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


__snake_case         =     logging.get_logger(__name__)

__snake_case         =     {
    '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       __snake_case (  snake_case__ ):
 __lowerCamelCase     : Any 	=   'blenderbot'
 __lowerCamelCase     : Any 	=   ['past_key_values']
 __lowerCamelCase     : int 	=   {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
 def __init__(	self		, snake_case__=8008		, snake_case__=128		, snake_case__=2		, snake_case__=1_0240		, snake_case__=32		, snake_case__=24		, snake_case__=1_0240		, snake_case__=32		, snake_case__=0.0		, snake_case__=0.0		, snake_case__=True		, snake_case__=True		, snake_case__="gelu"		, snake_case__=2560		, snake_case__=0.1		, snake_case__=0.0		, snake_case__=0.0		, snake_case__=0.02		, snake_case__=1		, snake_case__=False		, snake_case__=0		, snake_case__=1		, snake_case__=2		, snake_case__=3		, snake_case__=2		, **snake_case__		, )						->				str:
       '''simple docstring'''





       UpperCAmelCase     :			Dict     	=vocab_size
       UpperCAmelCase     :			str     	=max_position_embeddings
       UpperCAmelCase     :			List[Any]     	=d_model
       UpperCAmelCase     :			Dict     	=encoder_ffn_dim
       UpperCAmelCase     :			Union[str, Any]     	=encoder_layers
       UpperCAmelCase     :			Dict     	=encoder_attention_heads
       UpperCAmelCase     :			Optional[Any]     	=decoder_ffn_dim
       UpperCAmelCase     :			Optional[int]     	=decoder_layers
       UpperCAmelCase     :			Any     	=decoder_attention_heads
       UpperCAmelCase     :			List[Any]     	=dropout
       UpperCAmelCase     :			Union[str, Any]     	=attention_dropout
       UpperCAmelCase     :			Optional[Any]     	=activation_dropout
       UpperCAmelCase     :			Dict     	=activation_function
       UpperCAmelCase     :			List[Any]     	=init_std
       UpperCAmelCase     :			Union[str, Any]     	=encoder_layerdrop
       UpperCAmelCase     :			Optional[int]     	=decoder_layerdrop
       UpperCAmelCase     :			Optional[int]     	=use_cache
       UpperCAmelCase     :			Tuple     	=encoder_layers
       UpperCAmelCase     :			List[str]     	=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       __snake_case (  snake_case__ ):
 @property
 def 						UpperCAmelCase__							(	self    )						->				str:
       '''simple docstring'''





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





       if self.task in ["default", "seq2seq-lm"]:
             UpperCAmelCase     :			List[str]     	=super().outputs
       else:
             UpperCAmelCase     :			str     	=super(__snake_case		, self    ).outputs
             if self.use_past:
                   UpperCAmelCase     :			Optional[Any]     	=self.num_layers
                   for i in range(__snake_case    ):
                         UpperCAmelCase     :			List[Any]     	={0: '''batch''', 2: '''past_sequence + sequence'''}
                         UpperCAmelCase     :			Dict     	={0: '''batch''', 2: '''past_sequence + sequence'''}
       return common_outputs
 def 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__ = -1		, snake_case__ = -1		, snake_case__ = False		, snake_case__ = None		, )						->				Optional[Any]:
       '''simple docstring'''





       UpperCAmelCase     :			str     	=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           __snake_case		, __snake_case		, __snake_case		, __snake_case		, __snake_case    )
       # Generate decoder inputs
       UpperCAmelCase     :			int     	=seq_length if not self.use_past else 1
       UpperCAmelCase     :			List[Any]     	=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           __snake_case		, __snake_case		, __snake_case		, __snake_case		, __snake_case    )
       UpperCAmelCase     :			int     	={f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
       UpperCAmelCase     :			int     	=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
             UpperCAmelCase     :			Any     	=common_inputs['''input_ids'''].shape
             UpperCAmelCase     :			List[str]     	=common_inputs['''decoder_input_ids'''].shape[1]
             UpperCAmelCase     :			Union[str, Any]     	=self.num_attention_heads
             UpperCAmelCase     :			Dict     	=(
                 batch,
                 num_encoder_attention_heads,
                 encoder_seq_length,
                 self._config.hidden_size // num_encoder_attention_heads,
             )
             UpperCAmelCase     :			Tuple     	=decoder_seq_length
             UpperCAmelCase     :			int     	=(
                 batch,
                 num_decoder_attention_heads,
                 decoder_past_length,
                 self._config.hidden_size // num_decoder_attention_heads,
             )
             UpperCAmelCase     :			Any     	=torch.cat(
                 [common_inputs['''decoder_attention_mask'''], torch.ones(__snake_case		, __snake_case    )]		, dim=1    )
             UpperCAmelCase     :			Union[str, Any]     	=[]
             UpperCAmelCase     :			Any     	=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 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__ = -1		, snake_case__ = -1		, snake_case__ = False		, snake_case__ = None		, )						->				str:
       '''simple docstring'''





