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


UpperCAmelCase     						=			logging.get_logger(__name__)

UpperCAmelCase     						=			{
    '''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 				__lowerCAmelCase					(    a_  ):



 '''simple docstring'''
 _lowerCamelCase  :		Optional[Any]			      =	'mbart'
 _lowerCamelCase  :		str			      =	['past_key_values']
 _lowerCamelCase  :		Tuple			      =	{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}

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



 '''simple docstring'''

 @property
 def    __snake_case   (     self    :						Dict					)	->							Mapping[str, Mapping[int, str]]:
        if self.task in ["default", "seq2seq-lm"]:
               _UpperCamelCase       :    Any					  =					OrderedDict(
                   [
                       ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                       ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                   ]					)

               if self.use_past:
                      _UpperCamelCase       :    List[Any]					  =					{0: 'batch'}
                      _UpperCamelCase       :    int					  =					{0: 'batch', 1: 'past_decoder_sequence + sequence'}
               else:
                      _UpperCamelCase       :    List[Any]					  =					{0: 'batch', 1: 'decoder_sequence'}
                      _UpperCamelCase       :    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.
               _UpperCamelCase       :    Union[str, Any]					  =					OrderedDict(
                   [
                       ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                       ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                   ]					)
               if self.use_past:
                      _UpperCamelCase  , _UpperCamelCase       :    Union[str, Any]					  =					self.num_layers
                      for i in range(lowerCamelCase					):
                             _UpperCamelCase       :    Optional[Any]					  =					{0: 'batch', 2: 'past_sequence + sequence'}
                             _UpperCamelCase       :    int					  =					{0: 'batch', 2: 'past_sequence + sequence'}
        else:
               _UpperCamelCase       :    Optional[Any]					  =					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    __snake_case   (     self    :						Any					)	->							Mapping[str, Mapping[int, str]]:
        if self.task in ["default", "seq2seq-lm"]:
               _UpperCamelCase       :    str					  =					super().outputs
        else:
               _UpperCamelCase       :    List[str]					  =					super(lowerCamelCase    ,     self					).outputs
               if self.use_past:
                      _UpperCamelCase  , _UpperCamelCase       :    Dict					  =					self.num_layers
                      for i in range(lowerCamelCase					):
                             _UpperCamelCase       :    Tuple					  =					{0: 'batch', 2: 'past_sequence + sequence'}
                             _UpperCamelCase       :    Any					  =					{0: 'batch', 2: 'past_sequence + sequence'}
        return common_outputs

 def    __snake_case   (     self    :						Optional[int]    ,     lowerCamelCase    :						PreTrainedTokenizer    ,     lowerCamelCase    :						int = -1    ,     lowerCamelCase    :						int = -1    ,     lowerCamelCase    :						bool = False    ,     lowerCamelCase    :						Optional[TensorType] = None    ,     )	->							Mapping[str, Any]:
        _UpperCamelCase       :    int					  =					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            lowerCamelCase    ,     lowerCamelCase    ,     lowerCamelCase    ,     lowerCamelCase    ,     lowerCamelCase					)

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

               _UpperCamelCase       :    Optional[Any]					  =					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  , _UpperCamelCase       :    str					  =					self.num_layers
               _UpperCamelCase       :    Optional[Any]					  =					min(lowerCamelCase    ,     lowerCamelCase					)
               _UpperCamelCase       :    Any					  =					max(lowerCamelCase    ,     lowerCamelCase					) - min_num_layers
               _UpperCamelCase       :    List[str]					  =					'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       :    Optional[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

 def    __snake_case   (     self    :						Dict    ,     lowerCamelCase    :						PreTrainedTokenizer    ,     lowerCamelCase    :						int = -1    ,     lowerCamelCase    :						int = -1    ,     lowerCamelCase    :						bool = False    ,     lowerCamelCase    :						Optional[TensorType] = None    ,     )	->							Mapping[str, Any]:
        _UpperCamelCase       :    str					  =					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
               _UpperCamelCase  , _UpperCamelCase       :    List[Any]					  =					common_inputs['input_ids'].shape
               # Not using the same length for past_key_values
               _UpperCamelCase       :    List[str]					  =					seqlen + 2
               _UpperCamelCase  , _UpperCamelCase       :    Optional[Any]					  =					self.num_layers
               _UpperCamelCase  , _UpperCamelCase       :    int					  =					self.num_attention_heads
               _UpperCamelCase       :    Tuple					  =					(
                   batch,
                   num_encoder_attention_heads,
                   past_key_values_length,
                   self._config.hidden_size // num_encoder_attention_heads,
               )

