'''simple docstring'''





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_:					Optional[Any]  =logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:					List[Any]  ={
    '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json''',
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}




class 	__A				(			lowerCamelCase_   ):
    a__	:						int	       =  '''blenderbot-small'''
    a__	:						str	       =  ['''past_key_values''']
    a__	:						int	       =  {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}



    def __init__(self   :   Any       ,      __a   :   Union[str, Any]=50265       ,      __a   :   Optional[int]=512       ,      __a   :   Optional[Any]=8       ,      __a   :   Tuple=2048       ,      __a   :   Union[str, Any]=16       ,      __a   :   Union[str, Any]=8       ,      __a   :   int=2048       ,      __a   :   Optional[int]=16       ,      __a   :   List[Any]=0.0       ,      __a   :   List[str]=0.0       ,      __a   :   Optional[Any]=True       ,      __a   :   Dict=True       ,      __a   :   int="gelu"       ,      __a   :   Optional[int]=512       ,      __a   :   str=0.1       ,      __a   :   int=0.0       ,      __a   :   str=0.0       ,      __a   :   Any=0.02       ,      __a   :   int=1       ,      __a   :   List[Any]=False       ,      __a   :   List[str]=0       ,      __a   :   List[Any]=1       ,      __a   :   List[Any]=2       ,      __a   :   Dict=2       ,      **__a   :   Optional[int]       ,      ):
         UpperCAmelCase_	   =       vocab_size
         UpperCAmelCase_	   =       max_position_embeddings
         UpperCAmelCase_	   =       d_model
         UpperCAmelCase_	   =       encoder_ffn_dim
         UpperCAmelCase_	   =       encoder_layers
         UpperCAmelCase_	   =       encoder_attention_heads
         UpperCAmelCase_	   =       decoder_ffn_dim
         UpperCAmelCase_	   =       decoder_layers
         UpperCAmelCase_	   =       decoder_attention_heads
         UpperCAmelCase_	   =       dropout
         UpperCAmelCase_	   =       attention_dropout
         UpperCAmelCase_	   =       activation_dropout
         UpperCAmelCase_	   =       activation_function
         UpperCAmelCase_	   =       init_std
         UpperCAmelCase_	   =       encoder_layerdrop
         UpperCAmelCase_	   =       decoder_layerdrop
         UpperCAmelCase_	   =       use_cache
         UpperCAmelCase_	   =       encoder_layers
         UpperCAmelCase_	   =       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       ,      forced_eos_token_id=__snake_case       ,      **__snake_case       ,      )




class 	__A				(			lowerCamelCase_   ):



    @property
    def  _lowercase						(self   :   Optional[int]			):
         if self.task in ["default", "seq2seq-lm"]:
              UpperCAmelCase_	   =       OrderedDict(
                  [
                      ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                      ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                  ]			)

              if self.use_past:
                   UpperCAmelCase_	   =       {0: "batch"}
                   UpperCAmelCase_	   =       {0: "batch", 1: "past_decoder_sequence + sequence"}
              else:
                   UpperCAmelCase_	   =       {0: "batch", 1: "decoder_sequence"}
                   UpperCAmelCase_	   =       {0: "batch", 1: "decoder_sequence"}

              if self.use_past:
                   self.fill_with_past_key_values_(__snake_case       ,      direction="inputs"			)
         elif self.task == "causal-lm":
              # TODO: figure this case out.
              UpperCAmelCase_	   =       OrderedDict(
                  [
                      ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                      ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                  ]			)
              if self.use_past:
                   UpperCAmelCase_   ,			UpperCAmelCase_	   =       self.num_layers
                   for i in range(__snake_case			):
                        UpperCAmelCase_	   =       {0: "batch", 2: "past_sequence + sequence"}
                        UpperCAmelCase_	   =       {0: "batch", 2: "past_sequence + sequence"}
         else:
              UpperCAmelCase_	   =       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
    def  _lowercase						(self   :   Dict			):
         if self.task in ["default", "seq2seq-lm"]:
              UpperCAmelCase_	   =       super().outputs
         else:
              UpperCAmelCase_	   =       super(__snake_case       ,      self			).outputs
              if self.use_past:
                   UpperCAmelCase_   ,			UpperCAmelCase_	   =       self.num_layers
                   for i in range(__snake_case			):
                        UpperCAmelCase_	   =       {0: "batch", 2: "past_sequence + sequence"}
                        UpperCAmelCase_	   =       {0: "batch", 2: "past_sequence + sequence"}
         return common_outputs



    def  _lowercase						(self   :   List[Any]       ,      __a   :   PreTrainedTokenizer       ,      __a   :   int = -1       ,      __a   :   int = -1       ,      __a   :   bool = False       ,      __a   :   Optional[TensorType] = None       ,      ):
         UpperCAmelCase_	   =       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             __snake_case       ,      __snake_case       ,      __snake_case       ,      __snake_case       ,      __snake_case			)

