"""simple docstring"""



import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer


SCREAMING_SNAKE_CASE__:Any		       =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__:List[Any]		       =       {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

SCREAMING_SNAKE_CASE__:Optional[Any]		       =       {
    '''vocab_file''': {
        '''facebook/dpr-ctx_encoder-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt'''
        ),
        '''facebook/dpr-ctx_encoder-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/dpr-ctx_encoder-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json'''
        ),
        '''facebook/dpr-ctx_encoder-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json'''
        ),
    },
}
SCREAMING_SNAKE_CASE__:Union[str, Any]		       =       {
    '''vocab_file''': {
        '''facebook/dpr-question_encoder-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt'''
        ),
        '''facebook/dpr-question_encoder-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/dpr-question_encoder-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json'''
        ),
        '''facebook/dpr-question_encoder-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json'''
        ),
    },
}
SCREAMING_SNAKE_CASE__:Optional[int]		       =       {
    '''vocab_file''': {
        '''facebook/dpr-reader-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt'''
        ),
        '''facebook/dpr-reader-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/dpr-reader-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json'''
        ),
        '''facebook/dpr-reader-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json'''
        ),
    },
}

SCREAMING_SNAKE_CASE__:str		       =       {
    '''facebook/dpr-ctx_encoder-single-nq-base''': 512,
    '''facebook/dpr-ctx_encoder-multiset-base''': 512,
}
SCREAMING_SNAKE_CASE__:Any		       =       {
    '''facebook/dpr-question_encoder-single-nq-base''': 512,
    '''facebook/dpr-question_encoder-multiset-base''': 512,
}
SCREAMING_SNAKE_CASE__:Optional[int]		       =       {
    '''facebook/dpr-reader-single-nq-base''': 512,
    '''facebook/dpr-reader-multiset-base''': 512,
}


SCREAMING_SNAKE_CASE__:List[Any]		       =       {
    '''facebook/dpr-ctx_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-ctx_encoder-multiset-base''': {'''do_lower_case''': True},
}
SCREAMING_SNAKE_CASE__:Tuple		       =       {
    '''facebook/dpr-question_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-question_encoder-multiset-base''': {'''do_lower_case''': True},
}
SCREAMING_SNAKE_CASE__:int		       =       {
    '''facebook/dpr-reader-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-reader-multiset-base''': {'''do_lower_case''': True},
}






class       snake_case__	(    UpperCAmelCase__    ):
 _snake_case    : Tuple				   =		VOCAB_FILES_NAMES
 _snake_case    : int				   =		CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
 _snake_case    : Optional[int]				   =		CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _snake_case    : int				   =		CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION






class       snake_case__	(    UpperCAmelCase__    ):
 _snake_case    : List[Any]				   =		VOCAB_FILES_NAMES
 _snake_case    : Tuple				   =		QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
 _snake_case    : Dict				   =		QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _snake_case    : int				   =		QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
SCREAMING_SNAKE_CASE__:List[str]		       =       collections.namedtuple(
    """DPRSpanPrediction""", ["""span_score""", """relevance_score""", """doc_id""", """start_index""", """end_index""", """text"""]
)

SCREAMING_SNAKE_CASE__:List[Any]		       =       collections.namedtuple("""DPRReaderOutput""", ["""start_logits""", """end_logits""", """relevance_logits"""])


SCREAMING_SNAKE_CASE__:Union[str, Any]		       =       r'''
    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.
    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),
    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`
    with the format:

    ```
    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>
    ```

    Args:
        questions (`str` or `List[str]`):
            The questions to be encoded. You can specify one question for many passages. In this case, the question
            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in
            `titles` or `texts`.
        titles (`str` or `List[str]`):
            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.
        texts (`str` or `List[str]`):
            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.
        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
            Activates and controls padding. Accepts the following values:

            - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence
              if provided).
            - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided.
            - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different
              lengths).
        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
            Activates and controls truncation. Accepts the following values:

            - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to
              the maximum acceptable input length for the model if that argument is not provided. This will truncate
              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch
              of pairs) is provided.
            - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the first
              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the
              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths
              greater than the model maximum admissible input size).
        max_length (`int`, *optional*):
                Controls the maximum length to use by one of the truncation/padding parameters.

