

import warnings

from ...utils import logging
from .image_processing_detr import DetrImageProcessor


lowerCamelCase									= logging.get_logger(__name__)







class 						__magic_name__      (							SCREAMING_SNAKE_CASE_							):

   '''simple docstring'''




   def __init__(    self,   *lowercase_,   **lowercase_	)							->   None:






     """simple docstring"""






     warnings.warn(
         '''The class DetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
         ''' Please use DetrImageProcessor instead.''',   SCREAMING_SNAKE_CASE_,   )
     super().__init__(*SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_	)



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


lowerCamelCase									= logging.get_logger(__name__)

lowerCamelCase									= {
    '''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 						__magic_name__      (							lowerCamelCase__							):

   '''simple docstring'''




   lowerCamelCase__			:     List[Any]    =       'blenderbot'
   lowerCamelCase__			:     int    =       ['past_key_values']
   lowerCamelCase__			:     Optional[Any]    =       {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}



   def __init__(    self,   lowercase_=8008,   lowercase_=128,   lowercase_=2,   lowercase_=10240,   lowercase_=32,   lowercase_=24,   lowercase_=10240,   lowercase_=32,   lowercase_=0.0,   lowercase_=0.0,   lowercase_=True,   lowercase_=True,   lowercase_="gelu",   lowercase_=2560,   lowercase_=0.1,   lowercase_=0.0,   lowercase_=0.0,   lowercase_=0.02,   lowercase_=1,   lowercase_=False,   lowercase_=0,   lowercase_=1,   lowercase_=2,   lowercase_=3,   lowercase_=2,   **lowercase_,   )							->   Any:






     """simple docstring"""






     a__  =vocab_size
     a__  =max_position_embeddings
     a__  =d_model
     a__  =encoder_ffn_dim
     a__  =encoder_layers
     a__  =encoder_attention_heads
     a__  =decoder_ffn_dim
     a__  =decoder_layers
     a__  =decoder_attention_heads
     a__  =dropout
     a__  =attention_dropout
     a__  =activation_dropout
     a__  =activation_function
     a__  =init_std
     a__  =encoder_layerdrop
     a__  =decoder_layerdrop
     a__  =use_cache
     a__  =encoder_layers
     a__  =scale_embedding  # scale factor will be sqrt(d_model) if True

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







class 						__magic_name__      (							lowerCamelCase__							):

   '''simple docstring'''




   @property
   def 			_UpperCAmelCase			(    self	)							->   Mapping[str, Mapping[int, str]]:






     """simple docstring"""






     if self.task in ["default", "seq2seq-lm"]:
       a__  =OrderedDict(
           [
               ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
               ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
           ]	)
       if self.use_past:
         a__  ={0: '''batch'''}
         a__  ={0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
       else:
         a__  ={0: '''batch''', 1: '''decoder_sequence'''}
         a__  ={0: '''batch''', 1: '''decoder_sequence'''}
       if self.use_past:
         self.fill_with_past_key_values_(lowercase_,   direction='''inputs'''	)
     elif self.task == "causal-lm":
       a__  =OrderedDict(
           [
               ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
               ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
           ]	)
       if self.use_past:
         a__,				a__  =self.num_layers
         for i in range(lowercase_	):
           a__  ={0: '''batch''', 2: '''past_sequence + sequence'''}
           a__  ={0: '''batch''', 2: '''past_sequence + sequence'''}
     else:
       a__  =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	)							->   Mapping[str, Mapping[int, str]]:






     """simple docstring"""






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



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_ = -1,   lowercase_ = -1,   lowercase_ = False,   lowercase_ = None,   )							->   Mapping[str, Any]:






     """simple docstring"""






     a__  =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_	)
     # Generate decoder inputs
     a__  =seq_length if not self.use_past else 1
     a__  =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_	)
     a__  ={F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
     a__  =dict(**lowercase_,   **lowercase_	)

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

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



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_ = -1,   lowercase_ = -1,   lowercase_ = False,   lowercase_ = None,   )							->   Mapping[str, Any]:






     """simple docstring"""






     a__  =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_	)

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



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_ = -1,   lowercase_ = -1,   lowercase_ = False,   lowercase_ = None,   )							->   Mapping[str, Any]:






     """simple docstring"""






     a__  =compute_effective_axis_dimension(
         lowercase_,   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
     a__  =tokenizer.num_special_tokens_to_add(lowercase_	)
     a__  =compute_effective_axis_dimension(
         lowercase_,   fixed_dimension=OnnxConfig.default_fixed_sequence,   num_token_to_add=lowercase_	)

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



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_ = -1,   lowercase_ = -1,   lowercase_ = False,   lowercase_ = None,   )							->   Mapping[str, Any]:






     """simple docstring"""






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

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

     return common_inputs



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_,   lowercase_,   lowercase_	)							->   Optional[int]:






     """simple docstring"""






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



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_	)							->   Tuple:






     """simple docstring"""






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

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

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

     for i in range(lowercase_	):
       a__  ={0: '''batch''', 2: decoder_sequence}
       a__  ={0: '''batch''', 2: decoder_sequence}
       a__  ={0: '''batch''', 2: encoder_sequence}
       a__  ={0: '''batch''', 2: encoder_sequence}
