from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding





class 					__snake_case    (     lowercase_      ):
					__lowerCamelCase			   =  ["""image_processor""", """tokenizer"""]
					__lowerCamelCase			   =  """EfficientNetImageProcessor"""
					__lowerCamelCase			   =  ("""BertTokenizer""", """BertTokenizerFast""")




					def __init__(  self       ,			__UpperCamelCase       ,			__UpperCamelCase						)	->    List[Any]:



									'''simple docstring'''







									super().__init__(__UpperCamelCase       ,			__UpperCamelCase						)




					def __call__(  self       ,			__UpperCamelCase=None       ,			__UpperCamelCase=None       ,			__UpperCamelCase="max_length"       ,			__UpperCamelCase=64       ,			__UpperCamelCase=None       ,			**__UpperCamelCase						)	->    int:



									'''simple docstring'''







									if text is None and images is None:
													raise ValueError('You have to specify either text or images. Both cannot be none.'						)

									if text is not None:
													snake_case__   :      Union[str, Any]   				= self.tokenizer(
													    __UpperCamelCase       ,			padding=__UpperCamelCase       ,			max_length=__UpperCamelCase       ,			return_tensors=__UpperCamelCase       ,			**__UpperCamelCase						)

									if images is not None:
													snake_case__   :      Tuple   				= self.image_processor(__UpperCamelCase       ,			return_tensors=__UpperCamelCase       ,			**__UpperCamelCase						)

									if text is not None and images is not None:
													snake_case__   :      List[str]   				= image_features.pixel_values
													return encoding
									elif text is not None:
													return encoding
									else:
													return BatchEncoding(data=dict(**__UpperCamelCase						)       ,			tensor_type=__UpperCamelCase						)




					def      __a     (  self       ,			*__UpperCamelCase       ,			**__UpperCamelCase						)	->    List[str]:



									'''simple docstring'''







									return self.tokenizer.batch_decode(*__UpperCamelCase       ,			**__UpperCamelCase						)




					def      __a     (  self       ,			*__UpperCamelCase       ,			**__UpperCamelCase						)	->    Tuple:



									'''simple docstring'''







									return self.tokenizer.decode(*__UpperCamelCase       ,			**__UpperCamelCase						)




					@property
					def      __a     (  self						)	->    List[Any]:



									'''simple docstring'''







									snake_case__   :      Optional[int]   				= self.tokenizer.model_input_names
									snake_case__   :      List[Any]   				= self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)









"""simple docstring"""
import copy

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


_snake_case	:						int					=					logging.get_logger(__name__)

_snake_case	:						Optional[int]					=					{}






class    _UpperCAmelCase  (      lowercase_					):
					UpperCamelCase						=				'''fsmt_decoder'''


					def __init__(   self     :Optional[int]	,			__UpperCamelCase     :Tuple=0	,			__UpperCamelCase     :List[str]=0						):
												super().__init__()
												A						=    vocab_size
												A						=    bos_token_id







class    _UpperCAmelCase  (      lowercase_					):
					UpperCamelCase						=				'''fsmt'''
					UpperCamelCase						=				{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}


					def __init__(   self     :Dict	,			__UpperCamelCase     :List[str]=["en", "de"]	,			__UpperCamelCase     :List[str]=4_20_24	,			__UpperCamelCase     :Dict=4_20_24	,			__UpperCamelCase     :Tuple="relu"	,			__UpperCamelCase     :Optional[Any]=10_24	,			__UpperCamelCase     :Dict=2_00	,			__UpperCamelCase     :Optional[Any]=10_24	,			__UpperCamelCase     :Optional[Any]=40_96	,			__UpperCamelCase     :Union[str, Any]=12	,			__UpperCamelCase     :Dict=16	,			__UpperCamelCase     :Optional[Any]=0.0	,			__UpperCamelCase     :Dict=40_96	,			__UpperCamelCase     :Dict=12	,			__UpperCamelCase     :str=16	,			__UpperCamelCase     :Any=0.0	,			__UpperCamelCase     :str=0.0	,			__UpperCamelCase     :List[Any]=0.1	,			__UpperCamelCase     :List[Any]=0.0	,			__UpperCamelCase     :str=0.02	,			__UpperCamelCase     :List[str]=2	,			__UpperCamelCase     :Union[str, Any]=True	,			__UpperCamelCase     :Optional[Any]=True	,			__UpperCamelCase     :int=False	,			__UpperCamelCase     :List[str]=5	,			__UpperCamelCase     :Optional[int]=1.0	,			__UpperCamelCase     :List[str]=False	,			__UpperCamelCase     :Any=True	,			__UpperCamelCase     :List[str]=1	,			__UpperCamelCase     :int=0	,			__UpperCamelCase     :str=2	,			__UpperCamelCase     :Dict=2	,			**__UpperCamelCase     :Optional[int]	,			):
												A						=    langs
												A						=    src_vocab_size
												A						=    tgt_vocab_size
												A						=    d_model  # encoder_embed_dim and decoder_embed_dim

												A						=    encoder_ffn_dim
												A						=    A						=    encoder_layers
												A						=    encoder_attention_heads
												A						=    encoder_layerdrop
												A						=    decoder_layerdrop
												A						=    decoder_ffn_dim
												A						=    decoder_layers
												A						=    decoder_attention_heads
												A						=    max_position_embeddings
												A						=    init_std  # Normal(0, this parameter)
												A						=    activation_function

												A						=    DecoderConfig(vocab_size=__UpperCamelCase	,			bos_token_id=__UpperCamelCase						)
												if "decoder" in common_kwargs:
																			del common_kwargs["decoder"]

												A						=    scale_embedding  # scale factor will be sqrt(d_model) if True

												# 3 Types of Dropout
												A						=    attention_dropout
												A						=    activation_dropout
												A						=    dropout

												A						=    use_cache
												super().__init__(
												    pad_token_id=__UpperCamelCase	,			bos_token_id=__UpperCamelCase	,			eos_token_id=__UpperCamelCase	,			decoder_start_token_id=__UpperCamelCase	,			is_encoder_decoder=__UpperCamelCase	,			tie_word_embeddings=__UpperCamelCase	,			forced_eos_token_id=__UpperCamelCase	,			max_length=__UpperCamelCase	,			num_beams=__UpperCamelCase	,			length_penalty=__UpperCamelCase	,			early_stopping=__UpperCamelCase	,			**__UpperCamelCase	,			)


					def       lowerCamelCase			(   self     :List[str]						):
												A						=    copy.deepcopy(self.__dict__						)
												A						=    self.decoder.to_dict()
												A						=    self.__class__.model_type
												return output


