






'''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_					=	'\\n\n'

a_					=	'\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_					=	'\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        __SCREAMING_SNAKE_CASE (     datasets.Metric			):




 def        __magic_name__		( self		:							Optional[int] )		->  int:
       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        __magic_name__		( self		:							Dict   , __lowercase		:							Union[str, Any]   , __lowercase		:							Tuple   , __lowercase		:							int = 16   , __lowercase		:							bool = True   , __lowercase		:							Tuple=None )		->  Dict:
       if device is not None:
             assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
             if device == "gpu":
                   SCREAMING_SNAKE_CASE__       :			Any													='''cuda'''
       else:
             SCREAMING_SNAKE_CASE__       :			List[Any]													='''cuda''' if torch.cuda.is_available() else '''cpu'''

       SCREAMING_SNAKE_CASE__       :			str													=AutoModelForCausalLM.from_pretrained(__lowercase )
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=model.to(__lowercase )

       SCREAMING_SNAKE_CASE__       :			int													=AutoTokenizer.from_pretrained(__lowercase )

       # 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:
             SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=list(tokenizer.special_tokens_map_extended.values() )
             # check that the model already has at least one special token defined
             assert (
                 len(__lowercase ) > 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"
             SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=model.config.max_length - 1
       else:
             SCREAMING_SNAKE_CASE__       :			Optional[int]													=model.config.max_length

       SCREAMING_SNAKE_CASE__       :			Dict													=tokenizer(
           __lowercase   , add_special_tokens=__lowercase   , padding=__lowercase   , truncation=__lowercase   , max_length=__lowercase   , return_tensors='''pt'''   , return_attention_mask=__lowercase   , ).to(__lowercase )

       SCREAMING_SNAKE_CASE__       :			Tuple													=encodings['''input_ids''']
       SCREAMING_SNAKE_CASE__       :			List[str]													=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."

       SCREAMING_SNAKE_CASE__       :			Tuple													=[]
       SCREAMING_SNAKE_CASE__       :			List[Any]													=CrossEntropyLoss(reduction='''none''' )

       for start_index in logging.tqdm(range(0   , len(__lowercase )   , __lowercase ) ):
             SCREAMING_SNAKE_CASE__       :			List[str]													=min(start_index + batch_size   , len(__lowercase ) )
             SCREAMING_SNAKE_CASE__       :			List[str]													=encoded_texts[start_index:end_index]
             SCREAMING_SNAKE_CASE__       :			Tuple													=attn_masks[start_index:end_index]

             if add_start_token:
                   SCREAMING_SNAKE_CASE__       :			int													=torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0 ) ).to(__lowercase )
                   SCREAMING_SNAKE_CASE__       :			Optional[Any]													=torch.cat([bos_tokens_tensor, encoded_batch]   , dim=1 )
                   SCREAMING_SNAKE_CASE__       :			Optional[Any]													=torch.cat(
                       [torch.ones(bos_tokens_tensor.size()   , dtype=torch.intaa ).to(__lowercase ), attn_mask]   , dim=1 )

             SCREAMING_SNAKE_CASE__       :			List[Any]													=encoded_batch

             with torch.no_grad():
                   SCREAMING_SNAKE_CASE__       :			str													=model(__lowercase   , attention_mask=__lowercase ).logits

             SCREAMING_SNAKE_CASE__       :			Dict													=out_logits[..., :-1, :].contiguous()
             SCREAMING_SNAKE_CASE__       :			List[Any]													=labels[..., 1:].contiguous()
             SCREAMING_SNAKE_CASE__       :			List[str]													=attn_mask[..., 1:].contiguous()

             SCREAMING_SNAKE_CASE__       :			Tuple													=torch.expa(
                 (loss_fct(shift_logits.transpose(1   , 2 )   , __lowercase ) * shift_attention_mask_batch).sum(1 )
                 / shift_attention_mask_batch.sum(1 ) )

             ppls += perplexity_batch.tolist()

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







'''simple docstring'''
import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
a_					=	'src/diffusers'

# Matches is_xxx_available()
a_					=	re.compile(R'is\_([a-z_]*)_available\(\)')
# Matches from xxx import bla
a_					=	re.compile(R'\s+from\s+\S*\s+import\s+([^\(\s].*)\n')


a_					=	'\n{0} = None\n'

a_					=	'\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n'


a_					=	'\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n'





def 	_a(					UpperCamelCase__			:  Any							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			Dict													=_re_backend.findall(UpperCamelCase__							)
      if len(UpperCamelCase__							) == 0:
            return None

      return "_and_".join(UpperCamelCase__							)





def 	_a(					):




      '''simple docstring'''






      with open(os.path.join(UpperCamelCase__,		'''__init__.py'''							),		'''r''',		encoding='''utf-8''',		newline='''\n'''							) as f:
            SCREAMING_SNAKE_CASE__       :			List[str]													=f.readlines()

