

import copy
import json
import os
import tempfile

from transformers import is_torch_available

from .test_configuration_utils import config_common_kwargs






class      lowerCamelCase_      (	_lowercase	):




	def __init__(     self						:     int					,      __A						:     List[str]					,      __A						:     Optional[int]=None					,      __A						:     Any=True					,      __A						:     List[Any]=None					,      **__A						:     List[str]							):
		__A :			str		    =			parent
		__A :			str		    =			config_class
		__A :			List[str]		    =			has_text_modality
		__A :			int		    =			kwargs
		__A :			str		    =			common_properties




	def        lowerCAmelCase_       (     self						:     List[Any]							):
		__A :			List[Any]		    =			self.config_class(**self.inputs_dict							)
		__A :			List[Any]		    =			(
		    ["""hidden_size""", """num_attention_heads""", """num_hidden_layers"""]
		    if self.common_properties is None
		    else self.common_properties
		)

		# Add common fields for text models
		if self.has_text_modality:
			common_properties.extend(["""vocab_size"""]							)

		# Test that config has the common properties as getters
		for prop in common_properties:
			self.parent.assertTrue(hasattr(__A					,      __A							)					,      msg=F"""`{prop}` does not exist"""							)

		# Test that config has the common properties as setter
		for idx, name in enumerate(__A							):
			try:
				setattr(__A					,      __A					,      __A							)
				self.parent.assertEqual(
				    getattr(__A					,      __A							)					,      __A					,      msg=F"""`{name} value {idx} expected, but was {getattr(__A					,      __A							)}"""							)
			except NotImplementedError:
				# Some models might not be able to implement setters for common_properties
				# In that case, a NotImplementedError is raised
				pass

        # Test if config class can be called with Config(prop_name=..)
		for idx, name in enumerate(__A							):
			try:
				__A :			Dict		    =			self.config_class(**{name: idx}							)
				self.parent.assertEqual(
				    getattr(__A					,      __A							)					,      __A					,      msg=F"""`{name} value {idx} expected, but was {getattr(__A					,      __A							)}"""							)
			except NotImplementedError:
				# Some models might not be able to implement setters for common_properties
				# In that case, a NotImplementedError is raised
				pass




	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		__A :			List[str]		    =			self.config_class(**self.inputs_dict							)
		__A :			Optional[Any]		    =			json.loads(config.to_json_string()							)
		for key, value in self.inputs_dict.items():
			self.parent.assertEqual(obj[key]					,      __A							)




	def        lowerCAmelCase_       (     self						:     List[str]							):
		__A :			List[str]		    =			self.config_class(**self.inputs_dict							)

		with tempfile.TemporaryDirectory() as tmpdirname:
			__A :			List[Any]		    =			os.path.join(__A					,      """config.json"""							)
			config_first.to_json_file(__A							)
			__A :			List[str]		    =			self.config_class.from_json_file(__A							)

		self.parent.assertEqual(config_second.to_dict()					,      config_first.to_dict()							)




	def        lowerCAmelCase_       (     self						:     List[Any]							):
		__A :			Tuple		    =			self.config_class(**self.inputs_dict							)

		with tempfile.TemporaryDirectory() as tmpdirname:
			config_first.save_pretrained(__A							)
			__A :			str		    =			self.config_class.from_pretrained(__A							)

		self.parent.assertEqual(config_second.to_dict()					,      config_first.to_dict()							)




	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		__A :			str		    =			self.config_class(**self.inputs_dict							)

		__A :			Dict		    =			"""test"""
		with tempfile.TemporaryDirectory() as tmpdirname:
			__A :			Union[str, Any]		    =			os.path.join(__A					,      __A							)
			config_first.save_pretrained(__A							)
			__A :			Dict		    =			self.config_class.from_pretrained(__A					,      subfolder=__A							)

		self.parent.assertEqual(config_second.to_dict()					,      config_first.to_dict()							)




