





"""simple docstring"""



import copy

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


a_				=    logging.get_logger(__name__)

a_				=    {}






class 	__snake_case      (	SCREAMING_SNAKE_CASE__  ):





 """simple docstring"""


 _lowerCamelCase				 =  """fsmt_decoder"""

 def __init__(					self      ,		__lowerCamelCase=0      ,		__lowerCamelCase=0						):



     '''simple docstring'''




     super().__init__()
     __A					:		int						=  vocab_size
     __A					:		List[Any]						=  bos_token_id






class 	__snake_case      (	SCREAMING_SNAKE_CASE__  ):





 """simple docstring"""


 _lowerCamelCase				 =  """fsmt"""
 _lowerCamelCase				 =  {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}

 def __init__(					self      ,		__lowerCamelCase=["en", "de"]      ,		__lowerCamelCase=4_2024      ,		__lowerCamelCase=4_2024      ,		__lowerCamelCase="relu"      ,		__lowerCamelCase=1024      ,		__lowerCamelCase=200      ,		__lowerCamelCase=1024      ,		__lowerCamelCase=4096      ,		__lowerCamelCase=12      ,		__lowerCamelCase=16      ,		__lowerCamelCase=0.0      ,		__lowerCamelCase=4096      ,		__lowerCamelCase=12      ,		__lowerCamelCase=16      ,		__lowerCamelCase=0.0      ,		__lowerCamelCase=0.0      ,		__lowerCamelCase=0.1      ,		__lowerCamelCase=0.0      ,		__lowerCamelCase=0.0_2      ,		__lowerCamelCase=2      ,		__lowerCamelCase=True      ,		__lowerCamelCase=True      ,		__lowerCamelCase=False      ,		__lowerCamelCase=5      ,		__lowerCamelCase=1.0      ,		__lowerCamelCase=False      ,		__lowerCamelCase=True      ,		__lowerCamelCase=1      ,		__lowerCamelCase=0      ,		__lowerCamelCase=2      ,		__lowerCamelCase=2      ,		**__lowerCamelCase      ,		):



     '''simple docstring'''




     __A					:		Union[str, Any]						=  langs
     __A					:		List[str]						=  src_vocab_size
     __A					:		int						=  tgt_vocab_size
     __A					:		List[str]						=  d_model  # encoder_embed_dim and decoder_embed_dim

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

     __A					:		Optional[int]						=  DecoderConfig(vocab_size=__lowerCamelCase      ,		bos_token_id=__lowerCamelCase						)
     if "decoder" in common_kwargs:
         del common_kwargs["decoder"]

     __A					:		int						=  scale_embedding  # scale factor will be sqrt(d_model) if True

     # 3 Types of Dropout
     __A					:		List[Any]						=  attention_dropout
     __A					:		Any						=  activation_dropout
     __A					:		str						=  dropout

     __A					:		Tuple						=  use_cache
     super().__init__(
         pad_token_id=__lowerCamelCase      ,		bos_token_id=__lowerCamelCase      ,		eos_token_id=__lowerCamelCase      ,		decoder_start_token_id=__lowerCamelCase      ,		is_encoder_decoder=__lowerCamelCase      ,		tie_word_embeddings=__lowerCamelCase      ,		forced_eos_token_id=__lowerCamelCase      ,		max_length=__lowerCamelCase      ,		num_beams=__lowerCamelCase      ,		length_penalty=__lowerCamelCase      ,		early_stopping=__lowerCamelCase      ,		**__lowerCamelCase      ,		)







 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     __A					:		List[str]						=  copy.deepcopy(self.__dict__						)
     __A					:		Union[str, Any]						=  self.decoder.to_dict()
     __A					:		Optional[int]						=  self.__class__.model_type
     return output











"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a_				=    {
    """configuration_layoutlm""": ["""LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LayoutLMConfig""", """LayoutLMOnnxConfig"""],
    """tokenization_layoutlm""": ["""LayoutLMTokenizer"""],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_				=    ["""LayoutLMTokenizerFast"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_				=    [
           """LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """LayoutLMForMaskedLM""",
           """LayoutLMForSequenceClassification""",
           """LayoutLMForTokenClassification""",
           """LayoutLMForQuestionAnswering""",
           """LayoutLMModel""",
           """LayoutLMPreTrainedModel""",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_				=    [
           """TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """TFLayoutLMForMaskedLM""",
           """TFLayoutLMForSequenceClassification""",
           """TFLayoutLMForTokenClassification""",
           """TFLayoutLMForQuestionAnswering""",
           """TFLayoutLMMainLayer""",
           """TFLayoutLMModel""",
           """TFLayoutLMPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
       from .tokenization_layoutlm import LayoutLMTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_layoutlm import (
                  LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  LayoutLMForMaskedLM,
                  LayoutLMForQuestionAnswering,
                  LayoutLMForSequenceClassification,
                  LayoutLMForTokenClassification,
                  LayoutLMModel,
                  LayoutLMPreTrainedModel,
              )
       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_layoutlm import (
                  TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFLayoutLMForMaskedLM,
                  TFLayoutLMForQuestionAnswering,
                  TFLayoutLMForSequenceClassification,
                  TFLayoutLMForTokenClassification,
                  TFLayoutLMMainLayer,
                  TFLayoutLMModel,
                  TFLayoutLMPreTrainedModel,
              )

else:
       import sys

       a_				=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




