



"""simple docstring"""






from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_lowercase : int      						=						logging.get_logger(__name__)

_lowercase : Union[str, Any]      						=						{
    "microsoft/swin-tiny-patch4-window7-224": (
        "https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}

class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_		,   lowerCAmelCase_   ):


					'''simple docstring'''





					_a   			=							'swin'

					_a   			=							{
					    'num_attention_heads': 'num_heads',
					    'num_hidden_layers': 'num_layers',
					}



					def __init__(       self							:  Union[str, Any],  lowerCamelCase							:  Optional[int]=224,  lowerCamelCase							:  Any=4,  lowerCamelCase							:  Union[str, Any]=3,  lowerCamelCase							:  str=96,  lowerCamelCase							:  Optional[int]=[2, 2, 6, 2],  lowerCamelCase							:  Optional[int]=[3, 6, 12, 24],  lowerCamelCase							:  int=7,  lowerCamelCase							:  Any=4.0,  lowerCamelCase							:  Dict=True,  lowerCamelCase							:  Optional[int]=0.0,  lowerCamelCase							:  Optional[int]=0.0,  lowerCamelCase							:  int=0.1,  lowerCamelCase							:  str="gelu",  lowerCamelCase							:  Union[str, Any]=False,  lowerCamelCase							:  int=0.02,  lowerCamelCase							:  List[Any]=1E-5,  lowerCamelCase							:  List[str]=32,  lowerCamelCase							:  List[str]=None,  lowerCamelCase							:  int=None,  **lowerCamelCase							:  int,  )->					Dict:
								super().__init__(**A_     )

								lowerCamelCase__		:		Any			=image_size
								lowerCamelCase__		:		Optional[int]			=patch_size
								lowerCamelCase__		:		Optional[int]			=num_channels
								lowerCamelCase__		:		Union[str, Any]			=embed_dim
								lowerCamelCase__		:		Dict			=depths
								lowerCamelCase__		:		List[Any]			=len(A_     )
								lowerCamelCase__		:		Union[str, Any]			=num_heads
								lowerCamelCase__		:		List[str]			=window_size
								lowerCamelCase__		:		Tuple			=mlp_ratio
								lowerCamelCase__		:		Optional[int]			=qkv_bias
								lowerCamelCase__		:		int			=hidden_dropout_prob
								lowerCamelCase__		:		Any			=attention_probs_dropout_prob
								lowerCamelCase__		:		int			=drop_path_rate
								lowerCamelCase__		:		Optional[int]			=hidden_act
								lowerCamelCase__		:		Optional[Any]			=use_absolute_embeddings
								lowerCamelCase__		:		Any			=layer_norm_eps
								lowerCamelCase__		:		Any			=initializer_range
								lowerCamelCase__		:		Union[str, Any]			=encoder_stride
								# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
								# this indicates the channel dimension after the last stage of the model
								lowerCamelCase__		:		Tuple			=int(embed_dim * 2 ** (len(A_     ) - 1)     )
								lowerCamelCase__		:		Optional[int]			=['''stem'''] + [F'''stage{idx}''' for idx in range(1,  len(A_     ) + 1     )]
								lowerCamelCase__	,   lowerCamelCase__		:		List[Any]			=get_aligned_output_features_output_indices(
								    out_features=A_,  out_indices=A_,  stage_names=self.stage_names     )





class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					_a   			=							version.parse('1.11'   )



					@property
					def 		snake_case       (       self							:  Any     )->					Mapping[str, Mapping[int, str]]:
								return OrderedDict(
								    [
								        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
								    ]     )




					@property
					def 		snake_case       (       self							:  List[Any]     )->					float:
								return 1E-4










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