                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
                is required by one of the truncation/padding parameters. If the model has no specific maximum input
                length (like XLNet) truncation/padding to a maximum length will be deactivated.
        return_tensors (`str` or [`~utils.TensorType`], *optional*):
                If set, will return tensors instead of list of python integers. Acceptable values are:

                - `\'tf\'`: Return TensorFlow `tf.constant` objects.
                - `\'pt\'`: Return PyTorch `torch.Tensor` objects.
                - `\'np\'`: Return Numpy `np.ndarray` objects.
        return_attention_mask (`bool`, *optional*):
            Whether or not to return the attention mask. If not set, will return the attention mask according to the
            specific tokenizer\'s default, defined by the `return_outputs` attribute.

            [What are attention masks?](../glossary#attention-mask)

    Returns:
        `Dict[str, List[List[int]]]`: A dictionary with the following keys:

        - `input_ids`: List of token ids to be fed to a model.
        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.
    '''






@add_start_docstrings(UpperCAmelCase__    )
class       snake_case__	:


 def __call__(      self  ,						lowerCamelCase  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = False  ,						lowerCamelCase = False  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						**lowerCamelCase  ,						):
        if titles is None and texts is None:
               return super().__call__(
                   SCREAMING_SNAKE_CASE__  ,						padding=SCREAMING_SNAKE_CASE__  ,						truncation=SCREAMING_SNAKE_CASE__  ,						max_length=SCREAMING_SNAKE_CASE__  ,						return_tensors=SCREAMING_SNAKE_CASE__  ,						return_attention_mask=SCREAMING_SNAKE_CASE__  ,						**SCREAMING_SNAKE_CASE__  ,						)
        elif titles is None or texts is None:
               __a	      =  titles if texts is None else texts
               return super().__call__(
                   SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						padding=SCREAMING_SNAKE_CASE__  ,						truncation=SCREAMING_SNAKE_CASE__  ,						max_length=SCREAMING_SNAKE_CASE__  ,						return_tensors=SCREAMING_SNAKE_CASE__  ,						return_attention_mask=SCREAMING_SNAKE_CASE__  ,						**SCREAMING_SNAKE_CASE__  ,						)
        __a	      =  titles if not isinstance(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__		) else [titles]
        __a	      =  texts if not isinstance(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__		) else [texts]
        __a	      =  len(SCREAMING_SNAKE_CASE__		)
        __a	      =  questions if not isinstance(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__		) else [questions] * n_passages
        if len(SCREAMING_SNAKE_CASE__		) != len(SCREAMING_SNAKE_CASE__		):
               raise ValueError(
                   F"There should be as many titles than texts but got {len(SCREAMING_SNAKE_CASE__		)} titles and {len(SCREAMING_SNAKE_CASE__		)} texts."		)
        __a	      =  super().__call__(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						padding=SCREAMING_SNAKE_CASE__  ,						truncation=SCREAMING_SNAKE_CASE__		)["input_ids"]
        __a	      =  super().__call__(SCREAMING_SNAKE_CASE__  ,						add_special_tokens=SCREAMING_SNAKE_CASE__  ,						padding=SCREAMING_SNAKE_CASE__  ,						truncation=SCREAMING_SNAKE_CASE__		)["input_ids"]
        __a	      =  {
            "input_ids": [
                (encoded_question_and_title + encoded_text)[:max_length]
                if max_length is not None and truncation
                else encoded_question_and_title + encoded_text
                for encoded_question_and_title, encoded_text in zip(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__		)
            ]
        }
        if return_attention_mask is not False:
               __a	      =  []
               for input_ids in encoded_inputs["input_ids"]:
                      attention_mask.append([int(input_id != self.pad_token_id		) for input_id in input_ids]		)
               __a	      =  attention_mask
        return self.pad(SCREAMING_SNAKE_CASE__  ,						padding=SCREAMING_SNAKE_CASE__  ,						max_length=SCREAMING_SNAKE_CASE__  ,						return_tensors=SCREAMING_SNAKE_CASE__		)


