






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__      (  lowercase    ,  lowercase    ,  lowercase					):

  """simple docstring"""






  with open(lowercase    ,  "r"    ,  encoding="utf-8"    ,  newline="\n"					) as f:
    SCREAMING_SNAKE_CASE							:   str						    =     f.read()
  SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   List[str]						    =     REPLACE_PATTERNS[pattern]
  SCREAMING_SNAKE_CASE							:   List[str]						    =     replace.replace("VERSION"    ,  lowercase					)
  SCREAMING_SNAKE_CASE							:   Any						    =     re_pattern.sub(lowercase    ,  lowercase					)
  with open(lowercase    ,  "w"    ,  encoding="utf-8"    ,  newline="\n"					) as f:
    f.write(lowercase					)


def 	lowerCamelCase__      (  lowercase					):

  """simple docstring"""






  for folder, directories, fnames in os.walk(lowercase					):
    # 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(lowercase    ,  lowercase					)    ,  lowercase    ,  pattern="examples"					)


def 	lowerCamelCase__      (  lowercase    ,  lowercase=False					):

  """simple docstring"""






  for pattern, fname in REPLACE_FILES.items():
    update_version_in_file(lowercase    ,  lowercase    ,  lowercase					)
  if not patch:
    update_version_in_examples(lowercase					)


def 	lowerCamelCase__      (  ):

  """simple docstring"""






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

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

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

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


def 	lowerCamelCase__      (  ):

  """simple docstring"""






  with open(REPLACE_FILES["init"]    ,  "r"					) as f:
    SCREAMING_SNAKE_CASE							:   int						    =     f.read()
  SCREAMING_SNAKE_CASE							:   str						    =     REPLACE_PATTERNS["init"][0].search(lowercase					).groups()[0]
  return packaging.version.parse(lowercase					)


def 	lowerCamelCase__      (  lowercase=False					):

  """simple docstring"""






  SCREAMING_SNAKE_CASE							:   List[str]						    =     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:
    SCREAMING_SNAKE_CASE							:   Dict						    =     default_version.base_version
  elif patch:
    SCREAMING_SNAKE_CASE							:   str						    =     F'''{default_version.major}.{default_version.minor}.{default_version.micro + 1}'''
  else:
    SCREAMING_SNAKE_CASE							:   str						    =     F'''{default_version.major}.{default_version.minor + 1}.0'''

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

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


def 	lowerCamelCase__      (  ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE							:   List[Any]						    =     get_version()
  SCREAMING_SNAKE_CASE							:   Dict						    =     F'''{current_version.major}.{current_version.minor + 1}.0.dev0'''
  SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     current_version.base_version

  # Check with the user we got that right.
  SCREAMING_SNAKE_CASE							:   List[Any]						    =     input(F'''Which version are we developing now? [{dev_version}]'''					)
  if len(lowercase					) == 0:
    SCREAMING_SNAKE_CASE							:   Optional[int]						    =     dev_version

  print(F'''Updating version to {version}.'''					)
  global_version_update(lowercase					)
  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()










"""simple docstring"""

import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class  lowerCamelCase__    (		_a      , unittest.TestCase     ):
      a			:  Union[str, Any]           =		BarthezTokenizer
      a			:  Any           =		BarthezTokenizerFast
      a			:  Tuple           =		True
      a			:  List[Any]           =		True
      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]				):






       '''simple docstring'''


       super().setUp()

       __lowercase						=						BarthezTokenizerFast.from_pretrained("""moussaKam/mbarthez"""				)
       tokenizer.save_pretrained(self.tmpdirname				)
       tokenizer.save_pretrained(self.tmpdirname   ,							legacy_format=A_				)
       __lowercase						=						tokenizer
      def SCREAMING_SNAKE_CASE_						(		self						:						Tuple				):






       '''simple docstring'''


       __lowercase						=						"""<pad>"""
       __lowercase						=						1

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(A_				)   ,							A_				)
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(A_				)   ,							A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]				):






       '''simple docstring'''


       __lowercase						=						list(self.get_tokenizer().get_vocab().keys()				)

       self.assertEqual(vocab_keys[0]   ,							"""<s>"""				)
       self.assertEqual(vocab_keys[1]   ,							"""<pad>"""				)
       self.assertEqual(vocab_keys[-1]   ,							"""<mask>"""				)
       self.assertEqual(len(A_				)   ,							1_0_1_1_2_2				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Union[str, Any]				):






