
'''simple docstring'''



from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


_lowercase          =						logging.get_logger(__name__)

_lowercase          =						{
    """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json""",
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}




class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						_lowercase						: str										=  '''blenderbot-small'''
						_lowercase						: List[Any]										=  ['''past_key_values''']
						_lowercase						: List[Any]										=  {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}




						def __init__(   self       ,  _lowercase=50_265       ,  _lowercase=512       ,  _lowercase=8       ,  _lowercase=2_048       ,  _lowercase=16       ,  _lowercase=8       ,  _lowercase=2_048       ,  _lowercase=16       ,  _lowercase=0.0       ,  _lowercase=0.0       ,  _lowercase=True       ,  _lowercase=True       ,  _lowercase="gelu"       ,  _lowercase=512       ,  _lowercase=0.1       ,  _lowercase=0.0       ,  _lowercase=0.0       ,  _lowercase=0.02       ,  _lowercase=1       ,  _lowercase=False       ,  _lowercase=0       ,  _lowercase=1       ,  _lowercase=2       ,  _lowercase=2       ,  **_lowercase       ,  ):

												"""simple docstring"""



												_lowerCAmelCase    		=					vocab_size
												_lowerCAmelCase    		=					max_position_embeddings
												_lowerCAmelCase    		=					d_model
												_lowerCAmelCase    		=					encoder_ffn_dim
												_lowerCAmelCase    		=					encoder_layers
												_lowerCAmelCase    		=					encoder_attention_heads
												_lowerCAmelCase    		=					decoder_ffn_dim
												_lowerCAmelCase    		=					decoder_layers
												_lowerCAmelCase    		=					decoder_attention_heads
												_lowerCAmelCase    		=					dropout
												_lowerCAmelCase    		=					attention_dropout
												_lowerCAmelCase    		=					activation_dropout
												_lowerCAmelCase    		=					activation_function
												_lowerCAmelCase    		=					init_std
												_lowerCAmelCase    		=					encoder_layerdrop
												_lowerCAmelCase    		=					decoder_layerdrop
												_lowerCAmelCase    		=					use_cache
												_lowerCAmelCase    		=					encoder_layers
												_lowerCAmelCase    		=					scale_embedding  # scale factor will be sqrt(d_model) if True

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






class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



												if self.task in ["default", "seq2seq-lm"]:
																		_lowerCAmelCase    		=					OrderedDict(
																		    [
																		        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
																		        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
																		    ] )

																		if self.use_past:
																								_lowerCAmelCase    		=					{0: """batch"""}
																								_lowerCAmelCase    		=					{0: """batch""", 1: """past_decoder_sequence + sequence"""}
																		else:
																								_lowerCAmelCase    		=					{0: """batch""", 1: """decoder_sequence"""}
																								_lowerCAmelCase    		=					{0: """batch""", 1: """decoder_sequence"""}

																		if self.use_past:
																								self.fill_with_past_key_values_(_lowercase       ,  direction="""inputs""" )
												elif self.task == "causal-lm":
																		# TODO: figure this case out.
																		_lowerCAmelCase    		=					OrderedDict(
																		    [
																		        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
																		        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
																		    ] )
																		if self.use_past:
																								_lowerCAmelCase		,       _lowerCAmelCase    		=					self.num_layers
																								for i in range(_lowercase ):
																														_lowerCAmelCase    		=					{0: """batch""", 2: """past_sequence + sequence"""}
																														_lowerCAmelCase    		=					{0: """batch""", 2: """past_sequence + sequence"""}
												else:
																		_lowerCAmelCase    		=					OrderedDict(
																		    [
																		        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
																		        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
																		        ("""decoder_input_ids""", {0: """batch""", 1: """decoder_sequence"""}),
																		        ("""decoder_attention_mask""", {0: """batch""", 1: """decoder_sequence"""}),
																		    ] )

												return common_inputs




						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



												if self.task in ["default", "seq2seq-lm"]:
																		_lowerCAmelCase    		=					super().outputs
												else:
																		_lowerCAmelCase    		=					super(_lowercase       ,  self ).outputs
																		if self.use_past:
																								_lowerCAmelCase		,       _lowerCAmelCase    		=					self.num_layers
																								for i in range(_lowercase ):
																														_lowerCAmelCase    		=					{0: """batch""", 2: """past_sequence + sequence"""}
																														_lowerCAmelCase    		=					{0: """batch""", 2: """past_sequence + sequence"""}
												return common_outputs




