

'''simple docstring'''

from typing import Dict, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    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

lowercase__         =      logging.get_logger(__name__)




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
 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 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_ = None     ):
 UpperCAmelCase	:     Optional[Any]						 =			tesseract_config if tesseract_config is not None else ''

 # apply OCR
 UpperCAmelCase	:     List[Any]						 =			to_pil_image(UpperCAmelCase_     )
 UpperCAmelCase ,			UpperCAmelCase	:     Dict						 =			pil_image.size
 UpperCAmelCase	:     List[Any]						 =			pytesseract.image_to_data(UpperCAmelCase_  ,	lang=UpperCAmelCase_  ,	output_type='dict'  ,	config=UpperCAmelCase_     )
 UpperCAmelCase ,			UpperCAmelCase ,			UpperCAmelCase ,			UpperCAmelCase ,			UpperCAmelCase	:     Tuple						 =			data['text'], data['left'], data['top'], data['width'], data['height']

 # filter empty words and corresponding coordinates
 UpperCAmelCase	:     str						 =			[idx for idx, word in enumerate(UpperCAmelCase_     ) if not word.strip()]
 UpperCAmelCase	:     Tuple						 =			[word for idx, word in enumerate(UpperCAmelCase_     ) if idx not in irrelevant_indices]
 UpperCAmelCase	:     int						 =			[coord for idx, coord in enumerate(UpperCAmelCase_     ) if idx not in irrelevant_indices]
 UpperCAmelCase	:     List[Any]						 =			[coord for idx, coord in enumerate(UpperCAmelCase_     ) if idx not in irrelevant_indices]
 UpperCAmelCase	:     str						 =			[coord for idx, coord in enumerate(UpperCAmelCase_     ) if idx not in irrelevant_indices]
 UpperCAmelCase	:     Optional[Any]						 =			[coord for idx, coord in enumerate(UpperCAmelCase_     ) if idx not in irrelevant_indices]

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

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

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

 return words, normalized_boxes




class 	A_			(    _snake_case							):






     '''simple docstring'''



     UpperCAmelCase_						:     Optional[int]  		=  ["""pixel_values"""]


     def __init__(		self			:					str					,      lowercase_			:					bool = True					,      lowercase_			:					Dict[str, int] = None					,      lowercase_			:					PILImageResampling = PILImageResampling.BILINEAR					,      lowercase_			:					bool = True					,      lowercase_			:					Optional[str] = None					,      lowercase_			:					Optional[str] = ""					,      **lowercase_			:					str					,      )						->     None:
      super().__init__(**lowercase_	)
      UpperCAmelCase	:     Tuple						 =			size if size is not None else {'height': 224, 'width': 224}
      UpperCAmelCase	:     Optional[Any]						 =			get_size_dict(lowercase_	)

      UpperCAmelCase	:     Union[str, Any]						 =			do_resize
      UpperCAmelCase	:     Optional[int]						 =			size
      UpperCAmelCase	:     List[Any]						 =			resample
      UpperCAmelCase	:     Dict						 =			apply_ocr
      UpperCAmelCase	:     Any						 =			ocr_lang
      UpperCAmelCase	:     int						 =			tesseract_config


     def        UpperCAmelCase_	(		self			:					List[Any]					,      lowercase_			:					np.ndarray					,      lowercase_			:					Dict[str, int]					,      lowercase_			:					PILImageResampling = PILImageResampling.BILINEAR					,      lowercase_			:					Optional[Union[str, ChannelDimension]] = None					,      **lowercase_			:					str					,      )						->     np.ndarray:
      UpperCAmelCase	:     List[Any]						 =			get_size_dict(lowercase_	)
      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()}"""	)
      UpperCAmelCase	:     Tuple						 =			(size['height'], size['width'])
      return resize(lowercase_					,      size=lowercase_					,      resample=lowercase_					,      data_format=lowercase_					,      **lowercase_	)