       '''simple docstring'''


       self.assertEqual(self.get_tokenizer().vocab_size   ,							1_0_1_1_2_2				)
      @require_torch
      def SCREAMING_SNAKE_CASE_						(		self						:						str				):






       '''simple docstring'''


       __lowercase						=						["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
       __lowercase						=						[0, 5_7, 3_0_1_8, 7_0_3_0_7, 9_1, 2]

       __lowercase						=						self.tokenizer(
           A_   ,							max_length=len(A_				)   ,							padding=A_   ,							truncation=A_   ,							return_tensors="""pt"""				)
       self.assertIsInstance(A_   ,							A_				)

       self.assertEqual((2, 6)   ,							batch.input_ids.shape				)
       self.assertEqual((2, 6)   ,							batch.attention_mask.shape				)
       __lowercase						=						batch.input_ids.tolist()[0]
       self.assertListEqual(A_   ,							A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						int				):






       '''simple docstring'''


       if not self.test_rust_tokenizer:
        return

       __lowercase						=						self.get_tokenizer()
       __lowercase						=						self.get_rust_tokenizer()

       __lowercase						=						"""I was born in 92000, and this is falsé."""

       __lowercase						=						tokenizer.tokenize(A_				)
       __lowercase						=						rust_tokenizer.tokenize(A_				)
       self.assertListEqual(A_   ,							A_				)

       __lowercase						=						tokenizer.encode(A_   ,							add_special_tokens=A_				)
       __lowercase						=						rust_tokenizer.encode(A_   ,							add_special_tokens=A_				)
       self.assertListEqual(A_   ,							A_				)

       __lowercase						=						self.get_rust_tokenizer()
       __lowercase						=						tokenizer.encode(A_				)
       __lowercase						=						rust_tokenizer.encode(A_				)
       self.assertListEqual(A_   ,							A_				)






      @slow
      def SCREAMING_SNAKE_CASE_						(		self						:						Union[str, Any]				):






       '''simple docstring'''


       __lowercase						=						{"""input_ids""": [[0, 4_9_0, 1_4_3_2_8, 4_5_0_7, 3_5_4, 4_7, 4_3_6_6_9, 9_5, 2_5, 7_8_1_1_7, 2_0_2_1_5, 1_9_7_7_9, 1_9_0, 2_2, 4_0_0, 4, 3_5_3_4_3, 8_0_3_1_0, 6_0_3, 8_6, 2_4_9_3_7, 1_0_5, 3_3_4_3_8, 9_4_7_6_2, 1_9_6, 3_9_6_4_2, 7, 1_5, 1_5_9_3_3, 1_7_3, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1_0_5_3_4, 8_7, 2_5, 6_6, 3_3_5_8, 1_9_6, 5_5_2_8_9, 8, 8_2_9_6_1, 8_1, 2_2_0_4, 7_5_2_0_3, 7, 1_5, 7_6_3, 1_2_9_5_6, 2_1_6, 1_7_8, 1_4_3_2_8, 9_5_9_5, 1_3_7_7, 6_9_6_9_3, 7, 4_4_8, 7_1_0_2_1, 1_9_6, 1_8_1_0_6, 1_4_3_7, 1_3_9_7_4, 1_0_8, 9_0_8_3, 4, 4_9_3_1_5, 7, 3_9, 8_6, 1_3_2_6, 2_7_9_3, 4_6_3_3_3, 4, 4_4_8, 1_9_6, 7_4_5_8_8, 7, 4_9_3_1_5, 7, 3_9, 2_1, 8_2_2, 3_8_4_7_0, 7_4, 2_1, 6_6_7_2_3, 6_2_4_8_0, 8, 2_2_0_5_0, 5, 2]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
       # fmt: on

       # moussaKam/mbarthez is a french model. So we also use french texts.
       __lowercase						=						[
           """Le transformeur est un modèle d'apprentissage profond introduit en 2017, """
           """utilisé principalement dans le domaine du traitement automatique des langues (TAL).""",
           """À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus """
           """pour gérer des données séquentielles, telles que le langage naturel, pour des tâches """
           """telles que la traduction et la synthèse de texte.""",
       ]

       self.tokenizer_integration_test_util(
           expected_encoding=A_   ,							model_name="""moussaKam/mbarthez"""   ,							revision="""c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6"""   ,							sequences=A_   ,							)
