





"""simple docstring"""



from typing import Callable, Optional, Union

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


a_				=    logging.get_logger(__name__)

a_				=    {
    """microsoft/prophetnet-large-uncased""": (
        """https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json"""
    ),
}






class 	__snake_case      (	SCREAMING_SNAKE_CASE__  ):





	"""simple docstring"""


	_lowerCamelCase				 =  """prophetnet"""
	_lowerCamelCase				 =  ["""past_key_values"""]
	_lowerCamelCase				 =  {
	    """num_attention_heads""": """num_encoder_attention_heads""",
	}

	def __init__(					self      ,		__lowerCamelCase = 0.1      ,		__lowerCamelCase = "gelu"      ,		__lowerCamelCase = 3_0522      ,		__lowerCamelCase = 1024      ,		__lowerCamelCase = 4096      ,		__lowerCamelCase = 12      ,		__lowerCamelCase = 16      ,		__lowerCamelCase = 4096      ,		__lowerCamelCase = 12      ,		__lowerCamelCase = 16      ,		__lowerCamelCase = 0.1      ,		__lowerCamelCase = 0.1      ,		__lowerCamelCase = 512      ,		__lowerCamelCase = 0.0_2      ,		__lowerCamelCase = True      ,		__lowerCamelCase = True      ,		__lowerCamelCase = 0      ,		__lowerCamelCase = 2      ,		__lowerCamelCase = 32      ,		__lowerCamelCase = 128      ,		__lowerCamelCase = False      ,		__lowerCamelCase = 0.0      ,		__lowerCamelCase = True      ,		__lowerCamelCase = 0      ,		__lowerCamelCase = 1      ,		__lowerCamelCase = 2      ,		**__lowerCamelCase      ,		):



					'''simple docstring'''




					__A					:		int						=  vocab_size
					__A					:		str						=  hidden_size
					__A					:		Union[str, Any]						=  encoder_ffn_dim
					__A					:		Union[str, Any]						=  num_encoder_layers
					__A					:		List[Any]						=  num_encoder_attention_heads
					__A					:		str						=  decoder_ffn_dim
					__A					:		str						=  num_decoder_layers
					__A					:		List[str]						=  num_decoder_attention_heads
					__A					:		str						=  max_position_embeddings
					__A					:		str						=  init_std  # Normal(0, this parameter)
					__A					:		Tuple						=  activation_function

					# parameters for prophetnet
					__A					:		Tuple						=  ngram
					__A					:		List[Any]						=  num_buckets
					__A					:		List[str]						=  relative_max_distance
					__A					:		List[Any]						=  disable_ngram_loss
					__A					:		Tuple						=  eps

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

					__A					:		List[str]						=  use_cache

					super().__init__(
					    pad_token_id=__lowerCamelCase      ,		bos_token_id=__lowerCamelCase      ,		eos_token_id=__lowerCamelCase      ,		is_encoder_decoder=__lowerCamelCase      ,		add_cross_attention=__lowerCamelCase      ,		decoder_start_token_id=__lowerCamelCase      ,		**__lowerCamelCase      ,		)

	@property
	def       UpperCamelCase__(					self						):



					'''simple docstring'''




					return self.num_encoder_layers + self.num_decoder_layers







	@num_hidden_layers.setter
	def       UpperCamelCase__(					self      ,		__lowerCamelCase						):



					'''simple docstring'''




					raise NotImplementedError(
					    '''This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'''
					    ''' `num_decoder_layers`.'''						)











"""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__)




