

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			=       logging.get_logger(__name__)

_a			=       {
    '''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 							A_			(		UpperCamelCase__ ):
     _lowercase      :					Union[str, Any]       =     'mbart'
     _lowercase      :					Optional[Any]       =     ['past_key_values']
     _lowercase      :					List[Any]       =     {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}


     def __init__(							self      :		Tuple      ,  UpperCAmelCase      :		Optional[Any]=5_0_2_6_5      ,  UpperCAmelCase      :		Dict=1_0_2_4      ,  UpperCAmelCase      :		Dict=1_2      ,  UpperCAmelCase      :		int=4_0_9_6      ,  UpperCAmelCase      :		List[Any]=1_6      ,  UpperCAmelCase      :		List[Any]=1_2      ,  UpperCAmelCase      :		int=4_0_9_6      ,  UpperCAmelCase      :		Any=1_6      ,  UpperCAmelCase      :		List[str]=0.0      ,  UpperCAmelCase      :		Union[str, Any]=0.0      ,  UpperCAmelCase      :		Optional[int]=True      ,  UpperCAmelCase      :		Optional[int]=True      ,  UpperCAmelCase      :		Union[str, Any]="gelu"      ,  UpperCAmelCase      :		Any=1_0_2_4      ,  UpperCAmelCase      :		Dict=0.1      ,  UpperCAmelCase      :		Dict=0.0      ,  UpperCAmelCase      :		Optional[int]=0.0      ,  UpperCAmelCase      :		Tuple=0.02      ,  UpperCAmelCase      :		List[Any]=0.0      ,  UpperCAmelCase      :		List[Any]=False      ,  UpperCAmelCase      :		int=1      ,  UpperCAmelCase      :		Optional[Any]=0      ,  UpperCAmelCase      :		Dict=2      ,  UpperCAmelCase      :		int=2      ,  **UpperCAmelCase      :		Union[str, Any]      ,  )							->     int:
      __lowerCAmelCase:       Optional[int]            =				vocab_size
      __lowerCAmelCase:       Union[str, Any]            =				max_position_embeddings
      __lowerCAmelCase:       Dict            =				d_model
      __lowerCAmelCase:       Tuple            =				encoder_ffn_dim
      __lowerCAmelCase:       Dict            =				encoder_layers
      __lowerCAmelCase:       int            =				encoder_attention_heads
      __lowerCAmelCase:       Any            =				decoder_ffn_dim
      __lowerCAmelCase:       List[Any]            =				decoder_layers
      __lowerCAmelCase:       Dict            =				decoder_attention_heads
      __lowerCAmelCase:       Dict            =				dropout
      __lowerCAmelCase:       Dict            =				attention_dropout
      __lowerCAmelCase:       int            =				activation_dropout
      __lowerCAmelCase:       Union[str, Any]            =				activation_function
      __lowerCAmelCase:       Union[str, Any]            =				init_std
      __lowerCAmelCase:       Tuple            =				encoder_layerdrop
      __lowerCAmelCase:       Dict            =				decoder_layerdrop
      __lowerCAmelCase:       str            =				classifier_dropout
      __lowerCAmelCase:       Union[str, Any]            =				use_cache
      __lowerCAmelCase:       int            =				encoder_layers
      __lowerCAmelCase:       List[Any]            =				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      ,  forced_eos_token_id=__lowerCamelCase      ,  **__lowerCamelCase      ,  )







class 							A_			(		UpperCamelCase__ ):


     @property
     def   UpperCAmelCase  (							self      :		Union[str, Any] )							->     Dict:


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

       if self.use_past:
        __lowerCAmelCase:       str            =				{0: "batch"}
        __lowerCAmelCase:       Dict            =				{0: "batch", 1: "past_decoder_sequence + sequence"}
       else:
        __lowerCAmelCase:       str            =				{0: "batch", 1: "decoder_sequence"}
        __lowerCAmelCase:       Tuple            =				{0: "batch", 1: "decoder_sequence"}

