





'''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

snake_case_     =	logging.get_logger(__name__)
def 							_lowerCamelCase(  UpperCamelCase__    : Optional[int]    ,       UpperCamelCase__    : Any    ,       UpperCamelCase__    : Tuple		)		->  int:
    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 							_lowerCamelCase(  UpperCamelCase__    : np.ndarray    ,       UpperCamelCase__    : Optional[str]    ,       UpperCamelCase__    : Optional[str] = None		)		->  Dict:
    A       :       int							=						tesseract_config if tesseract_config is not None else ''''''

    # apply OCR
    A       :       Dict							=						to_pil_image(UpperCamelCase__		)
    A,	A       :       Optional[Any]							=						pil_image.size
    A       :       Optional[Any]							=						pytesseract.image_to_data(UpperCamelCase__    ,       lang=UpperCamelCase__    ,       output_type='''dict'''    ,       config=UpperCamelCase__		)
    A,	A,	A,	A,	A       :       Any							=						data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

    # filter empty words and corresponding coordinates
    A       :       Dict							=						[idx for idx, word in enumerate(UpperCamelCase__		) if not word.strip()]
    A       :       List[str]							=						[word for idx, word in enumerate(UpperCamelCase__		) if idx not in irrelevant_indices]
    A       :       Optional[Any]							=						[coord for idx, coord in enumerate(UpperCamelCase__		) if idx not in irrelevant_indices]
    A       :       Optional[int]							=						[coord for idx, coord in enumerate(UpperCamelCase__		) if idx not in irrelevant_indices]
    A       :       Dict							=						[coord for idx, coord in enumerate(UpperCamelCase__		) if idx not in irrelevant_indices]
    A       :       Optional[int]							=						[coord for idx, coord in enumerate(UpperCamelCase__		) if idx not in irrelevant_indices]

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

    # finally, normalize the bounding boxes
    A       :       str							=						[]
    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						(							a				):
   _UpperCamelCase       		=   ["""pixel_values"""]


   def __init__( self    ,   _UpperCAmelCase = True    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = PILImageResampling.BILINEAR    ,   _UpperCAmelCase = True    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = ""    ,   **_UpperCAmelCase    ,   ):
       super().__init__(**_UpperCAmelCase					)
       A       :       Optional[Any]							=						size if size is not None else {'''height''': 224, '''width''': 224}
       A       :       Tuple							=						get_size_dict(_UpperCAmelCase					)

       A       :       Any							=						do_resize
       A       :       Tuple							=						size
       A       :       int							=						resample
       A       :       str							=						apply_ocr
       A       :       int							=						ocr_lang
       A       :       Optional[Any]							=						tesseract_config


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase = PILImageResampling.BILINEAR    ,   _UpperCAmelCase = None    ,   **_UpperCAmelCase    ,   ):
       A       :       Any							=						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       :       str							=						(size['''height'''], size['''width'''])
       return resize(_UpperCAmelCase    ,   size=_UpperCAmelCase    ,   resample=_UpperCAmelCase    ,   data_format=_UpperCAmelCase    ,   **_UpperCAmelCase					)


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = ChannelDimension.FIRST    ,   **_UpperCAmelCase    ,   ):
       A       :       Optional[Any]							=						do_resize if do_resize is not None else self.do_resize
       A       :       Tuple							=						size if size is not None else self.size
       A       :       List[str]							=						get_size_dict(_UpperCAmelCase					)
       A       :       Optional[Any]							=						resample if resample is not None else self.resample
       A       :       Dict							=						apply_ocr if apply_ocr is not None else self.apply_ocr
       A       :       Union[str, Any]							=						ocr_lang if ocr_lang is not None else self.ocr_lang
       A       :       Dict							=						tesseract_config if tesseract_config is not None else self.tesseract_config

       A       :       List[str]							=						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.'''					)

       # All transformations expect numpy arrays.
       A       :       Union[str, Any]							=						[to_numpy_array(_UpperCAmelCase					) for image in images]

       if apply_ocr:
           requires_backends(self    ,   '''pytesseract'''					)
           A       :       Union[str, Any]							=						[]
           A       :       List[Any]							=						[]
           for image in images:
               A,	A       :       List[str]							=						apply_tesseract(_UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase					)
               words_batch.append(_UpperCAmelCase					)
               boxes_batch.append(_UpperCAmelCase					)

       if do_resize:
           A       :       Any							=						[self.resize(image=_UpperCAmelCase    ,   size=_UpperCAmelCase    ,   resample=_UpperCAmelCase					) for image in images]

       # flip color channels from RGB to BGR (as Detectron2 requires this)
       A       :       Dict							=						[flip_channel_order(_UpperCAmelCase					) for image in images]
       A       :       int							=						[to_channel_dimension_format(_UpperCAmelCase    ,   _UpperCAmelCase					) for image in images]

