



'''simple docstring'''





import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer

import datasets
from datasets import logging


a_						:     int	     =		'\\n\n'

a_						:     Any	     =		'\nPerplexity (PPL) is one of the most common metrics for evaluating language models.\nIt is defined as the exponentiated average negative log-likelihood of a sequence.\n\nFor more information, see https://huggingface.co/docs/transformers/perplexity\n'

a_						:     List[Any]	     =		'\nArgs:\n    model_id (str): model used for calculating Perplexity\n            NOTE: Perplexity can only be calculated for causal language models.\n                    This includes models such as gpt2, causal variations of bert,\n                    causal versions of t5, and more (the full list can be found\n                    in the AutoModelForCausalLM documentation here:\n                    https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )\n\n    input_texts (list of str): input text, each separate text snippet\n        is one list entry.\n    batch_size (int): the batch size to run texts through the model. Defaults to 16.\n    add_start_token (bool): whether to add the start token to the texts,\n        so the perplexity can include the probability of the first word. Defaults to True.\n    device (str): device to run on, defaults to \'cuda\' when available\nReturns:\n    perplexity: dictionary containing the perplexity scores for the texts\n        in the input list, as well as the mean perplexity. If one of the input texts is\n        longer than the max input length of the model, then it is truncated to the\n        max length for the perplexity computation.\nExamples:\n    Example 1:\n        >>> perplexity = datasets.load_metric("perplexity")\n        >>> input_texts = ["lorem ipsum", "Happy Birthday!", "Bienvenue"]\n        >>> results = perplexity.compute(model_id=\'gpt2\',\n        ...                              add_start_token=False,\n        ...                              input_texts=input_texts) # doctest:+ELLIPSIS\n        >>> print(list(results.keys()))\n        [\'perplexities\', \'mean_perplexity\']\n        >>> print(round(results["mean_perplexity"], 2))\n        78.22\n        >>> print(round(results["perplexities"][0], 2))\n        11.11\n\n    Example 2:\n        >>> perplexity = datasets.load_metric("perplexity")\n        >>> input_texts = datasets.load_dataset("wikitext",\n        ...                                     "wikitext-2-raw-v1",\n        ...                                     split="test")["text"][:50] # doctest:+ELLIPSIS\n        [...]\n        >>> input_texts = [s for s in input_texts if s!=\'\']\n        >>> results = perplexity.compute(model_id=\'gpt2\',\n        ...                              input_texts=input_texts) # doctest:+ELLIPSIS\n        >>> print(list(results.keys()))\n        [\'perplexities\', \'mean_perplexity\']\n        >>> print(round(results["mean_perplexity"], 2))\n        60.35\n        >>> print(round(results["perplexities"][0], 2))\n        81.12\n'





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       ,    _KWARGS_DESCRIPTION)
class    lowerCamelCase__  (						datasets.Metric):
     """simple docstring"""



     def 						_a      (self    ):






           '''simple docstring'''



           return datasets.MetricInfo(
               description=_DESCRIPTION				,			citation=_CITATION				,			inputs_description=_KWARGS_DESCRIPTION				,			features=datasets.Features(
                   {
                       "input_texts": datasets.Value("string"    ),
                   }    )				,			reference_urls=["https://huggingface.co/docs/transformers/perplexity"]				,			)







     def 						_a      (self				,			__a				,			__a				,			__a = 16				,			__a = True				,			__a=None    ):






           '''simple docstring'''



           if device is not None:
                 assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
                 if device == "gpu":
                       lowerCamelCase								=		"cuda"
           else:
                 lowerCamelCase								=		"cuda" if torch.cuda.is_available() else "cpu"

           lowerCamelCase								=		AutoModelForCausalLM.from_pretrained(__a    )
           lowerCamelCase								=		model.to(__a    )

           lowerCamelCase								=		AutoTokenizer.from_pretrained(__a    )

           # if batch_size > 1 (which generally leads to padding being required), and
           # if there is not an already assigned pad_token, assign an existing
           # special token to also be the padding token
           if tokenizer.pad_token is None and batch_size > 1:
                 lowerCamelCase								=		list(tokenizer.special_tokens_map_extended.values()    )
                 # check that the model already has at least one special token defined
                 assert (
                     len(__a    ) > 0
                 ), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
                 # assign one of the special tokens to also be the pad token
                 tokenizer.add_special_tokens({"pad_token": existing_special_tokens[0]}    )

           if add_start_token:
                 # leave room for <BOS> token to be added:
                 assert (
                     tokenizer.bos_token is not None
                 ), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
                 lowerCamelCase								=		model.config.max_length - 1
           else:
                 lowerCamelCase								=		model.config.max_length

           lowerCamelCase								=		tokenizer(
               __a				,			add_special_tokens=__a				,			padding=__a				,			truncation=__a				,			max_length=__a				,			return_tensors="pt"				,			return_attention_mask=__a				,			).to(__a    )

           lowerCamelCase								=		encodings["input_ids"]
           lowerCamelCase								=		encodings["attention_mask"]