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase = -1       ,  _lowercase = -1       ,  _lowercase = False       ,  _lowercase = None       ,  ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
												    _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase )

												# Generate decoder inputs
												_lowerCAmelCase    		=					seq_length if not self.use_past else 1
												_lowerCAmelCase    		=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
												    _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase )
												_lowerCAmelCase    		=					{F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
												_lowerCAmelCase    		=					dict(**_lowercase       ,  **_lowercase )

												if self.use_past:
																		if not is_torch_available():
																								raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
																		else:
																								import torch
																		_lowerCAmelCase		,       _lowerCAmelCase    		=					common_inputs["""input_ids"""].shape
																		_lowerCAmelCase    		=					common_inputs["""decoder_input_ids"""].shape[1]
																		_lowerCAmelCase		,       _lowerCAmelCase    		=					self.num_attention_heads
																		_lowerCAmelCase    		=					(
																		    batch,
																		    num_encoder_attention_heads,
																		    encoder_seq_length,
																		    self._config.hidden_size // num_encoder_attention_heads,
																		)
																		_lowerCAmelCase    		=					decoder_seq_length + 3
																		_lowerCAmelCase    		=					(
																		    batch,
																		    num_decoder_attention_heads,
																		    decoder_past_length,
																		    self._config.hidden_size // num_decoder_attention_heads,
																		)

																		_lowerCAmelCase    		=					torch.cat(
																		    [common_inputs["""decoder_attention_mask"""], torch.ones(_lowercase       ,  _lowercase )]       ,  dim=1 )

																		_lowerCAmelCase    		=					[]
																		# If the number of encoder and decoder layers are present in the model configuration, both are considered
																		_lowerCAmelCase		,       _lowerCAmelCase    		=					self.num_layers
																		_lowerCAmelCase    		=					min(_lowercase       ,  _lowercase )
																		_lowerCAmelCase    		=					max(_lowercase       ,  _lowercase ) - min_num_layers
																		_lowerCAmelCase    		=					"""encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

																		for _ in range(_lowercase ):
																								common_inputs["past_key_values"].append(
																								    (
																								        torch.zeros(_lowercase ),
																								        torch.zeros(_lowercase ),
																								        torch.zeros(_lowercase ),
																								        torch.zeros(_lowercase ),
																								    ) )
																		# TODO: test this.
																		_lowerCAmelCase    		=					encoder_shape if remaining_side_name == """encoder""" else decoder_shape
																		for _ in range(_lowercase       ,  _lowercase ):
																								common_inputs["past_key_values"].append((torch.zeros(_lowercase ), torch.zeros(_lowercase )) )
												return common_inputs




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase = -1       ,  _lowercase = -1       ,  _lowercase = False       ,  _lowercase = None       ,  ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
												    _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase )

												if self.use_past:
																		if not is_torch_available():
																								raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
																		else:
																								import torch
																		_lowerCAmelCase		,       _lowerCAmelCase    		=					common_inputs["""input_ids"""].shape
																		# Not using the same length for past_key_values
																		_lowerCAmelCase    		=					seqlen + 2
																		_lowerCAmelCase		,       _lowerCAmelCase    		=					self.num_layers
																		_lowerCAmelCase		,       _lowerCAmelCase    		=					self.num_attention_heads
																		_lowerCAmelCase    		=					(
																		    batch,
																		    num_encoder_attention_heads,
																		    past_key_values_length,
																		    self._config.hidden_size // num_encoder_attention_heads,
																		)