       A       :       int							=						BatchFeature(data={'''pixel_values''': images}    ,   tensor_type=_UpperCAmelCase					)

       if apply_ocr:
           A       :       Optional[int]							=						words_batch
           A       :       List[Any]							=						boxes_batch
       return data







'''simple docstring'''






import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand
def 							_lowerCamelCase(  UpperCamelCase__    : Optional[int]		)		->  List[str]:
    return EnvironmentCommand()
def 							_lowerCamelCase(  UpperCamelCase__    : List[Any]		)		->  Tuple:
    return EnvironmentCommand(args.accelerate_config_file		)





class   _lowercase						(							a				):


   @staticmethod
   def 					snake_case				( _UpperCAmelCase					):
       A       :       Optional[Any]							=						parser.add_parser('''env'''					)
       download_parser.set_defaults(func=_UpperCAmelCase					)
       download_parser.add_argument(
           '''--accelerate-config_file'''    ,   default=_UpperCAmelCase    ,   help='''The accelerate config file to use for the default values in the launching script.'''    ,   )
       download_parser.set_defaults(func=_UpperCAmelCase					)


   def __init__( self    ,   _UpperCAmelCase    ,   *_UpperCAmelCase					):
       A       :       Any							=						accelerate_config_file


   def 					snake_case				( self					):
       A       :       Any							=						'''not installed'''
       if is_safetensors_available():
           import safetensors

           A       :       Dict							=						safetensors.__version__
       elif importlib.util.find_spec('''safetensors'''					) is not None:
           import safetensors

           A       :       int							=						f'''{safetensors.__version__} but is ignored because of PyTorch version too old.'''

       A       :       Tuple							=						'''not installed'''
       A       :       Tuple							=						'''not found'''
       if is_accelerate_available():
           import accelerate
           from accelerate.commands.config import default_config_file, load_config_from_file

           A       :       Any							=						accelerate.__version__
           # Get the default from the config file.
           if self._accelerate_config_file is not None or os.path.isfile(_UpperCAmelCase					):
               A       :       int							=						load_config_from_file(self._accelerate_config_file					).to_dict()

           A       :       Tuple							=						(
               '''\n'''.join([f'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()]					)
               if isinstance(_UpperCAmelCase    ,   _UpperCAmelCase					)
               else f'''\t{accelerate_config}'''
           )

       A       :       str							=						'''not installed'''
       A       :       Optional[Any]							=						'''NA'''
       if is_torch_available():
           import torch

           A       :       Optional[int]							=						torch.__version__
           A       :       List[Any]							=						torch.cuda.is_available()

       A       :       Dict							=						'''not installed'''
       A       :       Any							=						'''NA'''
       if is_tf_available():
           import tensorflow as tf

           A       :       Optional[int]							=						tf.__version__
           try:
               # deprecated in v2.1
               A       :       int							=						tf.test.is_gpu_available()
           except AttributeError:
               # returns list of devices, convert to bool
               A       :       Optional[int]							=						bool(tf.config.list_physical_devices('''GPU'''					)					)

       A       :       str							=						'''not installed'''
       A       :       Dict							=						'''not installed'''
       A       :       int							=						'''not installed'''
       A       :       Tuple							=						'''NA'''
       if is_flax_available():
           import flax
           import jax
           import jaxlib

           A       :       Any							=						flax.__version__
           A       :       Tuple							=						jax.__version__
           A       :       int							=						jaxlib.__version__
           A       :       List[Any]							=						jax.lib.xla_bridge.get_backend().platform

       A       :       Union[str, Any]							=						{
           '''`transformers` version''': version,
           '''Platform''': platform.platform(),
           '''Python version''': platform.python_version(),
           '''Huggingface_hub version''': huggingface_hub.__version__,
           '''Safetensors version''': f'''{safetensors_version}''',
           '''Accelerate version''': f'''{accelerate_version}''',
           '''Accelerate config''': f'''{accelerate_config_str}''',
           '''PyTorch version (GPU?)''': f'''{pt_version} ({pt_cuda_available})''',
           '''Tensorflow version (GPU?)''': f'''{tf_version} ({tf_cuda_available})''',
           '''Flax version (CPU?/GPU?/TPU?)''': f'''{flax_version} ({jax_backend})''',
           '''Jax version''': f'''{jax_version}''',
           '''JaxLib version''': f'''{jaxlib_version}''',
           '''Using GPU in script?''': '''<fill in>''',
           '''Using distributed or parallel set-up in script?''': '''<fill in>''',
       }

       print('''\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'''					)
       print(self.format_dict(_UpperCAmelCase					)					)

       return info


   @staticmethod
   def 					snake_case				( _UpperCAmelCase					):
       return "\n".join([f'''- {prop}: {val}''' for prop, val in d.items()]					) + "\n"
