

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

__lowercase				      =						logging.get_logger(__name__)







def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   ):
      '''simple docstring'''







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







def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   ):
      '''simple docstring'''







      A_							       =		to_pil_image(__UpperCamelCase   )
      A_       ,A_							       =		pil_image.size
      A_							       =		pytesseract.image_to_data(__UpperCamelCase		,				lang=__UpperCamelCase		,				output_type='''dict'''		,				config=__UpperCamelCase   )
      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(__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    _lowercase				(					__lowerCamelCase			):
   _lowercase     :       List[Any]  					=    ['pixel_values']

   def __init__(				self	:    Union[str, Any]			,						lowerCamelCase__	:    Dict = True			,						lowerCamelCase__	:    int = None			,						lowerCamelCase__	:    Optional[int] = PILImageResampling.BILINEAR			,						lowerCamelCase__	:    Union[str, Any] = True			,						lowerCamelCase__	:    List[Any] = 1 / 2_5_5			,						lowerCamelCase__	:    Any = True			,						lowerCamelCase__	:    Optional[int] = None			,						lowerCamelCase__	:    Union[str, Any] = None			,						lowerCamelCase__	:    Union[str, Any] = True			,						lowerCamelCase__	:    Optional[int] = None			,						lowerCamelCase__	:    Union[str, Any] = ""			,						**lowerCamelCase__	:    Optional[int]			,						) ->			None:
         """simple docstring"""





         super().__init__(**_UpperCamelCase       )
         A_							       =		size if size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
         A_							       =		get_size_dict(_UpperCamelCase       )

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

   def     UpperCamelCase					(				self	:    str			,						lowerCamelCase__	:    str			,						lowerCamelCase__	:    List[Any]			,						lowerCamelCase__	:    Tuple = PILImageResampling.BILINEAR			,						lowerCamelCase__	:    Optional[Any] = None			,						**lowerCamelCase__	:    List[str]			,						) ->			np.ndarray:
         """simple docstring"""





         A_							       =		get_size_dict(_UpperCamelCase       )
         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(_UpperCamelCase			,						size=_UpperCamelCase			,						resample=_UpperCamelCase			,						data_format=_UpperCamelCase			,						**_UpperCamelCase       )

   def     UpperCamelCase					(				self	:    Any			,						lowerCamelCase__	:    List[Any]			,						lowerCamelCase__	:    Any			,						lowerCamelCase__	:    int = None			,						**lowerCamelCase__	:    Tuple			,						) ->			np.ndarray:
         """simple docstring"""





         return rescale(_UpperCamelCase			,						scale=_UpperCamelCase			,						data_format=_UpperCamelCase			,						**_UpperCamelCase       )

   def     UpperCamelCase					(				self	:    Optional[Any]			,						lowerCamelCase__	:    Union[str, Any]			,						lowerCamelCase__	:    List[Any]			,						lowerCamelCase__	:    Union[str, Any]			,						lowerCamelCase__	:    Union[str, Any] = None			,						**lowerCamelCase__	:    Optional[Any]			,						) ->			np.ndarray:
         """simple docstring"""





         return normalize(_UpperCamelCase			,						mean=_UpperCamelCase			,						std=_UpperCamelCase			,						data_format=_UpperCamelCase			,						**_UpperCamelCase       )





   def     UpperCamelCase					(				self	:    Optional[Any]			,						lowerCamelCase__	:    Optional[int]			,						lowerCamelCase__	:    Tuple = None			,						lowerCamelCase__	:    Optional[Any] = None			,						lowerCamelCase__	:    Optional[Any]=None			,						lowerCamelCase__	:    Tuple = None			,						lowerCamelCase__	:    Optional[Any] = None			,						lowerCamelCase__	:    str = None			,						lowerCamelCase__	:    int = None			,						lowerCamelCase__	:    Tuple = None			,						lowerCamelCase__	:    Tuple = None			,						lowerCamelCase__	:    Tuple = None			,						lowerCamelCase__	:    Tuple = None			,						lowerCamelCase__	:    List[str] = None			,						lowerCamelCase__	:    Union[str, Any] = ChannelDimension.FIRST			,						**lowerCamelCase__	:    Union[str, Any]			,						) ->			PIL.Image.Image:
         """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(_UpperCamelCase       )
         A_							       =		resample if resample is not None else self.resample
         A_							       =		do_rescale if do_rescale is not None else self.do_rescale
         A_							       =		rescale_factor if rescale_factor is not None else self.rescale_factor
         A_							       =		do_normalize if do_normalize is not None else self.do_normalize
         A_							       =		image_mean if image_mean is not None else self.image_mean
         A_							       =		image_std if image_std is not None else self.image_std
         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(_UpperCamelCase       )

         if not valid_images(_UpperCamelCase       ):
               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.
         A_							       =		[to_numpy_array(_UpperCamelCase       ) for image in images]

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

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

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

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

         A_							       =		[to_channel_dimension_format(_UpperCamelCase			,						_UpperCamelCase       ) for image in images]

         A_							       =		BatchFeature(data={'''pixel_values''': images}			,						tensor_type=_UpperCamelCase       )

         if apply_ocr:
               A_							       =		words_batch
               A_							       =		boxes_batch
         return data









'''simple docstring'''
# 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


lowerCAmelCase						=					"""Create a default config file for Accelerate with only a few flags set."""

def 					lowerCamelCase_ ( __UpperCamelCase					:       Union[str, Any]="no"							,    __UpperCamelCase					:       str = default_json_config_file							,    __UpperCamelCase					:       bool = False		)					->	Tuple:







     """simple docstring"""
     _A				=							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				=							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				=							{
         'compute_environment': 'LOCAL_MACHINE',
         'mixed_precision': mixed_precision,
     }
     if torch.cuda.is_available():
          _A				=							torch.cuda.device_count()
          _A				=							num_gpus
          _A				=							False
          if num_gpus > 1:
               _A				=							'MULTI_GPU'
          else:
               _A				=							'NO'
     elif is_xpu_available() and use_xpu:
          _A				=							torch.xpu.device_count()
          _A				=							num_xpus
          _A				=							False
          if num_xpus > 1:
               _A				=							'MULTI_XPU'
          else:
               _A				=							'NO'
     elif is_npu_available():
          _A				=							torch.npu.device_count()
          _A				=							num_npus
          _A				=							False
          if num_npus > 1:
               _A				=							'MULTI_NPU'
          else:
               _A				=							'NO'
     else:
          _A				=							0
          _A				=							True
          _A				=							1
          _A				=							'NO'
     _A				=							ClusterConfig(**__UpperCamelCase		)
     config.to_json_file(__UpperCamelCase		)
     return path

def 					lowerCamelCase_ ( __UpperCamelCase					:       List[Any]							,    __UpperCamelCase					:       str		)					->	Any:







     """simple docstring"""
     _A				=							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					:       Any		)					->	Union[str, Any]:







     """simple docstring"""
     _A				=							write_basic_config(args.mixed_precision							,    args.save_location		)
     if config_file:
          print(F'accelerate configuration saved at {config_file}'		)