	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		__A :			Optional[Any]		    =			self.config_class(**self.inputs_dict					,      num_labels=5							)
		self.parent.assertEqual(len(config.idalabel							)					,      5							)
		self.parent.assertEqual(len(config.labelaid							)					,      5							)

		__A :			str		    =			3
		self.parent.assertEqual(len(config.idalabel							)					,      3							)
		self.parent.assertEqual(len(config.labelaid							)					,      3							)




	def        lowerCAmelCase_       (     self						:     Union[str, Any]							):
		if self.config_class.is_composition:
			return
		__A :			List[str]		    =			self.config_class()
		self.parent.assertIsNotNone(__A							)




	def        lowerCAmelCase_       (     self						:     Any							):
		__A :			Optional[Any]		    =			copy.deepcopy(__A							)
		__A :			Optional[int]		    =			self.config_class(**__A							)
		__A :			List[str]		    =			[]
		for key, value in config_common_kwargs.items():
			if key == "torch_dtype":
				if not is_torch_available():
					continue
				else:
					import torch

					if config.torch_dtype != torch.floataa:
						wrong_values.append(("""torch_dtype""", config.torch_dtype, torch.floataa)							)
			elif getattr(__A					,      __A							) != value:
				wrong_values.append((key, getattr(__A					,      __A							), value)							)

		if len(__A							) > 0:
			__A :			Dict		    =			"""\n""".join([F"""- {v[0]}: got {v[1]} instead of {v[2]}""" for v in wrong_values]							)
			raise ValueError(F"""The following keys were not properly set in the config:\n{errors}"""							)




	def        lowerCAmelCase_       (     self						:     Dict							):
		self.create_and_test_config_common_properties()
		self.create_and_test_config_to_json_string()
		self.create_and_test_config_to_json_file()
		self.create_and_test_config_from_and_save_pretrained()
		self.create_and_test_config_from_and_save_pretrained_subfolder()
		self.create_and_test_config_with_num_labels()
		self.check_config_can_be_init_without_params()
		self.check_config_arguments_init()






'''simple docstring'''


import argparse
import collections
import os
import re

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_table.py
__snake_case						  =		"""src/transformers"""
__snake_case						  =		"""docs/source/en"""
__snake_case						  =		"""."""

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

  lowercase_     =       start_index
  while not lines[end_index].startswith(SCREAMING_SNAKE_CASE_						):
    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


# Add here suffixes that are used to identify models, separated by |
__snake_case						  =		"""Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
__snake_case						  =		re.compile(r"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
__snake_case						  =		re.compile(r"""Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
__snake_case						  =		re.compile(r"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# This is to make sure the transformers module imported is the one in the repo.
__snake_case						  =		direct_transformers_import(TRANSFORMERS_PATH)

def   A_      (       SCREAMING_SNAKE_CASE_						)			->Dict:
  lowercase_     =       re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)""" ,	SCREAMING_SNAKE_CASE_						)
  return [m.group(0						) for m in matches]

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Optional[Any]:
  lowercase_     =       2 if text == """✅""" or text == """❌""" else len(SCREAMING_SNAKE_CASE_						)
  lowercase_     =       (width - text_length) // 2
  lowercase_     =       width - text_length - left_indent
  return " " * left_indent + text + " " * right_indent

def   A_      (       )			->Tuple:
  lowercase_     =       transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
  lowercase_     =       {
      name: config_maping_names[code]
      for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
      if code in config_maping_names
  }
  lowercase_     =       {name: config.replace("""Config""" ,	""""""						) for name, config in model_name_to_config.items()}

  # Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
  lowercase_     =       collections.defaultdict(SCREAMING_SNAKE_CASE_						)
  lowercase_     =       collections.defaultdict(SCREAMING_SNAKE_CASE_						)
  lowercase_     =       collections.defaultdict(SCREAMING_SNAKE_CASE_						)
  lowercase_     =       collections.defaultdict(SCREAMING_SNAKE_CASE_						)
  lowercase_     =       collections.defaultdict(SCREAMING_SNAKE_CASE_						)

