




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		:    List[str]	      =		logging.get_logger(__name__)

__lowerCAmelCase		:    str	      =		{
    '''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    snake_case__							(A__       ):
	"""simple docstring"""



	SCREAMING_SNAKE_CASE_	:							Optional[Any]										= """m2m_100"""
	SCREAMING_SNAKE_CASE_	:							Tuple										= ["""past_key_values"""]
	SCREAMING_SNAKE_CASE_	:							int										= {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}





	def __init__(				self    :					List[Any] ,				__lowerCamelCase    :					int=12_81_12 ,				__lowerCamelCase    :					Tuple=10_24 ,				__lowerCamelCase    :					List[Any]=12 ,				__lowerCamelCase    :					Tuple=40_96 ,				__lowerCamelCase    :					Any=16 ,				__lowerCamelCase    :					List[Any]=12 ,				__lowerCamelCase    :					int=40_96 ,				__lowerCamelCase    :					List[str]=16 ,				__lowerCamelCase    :					Union[str, Any]=0.05 ,				__lowerCamelCase    :					Optional[int]=0.05 ,				__lowerCamelCase    :					int=True ,				__lowerCamelCase    :					Optional[int]=True ,				__lowerCamelCase    :					List[Any]="relu" ,				__lowerCamelCase    :					str=10_24 ,				__lowerCamelCase    :					Union[str, Any]=0.1 ,				__lowerCamelCase    :					Union[str, Any]=0.1 ,				__lowerCamelCase    :					Optional[Any]=0.0 ,				__lowerCamelCase    :					List[Any]=0.02 ,				__lowerCamelCase    :					Dict=2 ,				__lowerCamelCase    :					List[Any]=True ,				__lowerCamelCase    :					Optional[int]=1 ,				__lowerCamelCase    :					Optional[int]=0 ,				__lowerCamelCase    :					Optional[Any]=2 ,				**__lowerCamelCase    :					List[str] ,				) ->					str:
				a   =      vocab_size
				a   =      max_position_embeddings
				a   =      d_model
				a   =      encoder_ffn_dim
				a   =      encoder_layers
				a   =      encoder_attention_heads
				a   =      decoder_ffn_dim
				a   =      decoder_layers
				a   =      decoder_attention_heads
				a   =      dropout
				a   =      attention_dropout
				a   =      activation_dropout
				a   =      activation_function
				a   =      init_std
				a   =      encoder_layerdrop
				a   =      decoder_layerdrop
				a   =      use_cache
				a   =      encoder_layers
				a   =      scale_embedding  # scale factor will be sqrt(d_model) if True

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




class    snake_case__							(A__       ):
	"""simple docstring"""



	@property
	def 							__UpperCAmelCase							(				self    :					Dict							) ->					Any:
				a   =      OrderedDict(
				    [
				        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
				        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
				    ]							)

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

				if self.use_past:
							self.fill_with_past_key_values_(_A ,				direction="inputs"							)
				return common_inputs





	def 							__UpperCAmelCase							(				self    :					int ,				__lowerCamelCase    :					PreTrainedTokenizer ,				__lowerCamelCase    :					int = -1 ,				__lowerCamelCase    :					int = -1 ,				__lowerCamelCase    :					bool = False ,				__lowerCamelCase    :					Optional[TensorType] = None ,				) ->					Optional[int]:
				a   =      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
				a   =      tokenizer.num_special_tokens_to_add(_A							)
				a   =      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
				a   =      [" ".join([tokenizer.unk_token]							) * seq_length] * batch_size
				a   =      dict(tokenizer(_A ,				return_tensors=_A							)							)
				return common_inputs





	def 							__UpperCAmelCase							(				self    :					Optional[Any] ,				__lowerCamelCase    :					PreTrainedTokenizer ,				__lowerCamelCase    :					int = -1 ,				__lowerCamelCase    :					int = -1 ,				__lowerCamelCase    :					bool = False ,				__lowerCamelCase    :					Optional[TensorType] = None ,				) ->					str:
				a   =      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
				    _A ,				_A ,				_A ,				_A ,				_A							)

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

							a   =      torch.cat(
							    [common_inputs["decoder_attention_mask"], torch.ones(_A ,				_A							)] ,				dim=1							)

							a   =      []
							# If the number of encoder and decoder layers are present in the model configuration, both are considered
							a   =      self.num_layers
							a   =      min(_A ,				_A							)
							a   =      max(_A ,				_A							) - min_num_layers
							a   =      "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.
							a   =      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
	SCREAMING_SNAKE_CASE_	:							List[str]										= _generate_dummy_inputs_for_default_and_seqaseq_lm












from ..utils import DummyObject, requires_backends




class    snake_case__							(metaclass=_UpperCamelCase       ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_	:							List[str]										= ["""torch""", """transformers""", """k_diffusion"""]





				def __init__(				self    :					Optional[int] ,				*__lowerCamelCase    :					Union[str, Any] ,				**__lowerCamelCase    :					List[Any]							) ->					Optional[Any]:
							requires_backends(self ,				["torch", "transformers", "k_diffusion"]							)





				@classmethod
				def 							__UpperCAmelCase							(				cls    :					str ,				*__lowerCamelCase    :					Union[str, Any] ,				**__lowerCamelCase    :					Tuple							) ->					List[Any]:
							requires_backends(cls ,				["torch", "transformers", "k_diffusion"]							)





				@classmethod
				def 							__UpperCAmelCase							(				cls    :					Optional[Any] ,				*__lowerCamelCase    :					List[str] ,				**__lowerCamelCase    :					int							) ->					Dict:
							requires_backends(cls ,				["torch", "transformers", "k_diffusion"]							)