       if self.use_past:
        self.fill_with_past_key_values_(__lowerCamelCase      ,  direction='inputs' )
      elif self.task == "causal-lm":
       # TODO: figure this case out.
       __lowerCAmelCase:       str            =				OrderedDict(
           [
               ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
               ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
           ] )
       if self.use_past:
        __lowerCAmelCase:       int            =				self.num_layers
        for i in range(__lowerCamelCase ):
         __lowerCAmelCase:       Union[str, Any]            =				{0: "batch", 2: "past_sequence + sequence"}
         __lowerCAmelCase:       List[str]            =				{0: "batch", 2: "past_sequence + sequence"}
      else:
       __lowerCAmelCase:       List[str]            =				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   UpperCAmelCase  (							self      :		Tuple )							->     List[str]:


      if self.task in ["default", "seq2seq-lm"]:
       __lowerCAmelCase:       int            =				super().outputs
      else:
       __lowerCAmelCase:       List[str]            =				super(__lowerCamelCase      ,  self ).outputs
       if self.use_past:
        __lowerCAmelCase:       Optional[Any]            =				self.num_layers
        for i in range(__lowerCamelCase ):
         __lowerCAmelCase:       Optional[int]            =				{0: "batch", 2: "past_sequence + sequence"}
         __lowerCAmelCase:       Optional[int]            =				{0: "batch", 2: "past_sequence + sequence"}
      return common_outputs


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

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

       __lowerCAmelCase:       Union[str, Any]            =				torch.cat(
           [common_inputs['decoder_attention_mask'], torch.ones(__lowerCamelCase      ,  __lowerCamelCase )]      ,  dim=1 )

       __lowerCAmelCase:       Tuple            =				[]
       # If the number of encoder and decoder layers are present in the model configuration, both are considered
       __lowerCAmelCase:       Optional[Any]            =				self.num_layers
       __lowerCAmelCase:       Optional[Any]            =				min(__lowerCamelCase      ,  __lowerCamelCase )
       __lowerCAmelCase:       Any            =				max(__lowerCamelCase      ,  __lowerCamelCase ) - min_num_layers
       __lowerCAmelCase:       int            =				"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.
       __lowerCAmelCase:       int            =				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


     def   UpperCAmelCase  (							self      :		Any      ,  UpperCAmelCase      :		PreTrainedTokenizer      ,  UpperCAmelCase      :		int = -1      ,  UpperCAmelCase      :		int = -1      ,  UpperCAmelCase      :		bool = False      ,  UpperCAmelCase      :		Optional[TensorType] = None      ,  )							->     Union[str, Any]:
      __lowerCAmelCase:       Union[str, Any]            =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          __lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase      ,  __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
       __lowerCAmelCase:       Tuple            =				common_inputs["input_ids"].shape
       # Not using the same length for past_key_values
       __lowerCAmelCase:       Optional[int]            =				seqlen + 2
       __lowerCAmelCase:       List[str]            =				self.num_layers
       __lowerCAmelCase:       Any            =				self.num_attention_heads
       __lowerCAmelCase:       Optional[int]            =				(
           batch,
           num_encoder_attention_heads,
           past_key_values_length,
           self._config.hidden_size // num_encoder_attention_heads,
       )

       __lowerCAmelCase:       Optional[Any]            =				common_inputs["attention_mask"].dtype
       __lowerCAmelCase:       Dict            =				torch.cat(
           [common_inputs['attention_mask'], torch.ones(__lowerCamelCase      ,  __lowerCamelCase      ,  dtype=__lowerCamelCase )]      ,  dim=1 )
       __lowerCAmelCase:       Union[str, Any]            =				[
           (torch.zeros(__lowerCamelCase ), torch.zeros(__lowerCamelCase )) for _ in range(__lowerCamelCase )
       ]
      return common_inputs


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


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


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

      elif self.task == "causal-lm":
       __lowerCAmelCase:       int            =				self._generate_dummy_inputs_for_causal_lm(
           __lowerCamelCase      ,  batch_size=__lowerCamelCase      ,  seq_length=__lowerCamelCase      ,  is_pair=__lowerCamelCase      ,  framework=__lowerCamelCase )
      else:
       __lowerCAmelCase:       List[Any]            =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           __lowerCamelCase      ,  batch_size=__lowerCamelCase      ,  seq_length=__lowerCamelCase      ,  is_pair=__lowerCamelCase      ,  framework=__lowerCamelCase )

