




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				:		List[str]     =   logging.get_logger(__name__)

lowerCamelCase				:		Optional[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(						UpperCamelCase   ):



 '''simple docstring'''


 UpperCamelCase						   =						'''blenderbot-small'''
 UpperCamelCase						   =						['''past_key_values''']
 UpperCamelCase						   =						{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}

 def __init__(				self					:  int						,							A_					:  Any=50265						,							A_					:  int=512						,							A_					:  int=8						,							A_					:  Optional[int]=2048						,							A_					:  Optional[int]=16						,							A_					:  List[Any]=8						,							A_					:  str=2048						,							A_					:  Union[str, Any]=16						,							A_					:  int=0.0						,							A_					:  Optional[Any]=0.0						,							A_					:  List[str]=True						,							A_					:  Optional[int]=True						,							A_					:  Dict="gelu"						,							A_					:  int=512						,							A_					:  str=0.1						,							A_					:  Optional[Any]=0.0						,							A_					:  Any=0.0						,							A_					:  str=0.02						,							A_					:  Optional[Any]=1						,							A_					:  Optional[Any]=False						,							A_					:  Optional[Any]=0						,							A_					:  str=1						,							A_					:  List[str]=2						,							A_					:  Optional[int]=2						,							**A_					:  List[str]						,							)   ->	List[Any]:

       """simple docstring"""






       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=A_						,							bos_token_id=A_						,							eos_token_id=A_						,							is_encoder_decoder=A_						,							decoder_start_token_id=A_						,							forced_eos_token_id=A_						,							**A_						,							)







class   A(						UpperCamelCase   ):



 '''simple docstring'''


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

       """simple docstring"""






       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_(A_						,							direction='inputs'  )
       elif self.task == "causal-lm":
             # TODO: figure this case out.
             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(A_  ):
                         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
 def 				a__ (				self					:  Any  )   ->	Mapping[str, Mapping[int, str]]:

       """simple docstring"""






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

 def 				a__ (				self					:  int						,							A_					:  PreTrainedTokenizer						,							A_					:  int = -1						,							A_					:  int = -1						,							A_					:  bool = False						,							A_					:  Optional[TensorType] = None						,							)   ->	Mapping[str, Any]:

       """simple docstring"""






       lowerCamelCase_		     =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           A_						,							A_						,							A_						,							A_						,							A_  )

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

       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 + 3
             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(A_						,							A_  )]						,							dim=1  )

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

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

 def 				a__ (				self					:  str						,							A_					:  PreTrainedTokenizer						,							A_					:  int = -1						,							A_					:  int = -1						,							A_					:  bool = False						,							A_					:  Optional[TensorType] = None						,							)   ->	Mapping[str, Any]:

       """simple docstring"""






       lowerCamelCase_		     =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           A_						,							A_						,							A_						,							A_						,							A_  )

       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
             # Not using the same length for past_key_values
             lowerCamelCase_		     =    seqlen + 2
             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(A_						,							A_						,							dtype=A_  )]						,							dim=1  )
             lowerCamelCase_		     =    [
                 (torch.zeros(A_  ), torch.zeros(A_  )) for _ in range(A_  )
             ]
       return common_inputs

 def 				a__ (				self					:  Tuple						,							A_					:  PreTrainedTokenizer						,							A_					:  int = -1						,							A_					:  int = -1						,							A_					:  bool = False						,							A_					:  Optional[TensorType] = None						,							)   ->	Mapping[str, Any]:

       """simple docstring"""






       lowerCamelCase_		     =    compute_effective_axis_dimension(
           A_						,							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(A_  )
       lowerCamelCase_		     =    compute_effective_axis_dimension(
           A_						,							fixed_dimension=OnnxConfig.default_fixed_sequence						,							num_token_to_add=A_  )

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

 def 				a__ (				self					:  int						,							A_					:  PreTrainedTokenizer						,							A_					:  int = -1						,							A_					:  int = -1						,							A_					:  bool = False						,							A_					:  Optional[TensorType] = None						,							)   ->	Mapping[str, Any]:

       """simple docstring"""






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

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

       return common_inputs





 def 				a__ (				self					:  Any						,							A_					:  Optional[int]						,							A_					:  Dict						,							A_					:  Optional[Any]						,							A_					:  Optional[int]  )   ->	Union[str, Any]:

       """simple docstring"""






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









from typing import Dict, Iterable, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
 import PIL

# soft dependency
if is_pytesseract_available():
 import pytesseract

lowerCamelCase				:		List[str]     =   logging.get_logger(__name__)

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Tuple	,							lowercase	:       Optional[int]	,							lowercase	:       Optional[int]     ):


      '''simple docstring'''



      return [
          int(10_00 * (box[0] / width)     ),
          int(10_00 * (box[1] / height)     ),
          int(10_00 * (box[2] / width)     ),
          int(10_00 * (box[3] / height)     ),
      ]

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       np.ndarray	,							lowercase	:       Optional[str]	,							lowercase	:       Optional[str]     ):


      '''simple docstring'''



      lowerCamelCase_		     =    to_pil_image(lowercase     )
      lowerCamelCase_     ,				lowerCamelCase_		     =    pil_image.size
      lowerCamelCase_		     =    pytesseract.image_to_data(lowercase	,							lang=lowercase	,							output_type='dict'	,							config=lowercase     )
      lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		     =    data['text'], data['left'], data['top'], data['width'], data['height']

      # filter empty words and corresponding coordinates
      lowerCamelCase_		     =    [idx for idx, word in enumerate(lowercase     ) if not word.strip()]
      lowerCamelCase_		     =    [word for idx, word in enumerate(lowercase     ) if idx not in irrelevant_indices]
      lowerCamelCase_		     =    [coord for idx, coord in enumerate(lowercase     ) if idx not in irrelevant_indices]
      lowerCamelCase_		     =    [coord for idx, coord in enumerate(lowercase     ) if idx not in irrelevant_indices]
      lowerCamelCase_		     =    [coord for idx, coord in enumerate(lowercase     ) if idx not in irrelevant_indices]
      lowerCamelCase_		     =    [coord for idx, coord in enumerate(lowercase     ) if idx not in irrelevant_indices]

      # turn coordinates into (left, top, left+width, top+height) format
      lowerCamelCase_		     =    []
      for x, y, w, h in zip(lowercase	,							lowercase	,							lowercase	,							lowercase     ):
            lowerCamelCase_		     =    [x, y, x + w, y + h]
            actual_boxes.append(lowercase     )

      # finally, normalize the bounding boxes
      lowerCamelCase_		     =    []
      for box in actual_boxes:
            normalized_boxes.append(normalize_box(lowercase	,							lowercase	,							lowercase     )     )

      assert len(lowercase     ) == len(lowercase     ), "Not as many words as there are bounding boxes"

      return words, normalized_boxes







class   A(						UpperCamelCase   ):



 '''simple docstring'''


 UpperCamelCase						   =						['''pixel_values''']

 def __init__(				self					:  int						,							A_					:  bool = True						,							A_					:  Dict[str, int] = None						,							A_					:  PILImageResampling = PILImageResampling.BILINEAR						,							A_					:  bool = True						,							A_					:  float = 1 / 255						,							A_					:  bool = True						,							A_					:  Union[float, Iterable[float]] = None						,							A_					:  Union[float, Iterable[float]] = None						,							A_					:  bool = True						,							A_					:  Optional[str] = None						,							A_					:  Optional[str] = ""						,							**A_					:  Optional[int]						,							)   ->	None:

       """simple docstring"""






       super().__init__(**A_  )
       lowerCamelCase_		     =    size if size is not None else {'height': 224, 'width': 224}
       lowerCamelCase_		     =    get_size_dict(A_  )

       lowerCamelCase_		     =    do_resize
       lowerCamelCase_		     =    size
       lowerCamelCase_		     =    resample
       lowerCamelCase_		     =    do_rescale
       lowerCamelCase_		     =    rescale_value
       lowerCamelCase_		     =    do_normalize
       lowerCamelCase_		     =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       lowerCamelCase_		     =    image_std if image_std is not None else IMAGENET_STANDARD_STD
       lowerCamelCase_		     =    apply_ocr
       lowerCamelCase_		     =    ocr_lang
       lowerCamelCase_		     =    tesseract_config

 def 				a__ (				self					:  str						,							A_					:  np.ndarray						,							A_					:  Dict[str, int]						,							A_					:  PILImageResampling = PILImageResampling.BILINEAR						,							A_					:  Optional[Union[str, ChannelDimension]] = None						,							**A_					:  str						,							)   ->	np.ndarray:

       """simple docstring"""






       lowerCamelCase_		     =    get_size_dict(A_  )
       if "height" not in size or "width" not in size:
             raise ValueError(f"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""  )
       lowerCamelCase_		     =    (size['height'], size['width'])
       return resize(A_						,							size=A_						,							resample=A_						,							data_format=A_						,							**A_  )

 def 				a__ (				self					:  Any						,							A_					:  np.ndarray						,							A_					:  Union[int, float]						,							A_					:  Optional[Union[str, ChannelDimension]] = None						,							**A_					:  Optional[Any]						,							)   ->	np.ndarray:

       """simple docstring"""






       return rescale(A_						,							scale=A_						,							data_format=A_						,							**A_  )

 def 				a__ (				self					:  Union[str, Any]						,							A_					:  np.ndarray						,							A_					:  Union[float, Iterable[float]]						,							A_					:  Union[float, Iterable[float]]						,							A_					:  Optional[Union[str, ChannelDimension]] = None						,							**A_					:  int						,							)   ->	np.ndarray:

       """simple docstring"""






       return normalize(A_						,							mean=A_						,							std=A_						,							data_format=A_						,							**A_  )





 def 				a__ (				self					:  List[Any]						,							A_					:  ImageInput						,							A_					:  bool = None						,							A_					:  Dict[str, int] = None						,							A_					:  Dict=None						,							A_					:  bool = None						,							A_					:  float = None						,							A_					:  bool = None						,							A_					:  Union[float, Iterable[float]] = None						,							A_					:  Union[float, Iterable[float]] = None						,							A_					:  bool = None						,							A_					:  Optional[str] = None						,							A_					:  Optional[str] = None						,							A_					:  Optional[Union[str, TensorType]] = None						,							A_					:  ChannelDimension = ChannelDimension.FIRST						,							**A_					:  Any						,							)   ->	PIL.Image.Image:

       """simple docstring"""






       lowerCamelCase_		     =    do_resize if do_resize is not None else self.do_resize
       lowerCamelCase_		     =    size if size is not None else self.size
       lowerCamelCase_		     =    get_size_dict(A_  )
       lowerCamelCase_		     =    resample if resample is not None else self.resample
       lowerCamelCase_		     =    do_rescale if do_rescale is not None else self.do_rescale
       lowerCamelCase_		     =    rescale_factor if rescale_factor is not None else self.rescale_factor
       lowerCamelCase_		     =    do_normalize if do_normalize is not None else self.do_normalize
       lowerCamelCase_		     =    image_mean if image_mean is not None else self.image_mean
       lowerCamelCase_		     =    image_std if image_std is not None else self.image_std
       lowerCamelCase_		     =    apply_ocr if apply_ocr is not None else self.apply_ocr
       lowerCamelCase_		     =    ocr_lang if ocr_lang is not None else self.ocr_lang
       lowerCamelCase_		     =    tesseract_config if tesseract_config is not None else self.tesseract_config

       lowerCamelCase_		     =    make_list_of_images(A_  )

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

       if do_normalize and (image_mean is None or image_std is None):
             raise ValueError('If do_normalize is True, image_mean and image_std must be specified.'  )

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

       # Tesseract OCR to get words + normalized bounding boxes
       if apply_ocr:
             requires_backends(self						,							'pytesseract'  )
             lowerCamelCase_		     =    []
             lowerCamelCase_		     =    []
             for image in images:
                   lowerCamelCase_     ,				lowerCamelCase_		     =    apply_tesseract(A_						,							A_						,							A_  )
                   words_batch.append(A_  )
                   boxes_batch.append(A_  )

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

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

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

       lowerCamelCase_		     =    [to_channel_dimension_format(A_						,							A_  ) for image in images]

       lowerCamelCase_		     =    BatchFeature(data={'pixel_values': images}						,							tensor_type=A_  )

       if apply_ocr:
             lowerCamelCase_		     =    words_batch
             lowerCamelCase_		     =    boxes_batch
       return data



