

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_a									=  {
    '''configuration_markuplm''': ['''MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MarkupLMConfig'''],
    '''feature_extraction_markuplm''': ['''MarkupLMFeatureExtractor'''],
    '''processing_markuplm''': ['''MarkupLMProcessor'''],
    '''tokenization_markuplm''': ['''MarkupLMTokenizer'''],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a									=  ['''MarkupLMTokenizerFast''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a									=  [
      '''MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''MarkupLMForQuestionAnswering''',
      '''MarkupLMForSequenceClassification''',
      '''MarkupLMForTokenClassification''',
      '''MarkupLMModel''',
      '''MarkupLMPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_markuplm import MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP, MarkupLMConfig
  from .feature_extraction_markuplm import MarkupLMFeatureExtractor
  from .processing_markuplm import MarkupLMProcessor
  from .tokenization_markuplm import MarkupLMTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_markuplm_fast import MarkupLMTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_markuplm import (
        MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        MarkupLMForQuestionAnswering,
        MarkupLMForSequenceClassification,
        MarkupLMForTokenClassification,
        MarkupLMModel,
        MarkupLMPreTrainedModel,
    )


else:
  import sys

  _a									=  _LazyModule(__name__, globals()['''__file__'''], _import_structure)








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

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


_a									=  logging.get_logger(__name__)

_a									=  {
    '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/config.json''',
    # See all M2M100 models at https://huggingface.co/models?filter=m2m_100
}






class 				A_					(						snake_case__   ):
     _lowercase     :	List[Any]        =      'm2m_100'
     _lowercase     :	str        =      ['past_key_values']
     _lowercase     :	List[Any]        =      {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}


     def __init__(				self     :						int     ,    UpperCAmelCase     :						Any=1_2_8_1_1_2     ,    UpperCAmelCase     :						Optional[int]=1_0_2_4     ,    UpperCAmelCase     :						Tuple=1_2     ,    UpperCAmelCase     :						List[str]=4_0_9_6     ,    UpperCAmelCase     :						Dict=1_6     ,    UpperCAmelCase     :						int=1_2     ,    UpperCAmelCase     :						int=4_0_9_6     ,    UpperCAmelCase     :						List[str]=1_6     ,    UpperCAmelCase     :						Optional[Any]=0.05     ,    UpperCAmelCase     :						Dict=0.05     ,    UpperCAmelCase     :						Union[str, Any]=True     ,    UpperCAmelCase     :						Dict=True     ,    UpperCAmelCase     :						List[str]="relu"     ,    UpperCAmelCase     :						Dict=1_0_2_4     ,    UpperCAmelCase     :						str=0.1     ,    UpperCAmelCase     :						Dict=0.1     ,    UpperCAmelCase     :						List[str]=0.0     ,    UpperCAmelCase     :						Union[str, Any]=0.02     ,    UpperCAmelCase     :						Optional[Any]=2     ,    UpperCAmelCase     :						str=True     ,    UpperCAmelCase     :						Optional[int]=1     ,    UpperCAmelCase     :						List[Any]=0     ,    UpperCAmelCase     :						Union[str, Any]=2     ,    **UpperCAmelCase     :						Union[str, Any]     ,    )				->      Tuple:
      __lowerCAmelCase:  Optional[Any]        = vocab_size
      __lowerCAmelCase:  Dict        = max_position_embeddings
      __lowerCAmelCase:  Tuple        = d_model
      __lowerCAmelCase:  List[str]        = encoder_ffn_dim
      __lowerCAmelCase:  Any        = encoder_layers
      __lowerCAmelCase:  int        = encoder_attention_heads
      __lowerCAmelCase:  str        = decoder_ffn_dim
      __lowerCAmelCase:  Tuple        = decoder_layers
      __lowerCAmelCase:  Tuple        = decoder_attention_heads
      __lowerCAmelCase:  Any        = dropout
      __lowerCAmelCase:  Optional[int]        = attention_dropout
      __lowerCAmelCase:  Tuple        = activation_dropout
      __lowerCAmelCase:  Optional[Any]        = activation_function
      __lowerCAmelCase:  Union[str, Any]        = init_std
      __lowerCAmelCase:  List[str]        = encoder_layerdrop
      __lowerCAmelCase:  Optional[Any]        = decoder_layerdrop
      __lowerCAmelCase:  Dict        = use_cache
      __lowerCAmelCase:  Any        = encoder_layers
      __lowerCAmelCase:  Tuple        = scale_embedding  # scale factor will be sqrt(d_model) if True