  # Let's lookup through all transformers object (once).
  for attr_name in dir(SCREAMING_SNAKE_CASE_						):
    lowercase_     =       None
    if attr_name.endswith("""Tokenizer"""						):
      lowercase_     =       slow_tokenizers
      lowercase_     =       attr_name[:-9]
    elif attr_name.endswith("""TokenizerFast"""						):
      lowercase_     =       fast_tokenizers
      lowercase_     =       attr_name[:-13]
    elif _re_tf_models.match(SCREAMING_SNAKE_CASE_						) is not None:
      lowercase_     =       tf_models
      lowercase_     =       _re_tf_models.match(SCREAMING_SNAKE_CASE_						).groups()[0]
    elif _re_flax_models.match(SCREAMING_SNAKE_CASE_						) is not None:
      lowercase_     =       flax_models
      lowercase_     =       _re_flax_models.match(SCREAMING_SNAKE_CASE_						).groups()[0]
    elif _re_pt_models.match(SCREAMING_SNAKE_CASE_						) is not None:
      lowercase_     =       pt_models
      lowercase_     =       _re_pt_models.match(SCREAMING_SNAKE_CASE_						).groups()[0]

    if lookup_dict is not None:
      while len(SCREAMING_SNAKE_CASE_						) > 0:
        if attr_name in model_name_to_prefix.values():
          lowercase_     =       True
          break
        # Try again after removing the last word in the name
        lowercase_     =       """""".join(camel_case_split(SCREAMING_SNAKE_CASE_						)[:-1]						)

    # Let's build that table!
  lowercase_     =       list(model_name_to_config.keys()						)
  model_names.sort(key=str.lower						)
  lowercase_     =       ["""Model""", """Tokenizer slow""", """Tokenizer fast""", """PyTorch support""", """TensorFlow support""", """Flax Support"""]
  # We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
  lowercase_     =       [len(SCREAMING_SNAKE_CASE_						) + 2 for c in columns]
  lowercase_     =       max([len(SCREAMING_SNAKE_CASE_						) for name in model_names]						) + 2

  # Build the table per se
  lowercase_     =       """|""" + """|""".join([_center_text(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						) for c, w in zip(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)]						) + """|\n"""
  # Use ":-----:" format to center-aligned table cell texts
  table += "|" + "|".join([""":""" + """-""" * (w - 2) + """:""" for w in widths]						) + "|\n"

  lowercase_     =       {True: """✅""", False: """❌"""}
  for name in model_names:
    lowercase_     =       model_name_to_prefix[name]
    lowercase_     =       [
        name,
        check[slow_tokenizers[prefix]],
        check[fast_tokenizers[prefix]],
        check[pt_models[prefix]],
        check[tf_models[prefix]],
        check[flax_models[prefix]],
    ]
    table += "|" + "|".join([_center_text(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						) for l, w in zip(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)]						) + "|\n"
  return table

def   A_      (       SCREAMING_SNAKE_CASE_=False						)			->Dict:
  lowercase_     ,	lowercase_     ,	lowercase_     ,	lowercase_     =       _find_text_in_file(
      filename=os.path.join(SCREAMING_SNAKE_CASE_ ,	"""index.md"""						) ,	start_prompt="""<!--This table is updated automatically from the auto modules""" ,	end_prompt="""<!-- End table-->""" ,	)
  lowercase_     =       get_model_table_from_auto_modules()

  if current_table != new_table:
    if overwrite:
      with open(os.path.join(SCREAMING_SNAKE_CASE_ ,	"""index.md"""						) ,	"""w""" ,	encoding="""utf-8""" ,	newline="""\n"""						) as f:
        f.writelines(lines[:start_index] + [new_table] + lines[end_index:]						)
    else:
      raise ValueError(
          """The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this."""						)


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

       check_model_table(args.fix_and_overwrite)

