from typing import Mapping

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


__a  :					int          =  logging.get_logger(__name__)

__a  :					Any          =  {
    """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				(						_UpperCAmelCase			):


			"""simple docstring"""






			__a  :			Union[str, Any]      			=						'''longt5'''
			__a  :			Union[str, Any]      			=						['''past_key_values''']
			__a  :			Tuple      			=						{'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}





			def __init__(       self				,       lowerCAmelCase__=3_21_28				,       lowerCAmelCase__=5_12				,       lowerCAmelCase__=64				,       lowerCAmelCase__=20_48				,       lowerCAmelCase__=6				,       lowerCAmelCase__=None				,       lowerCAmelCase__=8				,       lowerCAmelCase__=1_27				,       lowerCAmelCase__=16				,       lowerCAmelCase__=32				,       lowerCAmelCase__=1_28				,       lowerCAmelCase__=0.1				,       lowerCAmelCase__=1E-6				,       lowerCAmelCase__=1.0				,       lowerCAmelCase__="relu"				,       lowerCAmelCase__=True				,       lowerCAmelCase__="local"				,       lowerCAmelCase__=True				,       lowerCAmelCase__=0				,       lowerCAmelCase__=1				,       **lowerCAmelCase__				,       )					-> Any:

							'''simple docstring'''


							__lowercase			      =							vocab_size
							__lowercase			      =							d_model
							__lowercase			      =							d_kv
							__lowercase			      =							d_ff
							__lowercase			      =							num_layers
							# default = symmetry
							__lowercase			      =							num_decoder_layers if num_decoder_layers is not None else self.num_layers
							__lowercase			      =							num_heads
							__lowercase			      =							local_radius
							__lowercase			      =							global_block_size
							__lowercase			      =							relative_attention_num_buckets
							__lowercase			      =							relative_attention_max_distance
							__lowercase			      =							dropout_rate
							__lowercase			      =							layer_norm_epsilon
							__lowercase			      =							initializer_factor
							__lowercase			      =							feed_forward_proj
							__lowercase			      =							encoder_attention_type
							__lowercase			      =							use_cache

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

							if len(lowerCAmelCase__						) > 1 and act_info[0] != "gated" or len(lowerCAmelCase__						) > 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":
											__lowercase			      =							'''gelu_new'''

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




class        _UpperCamelCase				(						_UpperCAmelCase			):


			"""simple docstring"""






			@property
			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Mapping[str, Mapping[int, str]]:

							'''simple docstring'''


							__lowercase			      =							{
							    '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
							    '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
							}
							if self.use_past:
											__lowercase			      =							'''past_encoder_sequence + sequence'''
											__lowercase			      =							{0: '''batch'''}
											__lowercase			      =							{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
							else:
											__lowercase			      =							{0: '''batch''', 1: '''decoder_sequence'''}
											__lowercase			      =							{0: '''batch''', 1: '''decoder_sequence'''}

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

							return common_inputs





			@property
			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> int:

							'''simple docstring'''


							return 13
from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PretrainedConfig, PreTrainedTokenizer
from ...onnx import OnnxConfig, PatchingSpec
from ...utils import TensorType, is_torch_available, logging


__a  :					int          =  logging.get_logger(__name__)

__a  :					Optional[Any]          =  {
    """microsoft/layoutlm-base-uncased""": (
        """https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/config.json"""
    ),
    """microsoft/layoutlm-large-uncased""": (
        """https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/config.json"""
    ),
}




class        _UpperCamelCase				(						_UpperCAmelCase			):


			"""simple docstring"""






			__a  :			List[Any]      			=						'''layoutlm'''





			def __init__(       self				,       lowerCAmelCase__=3_05_22				,       lowerCAmelCase__=7_68				,       lowerCAmelCase__=12				,       lowerCAmelCase__=12				,       lowerCAmelCase__=30_72				,       lowerCAmelCase__="gelu"				,       lowerCAmelCase__=0.1				,       lowerCAmelCase__=0.1				,       lowerCAmelCase__=5_12				,       lowerCAmelCase__=2				,       lowerCAmelCase__=0.02				,       lowerCAmelCase__=1E-12				,       lowerCAmelCase__=0				,       lowerCAmelCase__="absolute"				,       lowerCAmelCase__=True				,       lowerCAmelCase__=10_24				,       **lowerCAmelCase__				,       )					-> Tuple:

							'''simple docstring'''


							super().__init__(pad_token_id=lowerCAmelCase__				,       **lowerCAmelCase__						)
							__lowercase			      =							vocab_size
							__lowercase			      =							hidden_size
							__lowercase			      =							num_hidden_layers
							__lowercase			      =							num_attention_heads
							__lowercase			      =							hidden_act
							__lowercase			      =							intermediate_size
							__lowercase			      =							hidden_dropout_prob
							__lowercase			      =							attention_probs_dropout_prob
							__lowercase			      =							max_position_embeddings
							__lowercase			      =							type_vocab_size
							__lowercase			      =							initializer_range
							__lowercase			      =							layer_norm_eps
							__lowercase			      =							position_embedding_type
							__lowercase			      =							use_cache
							__lowercase			      =							max_ad_position_embeddings




class        _UpperCamelCase				(						_UpperCAmelCase			):


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__				,       lowerCAmelCase__ = "default"				,       lowerCAmelCase__ = None				,       )					-> str:

							'''simple docstring'''


							super().__init__(lowerCAmelCase__				,       task=lowerCAmelCase__				,       patching_specs=lowerCAmelCase__						)
							__lowercase			      =							config.max_ad_position_embeddings - 1





			@property
			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Mapping[str, Mapping[int, str]]:

							'''simple docstring'''


							return OrderedDict(
							    [
							        ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
							        ('''bbox''', {0: '''batch''', 1: '''sequence'''}),
							        ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
							        ('''token_type_ids''', {0: '''batch''', 1: '''sequence'''}),
							    ]						)





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__ = -1				,       lowerCAmelCase__ = -1				,       lowerCAmelCase__ = False				,       lowerCAmelCase__ = None				,       )					-> Mapping[str, Any]:

							'''simple docstring'''


							__lowercase			      =							super().generate_dummy_inputs(
							    lowerCAmelCase__				,       batch_size=lowerCAmelCase__				,       seq_length=lowerCAmelCase__				,       is_pair=lowerCAmelCase__				,       framework=lowerCAmelCase__						)

							# Generate a dummy bbox
							__lowercase			      =							[48, 84, 73, 1_28]

							if not framework == TensorType.PYTORCH:
											raise NotImplementedError('''Exporting LayoutLM to ONNX is currently only supported for PyTorch.'''						)

							if not is_torch_available():
											raise ValueError('''Cannot generate dummy inputs without PyTorch installed.'''						)
							import torch

							__lowercase      ,					__lowercase			      =							input_dict['''input_ids'''].shape
							__lowercase			      =							torch.tensor([*[box] * seq_length]						).tile(lowerCAmelCase__				,       1				,       1						)
							return input_dict