





'''simple docstring'''







import numpy as np
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from PIL import Image





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Optional[Any],      SCREAMING_SNAKE_CASE__   :      Optional[int]=256 )			->						str:
						UpperCAmelCase_      :   str      =	min(img.size )

						if s < target_image_size:
												raise ValueError(F"""min dim for image {s} < {target_image_size}""" )

						UpperCAmelCase_      :   Any      =	target_image_size / s
						UpperCAmelCase_      :   Union[str, Any]      =	(round(r * img.size[1] ), round(r * img.size[0] ))
						UpperCAmelCase_      :   Any      =	TF.resize(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__,      interpolation=PIL.Image.LANCZOS )
						UpperCAmelCase_      :   Optional[Any]      =	TF.center_crop(SCREAMING_SNAKE_CASE__,      output_size=2 * [target_image_size] )
						UpperCAmelCase_      :   int      =	torch.unsqueeze(T.ToTensor()(SCREAMING_SNAKE_CASE__ ),      0 )
						return img





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      int )			->						Dict:
						UpperCAmelCase_      :   str      =	2.0 * x - 1.0
						return x





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Tuple,      SCREAMING_SNAKE_CASE__   :      Optional[int]=True,      SCREAMING_SNAKE_CASE__   :      int="RGB" )			->						Optional[Any]:
						UpperCAmelCase_      :   Optional[Any]      =	x.detach().cpu()
						if process:
												UpperCAmelCase_      :   Tuple      =	post_process_tensor(SCREAMING_SNAKE_CASE__ )
						UpperCAmelCase_      :   str      =	x.numpy()
						if process:
												UpperCAmelCase_      :   int      =	(255 * x).astype(np.uinta )
						UpperCAmelCase_      :   Optional[int]      =	Image.fromarray(SCREAMING_SNAKE_CASE__ )
						if not x.mode == mode:
												UpperCAmelCase_      :   Any      =	x.convert(SCREAMING_SNAKE_CASE__ )
						return x





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      int )			->						Optional[Any]:
						UpperCAmelCase_      :   List[str]      =	torch.clamp(SCREAMING_SNAKE_CASE__,      -1.0,      1.0 )
						UpperCAmelCase_      :   List[Any]      =	(x + 1.0) / 2.0
						UpperCAmelCase_      :   List[Any]      =	x.permute(1,      2,      0 )
						return x





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Union[str, Any] )			->						Optional[Any]:
						UpperCAmelCase_      :   str      =	post_process_tensor(x.squeeze() )
						return x.permute(2,      0,      1 ).unsqueeze(0 )







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__						:	Any	     = logging.get_logger(__name__)

UpperCAmelCase__						:	Optional[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 UpperCAmelCase  (		SCREAMING_SNAKE_CASE__  ):





   '''simple docstring'''
   __UpperCamelCase						:       Optional[Any]												=       '''blenderbot'''
   __UpperCamelCase						:       Optional[int]												=       ['''past_key_values''']
   __UpperCamelCase						:       Union[str, Any]												=       {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}


   def __init__(   self	:				Any     ,     lowerCAmelCase_	:				List[str]=8_0_0_8     ,     lowerCAmelCase_	:				Any=1_2_8     ,     lowerCAmelCase_	:				List[str]=2     ,     lowerCAmelCase_	:				Dict=1_0_2_4_0     ,     lowerCAmelCase_	:				Dict=3_2     ,     lowerCAmelCase_	:				Union[str, Any]=2_4     ,     lowerCAmelCase_	:				Union[str, Any]=1_0_2_4_0     ,     lowerCAmelCase_	:				Union[str, Any]=3_2     ,     lowerCAmelCase_	:				Optional[Any]=0.0     ,     lowerCAmelCase_	:				Optional[int]=0.0     ,     lowerCAmelCase_	:				Optional[int]=True     ,     lowerCAmelCase_	:				Optional[Any]=True     ,     lowerCAmelCase_	:				Union[str, Any]="gelu"     ,     lowerCAmelCase_	:				Optional[int]=2_5_6_0     ,     lowerCAmelCase_	:				List[str]=0.1     ,     lowerCAmelCase_	:				Any=0.0     ,     lowerCAmelCase_	:				Union[str, Any]=0.0     ,     lowerCAmelCase_	:				Any=0.02     ,     lowerCAmelCase_	:				List[Any]=1     ,     lowerCAmelCase_	:				List[str]=False     ,     lowerCAmelCase_	:				str=0     ,     lowerCAmelCase_	:				str=1     ,     lowerCAmelCase_	:				List[Any]=2     ,     lowerCAmelCase_	:				List[str]=3     ,     lowerCAmelCase_	:				int=2     ,     **lowerCAmelCase_	:				Tuple     ,     ):

