


"""simple docstring"""






import json
import os
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCamelCase__				:List[str]			   =	logging.get_logger(__name__)


UpperCamelCase__				:Union[str, Any]			   =	{
    '''vocab_file''': '''vocab.json''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
    '''merges_file''': '''merges.txt''',
}

UpperCamelCase__				:Optional[Any]			   =	{
    '''vocab_file''': {
        '''facebook/s2t-wav2vec2-large-en-de''': (
            '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json'''
        ),
    },
    '''tokenizer_config_file''': {
        '''facebook/s2t-wav2vec2-large-en-de''': (
            '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json'''
        ),
    },
    '''merges_file''': {
        '''facebook/s2t-wav2vec2-large-en-de''': (
            '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt'''
        ),
    },
}

UpperCamelCase__				:Optional[Any]			   =	'''</w>'''
UpperCamelCase__				:List[Any]			   =	'''@@ '''





def 				A_       ( snake_case__		)					->  str:
			_UpperCamelCase					:List[Any]					     =	set()
			_UpperCamelCase					:int					     =	word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)		)
						_UpperCamelCase					:Union[str, Any]					     =	char
			return pairs


# Speech2Text2 has no max input length
UpperCamelCase__				:Union[str, Any]			   =	{'''facebook/s2t-wav2vec2-large-en-de''': 1_024}






class      A(  lowerCAmelCase__		):

		"""simple docstring"""





		A    =							VOCAB_FILES_NAMES
		A    =							PRETRAINED_VOCAB_FILES_MAP
		A    =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		A    =							["input_ids", "attention_mask"]
		def __init__( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__="<s>"					,				SCREAMING_SNAKE_CASE__="<pad>"					,				SCREAMING_SNAKE_CASE__="</s>"					,				SCREAMING_SNAKE_CASE__="<unk>"					,				SCREAMING_SNAKE_CASE__=False					,				SCREAMING_SNAKE_CASE__=None					,				**SCREAMING_SNAKE_CASE__					,				)      ->	Union[str, Any]:

					"""simple docstring"""



					super().__init__(
					    unk_token=snake_case_					,				bos_token=snake_case_					,				eos_token=snake_case_					,				pad_token=snake_case_					,				do_lower_case=snake_case_					,				**snake_case_					,				)

					_UpperCamelCase					:List[Any]					     =	do_lower_case

					with open(snake_case_					,				encoding='''utf-8'''   ) as vocab_handle:
								_UpperCamelCase					:int					     =	json.load(snake_case_   )
					_UpperCamelCase					:Tuple					     =	{v: k for k, v in self.encoder.items()}

					if merges_file is None:
								logger.info(f"No merges files provided. {self.__class__.__name__} can only be used for decoding."   )

								_UpperCamelCase					:List[Any]					     =	None
								_UpperCamelCase					:Union[str, Any]					     =	None
					else:
								with open(snake_case_					,				encoding='''utf-8'''   ) as merges_handle:
											_UpperCamelCase					:Union[str, Any]					     =	merges_handle.read().split('''\n'''   )[:-1]

								_UpperCamelCase					:Union[str, Any]					     =	[tuple(merge.split()[:2]   ) for merge in merges]
								_UpperCamelCase					:Dict					     =	dict(zip(snake_case_					,				range(len(snake_case_   )   )   )   )
								_UpperCamelCase					:Optional[int]					     =	{}
		@property
		def   _UpperCamelCase( self   )      ->	int:

					"""simple docstring"""



					return len(self.decoder   )
		def   _UpperCamelCase( self   )      ->	Dict:

					"""simple docstring"""



					return dict(self.encoder					,				**self.added_tokens_encoder   )
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	List[Any]:

					"""simple docstring"""



					_UpperCamelCase					:Optional[Any]					     =	tuple(token[:-1]   ) + (token[-1] + BPE_TOKEN_MERGES,)
					if token in self.cache:
								return self.cache[token]
					_UpperCamelCase					:Dict					     =	get_pairs(snake_case_   )

					if not pairs:
								return token

					while True:
								_UpperCamelCase					:Optional[int]					     =	min(snake_case_					,				key=lambda SCREAMING_SNAKE_CASE__   : self.bpe_ranks.get(snake_case_					,				float('''inf'''   )   )   )
								if bigram not in self.bpe_ranks:
											break
								_UpperCamelCase     ,      _UpperCamelCase					:Optional[Any]					     =	bigram
								_UpperCamelCase					:Any					     =	[]
								_UpperCamelCase					:Dict					     =	0
								while i < len(snake_case_   ):
											try:
														_UpperCamelCase					:Optional[Any]					     =	word.index(snake_case_					,				snake_case_   )
											except ValueError:
														new_word.extend(word[i:]   )
														break
											else:
														new_word.extend(word[i:j]   )
														_UpperCamelCase					:List[Any]					     =	j