       UpperCAmelCase     :			str     	=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
             UpperCAmelCase     :			Optional[int]     	=common_inputs['''input_ids'''].shape
             UpperCAmelCase     :			List[str]     	=seqlen
             UpperCAmelCase     :			str     	=self.num_layers
             UpperCAmelCase     :			Dict     	=self.num_attention_heads
             UpperCAmelCase     :			List[str]     	=(
                 batch,
                 num_encoder_attention_heads,
                 past_key_values_length,
                 self._config.hidden_size // num_encoder_attention_heads,
             )
             UpperCAmelCase     :			List[Any]     	=common_inputs['''attention_mask'''].dtype
             UpperCAmelCase     :			List[str]     	=torch.cat(
                 [common_inputs['''attention_mask'''], torch.ones(__snake_case		, __snake_case		, dtype=__snake_case    )]		, dim=1    )
             UpperCAmelCase     :			Tuple     	=[
                 (torch.zeros(__snake_case    ), torch.zeros(__snake_case    )) for _ in range(__snake_case    )
             ]
       return common_inputs
 def 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__ = -1		, snake_case__ = -1		, snake_case__ = False		, snake_case__ = None		, )						->				int:
       '''simple docstring'''





       UpperCAmelCase     :			Optional[int]     	=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
       UpperCAmelCase     :			List[Any]     	=tokenizer.num_special_tokens_to_add(__snake_case    )
       UpperCAmelCase     :			List[Any]     	=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
       UpperCAmelCase     :			Any     	=[''' '''.join([tokenizer.unk_token]    ) * seq_length] * batch_size
       UpperCAmelCase     :			List[Any]     	=dict(tokenizer(__snake_case		, return_tensors=__snake_case    )    )
       return common_inputs
 def 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__ = -1		, snake_case__ = -1		, snake_case__ = False		, snake_case__ = None		, )						->				Tuple:
       '''simple docstring'''





       if self.task in ["default", "seq2seq-lm"]:
             UpperCAmelCase     :			Union[str, Any]     	=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":
             UpperCAmelCase     :			Tuple     	=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:
             UpperCAmelCase     :			Any     	=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 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__		, snake_case__		, snake_case__    )						->				Optional[int]:
       '''simple docstring'''





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






 def 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__    )						->				Optional[int]:
       '''simple docstring'''





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

       UpperCAmelCase     :			Union[str, Any]     	='''past_key_values''' if direction == '''inputs''' else '''present'''
       UpperCAmelCase     :			str     	=self.num_layers

       UpperCAmelCase     :			List[Any]     	='''past_encoder_sequence'''
       UpperCAmelCase     :			Tuple     	='''past_decoder_sequence''' if direction == '''inputs''' else '''past_decoder_sequence + sequence'''

       for i in range(__snake_case    ):
             UpperCAmelCase     :			Tuple     	={0: '''batch''', 2: decoder_sequence}
             UpperCAmelCase     :			List[str]     	={0: '''batch''', 2: decoder_sequence}
             UpperCAmelCase     :			Union[str, Any]     	={0: '''batch''', 2: encoder_sequence}
             UpperCAmelCase     :			List[str]     	={0: '''batch''', 2: encoder_sequence}




def   snake_case_    (			__lowercase      ,							__lowercase      ,							__lowercase      ,							__lowercase							):
   UpperCAmelCase_				:   Optional[int]							= int((lower_bound + upper_bound) // 2							)
   if array[r] == value:
      return r
   if lower_bound >= upper_bound:
      return -1
   if array[r] < value:
      return binary_search(__lowercase      ,							r + 1      ,							__lowercase      ,							__lowercase							)
   else:
      return binary_search(__lowercase      ,							__lowercase      ,							r - 1      ,							__lowercase							)
def   snake_case_    (			__lowercase      ,							__lowercase							):
   UpperCAmelCase_				:   Union[str, Any]							= 0
   if matrix[index][0] == value:
      return [index, 0]
   while index < len(__lowercase							) and matrix[index][0] < value:
      UpperCAmelCase_				:   str							= binary_search(matrix[index]      ,							0      ,							len(matrix[index]							) - 1      ,							__lowercase							)
      if r != -1:
         return [index, r]
      index += 1
   return [-1, -1]


if __name__ == "__main__":
       import doctest

       doctest.testmod()