questions about our products or services, feel free to contact us at any time.\
- We look forward to hearing from you!']
+['Hugging Face Company is a family owned and operated business. We pride ourselves on being the best
+in the business and our customer service is second to none.\n\nIf you have any questions about our
+products or services, feel free to contact us at any time. We look forward to hearing from you!']
 ```
 
 ### Multinomial sampling
@@ -233,7 +233,8 @@ risk of repetition.
 To enable multinomial sampling set `do_sample=True` and `num_beams=1`.
 
 ```python
->>> from transformers import AutoTokenizer, AutoModelForCausalLM
+>>> from transformers import AutoTokenizer, AutoModelForCausalLM, set_seed
+>>> set_seed(0)  # For reproducibility
 
 >>> checkpoint = "gpt2-large"
 >>> tokenizer = AutoTokenizer.from_pretrained(checkpoint)
@@ -244,11 +245,8 @@ To enable multinomial sampling set `do_sample=True` and `num_beams=1`.
 
 >>> outputs = model.generate(**inputs, do_sample=True, num_beams=1, max_new_tokens=100)
 >>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
-['Today was an amazing day because we are now in the final stages of our trip to New York City which was very tough. \
-It is a difficult schedule and a challenging part of the year but still worth it. I have been taking things easier and \
-I feel stronger and more motivated to be out there on their tour. Hopefully, that experience is going to help them with \
-their upcoming events which are currently scheduled in Australia.\n\nWe love that they are here. They want to make a \
-name for themselves and become famous for what they']
+['Today was an amazing day because when you go to the World Cup and you don\'t, or when you don\'t get invited,
+that\'s a terrible feeling."']
 ```
 
 ### Beam-search decoding
@@ -272,7 +270,7 @@ To enable this decoding strategy, specify the `num_beams` (aka number of hypothe
 
 >>> outputs = model.generate(**inputs, num_beams=5, max_new_tokens=50)
 >>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
-['It is astonishing how one can have such a profound impact on the lives of so many people in such a short period of \
+['It is astonishing how one can have such a profound impact on the lives of so many people in such a short period of
 time."\n\nHe added: "I am very proud of the work I have been able to do in the last few years.\n\n"I have']
 ```
 
@@ -282,7 +280,8 @@ As the name implies, this decoding strategy combines beam search with multinomia
 the `num_beams` greater than 1, and set `do_sample=True` to use this decoding strategy.
 
 ```python
->>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+>>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, set_seed
+>>> set_seed(0)  # For reproducibility
 
 >>> prompt = "translate English to German: The house is wonderful."
 >>> checkpoint = "t5-small"
@@ -309,20 +308,22 @@ The diversily penalty ensures the outputs are distinct across groups, and beam s
 >>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
 >>> checkpoint = "google/pegasus-xsum"
->>> prompt = "The Permaculture Design Principles are a set of universal design principles \
->>> that can be applied to any location, climate and culture, and they allow us to design \
->>> the most efficient and sustainable human habitation and food production systems. \
->>> Permaculture is a design system that encompasses a wide variety of disciplines, such \
->>> as ecology, landscape design, environmental science and energy conservation, and the \
->>> Permaculture design principles are drawn from these various disciplines. Each individual \
->>> design principle itself embodies a complete conceptual framework based on sound \
->>> scientific principles. When we bring all these separate  principles together, we can \
->>> create a design system that both looks at whole systems, the parts that these systems \
->>> consist of, and how those parts interact with each other to create a complex, dynamic, \
->>> living system. Each design principle serves as a tool that allows us to integrate all \
->>> the separate parts of a design, referred to as elements, into a functional, synergistic, \
->>> whole system, where the elements harmoniously interact and work together in the most \
->>> efficient way possible."
+>>> prompt = (
+...     "The Permaculture Design Principles are a set of universal design principles "
+...     "that can be applied to any location, climate and culture, and they allow us to design "
+...     "the most efficient and sustainable human habitation and food production systems. "
+...     "Permaculture is a design system that encompasses a wide variety of disciplines, such "
+...     "as ecology, landscape design, environmental science and energy conservation, and the "
+...     "Permaculture design principles are drawn from these various disciplines. Each individual "
+...     "design principle itself embodies a complete conceptual framework based on sound "
+...     "scientific principles. When we bring all these separate  principles together, we can "
+...     "create a design system that both looks at whole systems, the parts that these systems "
+...     "consist of, and how those parts interact with each other to create a complex, dynamic, "
+...     "living system. Each design principle serves as a tool that allows us to integrate all "
+...     "the separate parts of a design, referred to as elements, into a functional, synergistic, "
+...     "whole system, where the elements harmoniously interact and work together in the most "
+...     "efficient way possible."
+... )
 
 >>> tokenizer = AutoTokenizer.from_pretrained(checkpoint)
 >>> inputs = tokenizer(prompt, return_tensors="pt")
@@ -369,7 +370,8 @@ When using assisted decoding with sampling methods, you can use the `temperarure
 just like in multinomial sampling. However, in assisted decoding, reducing the temperature will help improving latency.
 
