




import argparse
import collections
import importlib.util
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_table.py
__lowerCAmelCase		:    Any	      =		"""src/diffusers"""
__lowerCAmelCase		:    Any	      =		"""docs/source/en"""
__lowerCAmelCase		:    List[Any]	      =		"""."""




def       __magic_name__    (			A		: Dict,  A		: List[str],  A		: Dict       ):



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

			a   =      start_index
			while not lines[end_index].startswith(A       ):
						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 |
__lowerCAmelCase		:    str	      =		"""Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
__lowerCAmelCase		:    Tuple	      =		re.compile(r'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
__lowerCAmelCase		:    Dict	      =		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.
__lowerCAmelCase		:    int	      =		re.compile(r'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# This is to make sure the diffusers module imported is the one in the repo.
__lowerCAmelCase		:    Dict	      =		importlib.util.spec_from_file_location(
    'diffusers',
    os.path.join(TRANSFORMERS_PATH, '__init__.py'),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
__lowerCAmelCase		:    Union[str, Any]	      =		spec.loader.load_module()




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




def       __magic_name__    (			A		: List[Any],  A		: str       ):
			a   =      2 if text == "✅" or text == "❌" else len(A       )
			a   =      (width - text_length) // 2
			a   =      width - text_length - left_indent
			return " " * left_indent + text + " " * right_indent




def       __magic_name__    (			):
			a   =      diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
			a   =      {
			    name: config_mapping_names[code]
			    for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
			    if code in config_mapping_names
			}
			a   =      {name: config.replace("ConfigMixin",  ""       ) 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.
			a   =      collections.defaultdict(A       )
			a   =      collections.defaultdict(A       )
			a   =      collections.defaultdict(A       )
			a   =      collections.defaultdict(A       )
			a   =      collections.defaultdict(A       )

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

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

    # Let's build that table!
			a   =      list(model_name_to_config.keys()       )
			model_names.sort(key=str.lower       )
			a   =      ["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).
			a   =      [len(A       ) + 2 for c in columns]
			a   =      max([len(A       ) for name in model_names]       ) + 2

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

			a   =      {True: "✅", False: "❌"}
			for name in model_names:
						a   =      model_name_to_prefix[name]
						a   =      [
						    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(A,  A       ) for l, w in zip(A,  A       )]       ) + "|\n"
			return table




def       __magic_name__    (			A		: Optional[int]=False       ):
			a   ,		a   ,		a   ,		a   =      _find_text_in_file(
			    filename=os.path.join(A,  "index.mdx"       ),  start_prompt="<!--This table is updated automatically from the auto modules",  end_prompt="<!-- End table-->",  )
			a   =      get_model_table_from_auto_modules()

			if current_table != new_table:
						if overwrite:
									with open(os.path.join(A,  "index.mdx"       ),  "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.mdx` has not been updated. Run `make fix-copies` to fix this."       )


if __name__ == "__main__":
		__lowerCAmelCase		:    Optional[Any]	      =		argparse.ArgumentParser()
		parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
		__lowerCAmelCase		:    int	      =		parser.parse_args()

		check_model_table(args.fix_and_overwrite)












import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class    snake_case__							(_UpperCamelCase       ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_	:							List[Any]										= ["""image_processor""", """tokenizer"""]
				SCREAMING_SNAKE_CASE_	:							List[Any]										= """AutoImageProcessor"""
				SCREAMING_SNAKE_CASE_	:							Dict										= """AutoTokenizer"""





				def __init__(				self    :					int ,				__lowerCamelCase    :					Dict=None ,				__lowerCamelCase    :					int=None ,				**__lowerCamelCase    :					Optional[Any]							) ->					str:
							a   =      None
							if "feature_extractor" in kwargs:
										warnings.warn(
										    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
										    " instead." ,				__lowerCamelCase ,				)
										a   =      kwargs.pop("feature_extractor"							)

							a   =      image_processor if image_processor is not None else feature_extractor
							if image_processor is None:
										raise ValueError("You need to specify an `image_processor`."							)
							if tokenizer is None:
										raise ValueError("You need to specify a `tokenizer`."							)

							super().__init__(__lowerCamelCase ,				__lowerCamelCase							)
							a   =      self.image_processor
							a   =      False





				def __call__(				self    :					Any ,				*__lowerCamelCase    :					int ,				**__lowerCamelCase    :					Optional[int]							) ->					int:
							# For backward compatibility
							if self._in_target_context_manager:
										return self.current_processor(*__lowerCamelCase ,				**__lowerCamelCase							)

							a   =      kwargs.pop("images" ,				__lowerCamelCase							)
							a   =      kwargs.pop("text" ,				__lowerCamelCase							)
							if len(__lowerCamelCase							) > 0:
										a   =      args[0]
										a   =      args[1:]

							if images is None and text is None:
										raise ValueError("You need to specify either an `images` or `text` input to process."							)

							if images is not None:
										a   =      self.image_processor(__lowerCamelCase ,				*__lowerCamelCase ,				**__lowerCamelCase							)
							if text is not None:
										a   =      self.tokenizer(__lowerCamelCase ,				**__lowerCamelCase							)

							if text is None:
										return inputs
							elif images is None:
										return encodings
							else:
										a   =      encodings["input_ids"]
										return inputs





				def 							__UpperCAmelCase							(				self    :					Union[str, Any] ,				*__lowerCamelCase    :					str ,				**__lowerCamelCase    :					str							) ->					Optional[Any]:
							return self.tokenizer.batch_decode(*__lowerCamelCase ,				**__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					Optional[Any] ,				*__lowerCamelCase    :					Union[str, Any] ,				**__lowerCamelCase    :					Any							) ->					str:
							return self.tokenizer.decode(*__lowerCamelCase ,				**__lowerCamelCase							)





				@contextmanager
				def 							__UpperCAmelCase							(				self    :					Any							) ->					Optional[int]:
							warnings.warn(
							    "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
							    "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
							    "your images inputs, or in a separate call."							)
							a   =      True
							a   =      self.tokenizer
							yield
							a   =      self.image_processor
							a   =      False





				@property
				def 							__UpperCAmelCase							(				self    :					Dict							) ->					Optional[Any]:
							warnings.warn(
							    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." ,				__lowerCamelCase ,				)
							return self.image_processor_class





				@property
				def 							__UpperCAmelCase							(				self    :					int							) ->					Union[str, Any]:
							warnings.warn(
							    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." ,				__lowerCamelCase ,				)
							return self.image_processor