 def 		a__					(      self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase = 16  ,						lowerCamelCase = 64  ,						lowerCamelCase = 4  ,						):
        __a	      =  reader_input["input_ids"]
        __a      ,					__a      ,					__a	      =  reader_output[:3]
        __a	      =  len(SCREAMING_SNAKE_CASE__		)
        __a	      =  sorted(range(SCREAMING_SNAKE_CASE__		)  ,						reverse=SCREAMING_SNAKE_CASE__  ,						key=relevance_logits.__getitem__		)
        __a	      =  []
        for doc_id in sorted_docs:
               __a	      =  list(input_ids[doc_id]		)
               # assuming question & title information is at the beginning of the sequence
               __a	      =  sequence_ids.index(self.sep_token_id  ,						2		) + 1  # second sep id
               if sequence_ids[-1] == self.pad_token_id:
                      __a	      =  sequence_ids.index(self.pad_token_id		)
               else:
                      __a	      =  len(SCREAMING_SNAKE_CASE__		)

               __a	      =  self._get_best_spans(
                   start_logits=start_logits[doc_id][passage_offset:sequence_len]  ,						end_logits=end_logits[doc_id][passage_offset:sequence_len]  ,						max_answer_length=SCREAMING_SNAKE_CASE__  ,						top_spans=SCREAMING_SNAKE_CASE__  ,						)
               for start_index, end_index in best_spans:
                      start_index += passage_offset
                      end_index += passage_offset
                      nbest_spans_predictions.append(
                          DPRSpanPrediction(
                              span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index]  ,						relevance_score=relevance_logits[doc_id]  ,						doc_id=SCREAMING_SNAKE_CASE__  ,						start_index=SCREAMING_SNAKE_CASE__  ,						end_index=SCREAMING_SNAKE_CASE__  ,						text=self.decode(sequence_ids[start_index : end_index + 1]		)  ,						)		)
               if len(SCREAMING_SNAKE_CASE__		) >= num_spans:
                      break
        return nbest_spans_predictions[:num_spans]






 def 		a__					(      self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						):
        __a	      =  []
        for start_index, start_score in enumerate(SCREAMING_SNAKE_CASE__		):
               for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]		):
                      scores.append(((start_index, start_index + answer_length), start_score + end_score)		)
        __a	      =  sorted(SCREAMING_SNAKE_CASE__  ,						key=lambda lowerCamelCase		: x[1]  ,						reverse=SCREAMING_SNAKE_CASE__		)
        __a	      =  []
        for (start_index, end_index), score in scores:
               if start_index > end_index:
                      raise ValueError(F"Wrong span indices: [{start_index}:{end_index}]"		)
               __a	      =  end_index - start_index + 1
               if length > max_answer_length:
                      raise ValueError(F"Span is too long: {length} > {max_answer_length}"		)
               if any(
                   start_index <= prev_start_index <= prev_end_index <= end_index
                   or prev_start_index <= start_index <= end_index <= prev_end_index
                   for (prev_start_index, prev_end_index) in chosen_span_intervals		):
                      continue
               chosen_span_intervals.append((start_index, end_index)		)

               if len(SCREAMING_SNAKE_CASE__		) == top_spans:
                      break
        return chosen_span_intervals






@add_end_docstrings(UpperCAmelCase__    )
class       snake_case__	(    UpperCAmelCase__,		UpperCAmelCase__    ):
 _snake_case    : Tuple				   =		VOCAB_FILES_NAMES
 _snake_case    : List[str]				   =		READER_PRETRAINED_VOCAB_FILES_MAP
 _snake_case    : Optional[Any]				   =		READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _snake_case    : Tuple				   =		READER_PRETRAINED_INIT_CONFIGURATION
 _snake_case    : Optional[int]				   =		["""input_ids""", """attention_mask"""]

'''simple docstring'''

import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request

from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import version

from .. import __version__
from . import DIFFUSERS_DYNAMIC_MODULE_NAME, HF_MODULES_CACHE, logging


_A			:      List[Any]					      =				(
    '''https://raw.githubusercontent.com/huggingface/diffusers/{revision}/examples/community/{pipeline}.py'''
)


