



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
__SCREAMING_SNAKE_CASE     =	"""src/diffusers"""
__SCREAMING_SNAKE_CASE     =	"""docs/source/en"""
__SCREAMING_SNAKE_CASE     =	"""."""




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

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


# This is to make sure the diffusers module imported is the one in the repo.
__SCREAMING_SNAKE_CASE     =	importlib.util.spec_from_file_location(
    """diffusers""",
    os.path.join(TRANSFORMERS_PATH, """__init__.py"""),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
__SCREAMING_SNAKE_CASE     =	spec.loader.load_module()




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




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




def 						UpperCAmelCase (    ):
			A				:       List[str]														=    diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
			A				:       Dict														=    {
			    name: config_mapping_names[code]
			    for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
			    if code in config_mapping_names
			}
			A				:       Union[str, Any]														=    {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				:       Union[str, Any]														=    collections.defaultdict(_lowerCamelCase     )
			A				:       List[Any]														=    collections.defaultdict(_lowerCamelCase     )
			A				:       int														=    collections.defaultdict(_lowerCamelCase     )
			A				:       List[Any]														=    collections.defaultdict(_lowerCamelCase     )
			A				:       List[Any]														=    collections.defaultdict(_lowerCamelCase     )

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

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

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

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

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




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

			if current_table != new_table:
						if overwrite:
									with open(os.path.join(_lowerCamelCase			,     "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__":
							__SCREAMING_SNAKE_CASE     =	argparse.ArgumentParser()
							parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
							__SCREAMING_SNAKE_CASE     =	parser.parse_args()

							check_model_table(args.fix_and_overwrite)




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__SCREAMING_SNAKE_CASE     =	{"""configuration_yoso""": ["""YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """YosoConfig"""]}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__SCREAMING_SNAKE_CASE     =	[
							    """YOSO_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """YosoForMaskedLM""",
							    """YosoForMultipleChoice""",
							    """YosoForQuestionAnswering""",
							    """YosoForSequenceClassification""",
							    """YosoForTokenClassification""",
							    """YosoLayer""",
							    """YosoModel""",
							    """YosoPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_yoso import YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP, YosoConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_yoso import (
														    YOSO_PRETRAINED_MODEL_ARCHIVE_LIST,
														    YosoForMaskedLM,
														    YosoForMultipleChoice,
														    YosoForQuestionAnswering,
														    YosoForSequenceClassification,
														    YosoForTokenClassification,
														    YosoLayer,
														    YosoModel,
														    YosoPreTrainedModel,
														)


else:
							import sys

							__SCREAMING_SNAKE_CASE     =	_LazyModule(__name__, globals()["""__file__"""], _import_structure)