





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


UpperCAmelCase		:  Union[str, Any]         =	logging.get_logger(__name__)

UpperCAmelCase		:  List[str]         =	{
    '''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 		_A(   a__  ):


				"""simple docstring"""


				UpperCamelCase       :    Optional[Any]          =      '''marian'''
				UpperCamelCase       :    List[str]          =      ['''past_key_values''']
				UpperCamelCase       :    Tuple          =      {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}



				def __init__(     self	,   _A=58101	,   _A=None	,   _A=1024	,   _A=12	,   _A=4096	,   _A=16	,   _A=12	,   _A=4096	,   _A=16	,   _A=0.0	,   _A=0.0	,   _A=True	,   _A=True	,   _A="gelu"	,   _A=1024	,   _A=0.1	,   _A=0.0	,   _A=0.0	,   _A=0.0_2	,   _A=58100	,   _A=False	,   _A=58100	,   _A=0	,   _A=0	,   _A=True	,   **_A	,   ):
									__A      :		Dict         =							vocab_size
									__A      :		Optional[Any]         =							decoder_vocab_size or vocab_size
									__A      :		int         =							max_position_embeddings
									__A      :		Union[str, Any]         =							d_model
									__A      :		Union[str, Any]         =							encoder_ffn_dim
									__A      :		List[Any]         =							encoder_layers
									__A      :		Union[str, Any]         =							encoder_attention_heads
									__A      :		str         =							decoder_ffn_dim
									__A      :		Dict         =							decoder_layers
									__A      :		Union[str, Any]         =							decoder_attention_heads
									__A      :		List[str]         =							dropout
									__A      :		List[Any]         =							attention_dropout
									__A      :		Union[str, Any]         =							activation_dropout
									__A      :		int         =							activation_function
									__A      :		Optional[Any]         =							init_std
									__A      :		Any         =							encoder_layerdrop
									__A      :		Tuple         =							decoder_layerdrop
									__A      :		Optional[int]         =							use_cache
									__A      :		str         =							encoder_layers
									__A      :		List[Any]         =							scale_embedding  # scale factor will be sqrt(d_model) if True
									__A      :		List[Any]         =							share_encoder_decoder_embeddings
									super().__init__(
									    pad_token_id=_snake_case	,   eos_token_id=_snake_case	,   is_encoder_decoder=_snake_case	,   decoder_start_token_id=_snake_case	,   forced_eos_token_id=_snake_case	,   **_snake_case	,   )





class 		_A(   a__  ):


				"""simple docstring"""


				@property
				# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
				def    UpperCAmelCase_						(     self	):






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

														if self.use_past:
																			__A      :		Dict         =							{0: 'batch'}
																			__A      :		Optional[int]         =							{0: 'batch', 1: 'past_decoder_sequence + sequence'}
														else:
																			__A      :		Tuple         =							{0: 'batch', 1: 'decoder_sequence'}
																			__A      :		Tuple         =							{0: 'batch', 1: 'decoder_sequence'}

														if self.use_past:
																			self.fill_with_past_key_values_(_snake_case	,   direction='inputs'	)
									elif self.task == "causal-lm":
														# TODO: figure this case out.
														__A      :		int         =							OrderedDict(
														    [
														        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
														        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
														    ]	)
														if self.use_past:
																			__A       ,							__A      :		Tuple         =							self.num_layers
																			for i in range(_snake_case	):
																								__A      :		Optional[int]         =							{0: 'batch', 2: 'past_sequence + sequence'}
																								__A      :		Optional[Any]         =							{0: 'batch', 2: 'past_sequence + sequence'}
									else:
														__A      :		Tuple         =							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    UpperCAmelCase_						(     self	):






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



				def    UpperCAmelCase_						(     self	,   _A	,   _A = -1	,   _A = -1	,   _A = False	,   _A = None	,   ):
									__A      :		str         =							self._generate_dummy_inputs_for_encoder_and_decoder(
									    _snake_case	,   _snake_case	,   _snake_case	,   _snake_case	,   _snake_case	)