     def        UpperCAmelCase_	(		self			:					Any					,      lowercase_			:					ImageInput					,      lowercase_			:					bool = None					,      lowercase_			:					Dict[str, int] = None					,      lowercase_			:					PILImageResampling = None					,      lowercase_			:					bool = None					,      lowercase_			:					Optional[str] = None					,      lowercase_			:					Optional[str] = None					,      lowercase_			:					Optional[Union[str, TensorType]] = None					,      lowercase_			:					ChannelDimension = ChannelDimension.FIRST					,      **lowercase_			:					str					,      )						->     PIL.Image.Image:
      UpperCAmelCase	:     Union[str, Any]						 =			do_resize if do_resize is not None else self.do_resize
      UpperCAmelCase	:     Dict						 =			size if size is not None else self.size
      UpperCAmelCase	:     str						 =			get_size_dict(lowercase_	)
      UpperCAmelCase	:     str						 =			resample if resample is not None else self.resample
      UpperCAmelCase	:     Union[str, Any]						 =			apply_ocr if apply_ocr is not None else self.apply_ocr
      UpperCAmelCase	:     str						 =			ocr_lang if ocr_lang is not None else self.ocr_lang
      UpperCAmelCase	:     int						 =			tesseract_config if tesseract_config is not None else self.tesseract_config

      UpperCAmelCase	:     List[Any]						 =			make_list_of_images(lowercase_	)

      if not valid_images(lowercase_	):
       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.'	)

      # All transformations expect numpy arrays.
      UpperCAmelCase	:     List[str]						 =			[to_numpy_array(lowercase_	) for image in images]

      if apply_ocr:
       requires_backends(self					,      'pytesseract'	)
       UpperCAmelCase	:     List[str]						 =			[]
       UpperCAmelCase	:     Optional[Any]						 =			[]
       for image in images:
        UpperCAmelCase ,			UpperCAmelCase	:     Tuple						 =			apply_tesseract(lowercase_					,      lowercase_					,      lowercase_	)
        words_batch.append(lowercase_	)
        boxes_batch.append(lowercase_	)

      if do_resize:
       UpperCAmelCase	:     List[str]						 =			[self.resize(image=lowercase_					,      size=lowercase_					,      resample=lowercase_	) for image in images]

      # flip color channels from RGB to BGR (as Detectron2 requires this)
      UpperCAmelCase	:     Optional[Any]						 =			[flip_channel_order(lowercase_	) for image in images]
      UpperCAmelCase	:     Dict						 =			[to_channel_dimension_format(lowercase_					,      lowercase_	) for image in images]

      UpperCAmelCase	:     Any						 =			BatchFeature(data={'pixel_values': images}					,      tensor_type=lowercase_	)

      if apply_ocr:
       UpperCAmelCase	:     Dict						 =			words_batch
       UpperCAmelCase	:     str						 =			boxes_batch
      return data







'''simple docstring'''

from collections import OrderedDict
from typing import Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


lowercase__         =      logging.get_logger(__name__)

lowercase__         =      {
    "deepmind/language-perceiver": "https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json",
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}




class 	A_			(    _snake_case							):






     '''simple docstring'''



     UpperCAmelCase_						:     Optional[int]  		=  """perceiver"""


     def __init__(		self			:					str					,      lowercase_			:					List[str]=256					,      lowercase_			:					List[str]=1_280					,      lowercase_			:					str=768					,      lowercase_			:					Tuple=1					,      lowercase_			:					str=26					,      lowercase_			:					List[Any]=8					,      lowercase_			:					int=8					,      lowercase_			:					List[str]=None					,      lowercase_			:					Dict=None					,      lowercase_			:					int="kv"					,      lowercase_			:					Union[str, Any]=1					,      lowercase_			:					List[str]=1					,      lowercase_			:					Any="gelu"					,      lowercase_			:					Optional[Any]=0.1					,      lowercase_			:					str=0.02					,      lowercase_			:					Optional[Any]=1E-12					,      lowercase_			:					Any=True					,      lowercase_			:					Union[str, Any]=262					,      lowercase_			:					Union[str, Any]=2_048					,      lowercase_			:					Optional[int]=56					,      lowercase_			:					int=[368, 496]					,      lowercase_			:					str=16					,      lowercase_			:					Optional[int]=1_920					,      lowercase_			:					Tuple=16					,      lowercase_			:					int=[1, 16, 224, 224]					,      **lowercase_			:					Union[str, Any]					,      )						->     List[str]:
      super().__init__(**lowercase_	)

