




import argparse
import os
import re

import packaging.version


_snake_case            = "examples/"
_snake_case            = {
    "examples": (re.compile(r"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
    "init": (re.compile(r"^__version__\s+=\s+\"([^\"]+)\"\s*$", re.MULTILINE), "__version__ = \"VERSION\"\n"),
    "setup": (re.compile(r"^(\s*)version\s*=\s*\"[^\"]+\",", re.MULTILINE), r"\1version=\"VERSION\","),
    "doc": (re.compile(r"^(\s*)release\s*=\s*\"[^\"]+\"$", re.MULTILINE), "release = \"VERSION\"\n"),
}
_snake_case            = {
    "init": "src/transformers/__init__.py",
    "setup": "setup.py",
}
_snake_case            = "README.md"







def        lowerCAmelCase_			(  snake_case_,snake_case_,snake_case_  ):
      with open(snake_case_,"""r""",encoding="""utf-8""",newline="""\n"""  ) as f:
            _A							:  Optional[Any]    =		f.read()
      _A    ,     _A							:  Tuple    =		REPLACE_PATTERNS[pattern]
      _A							:  Union[str, Any]    =		replace.replace("""VERSION""",snake_case_  )
      _A							:  Dict    =		re_pattern.sub(snake_case_,snake_case_  )
      with open(snake_case_,"""w""",encoding="""utf-8""",newline="""\n"""  ) as f:
            f.write(snake_case_  )







def        lowerCAmelCase_			(  snake_case_  ):
      for folder, directories, fnames in os.walk(snake_case_  ):
            # Removing some of the folders with non-actively maintained examples from the walk
            if "research_projects" in directories:
                  directories.remove("""research_projects"""  )
            if "legacy" in directories:
                  directories.remove("""legacy"""  )
            for fname in fnames:
                  if fname.endswith(""".py"""  ):
                        update_version_in_file(os.path.join(snake_case_,snake_case_  ),snake_case_,pattern="""examples"""  )







def        lowerCAmelCase_			(  snake_case_,snake_case_=False  ):
      for pattern, fname in REPLACE_FILES.items():
            update_version_in_file(snake_case_,snake_case_,snake_case_  )
      if not patch:
            update_version_in_examples(snake_case_  )







def        lowerCAmelCase_			(  ):
      _A							:  Any    =		"""🤗 Transformers currently provides the following architectures"""
      _A							:  int    =		"""1. Want to contribute a new model?"""
      with open(snake_case_,"""r""",encoding="""utf-8""",newline="""\n"""  ) as f:
            _A							:  Union[str, Any]    =		f.readlines()

      # Find the start of the list.
      _A							:  Dict    =		0
      while not lines[start_index].startswith(_start_prompt  ):
            start_index += 1
      start_index += 1

      _A							:  Any    =		start_index
      # Update the lines in the model list.
      while not lines[index].startswith(_end_prompt  ):
            if lines[index].startswith("""1."""  ):
                  _A							:  Optional[int]    =		lines[index].replace(
                      """https://huggingface.co/docs/transformers/main/model_doc""","""https://huggingface.co/docs/transformers/model_doc""",)
            index += 1

      with open(snake_case_,"""w""",encoding="""utf-8""",newline="""\n"""  ) as f:
            f.writelines(snake_case_  )







def        lowerCAmelCase_			(  ):
      with open(REPLACE_FILES["""init"""],"""r"""  ) as f:
            _A							:  Dict    =		f.read()
      _A							:  str    =		REPLACE_PATTERNS["""init"""][0].search(snake_case_  ).groups()[0]
      return packaging.version.parse(snake_case_  )







def        lowerCAmelCase_			(  snake_case_=False  ):
      _A							:  Dict    =		get_version()
      if patch and default_version.is_devrelease:
            raise ValueError("""Can't create a patch version from the dev branch, checkout a released version!"""  )
      if default_version.is_devrelease:
            _A							:  Optional[Any]    =		default_version.base_version
      elif patch:
            _A							:  Any    =		f'''{default_version.major}.{default_version.minor}.{default_version.micro + 1}'''
      else:
            _A							:  str    =		f'''{default_version.major}.{default_version.minor + 1}.0'''

