"""simple docstring"""




from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


__lowerCamelCase									=	logging.get_logger(__name__)

__lowerCamelCase									=	{
    "google/long-t5-local-base": "https://huggingface.co/google/long-t5-local-base/blob/main/config.json",
    "google/long-t5-local-large": "https://huggingface.co/google/long-t5-local-large/blob/main/config.json",
    "google/long-t5-tglobal-base": "https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json",
    "google/long-t5-tglobal-large": "https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json",
}
class      UpperCamelCase__(      __A			):
						lowerCAmelCase__	:    Optional[int]    				=		'longt5'
						lowerCAmelCase__	:    Dict    				=		['past_key_values']
						lowerCAmelCase__	:    List[str]    				=		{'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}





						def __init__(							self		,__UpperCAmelCase=3_21_28		,__UpperCAmelCase=5_12		,__UpperCAmelCase=64		,__UpperCAmelCase=20_48		,__UpperCAmelCase=6		,__UpperCAmelCase=None		,__UpperCAmelCase=8		,__UpperCAmelCase=1_27		,__UpperCAmelCase=16		,__UpperCAmelCase=32		,__UpperCAmelCase=1_28		,__UpperCAmelCase=0.1		,__UpperCAmelCase=1e-6		,__UpperCAmelCase=1.0		,__UpperCAmelCase="relu"		,__UpperCAmelCase=True		,__UpperCAmelCase="local"		,__UpperCAmelCase=True		,__UpperCAmelCase=0		,__UpperCAmelCase=1		,**__UpperCAmelCase		,)       ->					Optional[int]:
													A__						  =				vocab_size
													A__						  =				d_model
													A__						  =				d_kv
													A__						  =				d_ff
													A__						  =				num_layers
													# default = symmetry
													A__						  =				num_decoder_layers if num_decoder_layers is not None else self.num_layers
													A__						  =				num_heads
													A__						  =				local_radius
													A__						  =				global_block_size
													A__						  =				relative_attention_num_buckets
													A__						  =				relative_attention_max_distance
													A__						  =				dropout_rate
													A__						  =				layer_norm_epsilon
													A__						  =				initializer_factor
													A__						  =				feed_forward_proj
													A__						  =				encoder_attention_type
													A__						  =				use_cache

													A__						  =				self.feed_forward_proj.split('-'  )
													A__						  =				act_info[-1]
													A__						  =				act_info[0] == 'gated'

													if len(__UpperCAmelCase  ) > 1 and act_info[0] != "gated" or len(__UpperCAmelCase  ) > 2:
																				raise ValueError(
																				    f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
																				    'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
																				    '\'gated-gelu\' or \'relu\''  )

													# for backwards compatibility
													if feed_forward_proj == "gated-gelu":
																				A__						  =				'gelu_new'

													super().__init__(
													    pad_token_id=__UpperCAmelCase		,eos_token_id=__UpperCAmelCase		,is_encoder_decoder=__UpperCAmelCase		,**__UpperCAmelCase		,)






class      UpperCamelCase__(      __A			):





						@property
						def 					snake_case__						(							self  )       ->					Mapping[str, Mapping[int, str]]:
													A__						  =				{
													    'input_ids': {0: 'batch', 1: 'encoder_sequence'},
													    'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
													}
													if self.use_past:
																				A__						  =				'past_encoder_sequence + sequence'
																				A__						  =				{0: 'batch'}
																				A__						  =				{0: 'batch', 1: 'past_decoder_sequence + sequence'}
													else:
																				A__						  =				{0: 'batch', 1: 'decoder_sequence'}
																				A__						  =				{0: 'batch', 1: 'decoder_sequence'}

													if self.use_past:
																				self.fill_with_past_key_values_(__UpperCAmelCase		,direction='inputs'  )

													return common_inputs





						@property
						def 					snake_case__						(							self  )       ->					int:
													return 13






"""simple docstring"""




from typing import TYPE_CHECKING

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


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

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

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

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase									=	[
				    "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

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





