






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

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


lowerCamelCase : Tuple						   =       logging.get_logger(__name__)

lowerCamelCase : Any						   =       {
    '''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json''',
    # See all Marian models at https://huggingface.co/models?filter=marian
}

class 		lowerCAmelCase     ( __a			):

					'''simple docstring'''

					_A						:			Any									=      '''marian'''
					_A						:			Optional[Any]									=      ['''past_key_values''']
					_A						:			Dict									=      {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}



					def __init__(  self				:	List[Any]		,     __a				:	Dict=58101		,     __a				:	Optional[int]=None		,     __a				:	int=1024		,     __a				:	Dict=12		,     __a				:	Optional[Any]=4096		,     __a				:	Union[str, Any]=16		,     __a				:	str=12		,     __a				:	Optional[int]=4096		,     __a				:	Optional[Any]=16		,     __a				:	Any=0.0		,     __a				:	Union[str, Any]=0.0		,     __a				:	Dict=True		,     __a				:	str=True		,     __a				:	str="gelu"		,     __a				:	Dict=1024		,     __a				:	Dict=0.1		,     __a				:	Tuple=0.0		,     __a				:	str=0.0		,     __a				:	Any=0.02		,     __a				:	Optional[Any]=58100		,     __a				:	Union[str, Any]=False		,     __a				:	Union[str, Any]=58100		,     __a				:	Optional[Any]=0		,     __a				:	int=0		,     __a				:	Dict=True		,     **__a				:	List[str]		,     )			->  Union[str, Any]:


												"""simple docstring"""

												__lowercase      :							Any				=     vocab_size
												__lowercase      :							str				=     decoder_vocab_size or vocab_size
												__lowercase      :							Any				=     max_position_embeddings
												__lowercase      :							Optional[Any]				=     d_model
												__lowercase      :							int				=     encoder_ffn_dim
												__lowercase      :							List[str]				=     encoder_layers
												__lowercase      :							Union[str, Any]				=     encoder_attention_heads
												__lowercase      :							Tuple				=     decoder_ffn_dim
												__lowercase      :							Any				=     decoder_layers
												__lowercase      :							Tuple				=     decoder_attention_heads
												__lowercase      :							Optional[Any]				=     dropout
												__lowercase      :							Dict				=     attention_dropout
												__lowercase      :							Optional[Any]				=     activation_dropout
												__lowercase      :							Optional[int]				=     activation_function
												__lowercase      :							Union[str, Any]				=     init_std
												__lowercase      :							Union[str, Any]				=     encoder_layerdrop
												__lowercase      :							Dict				=     decoder_layerdrop
												__lowercase      :							Optional[Any]				=     use_cache
												__lowercase      :							Union[str, Any]				=     encoder_layers
												__lowercase      :							List[str]				=     scale_embedding  # scale factor will be sqrt(d_model) if True
												__lowercase      :							List[Any]				=     share_encoder_decoder_embeddings
												super().__init__(
												    pad_token_id=__a		,     eos_token_id=__a		,     is_encoder_decoder=__a		,     decoder_start_token_id=__a		,     forced_eos_token_id=__a		,     **__a		,     )







class 		lowerCAmelCase     ( __a			):

					'''simple docstring'''

					@property
					# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
					def       lowerCAmelCase				(  self				:	Tuple							)			->  Mapping[str, Mapping[int, str]]:


												"""simple docstring"""

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

																			if self.use_past:
																										__lowercase      :							List[str]				=     {0: """batch"""}
																										__lowercase      :							Optional[int]				=     {0: """batch""", 1: """past_decoder_sequence + sequence"""}
																			else:
																										__lowercase      :							int				=     {0: """batch""", 1: """decoder_sequence"""}
																										__lowercase      :							List[str]				=     {0: """batch""", 1: """decoder_sequence"""}

																			if self.use_past:
																										self.fill_with_past_key_values_(__a		,     direction="""inputs"""							)
												elif self.task == "causal-lm":
																			# TODO: figure this case out.
																			__lowercase      :							List[Any]				=     OrderedDict(
																			    [
																			        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
																			        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
																			    ]							)
																			if self.use_past:
																										__lowercase      :							List[str]				=     self.num_layers
																										for i in range(__a							):
																																	__lowercase      :							Optional[Any]				=     {0: """batch""", 2: """past_sequence + sequence"""}
																																	__lowercase      :							Tuple				=     {0: """batch""", 2: """past_sequence + sequence"""}
												else:
																			__lowercase      :							Optional[int]				=     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
					# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
					def       lowerCAmelCase				(  self				:	Dict							)			->  Mapping[str, Mapping[int, str]]:


												"""simple docstring"""

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



					def       lowerCAmelCase				(  self				:	Any		,     __a				:	PreTrainedTokenizer		,     __a				:	int = -1		,     __a				:	int = -1		,     __a				:	bool = False		,     __a				:	Optional[TensorType] = None		,     )			->  Mapping[str, Any]:


												"""simple docstring"""

												__lowercase      :							Dict				=     self._generate_dummy_inputs_for_encoder_and_decoder(
												    __a		,     __a		,     __a		,     __a		,     __a							)

												# Generate decoder inputs
												__lowercase      :							Any				=     seq_length if not self.use_past else 1
												__lowercase      :							Union[str, Any]				=     self._generate_dummy_inputs_for_encoder_and_decoder(
												    __a		,     __a		,     __a		,     __a		,     __a							)
												__lowercase      :							Optional[Any]				=     {F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
												__lowercase      :							List[str]				=     dict(**__a		,     **__a							)

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

																			__lowercase      :							Optional[int]				=     torch.cat(
																			    [common_inputs["""decoder_attention_mask"""], torch.ones(__a		,     __a							)]		,     dim=1							)

																			__lowercase      :							Union[str, Any]				=     []
																			# If the number of encoder and decoder layers are present in the model configuration, both are considered
																			__lowercase      :							Any				=     self.num_layers
																			__lowercase      :							str				=     min(__a		,     __a							)
																			__lowercase      :							int				=     max(__a		,     __a							) - min_num_layers
																			__lowercase      :							List[str]				=     """encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

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



					def       lowerCAmelCase				(  self				:	Optional[int]		,     __a				:	PreTrainedTokenizer		,     __a				:	int = -1		,     __a				:	int = -1		,     __a				:	bool = False		,     __a				:	Optional[TensorType] = None		,     )			->  Mapping[str, Any]:


												"""simple docstring"""

												__lowercase      :							Tuple				=     self._generate_dummy_inputs_for_encoder_and_decoder(
												    __a		,     __a		,     __a		,     __a		,     __a							)

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

																			__lowercase      :							str				=     common_inputs["""attention_mask"""].dtype
																			__lowercase      :							Union[str, Any]				=     torch.cat(
																			    [common_inputs["""attention_mask"""], torch.ones(__a		,     __a		,     dtype=__a							)]		,     dim=1							)
																			__lowercase      :							Union[str, Any]				=     [
																			    (torch.zeros(__a							), torch.zeros(__a							)) for _ in range(__a							)
																			]
												return common_inputs



					def       lowerCAmelCase				(  self				:	List[str]		,     __a				:	PreTrainedTokenizer		,     __a				:	int = -1		,     __a				:	int = -1		,     __a				:	bool = False		,     __a				:	Optional[TensorType] = None		,     )			->  Mapping[str, Any]:


												"""simple docstring"""

												__lowercase      :							Union[str, Any]				=     compute_effective_axis_dimension(
												    __a		,     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
												__lowercase      :							str				=     tokenizer.num_special_tokens_to_add(__a							)
												__lowercase      :							Any				=     compute_effective_axis_dimension(
												    __a		,     fixed_dimension=OnnxConfig.default_fixed_sequence		,     num_token_to_add=__a							)

												# Generate dummy inputs according to compute batch and sequence
												__lowercase      :							int				=     [""" """.join([tokenizer.unk_token]							) * seq_length] * batch_size
												__lowercase      :							Union[str, Any]				=     dict(tokenizer(__a		,     return_tensors=__a							)							)
												return common_inputs



					def       lowerCAmelCase				(  self				:	int		,     __a				:	PreTrainedTokenizer		,     __a				:	int = -1		,     __a				:	int = -1		,     __a				:	bool = False		,     __a				:	Optional[TensorType] = None		,     )			->  Mapping[str, Any]:


												"""simple docstring"""

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

												else:
																			__lowercase      :							Optional[Any]				=     self._generate_dummy_inputs_for_causal_lm(
																			    __a		,     batch_size=__a		,     seq_length=__a		,     is_pair=__a		,     framework=__a							)

												return common_inputs



					def       lowerCAmelCase				(  self				:	Union[str, Any]		,     __a				:	Dict		,     __a				:	Any		,     __a				:	Optional[Any]		,     __a				:	str							)			->  int:


												"""simple docstring"""

												if self.task in ["default", "seq2seq-lm"]:
																			__lowercase      :							Tuple				=     super()._flatten_past_key_values_(__a		,     __a		,     __a		,     __a							)
												else:
																			__lowercase      :							List[str]				=     super(__a		,     self							)._flatten_past_key_values_(
																			    __a		,     __a		,     __a		,     __a							)