																		_lowerCAmelCase    		=					common_inputs["""attention_mask"""].dtype
																		_lowerCAmelCase    		=					torch.cat(
																		    [common_inputs["""attention_mask"""], torch.ones(_lowercase       ,  _lowercase       ,  dtype=_lowercase )]       ,  dim=1 )
																		_lowerCAmelCase    		=					[
																		    (torch.zeros(_lowercase ), torch.zeros(_lowercase )) for _ in range(_lowercase )
																		]
												return common_inputs




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase = -1       ,  _lowercase = -1       ,  _lowercase = False       ,  _lowercase = None       ,  ):

												"""simple docstring"""



												_lowerCAmelCase    		=					compute_effective_axis_dimension(
												    _lowercase       ,  fixed_dimension=OnnxConfig.default_fixed_batch       ,  num_token_to_add=0 )

												# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
												_lowerCAmelCase    		=					tokenizer.num_special_tokens_to_add(_lowercase )
												_lowerCAmelCase    		=					compute_effective_axis_dimension(
												    _lowercase       ,  fixed_dimension=OnnxConfig.default_fixed_sequence       ,  num_token_to_add=_lowercase )

												# Generate dummy inputs according to compute batch and sequence
												_lowerCAmelCase    		=					[""" """.join([tokenizer.unk_token] ) * seq_length] * batch_size
												_lowerCAmelCase    		=					dict(tokenizer(_lowercase       ,  return_tensors=_lowercase ) )
												return common_inputs




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase = -1       ,  _lowercase = -1       ,  _lowercase = False       ,  _lowercase = None       ,  ):

												"""simple docstring"""



												if self.task in ["default", "seq2seq-lm"]:
																		_lowerCAmelCase    		=					self._generate_dummy_inputs_for_default_and_seqaseq_lm(
																		    _lowercase       ,  batch_size=_lowercase       ,  seq_length=_lowercase       ,  is_pair=_lowercase       ,  framework=_lowercase )

												elif self.task == "causal-lm":
																		_lowerCAmelCase    		=					self._generate_dummy_inputs_for_causal_lm(
																		    _lowercase       ,  batch_size=_lowercase       ,  seq_length=_lowercase       ,  is_pair=_lowercase       ,  framework=_lowercase )
												else:
																		_lowerCAmelCase    		=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
																		    _lowercase       ,  batch_size=_lowercase       ,  seq_length=_lowercase       ,  is_pair=_lowercase       ,  framework=_lowercase )

												return common_inputs




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase ):

												"""simple docstring"""



												if self.task in ["default", "seq2seq-lm"]:
																		_lowerCAmelCase    		=					super()._flatten_past_key_values_(_lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase )
												else:
																		_lowerCAmelCase    		=					super(_lowercase       ,  self )._flatten_past_key_values_(
																		    _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase )






"""simple docstring"""






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
			from ...processing_utils import ProcessorMixin
			from ...utils import TensorType


lowercase__			=		logging.get_logger(__name__)

lowercase__			=		{
    """microsoft/layoutlmv3-base""": """https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json""",
}






class lowerCAmelCase__      (	lowercase		):






			'''simple docstring'''



