


"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__a			 =   {
    "configuration_markuplm": ["MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "MarkupLMConfig"],
    "feature_extraction_markuplm": ["MarkupLMFeatureExtractor"],
    "processing_markuplm": ["MarkupLMProcessor"],
    "tokenization_markuplm": ["MarkupLMTokenizer"],
}

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

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a			 =   [
						    "MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "MarkupLMForQuestionAnswering",
						    "MarkupLMForSequenceClassification",
						    "MarkupLMForTokenClassification",
						    "MarkupLMModel",
						    "MarkupLMPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_markuplm import MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP, MarkupLMConfig
						from .feature_extraction_markuplm import MarkupLMFeatureExtractor
						from .processing_markuplm import MarkupLMProcessor
						from .tokenization_markuplm import MarkupLMTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_markuplm_fast import MarkupLMTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_markuplm import (
												    MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MarkupLMForQuestionAnswering,
												    MarkupLMForSequenceClassification,
												    MarkupLMForTokenClassification,
												    MarkupLMModel,
												    MarkupLMPreTrainedModel,
												)


else:
						import sys

						__a			 =   _LazyModule(__name__, globals()["__file__"], _import_structure)



"""simple docstring"""







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


__a			 =   logging.get_logger(__name__)

__a			 =   {
    "google/pegasus-x-base": "https://huggingface.co/google/pegasus-x-base/resolve/main/config.json",
    "google/pegasus-x-large": "https://huggingface.co/google/pegasus-x-large/resolve/main/config.json",
    # See all PEGASUS-X models at https://huggingface.co/models?filter=pegasus-x
}
class 							lowerCamelCase       (			__SCREAMING_SNAKE_CASE ):


							"""simple docstring"""
							SCREAMING_SNAKE_CASE										=      """pegasus_x"""
							SCREAMING_SNAKE_CASE										=      ["""past_key_values"""]
							SCREAMING_SNAKE_CASE										=      {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}






							def __init__(						self :     int		, lowerCamelCase_ :     Any=96103		, lowerCamelCase_ :     Union[str, Any]=16384		, lowerCamelCase_ :     Any=16		, lowerCamelCase_ :     str=4096		, lowerCamelCase_ :     Tuple=16		, lowerCamelCase_ :     Optional[int]=16		, lowerCamelCase_ :     Tuple=4096		, lowerCamelCase_ :     Any=16		, lowerCamelCase_ :     List[str]=0.0		, lowerCamelCase_ :     int=0.0		, lowerCamelCase_ :     List[Any]=True		, lowerCamelCase_ :     str=True		, lowerCamelCase_ :     int="gelu"		, lowerCamelCase_ :     Optional[Any]=1024		, lowerCamelCase_ :     Optional[int]=0.1		, lowerCamelCase_ :     str=0.0		, lowerCamelCase_ :     Optional[Any]=0.0		, lowerCamelCase_ :     List[str]=0.02		, lowerCamelCase_ :     Optional[Any]=0		, lowerCamelCase_ :     List[str]=True		, lowerCamelCase_ :     List[Any]=0		, lowerCamelCase_ :     str=1		, lowerCamelCase_ :     Optional[Any]=1		, lowerCamelCase_ :     Tuple=32		, lowerCamelCase_ :     Dict=512		, lowerCamelCase_ :     List[Any]=True		, **lowerCamelCase_ :     int		, ):
													UpperCAmelCase__		=    vocab_size
													UpperCAmelCase__		=    max_position_embeddings
													UpperCAmelCase__		=    d_model
													UpperCAmelCase__		=    encoder_ffn_dim
													UpperCAmelCase__		=    encoder_layers
													UpperCAmelCase__		=    encoder_attention_heads
													UpperCAmelCase__		=    decoder_ffn_dim
													UpperCAmelCase__		=    decoder_layers
													UpperCAmelCase__		=    decoder_attention_heads
													UpperCAmelCase__		=    dropout
													UpperCAmelCase__		=    attention_dropout
													UpperCAmelCase__		=    activation_dropout
													UpperCAmelCase__		=    activation_function
													UpperCAmelCase__		=    init_std
													UpperCAmelCase__		=    encoder_layerdrop
													UpperCAmelCase__		=    decoder_layerdrop
													UpperCAmelCase__		=    use_cache
													UpperCAmelCase__		=    encoder_layers
													UpperCAmelCase__		=    scale_embedding  # scale factor will be sqrt(d_model) if True

													UpperCAmelCase__		=    num_global_tokens
													UpperCAmelCase__		=    block_size
													UpperCAmelCase__		=    stagger_local_blocks

													super().__init__(
													    pad_token_id=lowerCamelCase_		, eos_token_id=lowerCamelCase_		, is_encoder_decoder=lowerCamelCase_		, decoder_start_token_id=lowerCamelCase_		, forced_eos_token_id=lowerCamelCase_		, **lowerCamelCase_		, )






							@property
							def 					lowerCAmelCase     (						self :     Optional[int]				):
													return self.encoder_attention_heads






							@property
							def 					lowerCAmelCase     (						self :     Optional[int]				):
													return self.d_model