_A			:      List[Any]					      =				logging.get_logger(__name__)  # pylint: disable=invalid-name







def      UpperCamelCase_						(					)					->     Dict:






 '''simple docstring'''

 __lowerCAmelCase											=						"""https://pypi.org/pypi/diffusers/json"""
 __lowerCAmelCase											=						json.loads(request.urlopen(snake_case_      ).read()      )["""releases"""].keys()
 return sorted(snake_case_						,		key=lambda snake_case_      : version.Version(snake_case_      )      )







def      UpperCamelCase_						(					)					->     int:






 '''simple docstring'''

 if HF_MODULES_CACHE in sys.path:
  return

 sys.path.append(snake_case_      )
 os.makedirs(snake_case_						,		exist_ok=snake_case_      )
 __lowerCAmelCase											=						Path(snake_case_      ) / """__init__.py"""
 if not init_path.exists():
  init_path.touch()







def      UpperCamelCase_						(					snake_case_       :       Union[str, os.PathLike]      )					->     Dict:






 '''simple docstring'''

 init_hf_modules()
 __lowerCAmelCase											=						Path(snake_case_      ) / name
 # If the parent module does not exist yet, recursively create it.
 if not dynamic_module_path.parent.exists():
  create_dynamic_module(dynamic_module_path.parent      )
 os.makedirs(snake_case_						,		exist_ok=snake_case_      )
 __lowerCAmelCase											=						dynamic_module_path / """__init__.py"""
 if not init_path.exists():
  init_path.touch()







def      UpperCamelCase_						(					snake_case_       :       int      )					->     str:






 '''simple docstring'''

 with open(snake_case_						,		"""r"""						,		encoding="""utf-8"""      ) as f:
  __lowerCAmelCase											=						f.read()

 # Imports of the form `import .xxx`
 __lowerCAmelCase											=						re.findall("""^\s*import\s+\.(\S+)\s*$"""						,		snake_case_						,		flags=re.MULTILINE      )
 # Imports of the form `from .xxx import yyy`
 relative_imports += re.findall("""^\s*from\s+\.(\S+)\s+import"""						,		snake_case_						,		flags=re.MULTILINE      )
 # Unique-ify
 return list(set(snake_case_      )      )







def      UpperCamelCase_						(					snake_case_       :       int      )					->     Dict:






 '''simple docstring'''

 __lowerCAmelCase											=						False
 __lowerCAmelCase											=						[module_file]
 __lowerCAmelCase											=						[]

 # Let's recurse through all relative imports
 while not no_change:
  __lowerCAmelCase											=						[]
  for f in files_to_check:
   new_imports.extend(get_relative_imports(snake_case_      )      )

  __lowerCAmelCase											=						Path(snake_case_      ).parent
  __lowerCAmelCase											=						[str(module_path / m      ) for m in new_imports]
  __lowerCAmelCase											=						[f for f in new_import_files if f not in all_relative_imports]
  __lowerCAmelCase											=						[f"""{f}.py""" for f in new_import_files]

  __lowerCAmelCase											=						len(snake_case_      ) == 0
  all_relative_imports.extend(snake_case_      )

 return all_relative_imports







def      UpperCamelCase_						(					snake_case_       :       List[Any]      )					->     Optional[Any]:






 '''simple docstring'''

 with open(snake_case_						,		"""r"""						,		encoding="""utf-8"""      ) as f:
  __lowerCAmelCase											=						f.read()

 # Imports of the form `import xxx`
 __lowerCAmelCase											=						re.findall("""^\s*import\s+(\S+)\s*$"""						,		snake_case_						,		flags=re.MULTILINE      )
 # Imports of the form `from xxx import yyy`
 imports += re.findall("""^\s*from\s+(\S+)\s+import"""						,		snake_case_						,		flags=re.MULTILINE      )
 # Only keep the top-level module
 __lowerCAmelCase											=						[imp.split("""."""      )[0] for imp in imports if not imp.startswith("""."""      )]