      # Now let's ask nicely if that's the right one.
      _A							:  Dict    =		input(f'''Which version are you releasing? [{default_version}]'''  )
      if len(snake_case_  ) == 0:
            _A							:  List[str]    =		default_version

      print(f'''Updating version to {version}.'''  )
      global_version_update(snake_case_,patch=snake_case_  )
      if not patch:
            print("""Cleaning main README, don't forget to run `make fix-copies`."""  )
            clean_main_ref_in_model_list()







def        lowerCAmelCase_			(  ):
      _A							:  List[str]    =		get_version()
      _A							:  Union[str, Any]    =		f'''{current_version.major}.{current_version.minor + 1}.0.dev0'''
      _A							:  List[str]    =		current_version.base_version

      # Check with the user we got that right.
      _A							:  Union[str, Any]    =		input(f'''Which version are we developing now? [{dev_version}]'''  )
      if len(snake_case_  ) == 0:
            _A							:  Union[str, Any]    =		dev_version

      print(f'''Updating version to {version}.'''  )
      global_version_update(snake_case_  )
      print("""Cleaning main README, don't forget to run `make fix-copies`."""  )
      clean_main_ref_in_model_list()


if __name__ == "__main__":
  _snake_case            = argparse.ArgumentParser()
  parser.add_argument("--post_release", action="store_true", help="Whether this is pre or post release.")
  parser.add_argument("--patch", action="store_true", help="Whether or not this is a patch release.")
  _snake_case            = parser.parse_args()
  if not args.post_release:
    pre_release_work(patch=args.patch)
  elif args.patch:
    print("Nothing to do after a patch :-)")
  else:
    post_release_work()







import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer







class 		lowercase							(     tf.keras.layers.Layer	):

     def __init__(						self	,  _a	,  _a	,  _a = None	,  _a = None       )		->							Any:
           super().__init__()
           _A							:  Dict    =		pad_token_id
           _A							:  List[Any]    =		max_length
           _A							:  Optional[int]    =		vocab
           _A							:  Optional[int]    =		merges
           _A							:  Optional[int]    =		BytePairTokenizer(_a	,  _a	,  sequence_length=_a       )

     @classmethod
     def       a__			(						cls	,  _a	,  *_a	,  **_a       )		->							str:
           _A							:  Any    =		[""" """.join(_a       ) for m in tokenizer.bpe_ranks.keys()]
           _A							:  str    =		tokenizer.get_vocab()
           return cls(_a	,  _a	,  *_a	,  **_a       )

     @classmethod
     def       a__			(						cls	,  _a	,  *_a	,  **_a       )		->							List[Any]:
           _A							:  Union[str, Any]    =		GPTaTokenizer.from_pretrained(_a	,  *_a	,  **_a       )
           return cls.from_tokenizer(_a	,  *_a	,  **_a       )

     @classmethod
     def       a__			(						cls	,  _a       )		->							Union[str, Any]:
           return cls(**_a       )

     def       a__			(						self       )		->							Union[str, Any]:
           return {
               "vocab": self.vocab,
               "merges": self.merges,
               "max_length": self.max_length,
               "pad_token_id": self.pad_token_id,
           }



     def       a__			(						self	,  _a	,  _a = None       )		->							int:
           _A							:  Optional[int]    =		self.tf_tokenizer(_a       )
           _A							:  Tuple    =		tf.ones_like(_a       )

           if self.pad_token_id is not None:
                 # pad the tokens up to max length
                 _A							:  Dict    =		max_length if max_length is not None else self.max_length

                 if max_length is not None:
                       _A    ,     _A							:  Dict    =		pad_model_inputs(
                           _a	,  max_seq_length=_a	,  pad_value=self.pad_token_id       )

           return {"attention_mask": attention_mask, "input_ids": input_ids}

