
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


__lowerCAmelCase      :					Any   	=       logging.get_logger(__name__)

__lowerCAmelCase      :					Union[str, Any]   	=       {
    '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 	UpperCAmelCase_      (			_A					):


  '''simple docstring'''







  a__   	=		"""m2m_100"""
  a__   	=		["""past_key_values"""]
  a__   	=		{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
  def __init__(			self      :			Tuple      ,							UpperCamelCase__      :			int=12_8112      ,							UpperCamelCase__      :			str=1024      ,							UpperCamelCase__      :			Optional[Any]=12      ,							UpperCamelCase__      :			Tuple=4096      ,							UpperCamelCase__      :			Optional[int]=16      ,							UpperCamelCase__      :			List[Any]=12      ,							UpperCamelCase__      :			List[Any]=4096      ,							UpperCamelCase__      :			Optional[int]=16      ,							UpperCamelCase__      :			Optional[int]=0.05      ,							UpperCamelCase__      :			str=0.05      ,							UpperCamelCase__      :			Optional[Any]=True      ,							UpperCamelCase__      :			Dict=True      ,							UpperCamelCase__      :			List[str]="relu"      ,							UpperCamelCase__      :			Any=1024      ,							UpperCamelCase__      :			str=0.1      ,							UpperCamelCase__      :			Optional[int]=0.1      ,							UpperCamelCase__      :			Any=0.0      ,							UpperCamelCase__      :			Any=0.02      ,							UpperCamelCase__      :			Optional[Any]=2      ,							UpperCamelCase__      :			List[Any]=True      ,							UpperCamelCase__      :			List[str]=1      ,							UpperCamelCase__      :			str=0      ,							UpperCamelCase__      :			Any=2      ,							**UpperCamelCase__      :			Tuple      ,							)      ->     Union[str, Any]:







    """simple docstring"""






    __magic_name__    					=	vocab_size
    __magic_name__    					=	max_position_embeddings
    __magic_name__    					=	d_model
    __magic_name__    					=	encoder_ffn_dim
    __magic_name__    					=	encoder_layers
    __magic_name__    					=	encoder_attention_heads
    __magic_name__    					=	decoder_ffn_dim
    __magic_name__    					=	decoder_layers
    __magic_name__    					=	decoder_attention_heads
    __magic_name__    					=	dropout
    __magic_name__    					=	attention_dropout
    __magic_name__    					=	activation_dropout
    __magic_name__    					=	activation_function
    __magic_name__    					=	init_std
    __magic_name__    					=	encoder_layerdrop
    __magic_name__    					=	decoder_layerdrop
    __magic_name__    					=	use_cache
    __magic_name__    					=	encoder_layers
    __magic_name__    					=	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 	UpperCAmelCase_      (			_A					):


  '''simple docstring'''







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







    """simple docstring"""






    __magic_name__    					=	OrderedDict(
        [
            ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
            ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
        ]   )

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

    if self.use_past:
      self.fill_with_past_key_values_(UpperCamelCase__      ,							direction="""inputs"""   )
    return common_inputs
  def 							_lowercase							(			self      :			str      ,							UpperCamelCase__      :			PreTrainedTokenizer      ,							UpperCamelCase__      :			int = -1      ,							UpperCamelCase__      :			int = -1      ,							UpperCamelCase__      :			bool = False      ,							UpperCamelCase__      :			Optional[TensorType] = None      ,							)      ->     Mapping[str, Any]:







    """simple docstring"""






    __magic_name__    					=	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
    __magic_name__    					=	tokenizer.num_special_tokens_to_add(UpperCamelCase__   )
    __magic_name__    					=	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
    __magic_name__    					=	[""" """.join([tokenizer.unk_token]   ) * seq_length] * batch_size
    __magic_name__    					=	dict(tokenizer(UpperCamelCase__      ,							return_tensors=UpperCamelCase__   )   )
    return common_inputs
  def 							_lowercase							(			self      :			List[Any]      ,							UpperCamelCase__      :			PreTrainedTokenizer      ,							UpperCamelCase__      :			int = -1      ,							UpperCamelCase__      :			int = -1      ,							UpperCamelCase__      :			bool = False      ,							UpperCamelCase__      :			Optional[TensorType] = None      ,							)      ->     Mapping[str, Any]:







    """simple docstring"""






    __magic_name__    					=	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
        UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__   )

    # Generate decoder inputs
    __magic_name__    					=	seq_length if not self.use_past else 1
    __magic_name__    					=	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
        UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__   )
    __magic_name__    					=	{F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
    __magic_name__    					=	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
      __magic_name__					,   __magic_name__    					=	common_inputs["""input_ids"""].shape
      __magic_name__    					=	common_inputs["""decoder_input_ids"""].shape[1]
      __magic_name__					,   __magic_name__    					=	self.num_attention_heads
      __magic_name__    					=	(
          batch,
          num_encoder_attention_heads,
          encoder_seq_length,
          self._config.hidden_size // num_encoder_attention_heads,
      )
      __magic_name__    					=	decoder_seq_length + 3
      __magic_name__    					=	(
          batch,
          num_decoder_attention_heads,
          decoder_past_length,
          self._config.hidden_size // num_decoder_attention_heads,
      )

      __magic_name__    					=	torch.cat(
          [common_inputs["""decoder_attention_mask"""], torch.ones(UpperCamelCase__      ,							UpperCamelCase__   )]      ,							dim=1   )

      __magic_name__    					=	[]
      # If the number of encoder and decoder layers are present in the model configuration, both are considered
      __magic_name__					,   __magic_name__    					=	self.num_layers
      __magic_name__    					=	min(UpperCamelCase__      ,							UpperCamelCase__   )
      __magic_name__    					=	max(UpperCamelCase__      ,							UpperCamelCase__   ) - min_num_layers
      __magic_name__    					=	"""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.
      __magic_name__    					=	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





  a__   	=		_generate_dummy_inputs_for_default_and_seqaseq_lm



def a__   (	A_ ):






  '''simple docstring'''





  __magic_name__    					=	str(A_ ).strip()
  if not bin_string:
    raise ValueError("""Empty string was passed to the function""" )
  __magic_name__    					=	bin_string[0] == """-"""
  if is_negative:
    __magic_name__    					=	bin_string[1:]
  if not all(char in """01""" for char in bin_string ):
    raise ValueError("""Non-binary value was passed to the function""" )
  __magic_name__    					=	0
  for char in bin_string:
    __magic_name__    					=	2 * decimal_number + int(A_ )
  return -decimal_number if is_negative else decimal_number


if __name__ == "__main__":
    from doctest import testmod

    testmod()

