

"""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
_SCREAMING_SNAKE_CASE       :  List[str]        = """src/diffusers"""
_SCREAMING_SNAKE_CASE       :  Optional[int]        = """docs/source/en"""
_SCREAMING_SNAKE_CASE       :  Optional[Any]        = """."""



def        _lowerCAmelCase      (						UpperCAmelCase     :							List[str]      ,							UpperCAmelCase     :							int      ,							UpperCAmelCase     :							List[Any]				):


					'''simple docstring'''






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

					UpperCamelCase__		:    List[str]         =start_index
					while not lines[end_index].startswith(UpperCAmelCase				):
										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       :  Optional[Any]        = """Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
_SCREAMING_SNAKE_CASE       :  Optional[int]        = re.compile(r"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
_SCREAMING_SNAKE_CASE       :  List[Any]        = 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       :  Tuple        = 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       :  Optional[Any]        = importlib.util.spec_from_file_location(
    """diffusers""",
    os.path.join(TRANSFORMERS_PATH, """__init__.py"""),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
_SCREAMING_SNAKE_CASE       :  Any        = spec.loader.load_module()



def        _lowerCAmelCase      (						UpperCAmelCase     :							str				):


					'''simple docstring'''






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



def        _lowerCAmelCase      (						UpperCAmelCase     :							List[Any]      ,							UpperCAmelCase     :							Optional[int]				):


					'''simple docstring'''






					UpperCamelCase__		:    Optional[int]         =2 if text == '''✅''' or text == '''❌''' else len(UpperCAmelCase				)
					UpperCamelCase__		:    Optional[int]         =(width - text_length) // 2
					UpperCamelCase__		:    Optional[int]         =width - text_length - left_indent
					return " " * left_indent + text + " " * right_indent



def        _lowerCAmelCase      (						):


					'''simple docstring'''






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

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

										if lookup_dict is not None:
															while len(UpperCAmelCase				) > 0:
																				if attr_name in model_name_to_prefix.values():
																									UpperCamelCase__		:    str         =True
																									break
																				# Try again after removing the last word in the name
																				UpperCamelCase__		:    List[Any]         =''''''.join(camel_case_split(UpperCAmelCase				)[:-1]				)

    # Let's build that table!
					UpperCamelCase__		:    Union[str, Any]         =list(model_name_to_config.keys()				)
					model_names.sort(key=str.lower				)
					UpperCamelCase__		:    Optional[int]         =['''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__		:    Tuple         =[len(UpperCAmelCase				) + 2 for c in columns]
					UpperCamelCase__		:    Union[str, Any]         =max([len(UpperCAmelCase				) for name in model_names]				) + 2

					# Build the table per se
					UpperCamelCase__		:    Optional[int]         ='''|''' + '''|'''.join([_center_text(UpperCAmelCase      ,							UpperCAmelCase				) for c, w in zip(UpperCAmelCase      ,							UpperCAmelCase				)]				) + '''|\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__		:    int         =model_name_to_prefix[name]
										UpperCamelCase__		:    Union[str, 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(UpperCAmelCase      ,							UpperCAmelCase				) for l, w in zip(UpperCAmelCase      ,							UpperCAmelCase				)]				) + "|\n"
					return table



def        _lowerCAmelCase      (						UpperCAmelCase     :							Union[str, Any]=False				):


					'''simple docstring'''






					UpperCamelCase__  ,			UpperCamelCase__  ,			UpperCamelCase__  ,			UpperCamelCase__		:    Dict         =_find_text_in_file(
					    filename=os.path.join(UpperCAmelCase      ,							'''index.mdx'''				)      ,							start_prompt='''<!--This table is updated automatically from the auto modules'''      ,							end_prompt='''<!-- End table-->'''      ,							)
					UpperCamelCase__		:    Tuple         =get_model_table_from_auto_modules()

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

				check_model_table(args.fix_and_overwrite)





"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE       :  List[Any]        = {"""configuration_t5""": ["""T5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """T5Config""", """T5OnnxConfig"""]}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Optional[Any]        = ["""T5Tokenizer"""]

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  str        = ["""T5TokenizerFast"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Dict        = [
				    """T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """T5EncoderModel""",
				    """T5ForConditionalGeneration""",
				    """T5Model""",
				    """T5PreTrainedModel""",
				    """load_tf_weights_in_t5""",
				    """T5ForQuestionAnswering""",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Dict        = [
				    """TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """TFT5EncoderModel""",
				    """TFT5ForConditionalGeneration""",
				    """TFT5Model""",
				    """TFT5PreTrainedModel""",
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Any        = [
				    """FlaxT5EncoderModel""",
				    """FlaxT5ForConditionalGeneration""",
				    """FlaxT5Model""",
				    """FlaxT5PreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_ta import TaTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_ta_fast import TaTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_ta import (
								    T5_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TaEncoderModel,
								    TaForConditionalGeneration,
								    TaForQuestionAnswering,
								    TaModel,
								    TaPreTrainedModel,
								    load_tf_weights_in_ta,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_ta import (
								    TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFTaEncoderModel,
								    TFTaForConditionalGeneration,
								    TFTaModel,
								    TFTaPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_ta import (
								    FlaxTaEncoderModel,
								    FlaxTaForConditionalGeneration,
								    FlaxTaModel,
								    FlaxTaPreTrainedModel,
								)


else:
				import sys

				_SCREAMING_SNAKE_CASE       :  str        = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


