
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 				a						(							snake_case__:					str						,   snake_case__:					str						,   snake_case__:					Any					):


				'''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						(							snake_case__:					np.ndarray						,   snake_case__:					Optional[str]						,   snake_case__:					Optional[str] = None					):


				'''simple docstring'''
				lowercase_       =					tesseract_config if tesseract_config is not None else ''''''

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

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

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

				# finally, normalize the bounding boxes
				lowercase_       =					[]
				for box in actual_boxes:
								normalized_boxes.append(normalize_box(snake_case__						,   snake_case__						,   snake_case__					)					)

				assert len(snake_case__					) == len(snake_case__					), "Not as many words as there are bounding boxes"

				return words, normalized_boxes


class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	a			:Optional[Any]         =	['pixel_values']
	def __init__(       self  :			Union[str, Any]       ,							SCREAMING_SNAKE_CASE_  :			bool = True       ,							SCREAMING_SNAKE_CASE_  :			Dict[str, int] = None       ,							SCREAMING_SNAKE_CASE_  :			PILImageResampling = PILImageResampling.BILINEAR       ,							SCREAMING_SNAKE_CASE_  :			bool = True       ,							SCREAMING_SNAKE_CASE_  :			Optional[str] = None       ,							SCREAMING_SNAKE_CASE_  :			Optional[str] = ""       ,							**SCREAMING_SNAKE_CASE_  :			int       ,							)       ->     None:
					super().__init__(**SCREAMING_SNAKE_CASE_       )
					lowercase_       =					size if size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
					lowercase_       =					get_size_dict(SCREAMING_SNAKE_CASE_       )

					lowercase_       =					do_resize
					lowercase_       =					size
					lowercase_       =					resample
					lowercase_       =					apply_ocr
					lowercase_       =					ocr_lang
					lowercase_       =					tesseract_config
	def 						_lowercase  (       self  :			str       ,							SCREAMING_SNAKE_CASE_  :			np.ndarray       ,							SCREAMING_SNAKE_CASE_  :			Dict[str, int]       ,							SCREAMING_SNAKE_CASE_  :			PILImageResampling = PILImageResampling.BILINEAR       ,							SCREAMING_SNAKE_CASE_  :			Optional[Union[str, ChannelDimension]] = None       ,							**SCREAMING_SNAKE_CASE_  :			Dict       ,							)       ->     np.ndarray:
					lowercase_       =					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()}'''       )
					lowercase_       =					(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  :			Dict       ,							SCREAMING_SNAKE_CASE_  :			ImageInput       ,							SCREAMING_SNAKE_CASE_  :			bool = None       ,							SCREAMING_SNAKE_CASE_  :			Dict[str, int] = None       ,							SCREAMING_SNAKE_CASE_  :			PILImageResampling = None       ,							SCREAMING_SNAKE_CASE_  :			bool = None       ,							SCREAMING_SNAKE_CASE_  :			Optional[str] = None       ,							SCREAMING_SNAKE_CASE_  :			Optional[str] = None       ,							SCREAMING_SNAKE_CASE_  :			Optional[Union[str, TensorType]] = None       ,							SCREAMING_SNAKE_CASE_  :			ChannelDimension = ChannelDimension.FIRST       ,							**SCREAMING_SNAKE_CASE_  :			Dict       ,							)       ->     PIL.Image.Image:
					lowercase_       =					do_resize if do_resize is not None else self.do_resize
					lowercase_       =					size if size is not None else self.size
					lowercase_       =					get_size_dict(SCREAMING_SNAKE_CASE_       )
					lowercase_       =					resample if resample is not None else self.resample
					lowercase_       =					apply_ocr if apply_ocr is not None else self.apply_ocr
					lowercase_       =					ocr_lang if ocr_lang is not None else self.ocr_lang
					lowercase_       =					tesseract_config if tesseract_config is not None else self.tesseract_config

					lowercase_       =					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.'''       )

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

					if apply_ocr:
									requires_backends(self       ,							'''pytesseract'''       )
									lowercase_       =					[]
									lowercase_       =					[]
									for image in images:
													lowercase_  ,	lowercase_       =					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:
									lowercase_       =					[self.resize(image=SCREAMING_SNAKE_CASE_       ,							size=SCREAMING_SNAKE_CASE_       ,							resample=SCREAMING_SNAKE_CASE_       ) for image in images]