      """simple docstring"""
      _A:    Any						     =			vocab_size
      _A:    Optional[int]						     =			max_position_embeddings
      _A:    int						     =			d_model
      _A:    Optional[int]						     =			encoder_ffn_dim
      _A:    Optional[int]						     =			encoder_layers
      _A:    int						     =			encoder_attention_heads
      _A:    Optional[Any]						     =			decoder_ffn_dim
      _A:    Dict						     =			decoder_layers
      _A:    int						     =			decoder_attention_heads
      _A:    List[str]						     =			dropout
      _A:    Union[str, Any]						     =			attention_dropout
      _A:    Tuple						     =			activation_dropout
      _A:    Dict						     =			activation_function
      _A:    Optional[Any]						     =			init_std
      _A:    Optional[int]						     =			encoder_layerdrop
      _A:    Any						     =			decoder_layerdrop
      _A:    List[str]						     =			use_cache
      _A:    Dict						     =			encoder_layers
      _A:    int						     =			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_     ,     decoder_start_token_id=lowerCAmelCase_     ,     encoder_no_repeat_ngram_size=lowerCAmelCase_     ,     forced_eos_token_id=lowerCAmelCase_     ,     **lowerCAmelCase_     ,     )






class UpperCAmelCase  (		SCREAMING_SNAKE_CASE__  ):





   '''simple docstring'''


   @property
   def 					__magic_name__							(   self	:				Any	):

      """simple docstring"""
      if self.task in ["default", "seq2seq-lm"]:
         _A:    Union[str, Any]						     =			OrderedDict(
             [
                 ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                 ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
             ]	)
         if self.use_past:
            _A:    int						     =			{0: '''batch'''}
            _A:    Optional[int]						     =			{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
         else:
            _A:    List[Any]						     =			{0: '''batch''', 1: '''decoder_sequence'''}
            _A:    Tuple						     =			{0: '''batch''', 1: '''decoder_sequence'''}
         if self.use_past:
            self.fill_with_past_key_values_(lowerCAmelCase_     ,     direction='''inputs'''	)
      elif self.task == "causal-lm":
         _A:    str						     =			OrderedDict(
             [
                 ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                 ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
             ]	)
         if self.use_past:
            _A     ,    _A:    Tuple						     =			self.num_layers
            for i in range(lowerCAmelCase_	):
               _A:    str						     =			{0: '''batch''', 2: '''past_sequence + sequence'''}
               _A:    List[str]						     =			{0: '''batch''', 2: '''past_sequence + sequence'''}
      else:
         _A:    List[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 					__magic_name__							(   self	:				Tuple	):

      """simple docstring"""
      if self.task in ["default", "seq2seq-lm"]:
         _A:    List[str]						     =			super().outputs
      else:
         _A:    Tuple						     =			super(lowerCAmelCase_     ,     self	).outputs
         if self.use_past:
            _A     ,    _A:    Optional[int]						     =			self.num_layers
            for i in range(lowerCAmelCase_	):
               _A:    Union[str, Any]						     =			{0: '''batch''', 2: '''past_sequence + sequence'''}
               _A:    Optional[int]						     =			{0: '''batch''', 2: '''past_sequence + sequence'''}
      return common_outputs


   def 					__magic_name__							(   self	:				Tuple     ,     lowerCAmelCase_	:				PreTrainedTokenizer     ,     lowerCAmelCase_	:				int = -1     ,     lowerCAmelCase_	:				int = -1     ,     lowerCAmelCase_	:				bool = False     ,     lowerCAmelCase_	:				Optional[TensorType] = None     ,     ):

      """simple docstring"""
      _A:    Optional[Any]						     =			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_	)
      # Generate decoder inputs
      _A:    int						     =			seq_length if not self.use_past else 1
      _A:    Any						     =			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_	)
      _A:    Optional[int]						     =			{F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
      _A:    Union[str, Any]						     =			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
         _A     ,    _A:    str						     =			common_inputs['''input_ids'''].shape
         _A:    Optional[Any]						     =			common_inputs['''decoder_input_ids'''].shape[1]
         _A     ,    _A:    Optional[Any]						     =			self.num_attention_heads
         _A:    List[str]						     =			(
             batch,
             num_encoder_attention_heads,
             encoder_seq_length,
             self._config.hidden_size // num_encoder_attention_heads,
         )
         _A:    Union[str, Any]						     =			decoder_seq_length
         _A:    Tuple						     =			(
             batch,
             num_decoder_attention_heads,
             decoder_past_length,
             self._config.hidden_size // num_decoder_attention_heads,
         )
         _A:    Union[str, Any]						     =			torch.cat(
             [common_inputs['''decoder_attention_mask'''], torch.ones(lowerCAmelCase_     ,     lowerCAmelCase_	)]     ,     dim=1	)
         _A:    List[Any]						     =			[]
         _A     ,    _A:    Union[str, Any]						     =			self.num_layers

