



"""simple docstring"""


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

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

						UpperCAmelCase			 =					start_index
						while not lines[end_index].startswith(lowercase_		):
												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 diffusers module imported is the one in the repo.
snake_case_								=			importlib.util.spec_from_file_location(
    """diffusers""",
    os.path.join(TRANSFORMERS_PATH, """__init__.py"""),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
snake_case_								=			spec.loader.load_module()

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

def 					_lowerCAmelCase (  lowercase_						,      lowercase_		):
						UpperCAmelCase			 =					2 if text == '✅' or text == '❌' else len(lowercase_		)
						UpperCAmelCase			 =					(width - text_length) // 2
						UpperCAmelCase			 =					width - text_length - left_indent
						return " " * left_indent + text + " " * right_indent

def 					_lowerCAmelCase (  ):
						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(lowercase_		)
						UpperCAmelCase			 =					collections.defaultdict(lowercase_		)
						UpperCAmelCase			 =					collections.defaultdict(lowercase_		)
						UpperCAmelCase			 =					collections.defaultdict(lowercase_		)
						UpperCAmelCase			 =					collections.defaultdict(lowercase_		)

						# Let's lookup through all diffusers object (once).
						for attr_name in dir(lowercase_		):
												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(lowercase_		) is not None:
																		UpperCAmelCase			 =					tf_models
																		UpperCAmelCase			 =					_re_tf_models.match(lowercase_		).groups()[0]
												elif _re_flax_models.match(lowercase_		) is not None:
																		UpperCAmelCase			 =					flax_models
																		UpperCAmelCase			 =					_re_flax_models.match(lowercase_		).groups()[0]
												elif _re_pt_models.match(lowercase_		) is not None:
																		UpperCAmelCase			 =					pt_models
																		UpperCAmelCase			 =					_re_pt_models.match(lowercase_		).groups()[0]

												if lookup_dict is not None:
																		while len(lowercase_		) > 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(lowercase_		)[:-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(lowercase_		) + 2 for c in columns]
						UpperCAmelCase			 =					max([len(lowercase_		) for name in model_names]		) + 2

						# Build the table per se
						UpperCAmelCase			 =					'|' + '|'.join([_center_text(lowercase_						,      lowercase_		) for c, w in zip(lowercase_						,      lowercase_		)]		) + '|\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(lowercase_						,      lowercase_		) for l, w in zip(lowercase_						,      lowercase_		)]		) + "|\n"
						return table

def 					_lowerCAmelCase (  lowercase_=False		):
						UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase			 =					_find_text_in_file(
						    filename=os.path.join(lowercase_						,      '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(lowercase_						,      '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__":
				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)








from typing import TYPE_CHECKING

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


__A     						=      {'''configuration_hubert''': ['''HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''HubertConfig''']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A     						=      [
     '''HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''HubertForCTC''',
     '''HubertForSequenceClassification''',
     '''HubertModel''',
     '''HubertPreTrainedModel''',
 ]


try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A     						=      [
     '''TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''TFHubertForCTC''',
     '''TFHubertModel''',
     '''TFHubertPreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_hubert import (
      HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      HubertForCTC,
      HubertForSequenceClassification,
      HubertModel,
      HubertPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_hubert import (
      TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFHubertForCTC,
      TFHubertModel,
      TFHubertPreTrainedModel,
  )


else:
 import sys

 __A     						=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