         # Generate decoder inputs
         UpperCAmelCase_	   =       seq_length if not self.use_past else 1
         UpperCAmelCase_	   =       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             __snake_case       ,      __snake_case       ,      __snake_case       ,      __snake_case       ,      __snake_case			)
         UpperCAmelCase_	   =       {f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
         UpperCAmelCase_	   =       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_   ,			UpperCAmelCase_	   =       common_inputs["input_ids"].shape
              UpperCAmelCase_	   =       common_inputs["decoder_input_ids"].shape[1]
              UpperCAmelCase_   ,			UpperCAmelCase_	   =       self.num_attention_heads
              UpperCAmelCase_	   =       (
                  batch,
                  num_encoder_attention_heads,
                  encoder_seq_length,
                  self._config.hidden_size // num_encoder_attention_heads,
              )
              UpperCAmelCase_	   =       decoder_seq_length + 3
              UpperCAmelCase_	   =       (
                  batch,
                  num_decoder_attention_heads,
                  decoder_past_length,
                  self._config.hidden_size // num_decoder_attention_heads,
              )

              UpperCAmelCase_	   =       torch.cat(
                  [common_inputs["decoder_attention_mask"], torch.ones(__snake_case       ,      __snake_case			)]       ,      dim=1			)

              UpperCAmelCase_	   =       []
              # If the number of encoder and decoder layers are present in the model configuration, both are considered
              UpperCAmelCase_   ,			UpperCAmelCase_	   =       self.num_layers
              UpperCAmelCase_	   =       min(__snake_case       ,      __snake_case			)
              UpperCAmelCase_	   =       max(__snake_case       ,      __snake_case			) - min_num_layers
              UpperCAmelCase_	   =       "encoder" if num_encoder_layers > num_decoder_layers else "decoder"

              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			),
                       )			)
              # TODO: test this.
              UpperCAmelCase_	   =       encoder_shape if remaining_side_name == "encoder" else decoder_shape
              for _ in range(__snake_case       ,      __snake_case			):
                   common_inputs["past_key_values"].append((torch.zeros(__snake_case			), torch.zeros(__snake_case			))			)
         return common_inputs



    def  _lowercase						(self   :   Dict       ,      __a   :   PreTrainedTokenizer       ,      __a   :   int = -1       ,      __a   :   int = -1       ,      __a   :   bool = False       ,      __a   :   Optional[TensorType] = None       ,      ):
         UpperCAmelCase_	   =       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_   ,			UpperCAmelCase_	   =       common_inputs["input_ids"].shape
              # Not using the same length for past_key_values
              UpperCAmelCase_	   =       seqlen + 2
              UpperCAmelCase_   ,			UpperCAmelCase_	   =       self.num_layers
              UpperCAmelCase_   ,			UpperCAmelCase_	   =       self.num_attention_heads
              UpperCAmelCase_	   =       (
                  batch,
                  num_encoder_attention_heads,
                  past_key_values_length,
                  self._config.hidden_size // num_encoder_attention_heads,
              )

              UpperCAmelCase_	   =       common_inputs["attention_mask"].dtype
              UpperCAmelCase_	   =       torch.cat(
                  [common_inputs["attention_mask"], torch.ones(__snake_case       ,      __snake_case       ,      dtype=__snake_case			)]       ,      dim=1			)
              UpperCAmelCase_	   =       [
                  (torch.zeros(__snake_case			), torch.zeros(__snake_case			)) for _ in range(__snake_case			)
              ]
         return common_inputs