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


   def 					__magic_name__							(   self	:				Union[str, Any]     ,     lowerCAmelCase_	:				PreTrainedTokenizer     ,     lowerCAmelCase_	:				int = -1     ,     lowerCAmelCase_	:				int = -1     ,     lowerCAmelCase_	:				bool = False     ,     lowerCAmelCase_	:				Optional[TensorType] = None     ,     ):

      """simple docstring"""
      _A:    Tuple						     =			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
         _A     ,    _A:    Optional[int]						     =			common_inputs['''input_ids'''].shape
         _A:    Union[str, Any]						     =			seqlen
         _A     ,    _A:    Tuple						     =			self.num_layers
         _A     ,    _A:    Dict						     =			self.num_attention_heads
         _A:    Tuple						     =			(
             batch,
             num_encoder_attention_heads,
             past_key_values_length,
             self._config.hidden_size // num_encoder_attention_heads,
         )
         _A:    List[Any]						     =			common_inputs['''attention_mask'''].dtype
         _A:    Optional[int]						     =			torch.cat(
             [common_inputs['''attention_mask'''], torch.ones(lowerCAmelCase_     ,     lowerCAmelCase_     ,     dtype=lowerCAmelCase_	)]     ,     dim=1	)
         _A:    Tuple						     =			[
             (torch.zeros(lowerCAmelCase_	), torch.zeros(lowerCAmelCase_	)) for _ in range(lowerCAmelCase_	)
         ]
      return common_inputs


   def 					__magic_name__							(   self	:				Any     ,     lowerCAmelCase_	:				PreTrainedTokenizer     ,     lowerCAmelCase_	:				int = -1     ,     lowerCAmelCase_	:				int = -1     ,     lowerCAmelCase_	:				bool = False     ,     lowerCAmelCase_	:				Optional[TensorType] = None     ,     ):

      """simple docstring"""
      # 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
      _A:    Optional[int]						     =			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
      _A:    int						     =			tokenizer.num_special_tokens_to_add(lowerCAmelCase_	)
      _A:    Any						     =			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
      _A:    Tuple						     =			[''' '''.join([tokenizer.unk_token]	) * seq_length] * batch_size
      _A:    Any						     =			dict(tokenizer(lowerCAmelCase_     ,     return_tensors=lowerCAmelCase_	)	)
      return common_inputs


   def 					__magic_name__							(   self	:				Dict     ,     lowerCAmelCase_	:				PreTrainedTokenizer     ,     lowerCAmelCase_	:				int = -1     ,     lowerCAmelCase_	:				int = -1     ,     lowerCAmelCase_	:				bool = False     ,     lowerCAmelCase_	:				Optional[TensorType] = None     ,     ):

      """simple docstring"""
      if self.task in ["default", "seq2seq-lm"]:
         _A:    Union[str, Any]						     =			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":
         _A:    Optional[Any]						     =			self._generate_dummy_inputs_for_causal_lm(
             lowerCAmelCase_     ,     batch_size=lowerCAmelCase_     ,     seq_length=lowerCAmelCase_     ,     is_pair=lowerCAmelCase_     ,     framework=lowerCAmelCase_	)
      else:
         _A:    Tuple						     =			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 					__magic_name__							(   self	:				int     ,     lowerCAmelCase_	:				Tuple     ,     lowerCAmelCase_	:				Any     ,     lowerCAmelCase_	:				str     ,     lowerCAmelCase_	:				List[str]	):

      """simple docstring"""
      if self.task in ["default", "seq2seq-lm"]:
         _A:    int						     =			super()._flatten_past_key_values_(lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_	)
      else:
         _A:    Optional[int]						     =			super(lowerCAmelCase_     ,     self	)._flatten_past_key_values_(
             lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_	)


   def 					__magic_name__							(   self	:				Dict     ,     lowerCAmelCase_	:				Mapping[str, Mapping[int, str]]     ,     lowerCAmelCase_	:				str	):

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

      _A:    Tuple						     =			'''past_key_values''' if direction == '''inputs''' else '''present'''
      _A     ,    _A:    int						     =			self.num_layers

      _A:    int						     =			'''past_encoder_sequence'''
      _A:    int						     =			'''past_decoder_sequence''' if direction == '''inputs''' else '''past_decoder_sequence + sequence'''

      for i in range(lowerCAmelCase_	):
         _A:    Optional[int]						     =			{0: '''batch''', 2: decoder_sequence}
         _A:    Dict						     =			{0: '''batch''', 2: decoder_sequence}
         _A:    int						     =			{0: '''batch''', 2: encoder_sequence}
         _A:    Optional[int]						     =			{0: '''batch''', 2: encoder_sequence}



