






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


SCREAMING_SNAKE_CASE__		:       Tuple												=							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__		:       Optional[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_     (		__lowerCamelCase		):
     __lowerCamelCase										=     'm2m_100'
     __lowerCamelCase										=     ['past_key_values']
     __lowerCamelCase										=     {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}




     def __init__(							self  ,	_lowerCAmelCase=128112  ,	_lowerCAmelCase=1024  ,	_lowerCAmelCase=12  ,	_lowerCAmelCase=4096  ,	_lowerCAmelCase=16  ,	_lowerCAmelCase=12  ,	_lowerCAmelCase=4096  ,	_lowerCAmelCase=16  ,	_lowerCAmelCase=0.0_5  ,	_lowerCAmelCase=0.0_5  ,	_lowerCAmelCase=True  ,	_lowerCAmelCase=True  ,	_lowerCAmelCase="relu"  ,	_lowerCAmelCase=1024  ,	_lowerCAmelCase=0.1  ,	_lowerCAmelCase=0.1  ,	_lowerCAmelCase=0.0  ,	_lowerCAmelCase=0.0_2  ,	_lowerCAmelCase=2  ,	_lowerCAmelCase=True  ,	_lowerCAmelCase=1  ,	_lowerCAmelCase=0  ,	_lowerCAmelCase=2  ,	**_lowerCAmelCase  ,	):
          UpperCAmelCase__			:     str										= vocab_size
          UpperCAmelCase__			:     Tuple										= max_position_embeddings
          UpperCAmelCase__			:     Tuple										= d_model
          UpperCAmelCase__			:     str										= encoder_ffn_dim
          UpperCAmelCase__			:     Optional[int]										= encoder_layers
          UpperCAmelCase__			:     int										= encoder_attention_heads
          UpperCAmelCase__			:     Optional[Any]										= decoder_ffn_dim
          UpperCAmelCase__			:     Optional[Any]										= decoder_layers
          UpperCAmelCase__			:     List[str]										= decoder_attention_heads
          UpperCAmelCase__			:     int										= dropout
          UpperCAmelCase__			:     Any										= attention_dropout
          UpperCAmelCase__			:     Union[str, Any]										= activation_dropout
          UpperCAmelCase__			:     List[Any]										= activation_function
          UpperCAmelCase__			:     Tuple										= init_std
          UpperCAmelCase__			:     Dict										= encoder_layerdrop
          UpperCAmelCase__			:     List[str]										= decoder_layerdrop
          UpperCAmelCase__			:     int										= use_cache
          UpperCAmelCase__			:     List[str]										= encoder_layers
          UpperCAmelCase__			:     int										= scale_embedding  # scale factor will be sqrt(d_model) if True

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







class 							UpperCAmelCase_     (		__lowerCamelCase		):




     @property
     def      __UpperCAmelCase     (							self		):
          UpperCAmelCase__			:     str										= OrderedDict(
              [
                  ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                  ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
              ]		)

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

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




     def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase = -1  ,	_lowerCAmelCase = -1  ,	_lowerCAmelCase = False  ,	_lowerCAmelCase = None  ,	):
          # 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
          UpperCAmelCase__			:     Optional[Any]										= compute_effective_axis_dimension(
              _lowerCAmelCase  ,	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
          UpperCAmelCase__			:     List[str]										= tokenizer.num_special_tokens_to_add(_lowerCAmelCase		)
          UpperCAmelCase__			:     int										= compute_effective_axis_dimension(
              _lowerCAmelCase  ,	fixed_dimension=OnnxConfig.default_fixed_sequence  ,	num_token_to_add=_lowerCAmelCase		)

          # Generate dummy inputs according to compute batch and sequence
          UpperCAmelCase__			:     Any										= [""" """.join([tokenizer.unk_token]		) * seq_length] * batch_size
          UpperCAmelCase__			:     Tuple										= dict(tokenizer(_lowerCAmelCase  ,	return_tensors=_lowerCAmelCase		)		)
          return common_inputs




     def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase = -1  ,	_lowerCAmelCase = -1  ,	_lowerCAmelCase = False  ,	_lowerCAmelCase = None  ,	):
          UpperCAmelCase__			:     List[Any]										= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              _lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase		)

          # Generate decoder inputs
          UpperCAmelCase__			:     str										= seq_length if not self.use_past else 1
          UpperCAmelCase__			:     Optional[Any]										= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              _lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase		)
          UpperCAmelCase__			:     Dict										= {f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
          UpperCAmelCase__			:     List[str]										= dict(**_lowerCAmelCase  ,	**_lowerCAmelCase		)

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

               UpperCAmelCase__			:     Tuple										= torch.cat(
                   [common_inputs["""decoder_attention_mask"""], torch.ones(_lowerCAmelCase  ,	_lowerCAmelCase		)]  ,	dim=1		)

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

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




     __lowerCamelCase										=     _generate_dummy_inputs_for_default_and_seqaseq_lm














from __future__ import annotations







class 							UpperCAmelCase_     :




     def __init__(							self  ,	_lowerCAmelCase		):
          UpperCAmelCase__			:     List[Any]										= value
          UpperCAmelCase__			:     Node | None										= None
          UpperCAmelCase__			:     Node | None										= None







class 							UpperCAmelCase_     :
     __lowerCamelCase										=     42




     def __init__(							self		):
          UpperCAmelCase__			:     Any										= 0




     def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase		):
          if node is None:
               return

          if path_sum == self.target:
               self.paths += 1

          if node.left:
               self.depth_first_search(node.left  ,	path_sum + node.left.value		)
          if node.right:
               self.depth_first_search(node.right  ,	path_sum + node.right.value		)




     def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase = None		):
          if node is None:
               return 0
          if target is not None:
               UpperCAmelCase__			:     List[Any]										= target

          self.depth_first_search(_lowerCAmelCase  ,	node.value		)
          self.path_sum(node.left		)
          self.path_sum(node.right		)

          return self.paths


if __name__ == "__main__":
      import doctest

      doctest.testmod()