			lowerCamelCase__ =     """layoutlmv3"""
			def __init__(    self    , lowercase=50265    , lowercase=768    , lowercase=12    , lowercase=12    , lowercase=3072    , lowercase="gelu"    , lowercase=0.1    , lowercase=0.1    , lowercase=512    , lowercase=2    , lowercase=0.02    , lowercase=1E-5    , lowercase=1    , lowercase=0    , lowercase=2    , lowercase=1024    , lowercase=128    , lowercase=128    , lowercase=True    , lowercase=32    , lowercase=128    , lowercase=64    , lowercase=256    , lowercase=True    , lowercase=True    , lowercase=True    , lowercase=224    , lowercase=3    , lowercase=16    , lowercase=None    , **lowercase    , ):
							super().__init__(
							    vocab_size=lowercase    , hidden_size=lowercase    , num_hidden_layers=lowercase    , num_attention_heads=lowercase    , intermediate_size=lowercase    , hidden_act=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    , pad_token_id=lowercase    , bos_token_id=lowercase    , eos_token_id=lowercase    , **lowercase    , )
							_lowerCamelCase	:     List[str]       =       max_ad_position_embeddings
							_lowerCamelCase	:     Optional[Any]       =       coordinate_size
							_lowerCamelCase	:     int       =       shape_size
							_lowerCamelCase	:     Optional[Any]       =       has_relative_attention_bias
							_lowerCamelCase	:     Dict       =       rel_pos_bins
							_lowerCamelCase	:     Tuple       =       max_rel_pos
							_lowerCamelCase	:     int       =       has_spatial_attention_bias
							_lowerCamelCase	:     Optional[int]       =       rel_ad_pos_bins
							_lowerCamelCase	:     List[Any]       =       max_rel_ad_pos
							_lowerCamelCase	:     Any       =       text_embed
							_lowerCamelCase	:     List[Any]       =       visual_embed
							_lowerCamelCase	:     str       =       input_size
							_lowerCamelCase	:     Optional[Any]       =       num_channels
							_lowerCamelCase	:     Tuple       =       patch_size
							_lowerCamelCase	:     Optional[int]       =       classifier_dropout






class lowerCAmelCase__      (	lowercase		):






			'''simple docstring'''



			lowerCamelCase__ =     version.parse("""1.12"""		)
			@property
			def    A_       (    self    ):
							# The order of inputs is different for question answering and sequence classification
							if self.task in ["question-answering", "sequence-classification"]:
											return OrderedDict(
											    [
											        ('input_ids', {0: 'batch', 1: 'sequence'}),
											        ('attention_mask', {0: 'batch', 1: 'sequence'}),
											        ('bbox', {0: 'batch', 1: 'sequence'}),
											        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
											    ]    )
							else:
											return OrderedDict(
											    [
											        ('input_ids', {0: 'batch', 1: 'sequence'}),
											        ('bbox', {0: 'batch', 1: 'sequence'}),
											        ('attention_mask', {0: 'batch', 1: 'sequence'}),
											        ('pixel_values', {0: 'batch', 1: 'num_channels'}),
											    ]    )
			@property
			def    A_       (    self    ):
							return 1E-5
			@property
			def    A_       (    self    ):
							return 12






			def    A_       (    self    , lowercase    , lowercase = -1    , lowercase = -1    , lowercase = False    , lowercase = None    , lowercase = 3    , lowercase = 40    , lowercase = 40    , ):
							setattr(processor.image_processor    , 'apply_ocr'    , lowercase    )

							# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
							_lowerCamelCase	:     Any       =       compute_effective_axis_dimension(
							    lowercase    , fixed_dimension=OnnxConfig.default_fixed_batch    , num_token_to_add=0    )
							# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
							_lowerCamelCase	:     Tuple       =       processor.tokenizer.num_special_tokens_to_add(lowercase    )
							_lowerCamelCase	:     Any       =       compute_effective_axis_dimension(
							    lowercase    , fixed_dimension=OnnxConfig.default_fixed_sequence    , num_token_to_add=lowercase    )
							# Generate dummy inputs according to compute batch and sequence
							_lowerCamelCase	:     List[Any]       =       [[' '.join([processor.tokenizer.unk_token]    ) * seq_length]] * batch_size

							# Generate dummy bounding boxes
							_lowerCamelCase	:     List[str]       =       [[[48, 84, 73, 128]]] * batch_size

							# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
							# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
							_lowerCamelCase	:     Tuple       =       self._generate_dummy_images(lowercase    , lowercase    , lowercase    , lowercase    )

							_lowerCamelCase	:     str       =       dict(
							    processor(
							        lowercase    , text=lowercase    , boxes=lowercase    , return_tensors=lowercase    , )    )

							return inputs