





"""simple docstring"""


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


a__					:     Tuple							=				logging.get_logger(__name__)

a__					:     List[str]							=				{
    """facebook/mbart-large-cc25""": """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json""",
    # See all MBART models at https://huggingface.co/models?filter=mbart
}



class      lowercase_				(     __lowerCAmelCase							):
       snake_case__				:							Any											=     """mbart"""
       snake_case__				:							Union[str, Any]											=     ["""past_key_values"""]
       snake_case__				:							Optional[int]											=     {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}

       def __init__(			self   ,					__SCREAMING_SNAKE_CASE=5_0265   ,					__SCREAMING_SNAKE_CASE=1024   ,					__SCREAMING_SNAKE_CASE=12   ,					__SCREAMING_SNAKE_CASE=4096   ,					__SCREAMING_SNAKE_CASE=16   ,					__SCREAMING_SNAKE_CASE=12   ,					__SCREAMING_SNAKE_CASE=4096   ,					__SCREAMING_SNAKE_CASE=16   ,					__SCREAMING_SNAKE_CASE=0.0   ,					__SCREAMING_SNAKE_CASE=0.0   ,					__SCREAMING_SNAKE_CASE=True   ,					__SCREAMING_SNAKE_CASE=True   ,					__SCREAMING_SNAKE_CASE="gelu"   ,					__SCREAMING_SNAKE_CASE=1024   ,					__SCREAMING_SNAKE_CASE=0.1   ,					__SCREAMING_SNAKE_CASE=0.0   ,					__SCREAMING_SNAKE_CASE=0.0   ,					__SCREAMING_SNAKE_CASE=0.02   ,					__SCREAMING_SNAKE_CASE=0.0   ,					__SCREAMING_SNAKE_CASE=False   ,					__SCREAMING_SNAKE_CASE=1   ,					__SCREAMING_SNAKE_CASE=0   ,					__SCREAMING_SNAKE_CASE=2   ,					__SCREAMING_SNAKE_CASE=2   ,					**__SCREAMING_SNAKE_CASE   ,					):






        '''simple docstring'''

        snake_case__    							=				vocab_size
        snake_case__    							=				max_position_embeddings
        snake_case__    							=				d_model
        snake_case__    							=				encoder_ffn_dim
        snake_case__    							=				encoder_layers
        snake_case__    							=				encoder_attention_heads
        snake_case__    							=				decoder_ffn_dim
        snake_case__    							=				decoder_layers
        snake_case__    							=				decoder_attention_heads
        snake_case__    							=				dropout
        snake_case__    							=				attention_dropout
        snake_case__    							=				activation_dropout
        snake_case__    							=				activation_function
        snake_case__    							=				init_std
        snake_case__    							=				encoder_layerdrop
        snake_case__    							=				decoder_layerdrop
        snake_case__    							=				classifier_dropout
        snake_case__    							=				use_cache
        snake_case__    							=				encoder_layers
        snake_case__    							=				scale_embedding  # scale factor will be sqrt(d_model) if True
        super().__init__(
            pad_token_id=__SCREAMING_SNAKE_CASE   ,					bos_token_id=__SCREAMING_SNAKE_CASE   ,					eos_token_id=__SCREAMING_SNAKE_CASE   ,					is_encoder_decoder=__SCREAMING_SNAKE_CASE   ,					forced_eos_token_id=__SCREAMING_SNAKE_CASE   ,					**__SCREAMING_SNAKE_CASE   ,					)



class      lowercase_				(     __lowerCAmelCase							):

       @property
       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

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

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

         if self.use_past:
          self.fill_with_past_key_values_(__SCREAMING_SNAKE_CASE   ,					direction="inputs"     )
        elif self.task == "causal-lm":
         # TODO: figure this case out.
         snake_case__    							=				OrderedDict(
             [
                 ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                 ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
             ]     )
         if self.use_past:
          snake_case__							,   snake_case__    							=				self.num_layers
          for i in range(__SCREAMING_SNAKE_CASE     ):
           snake_case__    							=				{0: "batch", 2: "past_sequence + sequence"}
           snake_case__    							=				{0: "batch", 2: "past_sequence + sequence"}
        else:
         snake_case__    							=				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
       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        if self.task in ["default", "seq2seq-lm"]:
         snake_case__    							=				super().outputs
        else:
         snake_case__    							=				super(__SCREAMING_SNAKE_CASE   ,					self     ).outputs
         if self.use_past:
          snake_case__							,   snake_case__    							=				self.num_layers
          for i in range(__SCREAMING_SNAKE_CASE     ):
           snake_case__    							=				{0: "batch", 2: "past_sequence + sequence"}
           snake_case__    							=				{0: "batch", 2: "past_sequence + sequence"}
        return common_outputs

