






'''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

a_  			=			logging.get_logger(__name__)



def       __UpperCAmelCase					(lowercase__						,lowercase__						,lowercase__					)		->					int:

						'''simple docstring'''


						return [
						    int(1000 * (box[0] / width)					),
						    int(1000 * (box[1] / height)					),
						    int(1000 * (box[2] / width)					),
						    int(1000 * (box[3] / height)					),
						]



def       __UpperCAmelCase					(lowercase__						,lowercase__						,lowercase__ = None					)		->					int:

						'''simple docstring'''


						a_					  = tesseract_config if tesseract_config is not None else ""

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

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

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

						# finally, normalize the bounding boxes
						a_					  = []
						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 		SCREAMING_SNAKE_CASE__ (  lowercase_     ):
		_UpperCAmelCase								=['pixel_values']



		def __init__(     self:     Dict					, a:     bool = True					, a:     Dict[str, int] = None					, a:     PILImageResampling = PILImageResampling.BILINEAR					, a:     bool = True					, a:     Optional[str] = None					, a:     Optional[str] = ""					, **a:     Optional[int]					, )	->Optional[Any]:






								'''simple docstring'''


								super().__init__(**a)
								a_					  = size if size is not None else {"height": 2_24, "width": 2_24}
								a_					  = get_size_dict(a)

								a_					  = do_resize
								a_					  = size
								a_					  = resample
								a_					  = apply_ocr
								a_					  = ocr_lang
								a_					  = tesseract_config



		def _lowerCAmelCase  (     self:     List[Any]					, a:     np.ndarray					, a:     Dict[str, int]					, a:     PILImageResampling = PILImageResampling.BILINEAR					, a:     Optional[Union[str, ChannelDimension]] = None					, **a:     List[str]					, )	->Optional[Any]:






								'''simple docstring'''


								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 _lowerCAmelCase  (     self:     Optional[Any]					, a:     ImageInput					, a:     bool = None					, a:     Dict[str, int] = None					, a:     PILImageResampling = None					, a:     bool = None					, a:     Optional[str] = None					, a:     Optional[str] = None					, a:     Optional[Union[str, TensorType]] = None					, a:     ChannelDimension = ChannelDimension.FIRST					, **a:     List[str]					, )	->Tuple:






								'''simple docstring'''


								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_					  = 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











'''simple docstring'''
import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


UpperCamelCase__			=      ['small', 'medium', 'large']

UpperCamelCase__			=      'lm_head.decoder.weight'
UpperCamelCase__			=      'lm_head.weight'




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Optional[Any]							     =  torch.load(_UpperCamelCase				)
   lowercase_						:     List[Any]							     =  d.pop(_UpperCamelCase				)
   os.makedirs(_UpperCamelCase    ,		exist_ok=_UpperCamelCase				)
   torch.save(_UpperCamelCase    ,		os.path.join(_UpperCamelCase    ,		_UpperCamelCase				)				)


if __name__ == "__main__":
 UpperCamelCase__			=      argparse.ArgumentParser()
 parser.add_argument('--dialogpt_path', default='.', type=str)
 UpperCamelCase__			=      parser.parse_args()
 for MODEL in DIALOGPT_MODELS:
  UpperCamelCase__			=      os.path.join(args.dialogpt_path, f"""{MODEL}_ft.pkl""")
  UpperCamelCase__			=      f"""./DialoGPT-{MODEL}"""
  convert_dialogpt_checkpoint(
      checkpoint_path,
      pytorch_dump_folder_path,
  )




