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

_lowerCamelCase					:   Union[str, Any]							=		logging.get_logger(__name__)





def 	__lowerCamelCase							(UpperCAmelCase__     :							Optional[Any]      , UpperCAmelCase__     :							Union[str, Any]      , UpperCAmelCase__     :							List[str]  ):
					return [
					    int(1_0_0_0 * (box[0] / width)  ),
					    int(1_0_0_0 * (box[1] / height)  ),
					    int(1_0_0_0 * (box[2] / width)  ),
					    int(1_0_0_0 * (box[3] / height)  ),
					]





def 	__lowerCamelCase							(UpperCAmelCase__     :							np.ndarray      , UpperCAmelCase__     :							Optional[str]      , UpperCAmelCase__     :							Optional[str] = None  ):
					SCREAMING_SNAKE_CASE    			=       tesseract_config if tesseract_config is not None else """"""

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

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

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

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

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

					return words, normalized_boxes


class 						lowercase    (     _UpperCAmelCase     ):
							lowercase__						:       int								=							["""pixel_values"""]




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



												'''simple docstring'''

												super().__init__(**lowercase__      )
												SCREAMING_SNAKE_CASE    			=       size if size is not None else {"""height""": 224, """width""": 224}
												SCREAMING_SNAKE_CASE    			=       get_size_dict(lowercase__      )

												SCREAMING_SNAKE_CASE    			=       do_resize
												SCREAMING_SNAKE_CASE    			=       size
												SCREAMING_SNAKE_CASE    			=       resample
												SCREAMING_SNAKE_CASE    			=       apply_ocr
												SCREAMING_SNAKE_CASE    			=       ocr_lang
												SCREAMING_SNAKE_CASE    			=       tesseract_config




							def 	__snake_case(						self   :					List[Any]	,							_UpperCamelCase   :					Dict	,							_UpperCamelCase   :					Dict	,							_UpperCamelCase   :					List[str] = PILImageResampling.BILINEAR	,							_UpperCamelCase   :					int = None	,							**_UpperCamelCase   :					Optional[int]	,							)					->				Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       get_size_dict(lowercase__      )
												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()}"      )
												SCREAMING_SNAKE_CASE    			=       (size["""height"""], size["""width"""])
												return resize(lowercase__	,							size=lowercase__	,							resample=lowercase__	,							data_format=lowercase__	,							**lowercase__      )





							def 	__snake_case(						self   :					Optional[int]	,							_UpperCamelCase   :					List[Any]	,							_UpperCamelCase   :					List[str] = None	,							_UpperCamelCase   :					Union[str, Any] = None	,							_UpperCamelCase   :					int = None	,							_UpperCamelCase   :					Dict = None	,							_UpperCamelCase   :					Optional[int] = None	,							_UpperCamelCase   :					List[Any] = None	,							_UpperCamelCase   :					List[str] = None	,							_UpperCamelCase   :					Optional[int] = ChannelDimension.FIRST	,							**_UpperCamelCase   :					int	,							)					->				List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       do_resize if do_resize is not None else self.do_resize
												SCREAMING_SNAKE_CASE    			=       size if size is not None else self.size
												SCREAMING_SNAKE_CASE    			=       get_size_dict(lowercase__      )
												SCREAMING_SNAKE_CASE    			=       resample if resample is not None else self.resample
												SCREAMING_SNAKE_CASE    			=       apply_ocr if apply_ocr is not None else self.apply_ocr
												SCREAMING_SNAKE_CASE    			=       ocr_lang if ocr_lang is not None else self.ocr_lang
												SCREAMING_SNAKE_CASE    			=       tesseract_config if tesseract_config is not None else self.tesseract_config

												SCREAMING_SNAKE_CASE    			=       make_list_of_images(lowercase__      )

												if not valid_images(lowercase__      ):
																	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.
												SCREAMING_SNAKE_CASE    			=       [to_numpy_array(lowercase__      ) for image in images]

												if apply_ocr:
																	requires_backends(self	,							"pytesseract"      )
																	SCREAMING_SNAKE_CASE    			=       []
																	SCREAMING_SNAKE_CASE    			=       []
																	for image in images:
																						SCREAMING_SNAKE_CASE    			=       apply_tesseract(lowercase__	,							lowercase__	,							lowercase__      )
																						words_batch.append(lowercase__      )
																						boxes_batch.append(lowercase__      )