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE = -1   ,					__SCREAMING_SNAKE_CASE = -1   ,					__SCREAMING_SNAKE_CASE = False   ,					__SCREAMING_SNAKE_CASE = None   ,					):






        '''simple docstring'''

        snake_case__    							=				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            __SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     )

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

         snake_case__    							=				torch.cat(
             [common_inputs["decoder_attention_mask"], torch.ones(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     )]   ,					dim=1     )

         snake_case__    							=				[]
         # If the number of encoder and decoder layers are present in the model configuration, both are considered
         snake_case__							,   snake_case__    							=				self.num_layers
         snake_case__    							=				min(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     )
         snake_case__    							=				max(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     ) - min_num_layers
         snake_case__    							=				"encoder" if num_encoder_layers > num_decoder_layers else "decoder"

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

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE = -1   ,					__SCREAMING_SNAKE_CASE = -1   ,					__SCREAMING_SNAKE_CASE = False   ,					__SCREAMING_SNAKE_CASE = None   ,					):






        '''simple docstring'''

        snake_case__    							=				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            __SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_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
         snake_case__							,   snake_case__    							=				common_inputs["input_ids"].shape
         # Not using the same length for past_key_values
         snake_case__    							=				seqlen + 2
         snake_case__							,   snake_case__    							=				self.num_layers
         snake_case__							,   snake_case__    							=				self.num_attention_heads
         snake_case__    							=				(
             batch,
             num_encoder_attention_heads,
             past_key_values_length,
             self._config.hidden_size // num_encoder_attention_heads,
         )

         snake_case__    							=				common_inputs["attention_mask"].dtype
         snake_case__    							=				torch.cat(
             [common_inputs["attention_mask"], torch.ones(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					dtype=__SCREAMING_SNAKE_CASE     )]   ,					dim=1     )
         snake_case__    							=				[
             (torch.zeros(__SCREAMING_SNAKE_CASE     ), torch.zeros(__SCREAMING_SNAKE_CASE     )) for _ in range(__SCREAMING_SNAKE_CASE     )
         ]
        return common_inputs

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE = -1   ,					__SCREAMING_SNAKE_CASE = -1   ,					__SCREAMING_SNAKE_CASE = False   ,					__SCREAMING_SNAKE_CASE = None   ,					):






        '''simple docstring'''

        snake_case__    							=				compute_effective_axis_dimension(
            __SCREAMING_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
        snake_case__    							=				tokenizer.num_special_tokens_to_add(__SCREAMING_SNAKE_CASE     )
        snake_case__    							=				compute_effective_axis_dimension(
            __SCREAMING_SNAKE_CASE   ,					fixed_dimension=OnnxConfig.default_fixed_sequence   ,					num_token_to_add=__SCREAMING_SNAKE_CASE     )

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

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE = -1   ,					__SCREAMING_SNAKE_CASE = -1   ,					__SCREAMING_SNAKE_CASE = False   ,					__SCREAMING_SNAKE_CASE = None   ,					):






        '''simple docstring'''

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

        elif self.task == "causal-lm":
         snake_case__    							=				self._generate_dummy_inputs_for_causal_lm(
             __SCREAMING_SNAKE_CASE   ,					batch_size=__SCREAMING_SNAKE_CASE   ,					seq_length=__SCREAMING_SNAKE_CASE   ,					is_pair=__SCREAMING_SNAKE_CASE   ,					framework=__SCREAMING_SNAKE_CASE     )
        else:
         snake_case__    							=				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             __SCREAMING_SNAKE_CASE   ,					batch_size=__SCREAMING_SNAKE_CASE   ,					seq_length=__SCREAMING_SNAKE_CASE   ,					is_pair=__SCREAMING_SNAKE_CASE   ,					framework=__SCREAMING_SNAKE_CASE     )

        return common_inputs

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

        if self.task in ["default", "seq2seq-lm"]:
         snake_case__    							=				super()._flatten_past_key_values_(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     )
        else:
         snake_case__    							=				super(__SCREAMING_SNAKE_CASE   ,					self     )._flatten_past_key_values_(
             __SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     )








"""simple docstring"""


def 							a__			(  _UpperCAmelCase	)    ->  int:

 '''simple docstring'''






 return int(_UpperCAmelCase	) if x - int(_UpperCAmelCase	) <= 0 else int(_UpperCAmelCase	) + 1


if __name__ == "__main__":
   import doctest

   doctest.testmod()

