




'''simple docstring'''





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

UpperCamelCase_						:      Tuple       =						logging.get_logger(__name__)





def      __a     (       _UpperCamelCase:   int ,						_UpperCamelCase:   int ,						_UpperCamelCase:   Optional[Any]   )						->			str:

     """simple docstring"""




     return [
         int(1_000 * (box[0] / width)   ),
         int(1_000 * (box[1] / height)   ),
         int(1_000 * (box[2] / width)   ),
         int(1_000 * (box[3] / height)   ),
     ]





def      __a     (       _UpperCamelCase:   np.ndarray ,						_UpperCamelCase:   Optional[str] ,						_UpperCamelCase:   Optional[str]   )						->			Optional[int]:

     """simple docstring"""




     _snake_case							=				to_pil_image(_UpperCamelCase   )
     _snake_case							, _snake_case							=				pil_image.size
     _snake_case							=				pytesseract.image_to_data(_UpperCamelCase ,						lang=_UpperCamelCase ,						output_type="dict" ,						config=_UpperCamelCase   )
     _snake_case							, _snake_case							, _snake_case							, _snake_case							, _snake_case							=				data["text"], data["left"], data["top"], data["width"], data["height"]

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

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

     # finally, normalize the bounding boxes
     _snake_case							=				[]
     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    (						__lowerCAmelCase							):
     SCREAMING_SNAKE_CASE_							:   Dict       =						["""pixel_values"""]






     def __init__(			self ,_SCREAMING_SNAKE_CASE = True ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR ,_SCREAMING_SNAKE_CASE = True ,_SCREAMING_SNAKE_CASE = 1 / 255 ,_SCREAMING_SNAKE_CASE = True ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = True ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = "" ,**_SCREAMING_SNAKE_CASE ,)							->      None:
          super().__init__(**_SCREAMING_SNAKE_CASE   )
          _snake_case							=				size if size is not None else {"height": 224, "width": 224}
          _snake_case							=				get_size_dict(_SCREAMING_SNAKE_CASE   )

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






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR ,_SCREAMING_SNAKE_CASE = None ,**_SCREAMING_SNAKE_CASE ,)							->      np.ndarray:
          _snake_case							=				get_size_dict(_SCREAMING_SNAKE_CASE   )
          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()}"""   )
          _snake_case							=				(size["height"], size["width"])
          return resize(_SCREAMING_SNAKE_CASE ,size=_SCREAMING_SNAKE_CASE ,resample=_SCREAMING_SNAKE_CASE ,data_format=_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None ,**_SCREAMING_SNAKE_CASE ,)							->      np.ndarray:
          return rescale(_SCREAMING_SNAKE_CASE ,scale=_SCREAMING_SNAKE_CASE ,data_format=_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None ,**_SCREAMING_SNAKE_CASE ,)							->      np.ndarray:
          return normalize(_SCREAMING_SNAKE_CASE ,mean=_SCREAMING_SNAKE_CASE ,std=_SCREAMING_SNAKE_CASE ,data_format=_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE=None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = ChannelDimension.FIRST ,**_SCREAMING_SNAKE_CASE ,)							->      PIL.Image.Image:
          _snake_case							=				do_resize if do_resize is not None else self.do_resize
          _snake_case							=				size if size is not None else self.size
          _snake_case							=				get_size_dict(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				resample if resample is not None else self.resample
          _snake_case							=				do_rescale if do_rescale is not None else self.do_rescale
          _snake_case							=				rescale_factor if rescale_factor is not None else self.rescale_factor
          _snake_case							=				do_normalize if do_normalize is not None else self.do_normalize
          _snake_case							=				image_mean if image_mean is not None else self.image_mean
          _snake_case							=				image_std if image_std is not None else self.image_std
          _snake_case							=				apply_ocr if apply_ocr is not None else self.apply_ocr
          _snake_case							=				ocr_lang if ocr_lang is not None else self.ocr_lang
          _snake_case							=				tesseract_config if tesseract_config is not None else self.tesseract_config

          _snake_case							=				make_list_of_images(_SCREAMING_SNAKE_CASE   )

          if not valid_images(_SCREAMING_SNAKE_CASE   ):
               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.
          _snake_case							=				[to_numpy_array(_SCREAMING_SNAKE_CASE   ) for image in images]

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

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

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

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

          _snake_case							=				[to_channel_dimension_format(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ) for image in images]

          _snake_case							=				BatchFeature(data={"pixel_values": images} ,tensor_type=_SCREAMING_SNAKE_CASE   )

          if apply_ocr:
               _snake_case							=				words_batch
               _snake_case							=				boxes_batch
          return data








'''simple docstring'''





import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase_						:      str       =						logging.get_logger(__name__)

