





"""simple docstring"""



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
a_ =			"src/transformers"
a_ =			"docs/source/en"
a_ =			"."







def 		__lowercase     (   snake_case_  :  Optional[int]				,snake_case_  :  Tuple				,snake_case_  :  Optional[Any]						)   ->Tuple:



    '''simple docstring'''







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

    __A    :						List[str]						 =     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 |
a_ =			"Model|Encoder|Decoder|ForConditionalGeneration"
# Regexes that match TF/Flax/PT model names.
a_ =			re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
a_ =			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.
a_ =			re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# This is to make sure the transformers module imported is the one in the repo.
a_ =			direct_transformers_import(TRANSFORMERS_PATH)







def 		__lowercase     (   snake_case_  :  Optional[int]						)   ->str:



    '''simple docstring'''







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







def 		__lowercase     (   snake_case_  :  Any				,snake_case_  :  List[Any]						)   ->Any:



    '''simple docstring'''







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







def 		__lowercase     (   )   ->Union[str, Any]:



    '''simple docstring'''







    __A    :						Optional[int]						 =     transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
    __A    :						Dict						 =     {
        name: config_maping_names[code]
        for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
        if code in config_maping_names
    }
    __A    :						Dict						 =     {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.
    __A    :						Union[str, Any]						 =     collections.defaultdict(__SCREAMING_SNAKE_CASE						)
    __A    :						Dict						 =     collections.defaultdict(__SCREAMING_SNAKE_CASE						)
    __A    :						List[Any]						 =     collections.defaultdict(__SCREAMING_SNAKE_CASE						)
    __A    :						str						 =     collections.defaultdict(__SCREAMING_SNAKE_CASE						)
    __A    :						Optional[int]						 =     collections.defaultdict(__SCREAMING_SNAKE_CASE						)

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

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

    # Build the table per se
    __A    :						str						 =     '''|''' + '''|'''.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"

    __A    :						int						 =     {True: '''✅''', False: '''❌'''}
    for name in model_names:
        __A    :						Dict						 =     model_name_to_prefix[name]
        __A    :						Tuple						 =     [
            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 		__lowercase     (   snake_case_  :  Dict=False						)   ->Tuple:



    '''simple docstring'''







    __A    :						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-->'''				,)
    __A    :						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__":
       a_ =			argparse.ArgumentParser()
       parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
       a_ =			parser.parse_args()

       check_model_table(args.fix_and_overwrite)






'''simple docstring'''







import copy
import os
from typing import Union

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


_lowercase :  Optional[Any]    							=       logging.get_logger(__name__)

_lowercase :  List[str]    							=       {
    "google/pix2struct-textcaps-base": (
        "https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json"
    ),
}






class  lowerCAmelCase__   (  lowerCamelCase_     ):
					lowerCAmelCase_        =     '''pix2struct_text_model'''
					lowerCAmelCase_        =     ['''past_key_values''']
					lowerCAmelCase_        =     {
					    '''hidden_size''': '''hidden_size''',
					    '''num_attention_heads''': '''num_heads''',
					    '''num_hidden_layers''': '''num_layers''',
					}






					def __init__(   self				,							__SCREAMING_SNAKE_CASE=5_02_44				,							__SCREAMING_SNAKE_CASE=7_68				,							__SCREAMING_SNAKE_CASE=64				,							__SCREAMING_SNAKE_CASE=20_48				,							__SCREAMING_SNAKE_CASE=12				,							__SCREAMING_SNAKE_CASE=12				,							__SCREAMING_SNAKE_CASE=32				,							__SCREAMING_SNAKE_CASE=1_28				,							__SCREAMING_SNAKE_CASE=0.1				,							__SCREAMING_SNAKE_CASE=1E-6				,							__SCREAMING_SNAKE_CASE=1.0				,							__SCREAMING_SNAKE_CASE="gelu_new"				,							__SCREAMING_SNAKE_CASE=0				,							__SCREAMING_SNAKE_CASE=False				,							__SCREAMING_SNAKE_CASE=0				,							__SCREAMING_SNAKE_CASE=1				,							__SCREAMING_SNAKE_CASE=False				,							__SCREAMING_SNAKE_CASE=True				,							**__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





											lowercase_	:     Any													=       vocab_size
											lowercase_	:     Tuple													=       hidden_size
											lowercase_	:     Optional[Any]													=       d_kv
											lowercase_	:     List[str]													=       d_ff
											lowercase_	:     List[str]													=       num_layers
											lowercase_	:     Optional[Any]													=       num_heads
											lowercase_	:     Union[str, Any]													=       relative_attention_num_buckets
											lowercase_	:     Optional[int]													=       relative_attention_max_distance
											lowercase_	:     Union[str, Any]													=       dropout_rate
											lowercase_	:     Dict													=       layer_norm_epsilon
											lowercase_	:     Dict													=       initializer_factor
											lowercase_	:     List[Any]													=       use_cache

											lowercase_	:     Optional[int]													=       eos_token_id
											lowercase_	:     Optional[int]													=       decoder_start_token_id

											# for backwards compatibility
											lowercase_	:     Any													=       dense_act_fn

											super().__init__(
											    pad_token_id=__SCREAMING_SNAKE_CASE				,							eos_token_id=__SCREAMING_SNAKE_CASE				,							decoder_start_token_id=__SCREAMING_SNAKE_CASE				,							tie_word_embeddings=__SCREAMING_SNAKE_CASE				,							is_decoder=__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE				,							)






