




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
lowercase_     :    Optional[Any]      =			'src/diffusers'
lowercase_     :    Optional[int]      =			'docs/source/en'
lowercase_     :    Optional[Any]      =			'.'


def 							__SCREAMING_SNAKE_CASE  (      snake_case_     ,   snake_case_     ,   snake_case_						):

						'''simple docstring'''


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

						_UpperCAmelCase													=					start_index
						while not lines[end_index].startswith(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 |
lowercase_     :    Optional[Any]      =			'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
lowercase_     :    Union[str, Any]      =			re.compile(r'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
lowercase_     :    Optional[int]      =			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.
lowercase_     :    List[str]      =			re.compile(r'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


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


def 							__SCREAMING_SNAKE_CASE  (      snake_case_						):

						'''simple docstring'''


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


def 							__SCREAMING_SNAKE_CASE  (      snake_case_     ,   snake_case_						):

						'''simple docstring'''


						_UpperCAmelCase													=					2 if text == "✅" or text == "❌" else len(snake_case_						)
						_UpperCAmelCase													=					(width - text_length) // 2
						_UpperCAmelCase													=					width - text_length - left_indent
						return " " * left_indent + text + " " * right_indent


def 							__SCREAMING_SNAKE_CASE  (      ):

						'''simple docstring'''


						_UpperCAmelCase													=					diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
						_UpperCAmelCase													=					{
						    name: config_mapping_names[code]
						    for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
						    if code in config_mapping_names
						}
						_UpperCAmelCase													=					{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.
						_UpperCAmelCase													=					collections.defaultdict(snake_case_						)
						_UpperCAmelCase													=					collections.defaultdict(snake_case_						)
						_UpperCAmelCase													=					collections.defaultdict(snake_case_						)
						_UpperCAmelCase													=					collections.defaultdict(snake_case_						)
						_UpperCAmelCase													=					collections.defaultdict(snake_case_						)

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

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

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

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

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


def 							__SCREAMING_SNAKE_CASE  (      snake_case_=False						):

						'''simple docstring'''


						_UpperCAmelCase							,			_UpperCAmelCase							,			_UpperCAmelCase							,			_UpperCAmelCase													=					_find_text_in_file(
						    filename=os.path.join(snake_case_     ,   "index.mdx"						)     ,   start_prompt="<!--This table is updated automatically from the auto modules"     ,   end_prompt="<!-- End table-->"     ,   )
						_UpperCAmelCase													=					get_model_table_from_auto_modules()

						if current_table != new_table:
												if overwrite:
																		with open(os.path.join(snake_case_     ,   "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__":
				lowercase_     :    str      =			argparse.ArgumentParser()
				parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
				lowercase_     :    Union[str, Any]      =			parser.parse_args()

				check_model_table(args.fix_and_overwrite)










from typing import TYPE_CHECKING

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


lowercase_     :    int      =			{
    'configuration_videomae': ['VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VideoMAEConfig'],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_     :    List[str]      =			[
				    'VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'VideoMAEForPreTraining',
				    'VideoMAEModel',
				    'VideoMAEPreTrainedModel',
				    'VideoMAEForVideoClassification',
				]

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_     :    Any      =			['VideoMAEFeatureExtractor']
				lowercase_     :    Dict      =			['VideoMAEImageProcessor']

if TYPE_CHECKING:
				from .configuration_videomae import VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP, VideoMAEConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_videomae import (
								    VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST,
								    VideoMAEForPreTraining,
								    VideoMAEForVideoClassification,
								    VideoMAEModel,
								    VideoMAEPreTrainedModel,
								)

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_videomae import VideoMAEFeatureExtractor
								from .image_processing_videomae import VideoMAEImageProcessor

else:
				import sys

				lowercase_     :    int      =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




