


"""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

lowerCamelCase_   :   List[Any]									=  logging.get_logger(__name__)
def _A						(						lowercase       ,    lowercase       ,    lowercase		):







    """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 _A						(						lowercase       ,    lowercase       ,    lowercase = None		):







    """simple docstring"""
    a 		=tesseract_config if tesseract_config is not None else ''''''

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

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

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

    # finally, normalize the bounding boxes
    a 		=[]
    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				(       _SCREAMING_SNAKE_CASE			):






 """simple docstring"""




 __lowerCAmelCase 					=  ["pixel_values"]


 def __init__(		self ,    __A = True ,    __A = None ,    __A = PILImageResampling.BILINEAR ,    __A = True ,    __A = None ,    __A = "" ,    **__A ,    )					->     None:
     super().__init__(**__A						)
     a 		=size if size is not None else {'''height''': 224, '''width''': 224}
     a 		=get_size_dict(__A						)

     a 		=do_resize
     a 		=size
     a 		=resample
     a 		=apply_ocr
     a 		=ocr_lang
     a 		=tesseract_config


 def       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A ,    __A = PILImageResampling.BILINEAR ,    __A = None ,    **__A ,    )					->     np.ndarray:
     a 		=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()}'''						)
     a 		=(size['''height'''], size['''width'''])
     return resize(__A ,    size=__A ,    resample=__A ,    data_format=__A ,    **__A						)





 def       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A = None ,    __A = None ,    __A = None ,    __A = None ,    __A = None ,    __A = None ,    __A = None ,    __A = ChannelDimension.FIRST ,    **__A ,    )					->     PIL.Image.Image:
     a 		=do_resize if do_resize is not None else self.do_resize
     a 		=size if size is not None else self.size
     a 		=get_size_dict(__A						)
     a 		=resample if resample is not None else self.resample
     a 		=apply_ocr if apply_ocr is not None else self.apply_ocr
     a 		=ocr_lang if ocr_lang is not None else self.ocr_lang
     a 		=tesseract_config if tesseract_config is not None else self.tesseract_config

     a 		=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.'''						)

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

     if apply_ocr:
         requires_backends(self ,    '''pytesseract'''						)
         a 		=[]
         a 		=[]
         for image in images:
             a		,      a 		=apply_tesseract(__A ,    __A ,    __A						)
             words_batch.append(__A						)
             boxes_batch.append(__A						)

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

     # flip color channels from RGB to BGR (as Detectron2 requires this)
     a 		=[flip_channel_order(__A						) for image in images]
     a 		=[to_channel_dimension_format(__A ,    __A						) for image in images]

     a 		=BatchFeature(data={'''pixel_values''': images} ,    tensor_type=__A						)

     if apply_ocr:
         a 		=words_batch
         a 		=boxes_batch
     return data

import argparse
import os
import shutil

import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer






def        a_		(       __lowercase   :	Dict ) ->  List[Any]:
			_snake_case		=			args.pruning_method
			_snake_case		=			args.threshold

			_snake_case		=			args.model_name_or_path.rstrip('/' )
			_snake_case		=			args.target_model_path

			print(f'''Load fine-pruned model from {model_name_or_path}''' )
			_snake_case		=			torch.load(os.path.join(__lowercase       ,					'pytorch_model.bin' ) )
			_snake_case		=			{}

			for name, tensor in model.items():
						if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
									_snake_case		=			tensor
									print(f'''Copied layer {name}''' )
						elif "classifier" in name or "qa_output" in name:
									_snake_case		=			tensor
									print(f'''Copied layer {name}''' )
						elif "bias" in name:
									_snake_case		=			tensor
									print(f'''Copied layer {name}''' )
						else:
									if pruning_method == "magnitude":
												_snake_case		=			MagnitudeBinarizer.apply(inputs=__lowercase       ,					threshold=__lowercase )
												_snake_case		=			tensor * mask
												print(f'''Pruned layer {name}''' )
									elif pruning_method == "topK":
												if "mask_scores" in name:
															continue
												_snake_case		=			name[:-6]
												_snake_case		=			model[f'''{prefix_}mask_scores''']
												_snake_case		=			TopKBinarizer.apply(__lowercase       ,					__lowercase )
												_snake_case		=			tensor * mask
												print(f'''Pruned layer {name}''' )
									elif pruning_method == "sigmoied_threshold":
												if "mask_scores" in name:
															continue
												_snake_case		=			name[:-6]
												_snake_case		=			model[f'''{prefix_}mask_scores''']
												_snake_case		=			ThresholdBinarizer.apply(__lowercase       ,					__lowercase       ,					__lowercase )
												_snake_case		=			tensor * mask
												print(f'''Pruned layer {name}''' )
									elif pruning_method == "l0":
												if "mask_scores" in name:
															continue
												_snake_case		=			name[:-6]
												_snake_case		=			model[f'''{prefix_}mask_scores''']
												_snake_case	,					_snake_case		=			-0.1, 1.1
												_snake_case		=			torch.sigmoid(__lowercase )
												_snake_case		=			s * (r - l) + l
												_snake_case		=			s_bar.clamp(min=0.0       ,					max=1.0 )
												_snake_case		=			tensor * mask
												print(f'''Pruned layer {name}''' )
									else:
												raise ValueError('Unknown pruning method' )

			if target_model_path is None:
						_snake_case		=			os.path.join(
						    os.path.dirname(__lowercase )       ,					f'''bertarized_{os.path.basename(__lowercase )}''' )

			if not os.path.isdir(__lowercase ):
						shutil.copytree(__lowercase       ,					__lowercase )
						print(f'''\nCreated folder {target_model_path}''' )

			torch.save(__lowercase       ,					os.path.join(__lowercase       ,					'pytorch_model.bin' ) )
			print('\nPruned model saved! See you later!' )


if __name__ == "__main__":
			_lowerCamelCase       :				Dict       =			argparse.ArgumentParser()

			parser.add_argument(
			    '''--pruning_method''',
			    choices=['''l0''', '''magnitude''', '''topK''', '''sigmoied_threshold'''],
			    type=str,
			    required=True,
			    help=(
			        '''Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,'''
			        ''' sigmoied_threshold = Soft movement pruning)'''
			    ),
			)
			parser.add_argument(
			    '''--threshold''',
			    type=float,
			    required=False,
			    help=(
			        '''For `magnitude` and `topK`, it is the level of remaining weights (in %) in the fine-pruned model.'''
			        '''For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared.'''
			        '''Not needed for `l0`'''
			    ),
			)
			parser.add_argument(
			    '''--model_name_or_path''',
			    type=str,
			    required=True,
			    help='''Folder containing the model that was previously fine-pruned''',
			)
			parser.add_argument(
			    '''--target_model_path''',
			    default=None,
			    type=str,
			    required=False,
			    help='''Folder containing the model that was previously fine-pruned''',
			)

			_lowerCamelCase       :				int       =			parser.parse_args()

			main(args)