    def  _lowercase						(self   :   List[Any]       ,      __a   :   PreTrainedTokenizer       ,      __a   :   int = -1       ,      __a   :   int = -1       ,      __a   :   bool = False       ,      __a   :   Optional[TensorType] = None       ,      ):
         # 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
         UpperCAmelCase_	   =       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_	   =       tokenizer.num_special_tokens_to_add(__snake_case			)
         UpperCAmelCase_	   =       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_	   =       [" ".join([tokenizer.unk_token]			) * seq_length] * batch_size
         UpperCAmelCase_	   =       dict(tokenizer(__snake_case       ,      return_tensors=__snake_case			)			)
         return common_inputs



    def  _lowercase						(self   :   str       ,      __a   :   PreTrainedTokenizer       ,      __a   :   int = -1       ,      __a   :   int = -1       ,      __a   :   bool = False       ,      __a   :   Optional[TensorType] = None       ,      ):
         if self.task in ["default", "seq2seq-lm"]:
              UpperCAmelCase_	   =       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_	   =       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_	   =       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  _lowercase						(self   :   List[str]       ,      __a   :   Union[str, Any]       ,      __a   :   List[str]       ,      __a   :   List[str]       ,      __a   :   Optional[Any]			):
         if self.task in ["default", "seq2seq-lm"]:
              UpperCAmelCase_	   =       super()._flatten_past_key_values_(__snake_case       ,      __snake_case       ,      __snake_case       ,      __snake_case			)
         else:
              UpperCAmelCase_	   =       super(__snake_case       ,      self			)._flatten_past_key_values_(
                  __snake_case       ,      __snake_case       ,      __snake_case       ,      __snake_case			)





'''simple docstring'''





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


SCREAMING_SNAKE_CASE_:					Dict  =logging.get_logger(__name__)




class 	__A				(			UpperCamelCase__     ):
    a__	:						Any	       =  ["""pixel_values"""]



    def __init__(self   :   Any       ,      __a   :   bool = True       ,      __a   :   Optional[Dict[str, int]] = None       ,      __a   :   PILImageResampling = PILImageResampling.BICUBIC       ,      __a   :   bool = True       ,      __a   :   bool = True       ,      __a   :   Union[int, float] = 1 / 255       ,      __a   :   Dict[str, int] = None       ,      __a   :   bool = True       ,      __a   :   Optional[Union[float, List[float]]] = None       ,      __a   :   Optional[Union[float, List[float]]] = None       ,      **__a   :   Dict       ,      ):
         super().__init__(**__a			)
         UpperCAmelCase_	   =       size if size is not None else {"height": 224, "width": 224}
         UpperCAmelCase_	   =       get_size_dict(__a			)
         UpperCAmelCase_	   =       crop_size if crop_size is not None else {"height": 224, "width": 224}
         UpperCAmelCase_	   =       get_size_dict(__a       ,      default_to_square=__a       ,      param_name="crop_size"			)

         UpperCAmelCase_	   =       do_resize
         UpperCAmelCase_	   =       do_rescale
         UpperCAmelCase_	   =       do_normalize
         UpperCAmelCase_	   =       do_center_crop
         UpperCAmelCase_	   =       crop_size
         UpperCAmelCase_	   =       size
         UpperCAmelCase_	   =       resample
         UpperCAmelCase_	   =       rescale_factor
         UpperCAmelCase_	   =       image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
         UpperCAmelCase_	   =       image_std if image_std is not None else IMAGENET_DEFAULT_STD



    def  _lowercase						(self   :   Tuple       ,      __a   :   np.ndarray       ,      __a   :   Dict[str, int]       ,      __a   :   PILImageResampling = PILImageResampling.BILINEAR       ,      __a   :   Optional[Union[str, ChannelDimension]] = None       ,      **__a   :   Optional[Any]       ,      ):
         UpperCAmelCase_	   =       get_size_dict(__a			)

         if "shortest_edge" in size:
              UpperCAmelCase_	   =       get_resize_output_image_size(__a       ,      size=size["shortest_edge"]       ,      default_to_square=__a			)
              # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
         elif "height" in size and "width" in size:
              UpperCAmelCase_	   =       (size["height"], size["width"])
         else:
              raise ValueError(f"""Size must contain 'height' and 'width' keys or 'shortest_edge' key. Got {size.keys()}"""			)
         return resize(__a       ,      size=__a       ,      resample=__a       ,      data_format=__a       ,      **__a			)



