


'''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
lowerCamelCase__     						=  'src/diffusers'
lowerCamelCase__     						=  'docs/source/en'
lowerCamelCase__     						=  '.'






def 			__lowerCAmelCase			(__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase ):





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

		_UpperCAmelCase		:							Any             =   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 |
lowerCamelCase__     						=  'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
lowerCamelCase__     						=  re.compile(r'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
lowerCamelCase__     						=  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__     						=  re.compile(r'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


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






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






def 			__lowerCAmelCase			(__lowerCAmelCase   ,			__lowerCAmelCase ):
		_UpperCAmelCase		:							Union[str, Any]             =   2 if text == '✅' or text == '❌' else len(__lowerCAmelCase )
		_UpperCAmelCase		:							Any             =   (width - text_length) // 2
		_UpperCAmelCase		:							Tuple             =   width - text_length - left_indent
		return " " * left_indent + text + " " * right_indent






def 			__lowerCAmelCase			():
		_UpperCAmelCase		:							List[str]             =   diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
		_UpperCAmelCase		:							Optional[Any]             =   {
		    name: config_mapping_names[code]
		    for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
		    if code in config_mapping_names
		}
		_UpperCAmelCase		:							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.
		_UpperCAmelCase		:							Any             =   collections.defaultdict(__lowerCAmelCase )
		_UpperCAmelCase		:							Optional[Any]             =   collections.defaultdict(__lowerCAmelCase )
		_UpperCAmelCase		:							Optional[Any]             =   collections.defaultdict(__lowerCAmelCase )
		_UpperCAmelCase		:							Union[str, Any]             =   collections.defaultdict(__lowerCAmelCase )
		_UpperCAmelCase		:							List[str]             =   collections.defaultdict(__lowerCAmelCase )

		# Let's lookup through all diffusers object (once).
		for attr_name in dir(__lowerCAmelCase ):
				_UpperCAmelCase		:							str             =   None
				if attr_name.endswith("Tokenizer" ):
						_UpperCAmelCase		:							Any             =   slow_tokenizers
						_UpperCAmelCase		:							Optional[int]             =   attr_name[:-9]
				elif attr_name.endswith("TokenizerFast" ):
						_UpperCAmelCase		:							Union[str, Any]             =   fast_tokenizers
						_UpperCAmelCase		:							str             =   attr_name[:-13]
				elif _re_tf_models.match(__lowerCAmelCase ) is not None:
						_UpperCAmelCase		:							Optional[Any]             =   tf_models
						_UpperCAmelCase		:							Any             =   _re_tf_models.match(__lowerCAmelCase ).groups()[0]
				elif _re_flax_models.match(__lowerCAmelCase ) is not None:
						_UpperCAmelCase		:							List[str]             =   flax_models
						_UpperCAmelCase		:							Tuple             =   _re_flax_models.match(__lowerCAmelCase ).groups()[0]
				elif _re_pt_models.match(__lowerCAmelCase ) is not None:
						_UpperCAmelCase		:							List[str]             =   pt_models
						_UpperCAmelCase		:							Any             =   _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():
										_UpperCAmelCase		:							Union[str, Any]             =   True
										break
								# Try again after removing the last word in the name
								_UpperCAmelCase		:							Optional[int]             =   ''.join(camel_case_split(__lowerCAmelCase )[:-1] )

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

		# Build the table per se
		_UpperCAmelCase		:							Union[str, Any]             =   '|' + '|'.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"

		_UpperCAmelCase		:							Optional[Any]             =   {True: '✅', False: '❌'}
		for name in model_names:
				_UpperCAmelCase		:							Tuple             =   model_name_to_prefix[name]
				_UpperCAmelCase		:							Any             =   [
				    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 			__lowerCAmelCase			(__lowerCAmelCase=False ):
		_UpperCAmelCase		:							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-->"   ,			)
		_UpperCAmelCase		:							Optional[Any]             =   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__":
						lowerCamelCase__     						=  argparse.ArgumentParser()
						parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
						lowerCamelCase__     						=  parser.parse_args()

						check_model_table(args.fix_and_overwrite)









from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


UpperCAmelCase__    :							List[str]       		=     {
    'configuration_ernie_m': ['ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ErnieMConfig'],
}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__    :							Any       		=     ['ErnieMTokenizer']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__    :							Dict       		=     [
       'ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST',
       'ErnieMForMultipleChoice',
       'ErnieMForQuestionAnswering',
       'ErnieMForSequenceClassification',
       'ErnieMForTokenClassification',
       'ErnieMModel',
       'ErnieMPreTrainedModel',
       'ErnieMForInformationExtraction',
   ]


if TYPE_CHECKING:
   from .configuration_ernie_m import ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieMConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_ernie_m import ErnieMTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_ernie_m import (
          ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST,
          ErnieMForInformationExtraction,
          ErnieMForMultipleChoice,
          ErnieMForQuestionAnswering,
          ErnieMForSequenceClassification,
          ErnieMForTokenClassification,
          ErnieMModel,
          ErnieMPreTrainedModel,
      )


else:
   import sys

   UpperCAmelCase__    :							List[Any]       		=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
