




import functools
import operator

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


SCREAMING_SNAKE_CASE__       :      Union[str, Any]  =   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__       :      Any  =   {
    'microsoft/wavlm-base': 'https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json',
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}




class 		UpperCamelCase__	(lowerCAmelCase__     ):

    '''simple docstring'''



    lowerCamelCase_      :	Tuple	     =      """wavlm"""







    def __init__(    self     ,  UpperCamelCase__=32     ,  UpperCamelCase__=768     ,  UpperCamelCase__=12     ,  UpperCamelCase__=12     ,  UpperCamelCase__=3072     ,  UpperCamelCase__="gelu"     ,  UpperCamelCase__=0.1     ,  UpperCamelCase__=0.1     ,  UpperCamelCase__=0.1     ,  UpperCamelCase__=0.0     ,  UpperCamelCase__=0.1     ,  UpperCamelCase__=0.1     ,  UpperCamelCase__=0.02     ,  UpperCamelCase__=1e-5     ,  UpperCamelCase__="group"     ,  UpperCamelCase__="gelu"     ,  UpperCamelCase__=(512, 512, 512, 512, 512, 512, 512)     ,  UpperCamelCase__=(5, 2, 2, 2, 2, 2, 2)     ,  UpperCamelCase__=(10, 3, 3, 3, 3, 2, 2)     ,  UpperCamelCase__=False     ,  UpperCamelCase__=128     ,  UpperCamelCase__=16     ,  UpperCamelCase__=320     ,  UpperCamelCase__=800     ,  UpperCamelCase__=False     ,  UpperCamelCase__=True     ,  UpperCamelCase__=0.05     ,  UpperCamelCase__=10     ,  UpperCamelCase__=2     ,  UpperCamelCase__=0.0     ,  UpperCamelCase__=10     ,  UpperCamelCase__=320     ,  UpperCamelCase__=2     ,  UpperCamelCase__=0.1     ,  UpperCamelCase__=100     ,  UpperCamelCase__=256     ,  UpperCamelCase__=256     ,  UpperCamelCase__=0.1     ,  UpperCamelCase__="mean"     ,  UpperCamelCase__=False     ,  UpperCamelCase__=False     ,  UpperCamelCase__=256     ,  UpperCamelCase__=(512, 512, 512, 512, 1500)     ,  UpperCamelCase__=(5, 3, 3, 1, 1)     ,  UpperCamelCase__=(1, 2, 3, 1, 1)     ,  UpperCamelCase__=512     ,  UpperCamelCase__=80     ,  UpperCamelCase__=0     ,  UpperCamelCase__=1     ,  UpperCamelCase__=2     ,  UpperCamelCase__=False     ,  UpperCamelCase__=3     ,  UpperCamelCase__=2     ,  UpperCamelCase__=3     ,  UpperCamelCase__=None     ,  **UpperCamelCase__     ,  )     ->      Any:
           super().__init__(**UpperCamelCase__     ,  pad_token_id=UpperCamelCase__     ,  bos_token_id=UpperCamelCase__     ,  eos_token_id=UpperCamelCase__     )
           lowerCamelCase   :      Any			 =					hidden_size
           lowerCamelCase   :      Dict			 =					feat_extract_norm
           lowerCamelCase   :      Tuple			 =					feat_extract_activation
           lowerCamelCase   :      Dict			 =					list(UpperCamelCase__     )
           lowerCamelCase   :      int			 =					list(UpperCamelCase__     )
           lowerCamelCase   :      Any			 =					list(UpperCamelCase__     )
           lowerCamelCase   :      Any			 =					conv_bias
           lowerCamelCase   :      Dict			 =					num_buckets
           lowerCamelCase   :      Union[str, Any]			 =					max_bucket_distance
           lowerCamelCase   :      int			 =					num_conv_pos_embeddings
           lowerCamelCase   :      Optional[int]			 =					num_conv_pos_embedding_groups
           lowerCamelCase   :      Dict			 =					len(self.conv_dim     )
           lowerCamelCase   :      Dict			 =					num_hidden_layers
           lowerCamelCase   :      str			 =					intermediate_size
           lowerCamelCase   :      List[str]			 =					hidden_act
           lowerCamelCase   :      Tuple			 =					num_attention_heads
           lowerCamelCase   :      Tuple			 =					hidden_dropout
           lowerCamelCase   :      Dict			 =					attention_dropout
           lowerCamelCase   :      Any			 =					activation_dropout
           lowerCamelCase   :      str			 =					feat_proj_dropout
           lowerCamelCase   :      List[str]			 =					final_dropout
           lowerCamelCase   :      Tuple			 =					layerdrop
           lowerCamelCase   :      List[Any]			 =					layer_norm_eps
           lowerCamelCase   :      Any			 =					initializer_range
           lowerCamelCase   :      Dict			 =					num_ctc_classes
           lowerCamelCase   :      Optional[int]			 =					vocab_size
           lowerCamelCase   :      Tuple			 =					do_stable_layer_norm
           lowerCamelCase   :      Tuple			 =					use_weighted_layer_sum
           lowerCamelCase   :      List[Any]			 =					classifier_proj_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)`, but is `len(config.conv_dim) ="
                      F''' {len(self.conv_dim     )}`, `len(config.conv_stride) = {len(self.conv_stride     )}`,'''
                      F''' `len(config.conv_kernel) = {len(self.conv_kernel     )}`.'''     )

           # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
           lowerCamelCase   :      Dict			 =					apply_spec_augment
           lowerCamelCase   :      str			 =					mask_time_prob
           lowerCamelCase   :      List[Any]			 =					mask_time_length
           lowerCamelCase   :      str			 =					mask_time_min_masks
           lowerCamelCase   :      Union[str, Any]			 =					mask_feature_prob
           lowerCamelCase   :      List[str]			 =					mask_feature_length

           # parameters for pretraining with codevector quantized representations
           lowerCamelCase   :      Optional[int]			 =					num_codevectors_per_group
           lowerCamelCase   :      Optional[int]			 =					num_codevector_groups
           lowerCamelCase   :      Union[str, Any]			 =					contrastive_logits_temperature
           lowerCamelCase   :      List[str]			 =					num_negatives
           lowerCamelCase   :      Any			 =					codevector_dim
           lowerCamelCase   :      List[Any]			 =					proj_codevector_dim
           lowerCamelCase   :      Optional[Any]			 =					diversity_loss_weight

           # ctc loss
           lowerCamelCase   :      Union[str, Any]			 =					ctc_loss_reduction
           lowerCamelCase   :      int			 =					ctc_zero_infinity

           # adapter
           lowerCamelCase   :      Union[str, Any]			 =					add_adapter
           lowerCamelCase   :      List[str]			 =					adapter_kernel_size
           lowerCamelCase   :      Dict			 =					adapter_stride
           lowerCamelCase   :      Any			 =					num_adapter_layers
           lowerCamelCase   :      Optional[int]			 =					output_hidden_size or hidden_size

           # SequenceClassification-specific parameter. Feel free to ignore for other classes.
           lowerCamelCase   :      Union[str, Any]			 =					classifier_proj_size

           # XVector-specific parameters. Feel free to ignore for other classes.
           lowerCamelCase   :      Optional[int]			 =					list(UpperCamelCase__     )
           lowerCamelCase   :      List[str]			 =					list(UpperCamelCase__     )
           lowerCamelCase   :      Tuple			 =					list(UpperCamelCase__     )
           lowerCamelCase   :      Optional[int]			 =					xvector_output_dim







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












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