											if word[i] == first and i < len(snake_case_   ) - 1 and word[i + 1] == second:
														new_word.append(first + second   )
														i += 2
											else:
														new_word.append(word[i]   )
														i += 1
								_UpperCamelCase					:Dict					     =	tuple(snake_case_   )
								_UpperCamelCase					:str					     =	new_word
								if len(snake_case_   ) == 1:
											break
								else:
											_UpperCamelCase					:Optional[int]					     =	get_pairs(snake_case_   )
					_UpperCamelCase					:Optional[int]					     =	''' '''.join(snake_case_   )
					if word == "\n  " + BPE_TOKEN_MERGES:
								_UpperCamelCase					:Tuple					     =	'''\n''' + BPE_TOKEN_MERGES

					if word.endswith(snake_case_   ):
								_UpperCamelCase					:Any					     =	word.replace(snake_case_					,				''''''   )

					_UpperCamelCase					:Optional[int]					     =	word.replace(''' '''					,				snake_case_   )
					_UpperCamelCase					:Optional[int]					     =	word
					return word
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	Tuple:

					"""simple docstring"""



					if self.bpe_ranks is None:
								raise ValueError(
								    '''This tokenizer was instantiated without a `merges.txt` file, so'''
								    ''' that it can only be used for decoding, not for encoding.'''
								    '''Make sure to provide `merges.txt` file at instantiation to enable '''
								    '''encoding.'''   )

					if self.do_lower_case:
								_UpperCamelCase					:Optional[Any]					     =	text.lower()

					_UpperCamelCase					:Union[str, Any]					     =	text.split()

					_UpperCamelCase					:List[Any]					     =	[]
					for token in text:
								if token:
											split_tokens.extend(list(self.bpe(snake_case_   ).split(''' '''   )   )   )

					return split_tokens
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	int:

					"""simple docstring"""



					return self.encoder.get(snake_case_					,				self.encoder.get(self.unk_token   )   )
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	str:

					"""simple docstring"""



					_UpperCamelCase					:Tuple					     =	self.decoder.get(snake_case_					,				self.unk_token   )
					return result
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	str:

					"""simple docstring"""



					_UpperCamelCase					:Tuple					     =	''' '''.join(snake_case_   )

					# make sure @@ tokens are concatenated
					_UpperCamelCase					:Union[str, Any]					     =	''''''.join(string.split(snake_case_   )   )

					return string





		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__ = None   )      ->	Tuple[str]:

					"""simple docstring"""



					if not os.path.isdir(snake_case_   ):
								logger.error(f"Vocabulary path ({save_directory}) should be a directory"   )
								return
					_UpperCamelCase					:Union[str, Any]					     =	os.path.join(
					    snake_case_					,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )
					_UpperCamelCase					:Union[str, Any]					     =	os.path.join(
					    snake_case_					,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']   )

					with open(snake_case_					,				'''w'''					,				encoding='''utf-8'''   ) as f:
								f.write(json.dumps(self.encoder					,				indent=2					,				sort_keys=snake_case_					,				ensure_ascii=snake_case_   ) + '''\n'''   )

					_UpperCamelCase					:List[str]					     =	0
					if self.bpe_ranks is None:
								return (vocab_file,)

					with open(snake_case_					,				'''w'''					,				encoding='''utf-8'''   ) as writer:
								for bpe_tokens, token_index in sorted(self.bpe_ranks.items()					,				key=lambda SCREAMING_SNAKE_CASE__   : kv[1]   ):
											if index != token_index:
														logger.warning(
														    f"Saving vocabulary to {merges_file}: BPE merge indices are not consecutive."
														    ''' Please check that the tokenizer is not corrupted!'''   )
														_UpperCamelCase					:Tuple					     =	token_index
											writer.write(''' '''.join(snake_case_   ) + '''\n'''   )
											index += 1

					return (vocab_file, merges_file)





import copy
import os
from typing import Union

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


a			:			Optional[int]												=					logging.get_logger(__name__)

a			:			Dict												=					{
    '''google/pix2struct-textcaps-base''': (
        '''https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json'''
    ),
}
class 						lowerCamelCase_							(					lowerCAmelCase__					):



					'''simple docstring'''







					__UpperCAmelCase							  =					"pix2struct_text_model"
					__UpperCAmelCase							  =					["past_key_values"]
					__UpperCAmelCase							  =					{
					    "hidden_size": "hidden_size",
					    "num_attention_heads": "num_heads",
					    "num_hidden_layers": "num_layers",
					}






					def __init__(					self			,							snake_case_=5_0_2_4_4			,							snake_case_=7_6_8			,							snake_case_=6_4			,							snake_case_=2_0_4_8			,							snake_case_=1_2			,							snake_case_=1_2			,							snake_case_=3_2			,							snake_case_=1_2_8			,							snake_case_=0.1			,							snake_case_=1e-6			,							snake_case_=1.0			,							snake_case_="gelu_new"			,							snake_case_=0			,							snake_case_=False			,							snake_case_=0			,							snake_case_=1			,							snake_case_=False			,							snake_case_=True			,							**snake_case_			,							) ->      Optional[int]:







								'''simple docstring'''

								__lowercase			       = vocab_size
								__lowercase			       = hidden_size
								__lowercase			       = d_kv
								__lowercase			       = d_ff
								__lowercase			       = num_layers
								__lowercase			       = num_heads
								__lowercase			       = relative_attention_num_buckets
								__lowercase			       = relative_attention_max_distance
								__lowercase			       = dropout_rate
								__lowercase			       = layer_norm_epsilon
								__lowercase			       = initializer_factor
								__lowercase			       = use_cache

								__lowercase			       = eos_token_id
								__lowercase			       = decoder_start_token_id

								# for backwards compatibility
								__lowercase			       = dense_act_fn

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






					@classmethod
					def A   (					cls			,							snake_case_			,							**snake_case_    ) ->      "PretrainedConfig":







								'''simple docstring'''

								cls._set_token_in_kwargs(snake_case_    )

								__lowercase       ,			__lowercase			       = cls.get_config_dict(snake_case_			,							**snake_case_    )

								# get the text config dict if we are loading from Pix2StructConfig
								if config_dict.get('''model_type'''    ) == "pix2struct":
											__lowercase			       = 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(snake_case_			,							**snake_case_    )




class 						lowerCamelCase_							(					lowerCAmelCase__					):



					'''simple docstring'''







					__UpperCAmelCase							  =					"pix2struct_vision_model"






					def __init__(					self			,							snake_case_=7_6_8			,							snake_case_=7_6_8			,							snake_case_=2_0_4_8			,							snake_case_=6_4			,							snake_case_=1_2			,							snake_case_=1_2			,							snake_case_="gelu_new"			,							snake_case_=1e-6			,							snake_case_=0.0			,							snake_case_=0.0			,							snake_case_=1e-1_0			,							snake_case_=1.0			,							snake_case_=4_0_9_6			,							snake_case_=3_2			,							snake_case_=1_2_8			,							**snake_case_			,							) ->      Union[str, Any]:







								'''simple docstring'''

								super().__init__(**snake_case_    )

								__lowercase			       = hidden_size
								__lowercase			       = patch_embed_hidden_size
								__lowercase			       = d_ff
								__lowercase			       = dropout_rate
								__lowercase			       = num_hidden_layers
								__lowercase			       = num_attention_heads
								__lowercase			       = initializer_range
								__lowercase			       = initializer_factor
								__lowercase			       = attention_dropout
								__lowercase			       = layer_norm_eps
								__lowercase			       = dense_act_fn
								__lowercase			       = seq_len
								__lowercase			       = relative_attention_num_buckets
								__lowercase			       = relative_attention_max_distance
								__lowercase			       = d_kv






					@classmethod
					def A   (					cls			,							snake_case_			,							**snake_case_    ) ->      "PretrainedConfig":







								'''simple docstring'''

								cls._set_token_in_kwargs(snake_case_    )

								__lowercase       ,			__lowercase			       = cls.get_config_dict(snake_case_			,							**snake_case_    )

								# get the vision config dict if we are loading from Pix2StructConfig
								if config_dict.get('''model_type'''    ) == "pix2struct":
											__lowercase			       = 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(snake_case_			,							**snake_case_    )




class 						lowerCamelCase_							(					lowerCAmelCase__					):



					'''simple docstring'''







					__UpperCAmelCase							  =					"pix2struct"
					__UpperCAmelCase							  =					True






					def __init__(					self			,							snake_case_=None			,							snake_case_=None			,							snake_case_=1.0			,							snake_case_=0.0_2			,							snake_case_=False			,							snake_case_=False			,							snake_case_=True			,							**snake_case_			,							) ->      Optional[int]:







								'''simple docstring'''

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

								if text_config is None:
											__lowercase			       = {}
											logger.info('''text_config is None. Initializing the Pix2StructTextConfig with default values.'''    )

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

								__lowercase			       = PixaStructTextConfig(**snake_case_    )
								__lowercase			       = PixaStructVisionConfig(**snake_case_    )

								__lowercase			       = self.text_config.decoder_start_token_id
								__lowercase			       = self.text_config.pad_token_id
								__lowercase			       = self.text_config.eos_token_id

								__lowercase			       = initializer_factor
								__lowercase			       = initializer_range

								__lowercase			       = self.initializer_range
								__lowercase			       = self.initializer_range

								__lowercase			       = is_vqa






					@classmethod
					def A   (					cls			,							snake_case_			,							snake_case_			,							**snake_case_    ) ->      List[str]:







								'''simple docstring'''

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






					def A   (					self    ) ->      Tuple:







								'''simple docstring'''

								__lowercase			       = copy.deepcopy(self.__dict__    )
								__lowercase			       = self.text_config.to_dict()
								__lowercase			       = self.vision_config.to_dict()
								__lowercase			       = self.__class__.model_type
								return output