      # Get to the point we do the actual imports for type checking
      SCREAMING_SNAKE_CASE__       :			Optional[int]													=0
      SCREAMING_SNAKE_CASE__       :			List[str]													={}
      # Go through the end of the file
      while line_index < len(UpperCamelCase__							):
            # If the line contains is_backend_available, we grab all objects associated with the `else` block
            SCREAMING_SNAKE_CASE__       :			List[Any]													=find_backend(lines[line_index]							)
            if backend is not None:
                  while not lines[line_index].startswith('''else:'''							):
                        line_index += 1
                  line_index += 1
                  SCREAMING_SNAKE_CASE__       :			List[Any]													=[]
                  # Until we unindent, add backend objects to the list
                  while line_index < len(UpperCamelCase__							) and len(lines[line_index]							) > 1:
                        SCREAMING_SNAKE_CASE__       :			Optional[Any]													=lines[line_index]
                        SCREAMING_SNAKE_CASE__       :			Optional[Any]													=_re_single_line_import.search(UpperCamelCase__							)
                        if single_line_import_search is not None:
                              objects.extend(single_line_import_search.groups()[0].split(''', '''							)							)
                        elif line.startswith(''' ''' * 8							):
                              objects.append(line[8:-2]							)
                        line_index += 1

                  if len(UpperCamelCase__							) > 0:
                        SCREAMING_SNAKE_CASE__       :			Any													=objects
            else:
                  line_index += 1

      return backend_specific_objects





def 	_a(					UpperCamelCase__			:  Union[str, Any],		UpperCamelCase__			:  List[Any]							):




      '''simple docstring'''






      if name.isupper():
            return DUMMY_CONSTANT.format(UpperCamelCase__							)
      elif name.islower():
            return DUMMY_FUNCTION.format(UpperCamelCase__,		UpperCamelCase__							)
      else:
            return DUMMY_CLASS.format(UpperCamelCase__,		UpperCamelCase__							)





def 	_a(					UpperCamelCase__			:  Any=None							):




      '''simple docstring'''






      if backend_specific_objects is None:
            SCREAMING_SNAKE_CASE__       :			int													=read_init()
      # For special correspondence backend to module name as used in the function requires_modulename
      SCREAMING_SNAKE_CASE__       :			Optional[int]													={}

      for backend, objects in backend_specific_objects.items():
            SCREAMING_SNAKE_CASE__       :			Tuple													='''[''' + ''', '''.join(f"\"{b}\"" for b in backend.split('''_and_'''							)							) + ''']'''
            SCREAMING_SNAKE_CASE__       :			List[str]													='''# This file is autogenerated by the command `make fix-copies`, do not edit.\n'''
            dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
            dummy_file += "\n".join([create_dummy_object(UpperCamelCase__,		UpperCamelCase__							) for o in objects]							)
            SCREAMING_SNAKE_CASE__       :			Tuple													=dummy_file

      return dummy_files





def 	_a(					UpperCamelCase__			:  Any=False							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			Tuple													=create_dummy_files()
      # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
      SCREAMING_SNAKE_CASE__       :			List[str]													={'''torch''': '''pt'''}

      # Locate actual dummy modules and read their content.
      SCREAMING_SNAKE_CASE__       :			List[str]													=os.path.join(UpperCamelCase__,		'''utils'''							)
      SCREAMING_SNAKE_CASE__       :			Union[str, Any]													={
          backend: os.path.join(UpperCamelCase__,		f"dummy_{short_names.get(UpperCamelCase__,		UpperCamelCase__							)}_objects.py"							)
          for backend in dummy_files.keys()
      }

      SCREAMING_SNAKE_CASE__       :			str													={}
      for backend, file_path in dummy_file_paths.items():
            if os.path.isfile(UpperCamelCase__							):
                  with open(UpperCamelCase__,		'''r''',		encoding='''utf-8''',		newline='''\n'''							) as f:
                        SCREAMING_SNAKE_CASE__       :			List[Any]													=f.read()
            else:
                  SCREAMING_SNAKE_CASE__       :			int													=''''''

      for backend in dummy_files.keys():
            if dummy_files[backend] != actual_dummies[backend]:
                  if overwrite:
                        print(
                            f"Updating diffusers.utils.dummy_{short_names.get(UpperCamelCase__,		UpperCamelCase__							)}_objects.py as the main "
                            '''__init__ has new objects.'''							)
                        with open(dummy_file_paths[backend],		'''w''',		encoding='''utf-8''',		newline='''\n'''							) as f:
                              f.write(dummy_files[backend]							)
                  else:
                        raise ValueError(
                            '''The main __init__ has objects that are not present in '''
                            f"diffusers.utils.dummy_{short_names.get(UpperCamelCase__,		UpperCamelCase__							)}_objects.py. Run `make fix-copies` "
                            '''to fix this.'''							)


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

  check_dummies(args.fix_and_overwrite)