
import argparse
import collections
import os
import re

from transformers.utils import direct_transformers_import


# 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__:    List[Any]      = """src/transformers"""
lowerCAmelCase__:    Tuple      = """docs/source/en"""
lowerCAmelCase__:    Dict      = """."""



def   __SCREAMING_SNAKE_CASE (		SCREAMING_SNAKE_CASE ,    SCREAMING_SNAKE_CASE ,    SCREAMING_SNAKE_CASE )		-> Optional[int]:






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

		SCREAMING_SNAKE_CASE_					:	Tuple		  =			start_index
		while not lines[end_index].startswith(SCREAMING_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 |
lowerCAmelCase__:    Dict      = """Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
lowerCAmelCase__:    Union[str, Any]      = re.compile(R"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
lowerCAmelCase__:    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.
lowerCAmelCase__:    List[str]      = re.compile(R"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")


# This is to make sure the transformers module imported is the one in the repo.
lowerCAmelCase__:    Optional[int]      = direct_transformers_import(TRANSFORMERS_PATH)



def   __SCREAMING_SNAKE_CASE (		SCREAMING_SNAKE_CASE )		-> str:
		SCREAMING_SNAKE_CASE_					:	Dict		  =			re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)' ,    SCREAMING_SNAKE_CASE_ )
		return [m.group(0 ) for m in matches]



def   __SCREAMING_SNAKE_CASE (		SCREAMING_SNAKE_CASE ,    SCREAMING_SNAKE_CASE )		-> List[str]:
		SCREAMING_SNAKE_CASE_					:	int		  =			2 if text == '✅' or text == '❌' else len(SCREAMING_SNAKE_CASE_ )
		SCREAMING_SNAKE_CASE_					:	Tuple		  =			(width - text_length) // 2
		SCREAMING_SNAKE_CASE_					:	Dict		  =			width - text_length - left_indent
		return " " * left_indent + text + " " * right_indent



def   __SCREAMING_SNAKE_CASE (		)		-> Any:
		SCREAMING_SNAKE_CASE_					:	int		  =			transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
		SCREAMING_SNAKE_CASE_					:	Optional[int]		  =			{
		    name: config_maping_names[code]
		    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
		    if code in config_maping_names
		}
		SCREAMING_SNAKE_CASE_					:	Tuple		  =			{name: config.replace('Config' ,    '' ) 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.
		SCREAMING_SNAKE_CASE_					:	Any		  =			collections.defaultdict(SCREAMING_SNAKE_CASE_ )
		SCREAMING_SNAKE_CASE_					:	List[Any]		  =			collections.defaultdict(SCREAMING_SNAKE_CASE_ )
		SCREAMING_SNAKE_CASE_					:	List[Any]		  =			collections.defaultdict(SCREAMING_SNAKE_CASE_ )
		SCREAMING_SNAKE_CASE_					:	int		  =			collections.defaultdict(SCREAMING_SNAKE_CASE_ )
		SCREAMING_SNAKE_CASE_					:	int		  =			collections.defaultdict(SCREAMING_SNAKE_CASE_ )

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

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

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

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

		SCREAMING_SNAKE_CASE_					:	Dict		  =			{True: '✅', False: '❌'}
		for name in model_names:
				SCREAMING_SNAKE_CASE_					:	List[str]		  =			model_name_to_prefix[name]
				SCREAMING_SNAKE_CASE_					:	Optional[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(SCREAMING_SNAKE_CASE_ ,    SCREAMING_SNAKE_CASE_ ) for l, w in zip(SCREAMING_SNAKE_CASE_ ,    SCREAMING_SNAKE_CASE_ )] ) + "|\n"
		return table



def   __SCREAMING_SNAKE_CASE (		SCREAMING_SNAKE_CASE=False )		-> Optional[Any]:
		SCREAMING_SNAKE_CASE_  ,      SCREAMING_SNAKE_CASE_  ,      SCREAMING_SNAKE_CASE_  ,      SCREAMING_SNAKE_CASE_					:	Optional[Any]		  =			_find_text_in_file(
		    filename=os.path.join(SCREAMING_SNAKE_CASE_ ,    'index.md' ) ,    start_prompt='<!--This table is updated automatically from the auto modules' ,    end_prompt='<!-- End table-->' ,    )
		SCREAMING_SNAKE_CASE_					:	str		  =			get_model_table_from_auto_modules()

		if current_table != new_table:
				if overwrite:
						with open(os.path.join(SCREAMING_SNAKE_CASE_ ,    'index.md' ) ,    '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.md` has not been updated. Run `make fix-copies` to fix this.' )


if __name__ == "__main__":
		lowerCAmelCase__:    List[Any]      = argparse.ArgumentParser()
		parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
		lowerCAmelCase__:    List[Any]      = parser.parse_args()

		check_model_table(args.fix_and_overwrite)









from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class 						__magic_name__					(							snake_case				):
     UpperCamelCase_	:Dict										=      ["""image_processor""", """tokenizer"""]
     UpperCamelCase_	:str										=      """BridgeTowerImageProcessor"""
     UpperCamelCase_	:Tuple										=      ("""RobertaTokenizer""", """RobertaTokenizerFast""")






     def __init__(				self		,						_lowercase		,						_lowercase	)->     Any:
         super().__init__(_lowercase		,						_lowercase	)






     def __call__(				self		,						_lowercase		,						_lowercase = None		,						_lowercase = True		,						_lowercase = False		,						_lowercase = None		,						_lowercase = None		,						_lowercase = 0		,						_lowercase = None		,						_lowercase = None		,						_lowercase = None		,						_lowercase = False		,						_lowercase = False		,						_lowercase = False		,						_lowercase = False		,						_lowercase = True		,						_lowercase = None		,						**_lowercase		,						)->     BatchEncoding:
         UpperCamelCase_			  =						self.tokenizer(
             text=_lowercase		,						add_special_tokens=_lowercase		,						padding=_lowercase		,						truncation=_lowercase		,						max_length=_lowercase		,						stride=_lowercase		,						pad_to_multiple_of=_lowercase		,						return_token_type_ids=_lowercase		,						return_attention_mask=_lowercase		,						return_overflowing_tokens=_lowercase		,						return_special_tokens_mask=_lowercase		,						return_offsets_mapping=_lowercase		,						return_length=_lowercase		,						verbose=_lowercase		,						return_tensors=_lowercase		,						**_lowercase		,						)
         # add pixel_values + pixel_mask
         UpperCamelCase_			  =						self.image_processor(
             _lowercase		,						return_tensors=_lowercase		,						do_normalize=_lowercase		,						do_center_crop=_lowercase		,						**_lowercase	)
         encoding.update(_lowercase	)

         return encoding






     def 	UpperCAmelCase_				(				self		,						*_lowercase		,						**_lowercase	)->     Union[str, Any]:
         return self.tokenizer.batch_decode(*_lowercase		,						**_lowercase	)






     def 	UpperCAmelCase_				(				self		,						*_lowercase		,						**_lowercase	)->     List[Any]:
         return self.tokenizer.decode(*_lowercase		,						**_lowercase	)






     @property
     def 	UpperCAmelCase_				(				self	)->     Any:
         UpperCamelCase_			  =						self.tokenizer.model_input_names
         UpperCamelCase_			  =						self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)