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

					lowercase_       =					BatchFeature(data={'''pixel_values''': images}       ,							tensor_type=SCREAMING_SNAKE_CASE_       )

					if apply_ocr:
									lowercase_       =					words_batch
									lowercase_       =					boxes_batch
					return data








# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


snake_case				:    int       				=     '''Create a default config file for Accelerate with only a few flags set.'''
def 				__lowerCamelCase						( UpperCAmelCase_						:		Optional[Any]="no" ,  UpperCAmelCase_						:		str = default_json_config_file ,  UpperCAmelCase_						:		bool = False						):




      """simple docstring"""





      a						:List[str] 							=       Path(UpperCAmelCase_						)
      path.parent.mkdir(parents=UpperCAmelCase_ ,  exist_ok=UpperCAmelCase_						)
      if path.exists():
            print(
                F'''Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.'''						)
            return False
      a						:Optional[Any] 							=       mixed_precision.lower()
      if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
            raise ValueError(
                F'''`mixed_precision` should be one of \'no\', \'fp16\', \'bf16\', or \'fp8\'. Received {mixed_precision}'''						)
      a						:List[Any] 							=       {
          '''compute_environment''': '''LOCAL_MACHINE''',
          '''mixed_precision''': mixed_precision,
      }
      if torch.cuda.is_available():
            a						:Dict 							=       torch.cuda.device_count()
            a						:Tuple 							=       num_gpus
            a						:int 							=       False
            if num_gpus > 1:
                  a						:str 							=       '''MULTI_GPU'''
            else:
                  a						:List[Any] 							=       '''NO'''
      elif is_xpu_available() and use_xpu:
            a						:List[Any] 							=       torch.xpu.device_count()
            a						:Optional[int] 							=       num_xpus
            a						:List[Any] 							=       False
            if num_xpus > 1:
                  a						:int 							=       '''MULTI_XPU'''
            else:
                  a						:str 							=       '''NO'''
      elif is_npu_available():
            a						:List[str] 							=       torch.npu.device_count()
            a						:Any 							=       num_npus
            a						:Optional[int] 							=       False
            if num_npus > 1:
                  a						:List[str] 							=       '''MULTI_NPU'''
            else:
                  a						:Dict 							=       '''NO'''
      else:
            a						:str 							=       0
            a						:Optional[Any] 							=       True
            a						:Optional[Any] 							=       1
            a						:str 							=       '''NO'''
      a						:List[str] 							=       ClusterConfig(**UpperCAmelCase_						)
      config.to_json_file(UpperCAmelCase_						)
      return path
def 				__lowerCamelCase						( UpperCAmelCase_						:		List[str] ,  UpperCAmelCase_						:		Union[str, Any]						):




      """simple docstring"""





      a						:List[Any] 							=       parser.add_parser('''default''' ,  parents=UpperCAmelCase_ ,  help=UpperCAmelCase_ ,  formatter_class=UpperCAmelCase_						)
      parser.add_argument(
          '''--config_file''' ,  default=UpperCAmelCase_ ,  help=(
              '''The path to use to store the config file. Will default to a file named default_config.yaml in the cache '''
              '''location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '''
              '''such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '''
              '''with \'huggingface\'.'''
          ) ,  dest='''save_location''' ,  )

      parser.add_argument(
          '''--mixed_precision''' ,  choices=['''no''', '''fp16''', '''bf16'''] ,  type=UpperCAmelCase_ ,  help='''Whether or not to use mixed precision training. '''
          '''Choose between FP16 and BF16 (bfloat16) training. '''
          '''BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.''' ,  default='''no''' ,  )
      parser.set_defaults(func=UpperCAmelCase_						)
      return parser






def 				__lowerCamelCase						( UpperCAmelCase_						:		int						):




      """simple docstring"""





      a						:Optional[Any] 							=       write_basic_config(args.mixed_precision ,  args.save_location						)
      if config_file:
            print(F'''accelerate configuration saved at {config_file}'''						)