 # Unique-ify and test we got them all
 __lowerCAmelCase											=						list(set(snake_case_      )      )
 __lowerCAmelCase											=						[]
 for imp in imports:
  try:
   importlib.import_module(snake_case_      )
  except ImportError:
   missing_packages.append(snake_case_      )

 if len(snake_case_      ) > 0:
  raise ImportError(
      """This modeling file requires the following packages that were not found in your environment: """
      f"""{", ".join(snake_case_      )}. Run `pip install {" ".join(snake_case_      )}`"""      )

 return get_relative_imports(snake_case_      )







def      UpperCamelCase_						(					snake_case_       :       Dict						,		snake_case_       :       Optional[int]      )					->     List[str]:






 '''simple docstring'''

 __lowerCAmelCase											=						module_path.replace(os.path.sep						,		"""."""      )
 __lowerCAmelCase											=						importlib.import_module(snake_case_      )

 if class_name is None:
  return find_pipeline_class(snake_case_      )
 return getattr(snake_case_						,		snake_case_      )







def      UpperCamelCase_						(					snake_case_       :       str      )					->     Optional[Any]:






 '''simple docstring'''

 from ..pipelines import DiffusionPipeline

 __lowerCAmelCase											=						dict(inspect.getmembers(snake_case_						,		inspect.isclass      )      )

 __lowerCAmelCase											=						None
 for cls_name, cls in cls_members.items():
  if (
      cls_name != DiffusionPipeline.__name__
      and issubclass(cls						,		snake_case_      )
      and cls.__module__.split("""."""      )[0] != "diffusers"
  ):
   if pipeline_class is not None:
    raise ValueError(
        f"""Multiple classes that inherit from {DiffusionPipeline.__name__} have been found:"""
        f""" {pipeline_class.__name__}, and {cls_name}. Please make sure to define only one in"""
        f""" {loaded_module}."""      )
   __lowerCAmelCase											=						cls

 return pipeline_class







def      UpperCamelCase_						(					snake_case_       :       Union[str, os.PathLike]						,		snake_case_       :       str						,		snake_case_       :       Optional[Union[str, os.PathLike]] = None						,		snake_case_       :       bool = False						,		snake_case_       :       bool = False						,		snake_case_       :       Optional[Dict[str, str]] = None						,		snake_case_       :       Optional[Union[bool, str]] = None						,		snake_case_       :       Optional[str] = None						,		snake_case_       :       bool = False						,		)					->     Any:






 '''simple docstring'''

 __lowerCAmelCase											=						str(snake_case_      )

 __lowerCAmelCase											=						os.path.join(snake_case_						,		snake_case_      )

 if os.path.isfile(snake_case_      ):
  __lowerCAmelCase											=						module_file_or_url
  __lowerCAmelCase											=						"""local"""
 elif pretrained_model_name_or_path.count("""/"""      ) == 0:
  __lowerCAmelCase											=						get_diffusers_versions()
  # cut ".dev0"
  __lowerCAmelCase											=						"""v""" + """.""".join(__version__.split("""."""      )[:3]      )

  # retrieve github version that matches
  if revision is None:
   __lowerCAmelCase											=						latest_version if latest_version[1:] in available_versions else """main"""
   logger.info(f"""Defaulting to latest_version: {revision}."""      )
  elif revision in available_versions:
   __lowerCAmelCase											=						f"""v{revision}"""
  elif revision == "main":
   __lowerCAmelCase											=						revision
  else:
   raise ValueError(
       f"""`custom_revision`: {revision} does not exist. Please make sure to choose one of"""
       f""" {", ".join(available_versions + ["main"]      )}."""      )