									# Generate decoder inputs
									__A      :		Dict         =							seq_length if not self.use_past else 1
									__A      :		Tuple         =							self._generate_dummy_inputs_for_encoder_and_decoder(
									    _snake_case	,   _snake_case	,   _snake_case	,   _snake_case	,   _snake_case	)
									__A      :		str         =							{F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
									__A      :		Any         =							dict(**_snake_case	,   **_snake_case	)

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

														__A      :		Optional[int]         =							torch.cat(
														    [common_inputs['decoder_attention_mask'], torch.ones(_snake_case	,   _snake_case	)]	,   dim=1	)

														__A      :		Any         =							[]
														# If the number of encoder and decoder layers are present in the model configuration, both are considered
														__A       ,							__A      :		int         =							self.num_layers
														__A      :		Optional[int]         =							min(_snake_case	,   _snake_case	)
														__A      :		Tuple         =							max(_snake_case	,   _snake_case	) - min_num_layers
														__A      :		Any         =							'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

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



				def    UpperCAmelCase_						(     self	,   _A	,   _A = -1	,   _A = -1	,   _A = False	,   _A = None	,   ):
									__A      :		Tuple         =							self._generate_dummy_inputs_for_encoder_and_decoder(
									    _snake_case	,   _snake_case	,   _snake_case	,   _snake_case	,   _snake_case	)

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

														__A      :		Optional[Any]         =							common_inputs['attention_mask'].dtype
														__A      :		List[Any]         =							torch.cat(
														    [common_inputs['attention_mask'], torch.ones(_snake_case	,   _snake_case	,   dtype=_snake_case	)]	,   dim=1	)
														__A      :		Any         =							[
														    (torch.zeros(_snake_case	), torch.zeros(_snake_case	)) for _ in range(_snake_case	)
														]
									return common_inputs



				def    UpperCAmelCase_						(     self	,   _A	,   _A = -1	,   _A = -1	,   _A = False	,   _A = None	,   ):
									__A      :		List[Any]         =							compute_effective_axis_dimension(
									    _snake_case	,   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
									__A      :		List[Any]         =							tokenizer.num_special_tokens_to_add(_snake_case	)
									__A      :		Optional[Any]         =							compute_effective_axis_dimension(
									    _snake_case	,   fixed_dimension=OnnxConfig.default_fixed_sequence	,   num_token_to_add=_snake_case	)

									# Generate dummy inputs according to compute batch and sequence
									__A      :		List[Any]         =							[' '.join([tokenizer.unk_token]	) * seq_length] * batch_size
									__A      :		int         =							dict(tokenizer(_snake_case	,   return_tensors=_snake_case	)	)
									return common_inputs



				def    UpperCAmelCase_						(     self	,   _A	,   _A = -1	,   _A = -1	,   _A = False	,   _A = None	,   ):






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

									else:
														__A      :		Union[str, Any]         =							self._generate_dummy_inputs_for_causal_lm(
														    _snake_case	,   batch_size=_snake_case	,   seq_length=_snake_case	,   is_pair=_snake_case	,   framework=_snake_case	)

									return common_inputs



				def    UpperCAmelCase_						(     self	,   _A	,   _A	,   _A	,   _A	):






									if self.task in ["default", "seq2seq-lm"]:
														__A      :		Optional[Any]         =							super()._flatten_past_key_values_(_snake_case	,   _snake_case	,   _snake_case	,   _snake_case	)
									else:
														__A      :		Dict         =							super(_snake_case	,   self	)._flatten_past_key_values_(
														    _snake_case	,   _snake_case	,   _snake_case	,   _snake_case	)





				@property
				def    UpperCAmelCase_						(     self	):
									return 1e-4











"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__UpperCamelCase     :						Dict						       =  {'''processing_layoutxlm''': ['''LayoutXLMProcessor''']}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase     :						int						       =  ['''LayoutXLMTokenizer''']

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase     :						Any						       =  ['''LayoutXLMTokenizerFast''']

if TYPE_CHECKING:
       from .processing_layoutxlm import LayoutXLMProcessor

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_layoutxlm import LayoutXLMTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast

else:
       import sys

       __UpperCamelCase     :						Dict						       =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





