Fix implementation

lm_eval/models/deepsparse.py

-import torch
+from typing import List, Optional, Tuple, Union
+from tqdm import tqdm
+import random
+
 import deepsparse
-from typing import Optional, Union
+
+from lm_eval import utils
 from lm_eval.base import BaseLM
 
 
 class DeepSparseLM(BaseLM):
-
     _DEFAULT_MAX_LENGTH = 2048
 
     def __init__(
@@ -23,7 +26,7 @@ class DeepSparseLM(BaseLM):
         self.model = deepsparse.Pipeline.create(
             task="text-generation",
             model_path=pretrained,
-            sequence_length=max_length or _DEFAULT_MAX_LENGTH,
+            sequence_length=max_length or self._DEFAULT_MAX_LENGTH,
             trust_remote_code=trust_remote_code,
             batch_size=batch_size,
         )
@@ -36,8 +39,11 @@ class DeepSparseLM(BaseLM):
         self._max_gen_toks = max_gen_toks
 
     @property
-    def eot_token_id(self):
-        # we use EOT because end of *text* is more accurate for what we're doing than end of *sentence*
+    def eot_token(self) -> str:
+        return self.tokenizer.eos_token
+
+    @property
+    def eot_token_id(self) -> int:
         return self.tokenizer.eos_token_id
 
     @property
@@ -125,6 +131,8 @@ class DeepSparseLM(BaseLM):
                 do_sample=False,
             )
 
+            responses = responses if type(responses) is list else [responses]
+
             for response in responses:
                 response = response.generations[0].text
                 # Ensure the generated responses do not contain the stop sequences.
@@ -136,3 +144,19 @@ class DeepSparseLM(BaseLM):
 
         return reorder.get_original(results)
 
+    def loglikelihood(self, requests):
+        raise NotImplementedError()
+
+    def loglikelihood_rolling(self, requests):
+        raise NotImplementedError()
+
+    def _loglikelihood_tokens(self, requests, disable_tqdm=False):
+        raise NotImplementedError("No support for logits.")
+
+    def _model_call(self, inps):
+        # Isn't used because we override _loglikelihood_tokens
+        raise NotImplementedError()
+
+    def _model_generate(self, context, max_length, eos_token_id):
+        # Isn't used because we override greedy_until
+        raise NotImplementedError()