      return common_inputs


     def   UpperCAmelCase  (							self      :		str      ,  UpperCAmelCase      :		Optional[int]      ,  UpperCAmelCase      :		List[str]      ,  UpperCAmelCase      :		Union[str, Any]      ,  UpperCAmelCase      :		int )							->     List[Any]:


      if self.task in ["default", "seq2seq-lm"]:
       __lowerCAmelCase:       Any            =				super()._flatten_past_key_values_(__lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase )
      else:
       __lowerCAmelCase:       List[str]            =				super(__lowerCamelCase      ,  self )._flatten_past_key_values_(
           __lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase )








from __future__ import annotations







def _a			(	SCREAMING_SNAKE_CASE		:      list[int]		)   ->   list[int]:

 """simple docstring"""






 if not list_of_ints:
  return []

 if min(SCREAMING_SNAKE_CASE		) < 0:
  raise ValueError('All numbers must be positive'		)

 __lowerCAmelCase:       Tuple            =				max(len(bin(SCREAMING_SNAKE_CASE		)[2:]		) for x in list_of_ints		)
 return _msd_radix_sort(SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE		)







def _a			(	SCREAMING_SNAKE_CASE		:      list[int]   , SCREAMING_SNAKE_CASE		:      int		)   ->   list[int]:

 """simple docstring"""






 if bit_position == 0 or len(SCREAMING_SNAKE_CASE		) in [0, 1]:
  return list_of_ints

 __lowerCAmelCase:       List[str]            =				[]
 __lowerCAmelCase:       Any            =				[]
 # Split numbers based on bit at bit_position from the right
 for number in list_of_ints:
  if (number >> (bit_position - 1)) & 1:
   # number has a one at bit bit_position
   ones.append(SCREAMING_SNAKE_CASE		)
  else:
   # number has a zero at bit bit_position
   zeros.append(SCREAMING_SNAKE_CASE		)

    # recursively split both lists further
 __lowerCAmelCase:       Dict            =				_msd_radix_sort(SCREAMING_SNAKE_CASE   , bit_position - 1		)
 __lowerCAmelCase:       Union[str, Any]            =				_msd_radix_sort(SCREAMING_SNAKE_CASE   , bit_position - 1		)

 # recombine lists
 __lowerCAmelCase:       List[str]            =				zeros
 res.extend(SCREAMING_SNAKE_CASE		)

 return res







def _a			(	SCREAMING_SNAKE_CASE		:      list[int]		)   ->   List[str]:

 """simple docstring"""






 __lowerCAmelCase:       Optional[int]            =				len(SCREAMING_SNAKE_CASE		)
 if not list_of_ints or length == 1:
  return

 if min(SCREAMING_SNAKE_CASE		) < 0:
  raise ValueError('All numbers must be positive'		)

 __lowerCAmelCase:       Optional[int]            =				max(len(bin(SCREAMING_SNAKE_CASE		)[2:]		) for x in list_of_ints		)
 _msd_radix_sort_inplace(SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , 0   , SCREAMING_SNAKE_CASE		)







def _a			(	SCREAMING_SNAKE_CASE		:      list[int]   , SCREAMING_SNAKE_CASE		:      int   , SCREAMING_SNAKE_CASE		:      int   , SCREAMING_SNAKE_CASE		:      int		)   ->   str:

 """simple docstring"""






 if bit_position == 0 or end_index - begin_index <= 1:
  return

 bit_position -= 1

 __lowerCAmelCase:       Optional[int]            =				begin_index
 __lowerCAmelCase:       Optional[Any]            =				end_index - 1
 while i <= j:
  __lowerCAmelCase:       Any            =				False
  if not (list_of_ints[i] >> bit_position) & 1:
   # found zero at the beginning
   i += 1
   __lowerCAmelCase:       Optional[Any]            =				True
  if (list_of_ints[j] >> bit_position) & 1:
   # found one at the end
   j -= 1
   __lowerCAmelCase:       List[str]            =				True

  if changed:
   continue

  __lowerCAmelCase     ,							__lowerCAmelCase:       Union[str, Any]            =				list_of_ints[j], list_of_ints[i]
  j -= 1
  if j != i:
   i += 1

 _msd_radix_sort_inplace(SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE		)
 _msd_radix_sort_inplace(SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE		)


if __name__ == "__main__":
  import doctest

  doctest.testmod()