           # check that each input is long enough:
           if add_start_token:
                 assert torch.all(torch.ge(attn_masks.sum(1    )				,			1    )    ), "Each input text must be at least one token long."
           else:
                 assert torch.all(
                     torch.ge(attn_masks.sum(1    )				,			2    )    ), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."

           lowerCamelCase								=		[]
           lowerCamelCase								=		CrossEntropyLoss(reduction="none"    )

           for start_index in logging.tqdm(range(0				,			len(__a    )				,			__a    )    ):
                 lowerCamelCase								=		min(start_index + batch_size				,			len(__a    )    )
                 lowerCamelCase								=		encoded_texts[start_index:end_index]
                 lowerCamelCase								=		attn_masks[start_index:end_index]

                 if add_start_token:
                       lowerCamelCase								=		torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0    )    ).to(__a    )
                       lowerCamelCase								=		torch.cat([bos_tokens_tensor, encoded_batch]				,			dim=1    )
                       lowerCamelCase								=		torch.cat(
                           [torch.ones(bos_tokens_tensor.size()				,			dtype=torch.intaa    ).to(__a    ), attn_mask]				,			dim=1    )

                 lowerCamelCase								=		encoded_batch

                 with torch.no_grad():
                       lowerCamelCase								=		model(__a				,			attention_mask=__a    ).logits

                 lowerCamelCase								=		out_logits[..., :-1, :].contiguous()
                 lowerCamelCase								=		labels[..., 1:].contiguous()
                 lowerCamelCase								=		attn_mask[..., 1:].contiguous()

                 lowerCamelCase								=		torch.expa(
                     (loss_fct(shift_logits.transpose(1				,			2    )				,			__a    ) * shift_attention_mask_batch).sum(1    )
                     / shift_attention_mask_batch.sum(1    )    )

                 ppls += perplexity_batch.tolist()

           return {"perplexities": ppls, "mean_perplexity": np.mean(__a    )}




import argparse
import os

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
a_						:     Tuple	     =		'src/transformers'
a_						:     Any	     =		'docs/source/en/tasks'

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      with open(UpperCAmelCase__   ,   "r"   ,   encoding="utf-8"   ,   newline="\n" ) as f:
            lowerCamelCase								=		f.readlines()
      # Find the start prompt.
      lowerCamelCase								=		0
      while not lines[start_index].startswith(UpperCAmelCase__ ):
            start_index += 1
      start_index += 1

      lowerCamelCase								=		start_index
      while not lines[end_index].startswith(UpperCAmelCase__ ):
            end_index += 1
      end_index -= 1

      while len(lines[start_index] ) <= 1:
            start_index += 1
      while len(lines[end_index] ) <= 1:
            end_index -= 1
      end_index += 1
      return "".join(lines[start_index:end_index] ), start_index, end_index, lines


# This is to make sure the transformers module imported is the one in the repo.
a_						:     Union[str, Any]	     =		direct_transformers_import(TRANSFORMERS_PATH)

a_						:     List[Any]	     =		{
    'asr.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
    'audio_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
    'language_modeling.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
    'image_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
    'masked_language_modeling.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
    'multiple_choice.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
    'object_detection.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
    'question_answering.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
    'semantic_segmentation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
    'sequence_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
    'summarization.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    'token_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
    'translation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    'video_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
    'document_question_answering.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
    'monocular_depth_estimation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
}

# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
a_						:     Union[str, Any]	     =		{
    'summarization.md': ('nllb',),
    'translation.md': ('nllb',),
}

def     __lowercase(	UpperCAmelCase__ ):


      """simple docstring"""



      lowerCamelCase								=		TASK_GUIDE_TO_MODELS[task_guide]
      lowerCamelCase								=		SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(UpperCAmelCase__   ,   set() )
      lowerCamelCase								=		{
          code: name
          for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
          if (code in model_maping_names or code in special_model_types)
      }
      return ", ".join([F"""[{name}](../model_doc/{code})""" for code, name in model_names.items()] ) + "\n"

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__=False ):


      """simple docstring"""



      lowerCamelCase			,							lowerCamelCase			,							lowerCamelCase			,							lowerCamelCase								=		_find_text_in_file(
          filename=os.path.join(UpperCAmelCase__   ,   UpperCAmelCase__ )   ,   start_prompt="<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->"   ,   end_prompt="<!--End of the generated tip-->"   ,   )

      lowerCamelCase								=		get_model_list_for_task(UpperCAmelCase__ )

      if current_list != new_list:
            if overwrite:
                  with open(os.path.join(UpperCAmelCase__   ,   UpperCAmelCase__ )   ,   "w"   ,   encoding="utf-8"   ,   newline="\n" ) as f:
                        f.writelines(lines[:start_index] + [new_list] + lines[end_index:] )
            else:
                  raise ValueError(
                      F"""The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`"""
                      " to fix this." )


if __name__ == "__main__":
     a_						:     str	     =		argparse.ArgumentParser()
     parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
     a_						:     str	     =		parser.parse_args()

     for task_guide in TASK_GUIDE_TO_MODELS.keys():
          check_model_list_for_task(task_guide, args.fix_and_overwrite)