"""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


SCREAMING_SNAKE_CASE__       =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__       =	{
    "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_     (      lowerCAmelCase       ):



				"""simple docstring"""
				_lowerCAmelCase  :     Optional[int]      	=    """mbart"""
				_lowerCAmelCase  :     List[Any]      	=    ["""past_key_values"""]
				_lowerCAmelCase  :     Optional[int]      	=    {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}

				def __init__(					self		,     lowerCAmelCase=5_02_65		,     lowerCAmelCase=10_24		,     lowerCAmelCase=12		,     lowerCAmelCase=40_96		,     lowerCAmelCase=16		,     lowerCAmelCase=12		,     lowerCAmelCase=40_96		,     lowerCAmelCase=16		,     lowerCAmelCase=0.0		,     lowerCAmelCase=0.0		,     lowerCAmelCase=True		,     lowerCAmelCase=True		,     lowerCAmelCase="gelu"		,     lowerCAmelCase=10_24		,     lowerCAmelCase=0.1		,     lowerCAmelCase=0.0		,     lowerCAmelCase=0.0		,     lowerCAmelCase=0.02		,     lowerCAmelCase=0.0		,     lowerCAmelCase=False		,     lowerCAmelCase=1		,     lowerCAmelCase=0		,     lowerCAmelCase=2		,     lowerCAmelCase=2		,     **lowerCAmelCase		,     ):






										"""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=lowerCAmelCase		,     bos_token_id=lowerCAmelCase		,     eos_token_id=lowerCAmelCase		,     is_encoder_decoder=lowerCAmelCase		,     forced_eos_token_id=lowerCAmelCase		,     **lowerCAmelCase		,     )





class 	lowerCAmelCase_     (      lowerCAmelCase       ):



				"""simple docstring"""

				@property
				def 		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_(lowerCAmelCase		,     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(lowerCAmelCase     ):
																												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 		snake_case			(					self     ):






										"""simple docstring"""

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

				def 		snake_case			(					self		,     lowerCAmelCase		,     lowerCAmelCase = -1		,     lowerCAmelCase = -1		,     lowerCAmelCase = False		,     lowerCAmelCase = None		,     ):






										"""simple docstring"""

										snake_case             =      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    lowerCAmelCase		,     lowerCAmelCase		,     lowerCAmelCase		,     lowerCAmelCase		,     lowerCAmelCase     )

										# 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(
										    lowerCAmelCase		,     lowerCAmelCase		,     lowerCAmelCase		,     lowerCAmelCase		,     lowerCAmelCase     )
										snake_case             =      {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
										snake_case             =      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
																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(lowerCAmelCase		,     lowerCAmelCase     )]		,     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(lowerCAmelCase		,     lowerCAmelCase     )
																snake_case             =      max(lowerCAmelCase		,     lowerCAmelCase     ) - min_num_layers
																snake_case             =      '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.
																snake_case             =      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		,     lowerCAmelCase		,     lowerCAmelCase = -1		,     lowerCAmelCase = -1		,     lowerCAmelCase = False		,     lowerCAmelCase = None		,     ):






										"""simple docstring"""

										snake_case             =      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
																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(lowerCAmelCase		,     lowerCAmelCase		,     dtype=lowerCAmelCase     )]		,     dim=1     )
																snake_case             =      [
																    (torch.zeros(lowerCAmelCase     ), torch.zeros(lowerCAmelCase     )) for _ in range(lowerCAmelCase     )
																]
										return common_inputs

				def 		snake_case			(					self		,     lowerCAmelCase		,     lowerCAmelCase = -1		,     lowerCAmelCase = -1		,     lowerCAmelCase = False		,     lowerCAmelCase = None		,     ):






										"""simple docstring"""

										snake_case             =      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
										snake_case             =      tokenizer.num_special_tokens_to_add(lowerCAmelCase     )
										snake_case             =      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
										snake_case             =      [' '.join([tokenizer.unk_token]     ) * seq_length] * batch_size
										snake_case             =      dict(tokenizer(lowerCAmelCase		,     return_tensors=lowerCAmelCase     )     )
										return common_inputs

				def 		snake_case			(					self		,     lowerCAmelCase		,     lowerCAmelCase = -1		,     lowerCAmelCase = -1		,     lowerCAmelCase = False		,     lowerCAmelCase = None		,     ):






										"""simple docstring"""

										if self.task in ["default", "seq2seq-lm"]:
																snake_case             =      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":
																snake_case             =      self._generate_dummy_inputs_for_causal_lm(
																    lowerCAmelCase		,     batch_size=lowerCAmelCase		,     seq_length=lowerCAmelCase		,     is_pair=lowerCAmelCase		,     framework=lowerCAmelCase     )
										else:
																snake_case             =      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		,     lowerCAmelCase		,     lowerCAmelCase		,     lowerCAmelCase		,     lowerCAmelCase     ):






										"""simple docstring"""

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



"""simple docstring"""
def  lowerCAmelCase__     (      _UpperCamelCase							:					list       )       ->					list:





						"""simple docstring"""






						if len(_UpperCamelCase       ) <= 1:
												return data
						else:
												return [
												    *quick_sort([e for e in data[1:] if e <= data[0]]       ),
												    data[0],
												    *quick_sort([e for e in data[1:] if e > data[0]]       ),
												]


if __name__ == "__main__":
					import doctest

					doctest.testmod()