					@classmethod
					def 	_snake_case    (   cls				,							__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											cls._set_token_in_kwargs(__SCREAMING_SNAKE_CASE		)

											lowercase_						, lowercase_	:     Optional[int]													=       cls.get_config_dict(__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		)

											# get the text config dict if we are loading from Pix2StructConfig
											if config_dict.get('''model_type'''		) == "pix2struct":
																	lowercase_	:     List[Any]													=       config_dict['''text_config''']

											if "model_type" in config_dict and hasattr(cls				,							'''model_type'''		) and config_dict["model_type"] != cls.model_type:
																	logger.warning(
																	    F'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
																	    F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''		)

											return cls.from_dict(__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		)







class  lowerCAmelCase__   (  lowerCamelCase_     ):
					lowerCAmelCase_        =     '''pix2struct_vision_model'''






					def __init__(   self				,							__SCREAMING_SNAKE_CASE=7_68				,							__SCREAMING_SNAKE_CASE=7_68				,							__SCREAMING_SNAKE_CASE=20_48				,							__SCREAMING_SNAKE_CASE=64				,							__SCREAMING_SNAKE_CASE=12				,							__SCREAMING_SNAKE_CASE=12				,							__SCREAMING_SNAKE_CASE="gelu_new"				,							__SCREAMING_SNAKE_CASE=1E-6				,							__SCREAMING_SNAKE_CASE=0.0				,							__SCREAMING_SNAKE_CASE=0.0				,							__SCREAMING_SNAKE_CASE=1E-1_0				,							__SCREAMING_SNAKE_CASE=1.0				,							__SCREAMING_SNAKE_CASE=40_96				,							__SCREAMING_SNAKE_CASE=32				,							__SCREAMING_SNAKE_CASE=1_28				,							**__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





											super().__init__(**__SCREAMING_SNAKE_CASE		)

											lowercase_	:     Union[str, Any]													=       hidden_size
											lowercase_	:     Any													=       patch_embed_hidden_size
											lowercase_	:     List[Any]													=       d_ff
											lowercase_	:     Dict													=       dropout_rate
											lowercase_	:     Any													=       num_hidden_layers
											lowercase_	:     Any													=       num_attention_heads
											lowercase_	:     int													=       initializer_range
											lowercase_	:     Dict													=       initializer_factor
											lowercase_	:     Dict													=       attention_dropout
											lowercase_	:     Optional[Any]													=       layer_norm_eps
											lowercase_	:     str													=       dense_act_fn
											lowercase_	:     Dict													=       seq_len
											lowercase_	:     List[Any]													=       relative_attention_num_buckets
											lowercase_	:     int													=       relative_attention_max_distance
											lowercase_	:     Optional[int]													=       d_kv






					@classmethod
					def 	_snake_case    (   cls				,							__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											cls._set_token_in_kwargs(__SCREAMING_SNAKE_CASE		)

											lowercase_						, lowercase_	:     str													=       cls.get_config_dict(__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		)

											# get the vision config dict if we are loading from Pix2StructConfig
											if config_dict.get('''model_type'''		) == "pix2struct":
																	lowercase_	:     Optional[int]													=       config_dict['''vision_config''']

											if "model_type" in config_dict and hasattr(cls				,							'''model_type'''		) and config_dict["model_type"] != cls.model_type:
																	logger.warning(
																	    F'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
																	    F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''		)

											return cls.from_dict(__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		)







class  lowerCAmelCase__   (  lowerCamelCase_     ):
					lowerCAmelCase_        =     '''pix2struct'''
					lowerCAmelCase_        =     True






					def __init__(   self				,							__SCREAMING_SNAKE_CASE=None				,							__SCREAMING_SNAKE_CASE=None				,							__SCREAMING_SNAKE_CASE=1.0				,							__SCREAMING_SNAKE_CASE=0.02				,							__SCREAMING_SNAKE_CASE=False				,							__SCREAMING_SNAKE_CASE=False				,							__SCREAMING_SNAKE_CASE=True				,							**__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





											super().__init__(tie_word_embeddings=__SCREAMING_SNAKE_CASE				,							is_encoder_decoder=__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		)

											if text_config is None:
																	lowercase_	:     Optional[Any]													=       {}
																	logger.info('''text_config is None. Initializing the Pix2StructTextConfig with default values.'''		)

											if vision_config is None:
																	lowercase_	:     Dict													=       {}
																	logger.info('''vision_config is None. Initializing the Pix2StructVisionConfig with default values.'''		)

											lowercase_	:     str													=       PixaStructTextConfig(**__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Tuple													=       PixaStructVisionConfig(**__SCREAMING_SNAKE_CASE		)

											lowercase_	:     Optional[Any]													=       self.text_config.decoder_start_token_id
											lowercase_	:     Union[str, Any]													=       self.text_config.pad_token_id
											lowercase_	:     Union[str, Any]													=       self.text_config.eos_token_id

											lowercase_	:     int													=       initializer_factor
											lowercase_	:     Any													=       initializer_range

											lowercase_	:     str													=       self.initializer_range
											lowercase_	:     str													=       self.initializer_range

											lowercase_	:     int													=       is_vqa






					@classmethod
					def 	_snake_case    (   cls				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											return cls(text_config=text_config.to_dict()				,							vision_config=vision_config.to_dict()				,							**__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Tuple													=       copy.deepcopy(self.__dict__		)
											lowercase_	:     Any													=       self.text_config.to_dict()
											lowercase_	:     Optional[Any]													=       self.vision_config.to_dict()
											lowercase_	:     Optional[int]													=       self.__class__.model_type
											return output



