




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


UpperCamelCase								=     logging.get_logger(__name__)

UpperCamelCase								=     {
    '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  __lowerCamelCase       (    _A    ):




    """simple docstring"""

    snake_case__					=      'blenderbot'
    snake_case__					=      ['past_key_values']
    snake_case__					=      {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}


    def __init__(       self     :     int   ,	SCREAMING_SNAKE_CASE__     :     Optional[Any]=8_008   ,	SCREAMING_SNAKE_CASE__     :     List[str]=128   ,	SCREAMING_SNAKE_CASE__     :     List[str]=2   ,	SCREAMING_SNAKE_CASE__     :     Tuple=10_240   ,	SCREAMING_SNAKE_CASE__     :     List[Any]=32   ,	SCREAMING_SNAKE_CASE__     :     int=24   ,	SCREAMING_SNAKE_CASE__     :     str=10_240   ,	SCREAMING_SNAKE_CASE__     :     Optional[int]=32   ,	SCREAMING_SNAKE_CASE__     :     Dict=0.0   ,	SCREAMING_SNAKE_CASE__     :     int=0.0   ,	SCREAMING_SNAKE_CASE__     :     str=True   ,	SCREAMING_SNAKE_CASE__     :     Dict=True   ,	SCREAMING_SNAKE_CASE__     :     Dict="gelu"   ,	SCREAMING_SNAKE_CASE__     :     List[str]=2_560   ,	SCREAMING_SNAKE_CASE__     :     List[Any]=0.1   ,	SCREAMING_SNAKE_CASE__     :     str=0.0   ,	SCREAMING_SNAKE_CASE__     :     Dict=0.0   ,	SCREAMING_SNAKE_CASE__     :     Tuple=0.02   ,	SCREAMING_SNAKE_CASE__     :     Any=1   ,	SCREAMING_SNAKE_CASE__     :     int=False   ,	SCREAMING_SNAKE_CASE__     :     Any=0   ,	SCREAMING_SNAKE_CASE__     :     Optional[Any]=1   ,	SCREAMING_SNAKE_CASE__     :     Optional[int]=2   ,	SCREAMING_SNAKE_CASE__     :     List[Any]=3   ,	SCREAMING_SNAKE_CASE__     :     int=2   ,	**SCREAMING_SNAKE_CASE__     :     Tuple   ,	)      ->       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=__snake_case   ,	bos_token_id=__snake_case   ,	eos_token_id=__snake_case   ,	is_encoder_decoder=__snake_case   ,	decoder_start_token_id=__snake_case   ,	encoder_no_repeat_ngram_size=__snake_case   ,	forced_eos_token_id=__snake_case   ,	**__snake_case   ,	)






class  __lowerCamelCase       (    _A    ):




    """simple docstring"""

    @property
    def    a      (       self     :     Any   )      ->       int:
         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_(__snake_case   ,	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(__snake_case   ):
                        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    a      (       self     :     Optional[Any]   )      ->       Optional[int]:
         if self.task in ["default", "seq2seq-lm"]:
              lowerCAmelCase__		= super().outputs
         else:
              lowerCAmelCase__		= super(__snake_case   ,	self   ).outputs
              if self.use_past:
                   lowerCAmelCase__	,		lowerCAmelCase__		= self.num_layers
                   for i in range(__snake_case   ):
                        lowerCAmelCase__		= {0: "batch", 2: "past_sequence + sequence"}
                        lowerCAmelCase__		= {0: "batch", 2: "past_sequence + sequence"}
         return common_outputs


    def    a      (       self     :     Optional[Any]   ,	SCREAMING_SNAKE_CASE__     :     PreTrainedTokenizer   ,	SCREAMING_SNAKE_CASE__     :     int = -1   ,	SCREAMING_SNAKE_CASE__     :     int = -1   ,	SCREAMING_SNAKE_CASE__     :     bool = False   ,	SCREAMING_SNAKE_CASE__     :     Optional[TensorType] = None   ,	)      ->       Dict:
         lowerCAmelCase__		= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             __snake_case   ,	__snake_case   ,	__snake_case   ,	__snake_case   ,	__snake_case   )
         # Generate decoder inputs
         lowerCAmelCase__		= seq_length if not self.use_past else 1
         lowerCAmelCase__		= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             __snake_case   ,	__snake_case   ,	__snake_case   ,	__snake_case   ,	__snake_case   )
         lowerCAmelCase__		= {f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
         lowerCAmelCase__		= dict(**__snake_case   ,	**__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
              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(__snake_case   ,	__snake_case   )]   ,	dim=1   )
              lowerCAmelCase__		= []
              lowerCAmelCase__	,		lowerCAmelCase__		= self.num_layers