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







class 				A_					(						snake_case__   ):


     @property
     def     UpperCAmelCase    (				self     :						List[Any]   )				->      Mapping[str, Mapping[int, str]]:
      __lowerCAmelCase:  Union[str, Any]        = OrderedDict(
          [
              ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
              ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
          ]   )

      if self.use_past:
       __lowerCAmelCase:  str        = {0: 'batch'}
       __lowerCAmelCase:  Union[str, Any]        = {0: 'batch', 1: 'past_decoder_sequence + sequence'}
      else:
       __lowerCAmelCase:  Any        = {0: 'batch', 1: 'decoder_sequence'}
       __lowerCAmelCase:  Dict        = {0: 'batch', 1: 'decoder_sequence'}

      if self.use_past:
       self.fill_with_past_key_values_(UpperCAmelCase     ,    direction='inputs'   )
      return common_inputs


     def     UpperCAmelCase    (				self     :						Tuple     ,    UpperCAmelCase     :						PreTrainedTokenizer     ,    UpperCAmelCase     :						int = -1     ,    UpperCAmelCase     :						int = -1     ,    UpperCAmelCase     :						bool = False     ,    UpperCAmelCase     :						Optional[TensorType] = None     ,    )				->      Mapping[str, Any]:
      # Copied from OnnxConfig.generate_dummy_inputs
      # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
      # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
      __lowerCAmelCase:  List[str]        = compute_effective_axis_dimension(
          UpperCAmelCase     ,    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        = tokenizer.num_special_tokens_to_add(UpperCAmelCase   )
      __lowerCAmelCase:  List[str]        = compute_effective_axis_dimension(
          UpperCAmelCase     ,    fixed_dimension=OnnxConfig.default_fixed_sequence     ,    num_token_to_add=UpperCAmelCase   )

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


     def     UpperCAmelCase    (				self     :						str     ,    UpperCAmelCase     :						PreTrainedTokenizer     ,    UpperCAmelCase     :						int = -1     ,    UpperCAmelCase     :						int = -1     ,    UpperCAmelCase     :						bool = False     ,    UpperCAmelCase     :						Optional[TensorType] = None     ,    )				->      Mapping[str, Any]:
      __lowerCAmelCase:  Optional[int]        = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          UpperCAmelCase     ,    UpperCAmelCase     ,    UpperCAmelCase     ,    UpperCAmelCase     ,    UpperCAmelCase   )

      # Generate decoder inputs
      __lowerCAmelCase:  Any        = seq_length if not self.use_past else 1
      __lowerCAmelCase:  Union[str, Any]        = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          UpperCAmelCase     ,    UpperCAmelCase     ,    UpperCAmelCase     ,    UpperCAmelCase     ,    UpperCAmelCase   )
      __lowerCAmelCase:  List[Any]        = {F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
      __lowerCAmelCase:  List[str]        = dict(**UpperCAmelCase     ,    **UpperCAmelCase   )

      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:  List[Any]        = common_inputs['input_ids'].shape
       __lowerCAmelCase:  int        = common_inputs['decoder_input_ids'].shape[1]
       __lowerCAmelCase      ,				__lowerCAmelCase:  str        = self.num_attention_heads
       __lowerCAmelCase:  str        = (
           batch,
           num_encoder_attention_heads,
           encoder_seq_length,
           self._config.hidden_size // num_encoder_attention_heads,
       )
       __lowerCAmelCase:  Optional[int]        = decoder_seq_length + 3
       __lowerCAmelCase:  List[str]        = (
           batch,
           num_decoder_attention_heads,
           decoder_past_length,
           self._config.hidden_size // num_decoder_attention_heads,
       )

       __lowerCAmelCase:  Dict        = torch.cat(
           [common_inputs['decoder_attention_mask'], torch.ones(UpperCAmelCase     ,    UpperCAmelCase   )]     ,    dim=1   )

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

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


     _lowercase     :	int        =      _generate_dummy_inputs_for_default_and_seqaseq_lm





