


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCAmelCase     :					str       =logging.get_logger(__name__)

_UpperCAmelCase     :					int       ={}







class 			snake_case__(							lowerCamelCase__							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__ :					Tuple   =							'fsmt_decoder'




						def __init__(			self				,  __lowercase=0				,  __lowercase=0							)   ->      str:
										super().__init__()
										lowerCAmelCase_	: str      			=			vocab_size
										lowerCAmelCase_	: str      			=			bos_token_id







class 			snake_case__(							lowerCamelCase__							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__ :					Union[str, Any]   =							'fsmt'
						SCREAMING_SNAKE_CASE__ :					Optional[Any]   =							{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}




						def __init__(			self				,  __lowercase=["en", "de"]				,  __lowercase=4_2_0_2_4				,  __lowercase=4_2_0_2_4				,  __lowercase="relu"				,  __lowercase=1_0_2_4				,  __lowercase=2_0_0				,  __lowercase=1_0_2_4				,  __lowercase=4_0_9_6				,  __lowercase=1_2				,  __lowercase=1_6				,  __lowercase=0.0				,  __lowercase=4_0_9_6				,  __lowercase=1_2				,  __lowercase=1_6				,  __lowercase=0.0				,  __lowercase=0.0				,  __lowercase=0.1				,  __lowercase=0.0				,  __lowercase=0.02				,  __lowercase=2				,  __lowercase=True				,  __lowercase=True				,  __lowercase=False				,  __lowercase=5				,  __lowercase=1.0				,  __lowercase=False				,  __lowercase=True				,  __lowercase=1				,  __lowercase=0				,  __lowercase=2				,  __lowercase=2				,  **__lowercase				,  )   ->      Optional[Any]:
										lowerCAmelCase_	: int      			=			langs
										lowerCAmelCase_	: Dict      			=			src_vocab_size
										lowerCAmelCase_	: Optional[int]      			=			tgt_vocab_size
										lowerCAmelCase_	: Any      			=			d_model  # encoder_embed_dim and decoder_embed_dim

										lowerCAmelCase_	: Optional[int]      			=			encoder_ffn_dim
										lowerCAmelCase_	: Dict      			=			encoder_layers
										lowerCAmelCase_	: List[str]      			=			encoder_attention_heads
										lowerCAmelCase_	: Tuple      			=			encoder_layerdrop
										lowerCAmelCase_	: Union[str, Any]      			=			decoder_layerdrop
										lowerCAmelCase_	: Any      			=			decoder_ffn_dim
										lowerCAmelCase_	: List[str]      			=			decoder_layers
										lowerCAmelCase_	: Union[str, Any]      			=			decoder_attention_heads
										lowerCAmelCase_	: Optional[Any]      			=			max_position_embeddings
										lowerCAmelCase_	: Dict      			=			init_std  # Normal(0, this parameter)
										lowerCAmelCase_	: Optional[int]      			=			activation_function

										lowerCAmelCase_	: Tuple      			=			DecoderConfig(vocab_size=lowercase_				,  bos_token_id=lowercase_							)
										if "decoder" in common_kwargs:
														del common_kwargs["decoder"]

										lowerCAmelCase_	: str      			=			scale_embedding  # scale factor will be sqrt(d_model) if True

										# 3 Types of Dropout
										lowerCAmelCase_	: Any      			=			attention_dropout
										lowerCAmelCase_	: Dict      			=			activation_dropout
										lowerCAmelCase_	: Union[str, Any]      			=			dropout

										lowerCAmelCase_	: str      			=			use_cache
										super().__init__(
										    pad_token_id=lowercase_				,  bos_token_id=lowercase_				,  eos_token_id=lowercase_				,  decoder_start_token_id=lowercase_				,  is_encoder_decoder=lowercase_				,  tie_word_embeddings=lowercase_				,  forced_eos_token_id=lowercase_				,  max_length=lowercase_				,  num_beams=lowercase_				,  length_penalty=lowercase_				,  early_stopping=lowercase_				,  **lowercase_				,  )




						def   lowercase_	(			self							)   ->      Any:
										lowerCAmelCase_	: Optional[int]      			=			copy.deepcopy(self.__dict__							)
										lowerCAmelCase_	: int      			=			self.decoder.to_dict()
										lowerCAmelCase_	: Optional[Any]      			=			self.__class__.model_type
										return output



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
_UpperCAmelCase     :					List[Any]       ="""src/diffusers"""
_UpperCAmelCase     :					Dict       ="""docs/source/en"""
_UpperCAmelCase     :					List[str]       ="""."""



def      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_	)->	List[str]:
				with open(lowerCAmelCase_	,				'''r'''	,				encoding='''utf-8'''	,				newline='''\n'''	) as f:
								lowerCAmelCase_	: Any      			=			f.readlines()
				# Find the start prompt.
				lowerCAmelCase_	: Optional[Any]      			=			0
				while not lines[start_index].startswith(lowerCAmelCase_	):
								start_index += 1
				start_index += 1

				lowerCAmelCase_	: List[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 |
_UpperCAmelCase     :					int       ="""Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
_UpperCAmelCase     :					Any       =re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
_UpperCAmelCase     :					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.
_UpperCAmelCase     :					Union[str, Any]       =re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


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



def      lowerCAmelCase	(						lowerCAmelCase_	)->	Dict:
				lowerCAmelCase_	: Any      			=			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_	)->	Any:
				lowerCAmelCase_	: Any      			=			2 if text == '''✅''' or text == '''❌''' else len(lowerCAmelCase_	)
				lowerCAmelCase_	: List[str]      			=			(width - text_length) // 2
				lowerCAmelCase_	: str      			=			width - text_length - left_indent
				return " " * left_indent + text + " " * right_indent



def      lowerCAmelCase	(						)->	Dict:
				lowerCAmelCase_	: Optional[int]      			=			diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
				lowerCAmelCase_	: Tuple      			=			{
				    name: config_mapping_names[code]
				    for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
				    if code in config_mapping_names
				}
				lowerCAmelCase_	: str      			=			{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.
				lowerCAmelCase_	: Union[str, Any]      			=			collections.defaultdict(lowerCAmelCase_	)
				lowerCAmelCase_	: Dict      			=			collections.defaultdict(lowerCAmelCase_	)
				lowerCAmelCase_	: Tuple      			=			collections.defaultdict(lowerCAmelCase_	)
				lowerCAmelCase_	: Optional[int]      			=			collections.defaultdict(lowerCAmelCase_	)
				lowerCAmelCase_	: Any      			=			collections.defaultdict(lowerCAmelCase_	)

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

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

				# Build the table per se
				lowerCAmelCase_	: List[str]      			=			'''|''' + '''|'''.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"

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

						check_model_table(args.fix_and_overwrite)