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


    def    a      (       self     :     Optional[Any]   ,	SCREAMING_SNAKE_CASE__     :     PreTrainedTokenizer   ,	SCREAMING_SNAKE_CASE__     :     int = -1   ,	SCREAMING_SNAKE_CASE__     :     int = -1   ,	SCREAMING_SNAKE_CASE__     :     bool = False   ,	SCREAMING_SNAKE_CASE__     :     Optional[TensorType] = None   ,	)      ->       List[Any]:
         lowerCAmelCase__		= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             __snake_case   ,	__snake_case   ,	__snake_case   ,	__snake_case   ,	__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
              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(__snake_case   ,	__snake_case   ,	dtype=__snake_case   )]   ,	dim=1   )
              lowerCAmelCase__		= [
                  (torch.zeros(__snake_case   ), torch.zeros(__snake_case   )) for _ in range(__snake_case   )
              ]
         return common_inputs


    def    a      (       self     :     Any   ,	SCREAMING_SNAKE_CASE__     :     PreTrainedTokenizer   ,	SCREAMING_SNAKE_CASE__     :     int = -1   ,	SCREAMING_SNAKE_CASE__     :     int = -1   ,	SCREAMING_SNAKE_CASE__     :     bool = False   ,	SCREAMING_SNAKE_CASE__     :     Optional[TensorType] = None   ,	)      ->       Union[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
         lowerCAmelCase__		= compute_effective_axis_dimension(
             __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
         lowerCAmelCase__		= tokenizer.num_special_tokens_to_add(__snake_case   )
         lowerCAmelCase__		= compute_effective_axis_dimension(
             __snake_case   ,	fixed_dimension=OnnxConfig.default_fixed_sequence   ,	num_token_to_add=__snake_case   )

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


    def    a      (       self     :     List[Any]   ,	SCREAMING_SNAKE_CASE__     :     PreTrainedTokenizer   ,	SCREAMING_SNAKE_CASE__     :     int = -1   ,	SCREAMING_SNAKE_CASE__     :     int = -1   ,	SCREAMING_SNAKE_CASE__     :     bool = False   ,	SCREAMING_SNAKE_CASE__     :     Optional[TensorType] = None   ,	)      ->       Tuple:
         if self.task in ["default", "seq2seq-lm"]:
              lowerCAmelCase__		= self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                  __snake_case   ,	batch_size=__snake_case   ,	seq_length=__snake_case   ,	is_pair=__snake_case   ,	framework=__snake_case   )

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

         return common_inputs


    def    a      (       self     :     Dict   ,	SCREAMING_SNAKE_CASE__     :     Optional[int]   ,	SCREAMING_SNAKE_CASE__     :     int   ,	SCREAMING_SNAKE_CASE__     :     str   ,	SCREAMING_SNAKE_CASE__     :     Optional[int]   )      ->       str:
         if self.task in ["default", "seq2seq-lm"]:
              lowerCAmelCase__		= super()._flatten_past_key_values_(__snake_case   ,	__snake_case   ,	__snake_case   ,	__snake_case   )
         else:
              lowerCAmelCase__		= super(__snake_case   ,	self   )._flatten_past_key_values_(
                  __snake_case   ,	__snake_case   ,	__snake_case   ,	__snake_case   )


    def    a      (       self     :     Dict   ,	SCREAMING_SNAKE_CASE__     :     Mapping[str, Mapping[int, str]]   ,	SCREAMING_SNAKE_CASE__     :     str   )      ->       Optional[int]:
         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(__snake_case   ):
              lowerCAmelCase__		= {0: "batch", 2: decoder_sequence}
              lowerCAmelCase__		= {0: "batch", 2: decoder_sequence}
              lowerCAmelCase__		= {0: "batch", 2: encoder_sequence}
              lowerCAmelCase__		= {0: "batch", 2: encoder_sequence}






def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						str      )  ->							int:
    UpperCAmelCase_							 =     5381
    for x in s:
        UpperCAmelCase_							 =     ((hash_value << 5) + hash_value) + ord(__UpperCamelCase      )
    return hash_value & 0xffff_ffff