    def  _lowercase						(self   :   List[Any]       ,      __a   :   np.ndarray       ,      __a   :   Dict[str, int]       ,      __a   :   Optional[Union[str, ChannelDimension]] = None       ,      **__a   :   int       ,      ):
         UpperCAmelCase_	   =       get_size_dict(__a			)
         if "height" not in size or "width" not in size:
              raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""			)
         return center_crop(__a       ,      size=(size["height"], size["width"])       ,      data_format=__a       ,      **__a			)



    def  _lowercase						(self   :   str       ,      __a   :   np.ndarray       ,      __a   :   float       ,      __a   :   Optional[Union[str, ChannelDimension]] = None       ,      **__a   :   List[str]			):
         return rescale(__a       ,      scale=__a       ,      data_format=__a       ,      **__a			)



    def  _lowercase						(self   :   Any       ,      __a   :   np.ndarray       ,      __a   :   Union[float, List[float]]       ,      __a   :   Union[float, List[float]]       ,      __a   :   Optional[Union[str, ChannelDimension]] = None       ,      **__a   :   Dict       ,      ):
         return normalize(__a       ,      mean=__a       ,      std=__a       ,      data_format=__a       ,      **__a			)






    def  _lowercase						(self   :   Dict       ,      __a   :   ImageInput       ,      __a   :   Optional[bool] = None       ,      __a   :   Dict[str, int] = None       ,      __a   :   PILImageResampling = None       ,      __a   :   bool = None       ,      __a   :   int = None       ,      __a   :   Optional[bool] = None       ,      __a   :   Optional[float] = None       ,      __a   :   Optional[bool] = None       ,      __a   :   Optional[Union[float, List[float]]] = None       ,      __a   :   Optional[Union[float, List[float]]] = None       ,      __a   :   Optional[Union[str, TensorType]] = None       ,      __a   :   Union[str, ChannelDimension] = ChannelDimension.FIRST       ,      **__a   :   Dict       ,      ):
         UpperCAmelCase_	   =       do_resize if do_resize is not None else self.do_resize
         UpperCAmelCase_	   =       do_rescale if do_rescale is not None else self.do_rescale
         UpperCAmelCase_	   =       do_normalize if do_normalize is not None else self.do_normalize
         UpperCAmelCase_	   =       do_center_crop if do_center_crop is not None else self.do_center_crop
         UpperCAmelCase_	   =       crop_size if crop_size is not None else self.crop_size
         UpperCAmelCase_	   =       get_size_dict(__a       ,      param_name="crop_size"       ,      default_to_square=__a			)
         UpperCAmelCase_	   =       resample if resample is not None else self.resample
         UpperCAmelCase_	   =       rescale_factor if rescale_factor is not None else self.rescale_factor
         UpperCAmelCase_	   =       image_mean if image_mean is not None else self.image_mean
         UpperCAmelCase_	   =       image_std if image_std is not None else self.image_std

         UpperCAmelCase_	   =       size if size is not None else self.size
         UpperCAmelCase_	   =       get_size_dict(__a			)

         if not is_batched(__a			):
              UpperCAmelCase_	   =       [images]

         if not valid_images(__a			):
              raise ValueError(
                  "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                  "torch.Tensor, tf.Tensor or jax.ndarray."			)

         if do_resize and size is None:
              raise ValueError("Size must be specified if do_resize is True."			)

         if do_center_crop and crop_size is None:
              raise ValueError("Crop size must be specified if do_center_crop is True."			)

         if do_rescale and rescale_factor is None:
              raise ValueError("Rescale factor must be specified if do_rescale is True."			)

         # All transformations expect numpy arrays.
         UpperCAmelCase_	   =       [to_numpy_array(__a			) for image in images]

         if do_resize:
              UpperCAmelCase_	   =       [self.resize(image=__a       ,      size=__a       ,      resample=__a			) for image in images]

         if do_center_crop:
              UpperCAmelCase_	   =       [self.center_crop(image=__a       ,      size=__a			) for image in images]

         if do_rescale:
              UpperCAmelCase_	   =       [self.rescale(image=__a       ,      scale=__a			) for image in images]

         if do_normalize:
              UpperCAmelCase_	   =       [self.normalize(image=__a       ,      mean=__a       ,      std=__a			) for image in images]

         UpperCAmelCase_	   =       [to_channel_dimension_format(__a       ,      __a			) for image in images]

         UpperCAmelCase_	   =       {"pixel_values": images}
         return BatchFeature(data=__a       ,      tensor_type=__a			)