  # community pipeline on GitHub
  __lowerCAmelCase											=						COMMUNITY_PIPELINES_URL.format(revision=snake_case_						,		pipeline=snake_case_      )
  try:
   __lowerCAmelCase											=						cached_download(
       snake_case_						,		cache_dir=snake_case_						,		force_download=snake_case_						,		proxies=snake_case_						,		resume_download=snake_case_						,		local_files_only=snake_case_						,		use_auth_token=snake_case_						,		)
   __lowerCAmelCase											=						"""git"""
   __lowerCAmelCase											=						pretrained_model_name_or_path + """.py"""
  except EnvironmentError:
   logger.error(f"""Could not locate the {module_file} inside {pretrained_model_name_or_path}."""      )
   raise
 else:
  try:
   # Load from URL or cache if already cached
   __lowerCAmelCase											=						hf_hub_download(
       snake_case_						,		snake_case_						,		cache_dir=snake_case_						,		force_download=snake_case_						,		proxies=snake_case_						,		resume_download=snake_case_						,		local_files_only=snake_case_						,		use_auth_token=snake_case_						,		)
   __lowerCAmelCase											=						os.path.join("""local"""						,		"""--""".join(pretrained_model_name_or_path.split("""/"""      )      )      )
  except EnvironmentError:
   logger.error(f"""Could not locate the {module_file} inside {pretrained_model_name_or_path}."""      )
   raise

    # Check we have all the requirements in our environment
 __lowerCAmelCase											=						check_imports(snake_case_      )

 # Now we move the module inside our cached dynamic modules.
 __lowerCAmelCase											=						DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
 create_dynamic_module(snake_case_      )
 __lowerCAmelCase											=						Path(snake_case_      ) / full_submodule
 if submodule == "local" or submodule == "git":
  # We always copy local files (we could hash the file to see if there was a change, and give them the name of
  # that hash, to only copy when there is a modification but it seems overkill for now).
  # The only reason we do the copy is to avoid putting too many folders in sys.path.
  shutil.copy(snake_case_						,		submodule_path / module_file      )
  for module_needed in modules_needed:
   __lowerCAmelCase											=						f"""{module_needed}.py"""
   shutil.copy(os.path.join(snake_case_						,		snake_case_      )						,		submodule_path / module_needed      )
 else:
  # Get the commit hash
  # TODO: we will get this info in the etag soon, so retrieve it from there and not here.
  if isinstance(snake_case_						,		snake_case_      ):
   __lowerCAmelCase											=						use_auth_token
  elif use_auth_token is True:
   __lowerCAmelCase											=						HfFolder.get_token()
  else:
   __lowerCAmelCase											=						None

  __lowerCAmelCase											=						model_info(snake_case_						,		revision=snake_case_						,		token=snake_case_      ).sha

  # The module file will end up being placed in a subfolder with the git hash of the repo. This way we get the
  # benefit of versioning.
  __lowerCAmelCase											=						submodule_path / commit_hash
  __lowerCAmelCase											=						full_submodule + os.path.sep + commit_hash
  create_dynamic_module(snake_case_      )

  if not (submodule_path / module_file).exists():
   shutil.copy(snake_case_						,		submodule_path / module_file      )
  # Make sure we also have every file with relative
  for module_needed in modules_needed:
   if not (submodule_path / module_needed).exists():
    get_cached_module_file(
        snake_case_						,		f"""{module_needed}.py"""						,		cache_dir=snake_case_						,		force_download=snake_case_						,		resume_download=snake_case_						,		proxies=snake_case_						,		use_auth_token=snake_case_						,		revision=snake_case_						,		local_files_only=snake_case_						,		)
 return os.path.join(snake_case_						,		snake_case_      )







def      UpperCamelCase_						(					snake_case_       :       Union[str, os.PathLike]						,		snake_case_       :       str						,		snake_case_       :       Optional[str] = None						,		snake_case_       :       Optional[Union[str, os.PathLike]] = None						,		snake_case_       :       bool = False						,		snake_case_       :       bool = False						,		snake_case_       :       Optional[Dict[str, str]] = None						,		snake_case_       :       Optional[Union[bool, str]] = None						,		snake_case_       :       Optional[str] = None						,		snake_case_       :       bool = False						,		**snake_case_       :       Dict						,		)					->     Tuple:






 '''simple docstring'''

 __lowerCAmelCase											=						get_cached_module_file(
     snake_case_						,		snake_case_						,		cache_dir=snake_case_						,		force_download=snake_case_						,		resume_download=snake_case_						,		proxies=snake_case_						,		use_auth_token=snake_case_						,		revision=snake_case_						,		local_files_only=snake_case_						,		)
 return get_class_in_module(snake_case_						,		final_module.replace(""".py"""						,		""""""      )      )