 ```python
->>> from transformers import AutoModelForCausalLM, AutoTokenizer
+>>> from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed
+>>> set_seed(42)  # For reproducibility
 
 >>> prompt = "Alice and Bob"
 >>> checkpoint = "EleutherAI/pythia-1.4b-deduped"
@@ -382,5 +384,5 @@ just like in multinomial sampling. However, in assisted decoding, reducing the t
 >>> assistant_model = AutoModelForCausalLM.from_pretrained(assistant_checkpoint)
 >>> outputs = model.generate(**inputs, assistant_model=assistant_model, do_sample=True, temperature=0.5)
 >>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
-["Alice and Bob are sitting on the sofa. Alice says, 'I'm going to my room"]
+['Alice and Bob are going to the same party. It is a small party, in a small']
 ```

utils/not_doctested.txt

@@ -14,14 +14,12 @@ docs/source/en/custom_models.md
 docs/source/en/custom_tools.md
 docs/source/en/debugging.md
 docs/source/en/fast_tokenizers.md
-docs/source/en/generation_strategies.md
 docs/source/en/glossary.md
 docs/source/en/hpo_train.md
 docs/source/en/index.md
 docs/source/en/installation.md
 docs/source/en/internal/audio_utils.md
 docs/source/en/internal/file_utils.md
-docs/source/en/internal/generation_utils.md
 docs/source/en/internal/image_processing_utils.md
 docs/source/en/internal/modeling_utils.md
 docs/source/en/internal/pipelines_utils.md
@@ -45,7 +43,6 @@ docs/source/en/main_classes/output.md
 docs/source/en/main_classes/pipelines.md
 docs/source/en/main_classes/processors.md
 docs/source/en/main_classes/quantization.md
-docs/source/en/main_classes/text_generation.md
 docs/source/en/main_classes/tokenizer.md
 docs/source/en/main_classes/trainer.md
 docs/source/en/model_doc/albert.md
@@ -367,16 +364,6 @@ src/transformers/dynamic_module_utils.py
 src/transformers/feature_extraction_sequence_utils.py
 src/transformers/feature_extraction_utils.py
 src/transformers/file_utils.py
-src/transformers/generation/beam_constraints.py
-src/transformers/generation/beam_search.py
-src/transformers/generation/flax_logits_process.py
-src/transformers/generation/flax_utils.py
-src/transformers/generation/stopping_criteria.py
-src/transformers/generation/streamers.py
-src/transformers/generation/tf_logits_process.py
-src/transformers/generation_flax_utils.py
-src/transformers/generation_tf_utils.py
-src/transformers/generation_utils.py
 src/transformers/hf_argparser.py
 src/transformers/hyperparameter_search.py
 src/transformers/image_processing_utils.py
@@ -413,7 +400,6 @@ src/transformers/models/auto/modeling_tf_auto.py
 src/transformers/models/autoformer/configuration_autoformer.py
 src/transformers/models/autoformer/modeling_autoformer.py
 src/transformers/models/bark/convert_suno_to_hf.py
-src/transformers/models/bark/generation_configuration_bark.py
 src/transformers/models/bart/convert_bart_original_pytorch_checkpoint_to_pytorch.py
 src/transformers/models/bart/modeling_flax_bart.py
 src/transformers/models/bart/modeling_tf_bart.py
@@ -925,9 +911,7 @@ src/transformers/pipelines/object_detection.py
 src/transformers/pipelines/pt_utils.py
 src/transformers/pipelines/question_answering.py
 src/transformers/pipelines/table_question_answering.py
-src/transformers/pipelines/text2text_generation.py
 src/transformers/pipelines/text_classification.py
-src/transformers/pipelines/text_generation.py
 src/transformers/pipelines/token_classification.py
 src/transformers/pipelines/video_classification.py
 src/transformers/pipelines/visual_question_answering.py

utils/slow_documentation_tests.txt

+docs/source/en/generation_strategies.md
 docs/source/en/task_summary.md

utils/tests_fetcher.py

@@ -395,6 +395,31 @@ def get_all_doctest_files() -> List[str]:
     return sorted(test_files_to_run)
 
 
+def get_new_doctest_files(repo, base_commit, branching_commit) -> List[str]:
+    """
+    Get the list of files that were removed from "utils/not_doctested.txt", between `base_commit` and
+    `branching_commit`.
+
+    Returns:
+        `List[str]`: List of files that were removed from "utils/not_doctested.txt".
+    """
+    for diff_obj in branching_commit.diff(base_commit):
+        # Ignores all but the "utils/not_doctested.txt" file.
+        if diff_obj.a_path != "utils/not_doctested.txt":
+            continue
+        # Loads the two versions
+        folder = Path(repo.working_dir)
+        with checkout_commit(repo, branching_commit):
+            with open(folder / "utils/not_doctested.txt", "r", encoding="utf-8") as f:
+                old_content = f.read()
+        with open(folder / "utils/not_doctested.txt", "r", encoding="utf-8") as f:
+            new_content = f.read()
+        # Compute the removed lines and return them
+        removed_content = set(old_content.split("\n")) - set(new_content.split("\n"))
+        return sorted(removed_content)
+    return []
+
+
 def get_doctest_files(diff_with_last_commit: bool = False) -> List[str]:
     """
     Return a list of python and Markdown files where doc example have been modified between:
@@ -426,6 +451,10 @@ def get_doctest_files(diff_with_last_commit: bool = False) -> List[str]:
 
     all_test_files_to_run = get_all_doctest_files()
 
+    # Add to the test files to run any removed entry from "utils/not_doctested.txt".
+    new_test_files = get_new_doctest_files(repo, repo.head.commit, repo.refs.main.commit)
+    test_files_to_run = list(set(test_files_to_run + new_test_files))
+
     # Do not run slow doctest tests on CircleCI
     with open("utils/slow_documentation_tests.txt") as fp:
         slow_documentation_tests = set(fp.read().strip().split("\n"))