					@property
					def       lowerCAmelCase				(  self				:	str							)			->  float:


												"""simple docstring"""

												return 1E-4







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


lowerCamelCase : Optional[int]						   =       logging.get_logger(__name__)

lowerCamelCase : Tuple						   =       {
    '''microsoft/layoutlmv3-base''': '''https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json''',
}

class 		lowerCAmelCase     ( __a			):

					'''simple docstring'''

					_A						:			int									=      '''layoutlmv3'''



					def __init__(  self				:	Dict		,     __a				:	List[str]=50265		,     __a				:	str=768		,     __a				:	List[Any]=12		,     __a				:	List[Any]=12		,     __a				:	List[str]=3072		,     __a				:	Optional[Any]="gelu"		,     __a				:	Optional[int]=0.1		,     __a				:	List[Any]=0.1		,     __a				:	Tuple=512		,     __a				:	int=2		,     __a				:	Any=0.02		,     __a				:	Union[str, Any]=1E-5		,     __a				:	List[str]=1		,     __a				:	List[Any]=0		,     __a				:	int=2		,     __a				:	str=1024		,     __a				:	str=128		,     __a				:	List[Any]=128		,     __a				:	Tuple=True		,     __a				:	Optional[int]=32		,     __a				:	Any=128		,     __a				:	List[Any]=64		,     __a				:	Tuple=256		,     __a				:	str=True		,     __a				:	int=True		,     __a				:	Optional[Any]=True		,     __a				:	Any=224		,     __a				:	str=3		,     __a				:	List[str]=16		,     __a				:	Union[str, Any]=None		,     **__a				:	List[Any]		,     )			->  List[str]:


												"""simple docstring"""

												super().__init__(
												    vocab_size=__a		,     hidden_size=__a		,     num_hidden_layers=__a		,     num_attention_heads=__a		,     intermediate_size=__a		,     hidden_act=__a		,     hidden_dropout_prob=__a		,     attention_probs_dropout_prob=__a		,     max_position_embeddings=__a		,     type_vocab_size=__a		,     initializer_range=__a		,     layer_norm_eps=__a		,     pad_token_id=__a		,     bos_token_id=__a		,     eos_token_id=__a		,     **__a		,     )
												__lowercase      :							int				=     max_ad_position_embeddings
												__lowercase      :							Any				=     coordinate_size
												__lowercase      :							Optional[Any]				=     shape_size
												__lowercase      :							str				=     has_relative_attention_bias
												__lowercase      :							int				=     rel_pos_bins
												__lowercase      :							Union[str, Any]				=     max_rel_pos
												__lowercase      :							str				=     has_spatial_attention_bias
												__lowercase      :							str				=     rel_ad_pos_bins
												__lowercase      :							List[Any]				=     max_rel_ad_pos
												__lowercase      :							Tuple				=     text_embed
												__lowercase      :							int				=     visual_embed
												__lowercase      :							Tuple				=     input_size
												__lowercase      :							Dict				=     num_channels
												__lowercase      :							str				=     patch_size
												__lowercase      :							Optional[int]				=     classifier_dropout







class 		lowerCAmelCase     ( __a			):

					'''simple docstring'''

					_A						:			str									=      version.parse('''1.12'''			)



					@property
					def       lowerCAmelCase				(  self				:	Optional[Any]							)			->  Mapping[str, Mapping[int, str]]:


												"""simple docstring"""

												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       lowerCAmelCase				(  self				:	Union[str, Any]							)			->  float:


												"""simple docstring"""

												return 1E-5



					@property
					def       lowerCAmelCase				(  self				:	str							)			->  int:


												"""simple docstring"""

												return 12



					def       lowerCAmelCase				(  self				:	List[Any]		,     __a				:	"ProcessorMixin"		,     __a				:	int = -1		,     __a				:	int = -1		,     __a				:	bool = False		,     __a				:	Optional["TensorType"] = None		,     __a				:	int = 3		,     __a				:	int = 40		,     __a				:	int = 40		,     )			->  Mapping[str, Any]:


												"""simple docstring"""

												setattr(processor.image_processor		,     """apply_ocr"""		,     __a							)

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

												# Generate dummy bounding boxes
												__lowercase      :							Tuple				=     [[[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)
												__lowercase      :							Tuple				=     self._generate_dummy_images(__a		,     __a		,     __a		,     __a							)

												__lowercase      :							int				=     dict(
												    processor(
												        __a		,     text=__a		,     boxes=__a		,     return_tensors=__a		,     )							)

												return inputs