




from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


__SCREAMING_SNAKE_CASE     : Tuple      =logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE     : Any      ={
    """facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json""",
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}






class       A      (		_lowerCAmelCase		):
     """simple docstring"""

     _snake_case:						int  ="""blenderbot"""
     _snake_case:						Optional[int]  =["""past_key_values"""]
     _snake_case:						int  ={"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}




     def __init__(     self	: List[Any]						,	A__	: Dict=8_0_0_8						,	A__	: Optional[int]=1_2_8						,	A__	: Optional[int]=2						,	A__	: Tuple=1_0_2_4_0						,	A__	: Optional[Any]=3_2						,	A__	: Tuple=2_4						,	A__	: int=1_0_2_4_0						,	A__	: Any=3_2						,	A__	: Any=0.0						,	A__	: List[Any]=0.0						,	A__	: Optional[Any]=True						,	A__	: List[Any]=True						,	A__	: Optional[int]="gelu"						,	A__	: Tuple=2_5_6_0						,	A__	: Dict=0.1						,	A__	: Union[str, Any]=0.0						,	A__	: Optional[Any]=0.0						,	A__	: int=0.02						,	A__	: Any=1						,	A__	: List[str]=False						,	A__	: List[Any]=0						,	A__	: str=1						,	A__	: int=2						,	A__	: Optional[int]=3						,	A__	: str=2						,	**A__	: Optional[int]						,	)					->							Optional[int]:
           lowerCamelCase__   	=					vocab_size
           lowerCamelCase__   	=					max_position_embeddings
           lowerCamelCase__   	=					d_model
           lowerCamelCase__   	=					encoder_ffn_dim
           lowerCamelCase__   	=					encoder_layers
           lowerCamelCase__   	=					encoder_attention_heads
           lowerCamelCase__   	=					decoder_ffn_dim
           lowerCamelCase__   	=					decoder_layers
           lowerCamelCase__   	=					decoder_attention_heads
           lowerCamelCase__   	=					dropout
           lowerCamelCase__   	=					attention_dropout
           lowerCamelCase__   	=					activation_dropout
           lowerCamelCase__   	=					activation_function
           lowerCamelCase__   	=					init_std
           lowerCamelCase__   	=					encoder_layerdrop
           lowerCamelCase__   	=					decoder_layerdrop
           lowerCamelCase__   	=					use_cache
           lowerCamelCase__   	=					encoder_layers
           lowerCamelCase__   	=					scale_embedding  # scale factor will be sqrt(d_model) if True

           super().__init__(
               pad_token_id=A__						,	bos_token_id=A__						,	eos_token_id=A__						,	is_encoder_decoder=A__						,	decoder_start_token_id=A__						,	encoder_no_repeat_ngram_size=A__						,	forced_eos_token_id=A__						,	**A__						,	)






class       A      (		_lowerCAmelCase		):
     """simple docstring"""

     @property
     def 			__snake_case (     self	: List[str]      )					->							Any:

           if self.task in ["default", "seq2seq-lm"]:
                 lowerCamelCase__   	=					OrderedDict(
                     [
                         ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                         ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                     ]      )
                 if self.use_past:
                       lowerCamelCase__   	=					{0: 'batch'}
                       lowerCamelCase__   	=					{0: 'batch', 1: 'past_decoder_sequence + sequence'}
                 else:
                       lowerCamelCase__   	=					{0: 'batch', 1: 'decoder_sequence'}
                       lowerCamelCase__   	=					{0: 'batch', 1: 'decoder_sequence'}
                 if self.use_past:
                       self.fill_with_past_key_values_(A__						,	direction='inputs'      )
           elif self.task == "causal-lm":
                 lowerCamelCase__   	=					OrderedDict(
                     [
                         ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                         ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                     ]      )
                 if self.use_past:
                       lowerCamelCase__	,		lowerCamelCase__   	=					self.num_layers
                       for i in range(A__      ):
                             lowerCamelCase__   	=					{0: 'batch', 2: 'past_sequence + sequence'}
                             lowerCamelCase__   	=					{0: 'batch', 2: 'past_sequence + sequence'}
           else:
                 lowerCamelCase__   	=					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
     # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
     def 			__snake_case (     self	: List[Any]      )					->							Any:

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




     def 			__snake_case (     self	: Any						,	A__	: str						,	A__	: Union[str, Any] = -1						,	A__	: Dict = -1						,	A__	: str = False						,	A__	: Dict = None						,	)					->							List[Any]:
           lowerCamelCase__   	=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               A__						,	A__						,	A__						,	A__						,	A__      )
           # Generate decoder inputs
           lowerCamelCase__   	=					seq_length if not self.use_past else 1
           lowerCamelCase__   	=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               A__						,	A__						,	A__						,	A__						,	A__      )
           lowerCamelCase__   	=					{F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
           lowerCamelCase__   	=					dict(**A__						,	**A__      )

           if self.use_past:
                 if not is_torch_available():
                       raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'      )
                 else:
                       import torch
                 lowerCamelCase__	,		lowerCamelCase__   	=					common_inputs['input_ids'].shape
                 lowerCamelCase__   	=					common_inputs['decoder_input_ids'].shape[1]
                 lowerCamelCase__	,		lowerCamelCase__   	=					self.num_attention_heads
                 lowerCamelCase__   	=					(
                     batch,
                     num_encoder_attention_heads,
                     encoder_seq_length,
                     self._config.hidden_size // num_encoder_attention_heads,
                 )
                 lowerCamelCase__   	=					decoder_seq_length
                 lowerCamelCase__   	=					(
                     batch,
                     num_decoder_attention_heads,
                     decoder_past_length,
                     self._config.hidden_size // num_decoder_attention_heads,
                 )
                 lowerCamelCase__   	=					torch.cat(
                     [common_inputs['decoder_attention_mask'], torch.ones(A__						,	A__      )]						,	dim=1      )
                 lowerCamelCase__   	=					[]
                 lowerCamelCase__	,		lowerCamelCase__   	=					self.num_layers

                 for _ in range(A__      ):
                       common_inputs["past_key_values"].append(
                           (
                               torch.zeros(A__      ),
                               torch.zeros(A__      ),
                               torch.zeros(A__      ),
                               torch.zeros(A__      ),
                           )      )
           return common_inputs




     def 			__snake_case (     self	: Union[str, Any]						,	A__	: str						,	A__	: Optional[int] = -1						,	A__	: Union[str, Any] = -1						,	A__	: Any = False						,	A__	: Optional[int] = None						,	)					->							Tuple:
           lowerCamelCase__   	=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               A__						,	A__						,	A__						,	A__						,	A__      )

           if self.use_past:
                 if not is_torch_available():
                       raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'      )
                 else:
                       import torch
                 lowerCamelCase__	,		lowerCamelCase__   	=					common_inputs['input_ids'].shape
                 lowerCamelCase__   	=					seqlen
                 lowerCamelCase__	,		lowerCamelCase__   	=					self.num_layers
                 lowerCamelCase__	,		lowerCamelCase__   	=					self.num_attention_heads
                 lowerCamelCase__   	=					(
                     batch,
                     num_encoder_attention_heads,
                     past_key_values_length,
                     self._config.hidden_size // num_encoder_attention_heads,
                 )
                 lowerCamelCase__   	=					common_inputs['attention_mask'].dtype
                 lowerCamelCase__   	=					torch.cat(
                     [common_inputs['attention_mask'], torch.ones(A__						,	A__						,	dtype=A__      )]						,	dim=1      )
                 lowerCamelCase__   	=					[
                     (torch.zeros(A__      ), torch.zeros(A__      )) for _ in range(A__      )
                 ]
           return common_inputs




     def 			__snake_case (     self	: Any						,	A__	: Dict						,	A__	: List[str] = -1						,	A__	: Optional[int] = -1						,	A__	: Optional[int] = False						,	A__	: Tuple = None						,	)					->							str:
           lowerCamelCase__   	=					compute_effective_axis_dimension(
               A__						,	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__   	=					tokenizer.num_special_tokens_to_add(A__      )
           lowerCamelCase__   	=					compute_effective_axis_dimension(
               A__						,	fixed_dimension=OnnxConfig.default_fixed_sequence						,	num_token_to_add=A__      )

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




     def 			__snake_case (     self	: Optional[int]						,	A__	: Tuple						,	A__	: Dict = -1						,	A__	: Tuple = -1						,	A__	: Tuple = False						,	A__	: Tuple = None						,	)					->							str:

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

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

           return common_inputs




     def 			__snake_case (     self	: int						,	A__	: List[Any]						,	A__	: List[str]						,	A__	: Tuple						,	A__	: Tuple      )					->							Optional[Any]:

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




     def 			__snake_case (     self	: Optional[Any]						,	A__	: str						,	A__	: Dict      )					->							str:

           if direction not in ["inputs", "outputs"]:
                 raise ValueError(F"""direction must either be \"inputs\" or \"outputs\", but {direction} was given"""      )

           lowerCamelCase__   	=					'past_key_values' if direction == 'inputs' else 'present'
           lowerCamelCase__	,		lowerCamelCase__   	=					self.num_layers

           lowerCamelCase__   	=					'past_encoder_sequence'
           lowerCamelCase__   	=					'past_decoder_sequence' if direction == 'inputs' else 'past_decoder_sequence + sequence'

           for i in range(A__      ):
                 lowerCamelCase__   	=					{0: 'batch', 2: decoder_sequence}
                 lowerCamelCase__   	=					{0: 'batch', 2: decoder_sequence}
                 lowerCamelCase__   	=					{0: 'batch', 2: encoder_sequence}
                 lowerCamelCase__   	=					{0: 'batch', 2: encoder_sequence}
"""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()