               _UpperCamelCase       :    List[str]					  =					common_inputs['attention_mask'].dtype
               _UpperCamelCase       :    Tuple					  =					torch.cat(
                   [common_inputs['attention_mask'], torch.ones(lowerCamelCase    ,     lowerCamelCase    ,     dtype=lowerCamelCase					)]    ,     dim=1					)
               _UpperCamelCase       :    int					  =					[
                   (torch.zeros(lowerCamelCase					), torch.zeros(lowerCamelCase					)) for _ in range(lowerCamelCase					)
               ]
        return common_inputs

 def    __snake_case   (     self    :						Optional[int]    ,     lowerCamelCase    :						PreTrainedTokenizer    ,     lowerCamelCase    :						int = -1    ,     lowerCamelCase    :						int = -1    ,     lowerCamelCase    :						bool = False    ,     lowerCamelCase    :						Optional[TensorType] = None    ,     )	->							Mapping[str, Any]:
        # 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       :    Dict					  =					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       :    Optional[Any]					  =					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       :    Optional[int]					  =					[' '.join([tokenizer.unk_token]					) * seq_length] * batch_size
        _UpperCamelCase       :    str					  =					dict(tokenizer(lowerCamelCase    ,     return_tensors=lowerCamelCase					)					)
        return common_inputs

 def    __snake_case   (     self    :						int    ,     lowerCamelCase    :						PreTrainedTokenizer    ,     lowerCamelCase    :						int = -1    ,     lowerCamelCase    :						int = -1    ,     lowerCamelCase    :						bool = False    ,     lowerCamelCase    :						Optional[TensorType] = None    ,     )	->							Mapping[str, Any]:
        if self.task in ["default", "seq2seq-lm"]:
               _UpperCamelCase       :    Any					  =					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":
               _UpperCamelCase       :    List[Any]					  =					self._generate_dummy_inputs_for_causal_lm(
                   lowerCamelCase    ,     batch_size=lowerCamelCase    ,     seq_length=lowerCamelCase    ,     is_pair=lowerCamelCase    ,     framework=lowerCamelCase					)
        else:
               _UpperCamelCase       :    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    __snake_case   (     self    :						Tuple    ,     lowerCamelCase    :						Optional[Any]    ,     lowerCamelCase    :						str    ,     lowerCamelCase    :						Union[str, Any]    ,     lowerCamelCase    :						Tuple					)	->							Any:
        if self.task in ["default", "seq2seq-lm"]:
               _UpperCamelCase       :    str					  =					super()._flatten_past_key_values_(lowerCamelCase    ,     lowerCamelCase    ,     lowerCamelCase    ,     lowerCamelCase					)
        else:
               _UpperCamelCase       :    Any					  =					super(lowerCamelCase    ,     self					)._flatten_past_key_values_(
                   lowerCamelCase    ,     lowerCamelCase    ,     lowerCamelCase    ,     lowerCamelCase					)




def SCREAMING_SNAKE_CASE			(       lowercase_:		list[int]			):

       # validate:
       if not isinstance(lowercase_						,  lowercase_			) or not all(
           isinstance(lowercase_						,  lowercase_			) and item >= 0 for item in citations			):
              raise ValueError('The citations should be a list of non negative integers.'			)

       citations.sort()
       _UpperCamelCase       :    List[str]					  =					len(lowercase_			)

       for i in range(lowercase_			):
              if citations[len_citations - 1 - i] <= i:
                     return i

       return len_citations


if __name__ == "__main__":
 import doctest

 doctest.testmod()