UpperCamelCase_						:      Optional[Any]       =						{
    '''asapp/sew-tiny-100k''': '''https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json''',
    # See all SEW models at https://huggingface.co/models?filter=sew
}

class        _a    (						__lowerCAmelCase							):
     SCREAMING_SNAKE_CASE_							:   Dict       =						"""sew"""






     def __init__(			self ,_SCREAMING_SNAKE_CASE=32 ,_SCREAMING_SNAKE_CASE=768 ,_SCREAMING_SNAKE_CASE=12 ,_SCREAMING_SNAKE_CASE=12 ,_SCREAMING_SNAKE_CASE=3_072 ,_SCREAMING_SNAKE_CASE=2 ,_SCREAMING_SNAKE_CASE="gelu" ,_SCREAMING_SNAKE_CASE=0.1 ,_SCREAMING_SNAKE_CASE=0.1 ,_SCREAMING_SNAKE_CASE=0.1 ,_SCREAMING_SNAKE_CASE=0.0 ,_SCREAMING_SNAKE_CASE=0.1 ,_SCREAMING_SNAKE_CASE=0.1 ,_SCREAMING_SNAKE_CASE=0.0_2 ,_SCREAMING_SNAKE_CASE=1e-5 ,_SCREAMING_SNAKE_CASE="group" ,_SCREAMING_SNAKE_CASE="gelu" ,_SCREAMING_SNAKE_CASE=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512) ,_SCREAMING_SNAKE_CASE=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) ,_SCREAMING_SNAKE_CASE=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) ,_SCREAMING_SNAKE_CASE=False ,_SCREAMING_SNAKE_CASE=128 ,_SCREAMING_SNAKE_CASE=16 ,_SCREAMING_SNAKE_CASE=True ,_SCREAMING_SNAKE_CASE=0.0_5 ,_SCREAMING_SNAKE_CASE=10 ,_SCREAMING_SNAKE_CASE=2 ,_SCREAMING_SNAKE_CASE=0.0 ,_SCREAMING_SNAKE_CASE=10 ,_SCREAMING_SNAKE_CASE=0 ,_SCREAMING_SNAKE_CASE="mean" ,_SCREAMING_SNAKE_CASE=False ,_SCREAMING_SNAKE_CASE=False ,_SCREAMING_SNAKE_CASE=256 ,_SCREAMING_SNAKE_CASE=0 ,_SCREAMING_SNAKE_CASE=1 ,_SCREAMING_SNAKE_CASE=2 ,**_SCREAMING_SNAKE_CASE ,)							->      str:
          super().__init__(**_SCREAMING_SNAKE_CASE ,pad_token_id=_SCREAMING_SNAKE_CASE ,bos_token_id=_SCREAMING_SNAKE_CASE ,eos_token_id=_SCREAMING_SNAKE_CASE   )
          _snake_case							=				hidden_size
          _snake_case							=				feat_extract_norm
          _snake_case							=				feat_extract_activation
          _snake_case							=				list(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				list(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				list(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				conv_bias
          _snake_case							=				num_conv_pos_embeddings
          _snake_case							=				num_conv_pos_embedding_groups
          _snake_case							=				len(self.conv_dim   )
          _snake_case							=				num_hidden_layers
          _snake_case							=				intermediate_size
          _snake_case							=				squeeze_factor
          _snake_case							=				hidden_act
          _snake_case							=				num_attention_heads
          _snake_case							=				hidden_dropout
          _snake_case							=				attention_dropout
          _snake_case							=				activation_dropout
          _snake_case							=				feat_proj_dropout
          _snake_case							=				final_dropout
          _snake_case							=				layerdrop
          _snake_case							=				layer_norm_eps
          _snake_case							=				initializer_range
          _snake_case							=				vocab_size

          if (
              (len(self.conv_stride   ) != self.num_feat_extract_layers)
              or (len(self.conv_kernel   ) != self.num_feat_extract_layers)
              or (len(self.conv_dim   ) != self.num_feat_extract_layers)
          ):
               raise ValueError(
                   "Configuration for convolutional layers is incorrect."
                   "It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"
                   f"""but is `len(config.conv_dim) = {len(self.conv_dim   )}`, `len(config.conv_stride)"""
                   f"""= {len(self.conv_stride   )}`, `len(config.conv_kernel) = {len(self.conv_kernel   )}`."""   )

          # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
          _snake_case							=				apply_spec_augment
          _snake_case							=				mask_time_prob
          _snake_case							=				mask_time_length
          _snake_case							=				mask_time_min_masks
          _snake_case							=				mask_feature_prob
          _snake_case							=				mask_feature_length
          _snake_case							=				mask_feature_min_masks

          # ctc loss
          _snake_case							=				ctc_loss_reduction
          _snake_case							=				ctc_zero_infinity

          # sequence classification
          _snake_case							=				use_weighted_layer_sum
          _snake_case							=				classifier_proj_size






     @property
     def     _lowercase     (			self   )							->      Optional[Any]:
          return functools.reduce(operator.mul ,self.conv_stride ,1   )


