





import os
import sys
import tempfile

import torch

from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a=()   , __a=None   , __a="no"   , __a="29500"   ):
	snake_case_		:			Tuple											=	False
	snake_case_		:			Tuple											=	False
	if any(key.startswith('KAGGLE'   ) for key in os.environ.keys()   ):
		snake_case_		:			Optional[Any]											=	True
	elif "IPython" in sys.modules:
		snake_case_		:			int											=	'''google.colab''' in str(sys.modules['IPython'].get_ipython()   )

	try:
		snake_case_		:			Tuple											=	PrecisionType(mixed_precision.lower()   )
	except ValueError:
		raise ValueError(
		    f"""Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}."""   )

	if (in_colab or in_kaggle) and (os.environ.get('TPU_NAME'   , a_   ) is not None):
		# TPU launch
		import torch_xla.distributed.xla_multiprocessing as xmp

		if len(AcceleratorState._shared_state   ) > 0:
			raise ValueError(
			    'To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside '
			    'your training function. Restart your notebook and make sure no cells initializes an '
			    '`Accelerator`.'   )
		if num_processes is None:
			snake_case_		:			Optional[int]											=	8

		snake_case_		:			Optional[Any]											=	PrepareForLaunch(a_   , distributed_type='TPU'   )
		print(f"""Launching a training on {num_processes} TPU cores."""   )
		xmp.spawn(a_   , args=a_   , nprocs=a_   , start_method='fork'   )
	elif in_colab:
		# No need for a distributed launch otherwise as it's either CPU or one GPU.
		if torch.cuda.is_available():
			print('Launching training on one GPU.'   )
		else:
			print('Launching training on one CPU.'   )
		function(*a_   )
	else:
		if num_processes is None:
			raise ValueError(
			    'You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call.'   )

		if num_processes > 1:
			# Multi-GPU launch
			from torch.multiprocessing import start_processes
			from torch.multiprocessing.spawn import ProcessRaisedException

			if len(AcceleratorState._shared_state   ) > 0:
				raise ValueError(
				    'To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized '
				    'inside your training function. Restart your notebook and make sure no cells initializes an '
				    '`Accelerator`.'   )

			if torch.cuda.is_initialized():
				raise ValueError(
				    'To launch a multi-GPU training from your notebook, you need to avoid running any instruction '
				    'using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA '
				    'function.'   )

			# torch.distributed will expect a few environment variable to be here. We set the ones common to each
			# process here (the other ones will be set be the launcher).
			with patch_environment(
			    world_size=a_   , master_addr='127.0.01'   , master_port=a_   , mixed_precision=a_   ):
				snake_case_		:			Any											=	PrepareForLaunch(a_   , distributed_type='MULTI_GPU'   )
				print(f"""Launching training on {num_processes} GPUs."""   )
				try:
					start_processes(a_   , args=a_   , nprocs=a_   , start_method='fork'   )
				except ProcessRaisedException as e:
					if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
						raise RuntimeError(
						    'CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. '
						    'This likely stems from an outside import causing issues once the `notebook_launcher()` is called. '
						    'Please review your imports and test them when running the `notebook_launcher()` to identify '
						    'which one is problematic.'   ) from e

		else:
			# No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
			if is_mps_available():
				snake_case_		:			Union[str, Any]											=	'''1'''
				print('Launching training on MPS.'   )
			elif torch.cuda.is_available():
				print('Launching training on one GPU.'   )
			else:
				print('Launching training on CPU.'   )
			function(*a_   )


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a=()   , __a=2   ):
	from torch.multiprocessing import start_processes

	with tempfile.NamedTemporaryFile() as tmp_file:
		# torch.distributed will expect a few environment variable to be here. We set the ones common to each
		# process here (the other ones will be set be the launcher).
		with patch_environment(
		    world_size=a_   , master_addr='127.0.01'   , master_port='29500'   , accelerate_mixed_precision='no'   , accelerate_debug_rdv_file=tmp_file.name   , accelerate_use_cpu='yes'   , ):
			snake_case_		:			Optional[Any]											=	PrepareForLaunch(a_   , debug=a_   )
			start_processes(a_   , args=a_   , nprocs=a_   , start_method='fork'   )








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

_SCREAMING_SNAKE_CASE      	=		logging.get_logger(__name__)


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   ):
	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 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a = None   ):
	snake_case_		:			int											=	tesseract_config if tesseract_config is not None else ''

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

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

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

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

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

	return words, normalized_boxes


class SCREAMING_SNAKE_CASE_						(					snake_case_					):
						__magic_name__:			str       =       ["pixel_values"]






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







							"""simple docstring"""


							super().__init__(**_A	)
							snake_case_		:			Optional[int]											=	size if size is not None else {'height': 224, 'width': 224}
							snake_case_		:			List[str]											=	get_size_dict(_A	)

							snake_case_		:			int											=	do_resize
							snake_case_		:			int											=	size
							snake_case_		:			Any											=	resample
							snake_case_		:			List[str]											=	apply_ocr
							snake_case_		:			str											=	ocr_lang
							snake_case_		:			Dict											=	tesseract_config






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







							"""simple docstring"""


							snake_case_		:			Optional[Any]											=	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()}"""	)
							snake_case_		:			Optional[Any]											=	(size['height'], size['width'])
							return resize(_A		,   size=_A		,   resample=_A		,   data_format=_A		,   **_A	)






						def UpperCAmelCase_				(      self      :      Optional[int]		,   _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]		,   )							->    PIL.Image.Image:







							"""simple docstring"""


							snake_case_		:			Dict											=	do_resize if do_resize is not None else self.do_resize
							snake_case_		:			Optional[Any]											=	size if size is not None else self.size
							snake_case_		:			Union[str, Any]											=	get_size_dict(_A	)
							snake_case_		:			Dict											=	resample if resample is not None else self.resample
							snake_case_		:			Tuple											=	apply_ocr if apply_ocr is not None else self.apply_ocr
							snake_case_		:			int											=	ocr_lang if ocr_lang is not None else self.ocr_lang
							snake_case_		:			int											=	tesseract_config if tesseract_config is not None else self.tesseract_config

							snake_case_		:			str											=	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.
							snake_case_		:			List[str]											=	[to_numpy_array(_A	) for image in images]

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

							if do_resize:
								snake_case_		:			Tuple											=	[self.resize(image=_A		,   size=_A		,   resample=_A	) for image in images]

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

							snake_case_		:			List[str]											=	BatchFeature(data={'pixel_values': images}		,   tensor_type=_A	)

							if apply_ocr:
								snake_case_		:			Dict											=	words_batch
								snake_case_		:			List[str]											=	boxes_batch
							return data