      UpperCAmelCase	:     Union[str, Any]						 =			num_latents
      UpperCAmelCase	:     List[Any]						 =			d_latents
      UpperCAmelCase	:     Dict						 =			d_model
      UpperCAmelCase	:     Dict						 =			num_blocks
      UpperCAmelCase	:     Optional[int]						 =			num_self_attends_per_block
      UpperCAmelCase	:     Optional[Any]						 =			num_self_attention_heads
      UpperCAmelCase	:     Optional[Any]						 =			num_cross_attention_heads
      UpperCAmelCase	:     Tuple						 =			qk_channels
      UpperCAmelCase	:     Optional[int]						 =			v_channels
      UpperCAmelCase	:     str						 =			cross_attention_shape_for_attention
      UpperCAmelCase	:     Union[str, Any]						 =			self_attention_widening_factor
      UpperCAmelCase	:     List[Any]						 =			cross_attention_widening_factor
      UpperCAmelCase	:     Optional[Any]						 =			hidden_act
      UpperCAmelCase	:     int						 =			attention_probs_dropout_prob
      UpperCAmelCase	:     Optional[int]						 =			initializer_range
      UpperCAmelCase	:     Dict						 =			layer_norm_eps
      UpperCAmelCase	:     Any						 =			use_query_residual
      # masked language modeling attributes
      UpperCAmelCase	:     Any						 =			vocab_size
      UpperCAmelCase	:     List[Any]						 =			max_position_embeddings
      # image classification attributes
      UpperCAmelCase	:     str						 =			image_size
      # flow attributes
      UpperCAmelCase	:     Any						 =			train_size
      # multimodal autoencoding attributes
      UpperCAmelCase	:     Any						 =			num_frames
      UpperCAmelCase	:     List[Any]						 =			audio_samples_per_frame
      UpperCAmelCase	:     Tuple						 =			samples_per_patch
      UpperCAmelCase	:     Union[str, Any]						 =			output_shape




class 	A_			(    _snake_case							):






     '''simple docstring'''



     @property
     def        UpperCAmelCase_	(		self			:					Union[str, Any]	)						->     Mapping[str, Mapping[int, str]]:
      if self.task == "multiple-choice":
       UpperCAmelCase	:     Tuple						 =			{0: 'batch', 1: 'choice', 2: 'sequence'}
      else:
       UpperCAmelCase	:     Tuple						 =			{0: 'batch', 1: 'sequence'}
      return OrderedDict(
          [
              ('inputs', dynamic_axis),
              ('attention_mask', dynamic_axis),
          ]	)


     @property
     def        UpperCAmelCase_	(		self			:					str	)						->     float:
      return 1E-4




     def        UpperCAmelCase_	(		self			:					str					,      lowercase_			:					Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]					,      lowercase_			:					int = -1					,      lowercase_			:					int = -1					,      lowercase_			:					int = -1					,      lowercase_			:					bool = False					,      lowercase_			:					Optional[TensorType] = None					,      lowercase_			:					int = 3					,      lowercase_			:					int = 40					,      lowercase_			:					int = 40					,      )						->     Mapping[str, Any]:
      # copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified

      if isinstance(lowercase_					,      lowercase_	):
       # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
       UpperCAmelCase	:     Tuple						 =			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
       UpperCAmelCase	:     int						 =			preprocessor.num_special_tokens_to_add(lowercase_	)
       UpperCAmelCase	:     Union[str, Any]						 =			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
       UpperCAmelCase	:     List[Any]						 =			[' '.join(['a']	) * seq_length] * batch_size
       UpperCAmelCase	:     Union[str, Any]						 =			dict(preprocessor(lowercase_					,      return_tensors=lowercase_	)	)
       UpperCAmelCase	:     Union[str, Any]						 =			inputs.pop('input_ids'	)
       return inputs
      elif isinstance(lowercase_					,      lowercase_	) and preprocessor.model_input_names[0] == "pixel_values":
       # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
       UpperCAmelCase	:     Tuple						 =			compute_effective_axis_dimension(lowercase_					,      fixed_dimension=OnnxConfig.default_fixed_batch	)
       UpperCAmelCase	:     Any						 =			self._generate_dummy_images(lowercase_					,      lowercase_					,      lowercase_					,      lowercase_	)
       UpperCAmelCase	:     Tuple						 =			dict(preprocessor(images=lowercase_					,      return_tensors=lowercase_	)	)
       UpperCAmelCase	:     Dict						 =			inputs.pop('pixel_values'	)
       return inputs
      else:
       raise ValueError(
           'Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor.'	)