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

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

												SCREAMING_SNAKE_CASE    			=       BatchFeature(data={"pixel_values": images}	,							tensor_type=lowercase__      )

												if apply_ocr:
																	SCREAMING_SNAKE_CASE    			=       words_batch
																	SCREAMING_SNAKE_CASE    			=       boxes_batch
												return data





import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment


class 						lowercase    (     unittest.TestCase     ):




							def 	__snake_case(						self   :					Union[str, Any]      )					->				List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       inspect.getfile(accelerate.test_utils      )
												SCREAMING_SNAKE_CASE    			=       os.path.sep.join(mod_file.split(os.path.sep      )[:-1] + ["scripts", "test_script.py"]      )
												SCREAMING_SNAKE_CASE    			=       os.path.sep.join(
												    mod_file.split(os.path.sep      )[:-1] + ["scripts", "test_distributed_data_loop.py"]      )
												SCREAMING_SNAKE_CASE    			=       os.path.sep.join(mod_file.split(os.path.sep      )[:-1] + ["scripts", "test_ops.py"]      )




							@require_multi_gpu
							def 	__snake_case(						self   :					Optional[int]      )					->				Any:



												'''simple docstring'''

												print(F"Found {torch.cuda.device_count()} devices."      )
												SCREAMING_SNAKE_CASE    			=       ["torchrun", F"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
												with patch_environment(omp_num_threads=1      ):
																	execute_subprocess_async(_UpperCamelCase	,							env=os.environ.copy()      )




							@require_multi_gpu
							def 	__snake_case(						self   :					List[Any]      )					->				int:



												'''simple docstring'''

												print(F"Found {torch.cuda.device_count()} devices."      )
												SCREAMING_SNAKE_CASE    			=       ["torchrun", F"--nproc_per_node={torch.cuda.device_count()}", self.operation_file_path]
												print(F"Command: {cmd}"      )
												with patch_environment(omp_num_threads=1      ):
																	execute_subprocess_async(_UpperCamelCase	,							env=os.environ.copy()      )




							@require_multi_gpu
							def 	__snake_case(						self   :					int      )					->				Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       ["torchrun", F"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__      )]
												with patch_environment(omp_num_threads=1      ):
																	execute_subprocess_async(_UpperCamelCase	,							env=os.environ.copy()      )





							@require_multi_gpu
							def 	__snake_case(						self   :					int      )					->				int:



												'''simple docstring'''

												print(F"Found {torch.cuda.device_count()} devices, using 2 devices only"      )
												SCREAMING_SNAKE_CASE    			=       ["torchrun", F"--nproc_per_node={torch.cuda.device_count()}", self.data_loop_file_path]
												with patch_environment(omp_num_threads=1	,							cuda_visible_devices="0,1"      ):
																	execute_subprocess_async(_UpperCamelCase	,							env=os.environ.copy()      )


if __name__ == "__main__":
							_lowerCamelCase					:   str							=		Accelerator()
							_lowerCamelCase					:   List[str]							=		(accelerator.state.process_index + 2, 10)
							_lowerCamelCase					:   str							=		torch.randint(0, 10, shape).to(accelerator.device)

							_lowerCamelCase					:   Optional[Any]							=		''''''

							_lowerCamelCase					:   str							=		accelerator.pad_across_processes(tensor)
							if tensora.shape[0] != accelerator.state.num_processes + 1:
														error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
							if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
														error_msg += "Tensors have different values."
							if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
														error_msg += "Padding was not done with the right value (0)."

							_lowerCamelCase					:   Any							=		accelerator.pad_across_processes(tensor, pad_first=True)
							if tensora.shape[0] != accelerator.state.num_processes + 1:
														error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
							_lowerCamelCase					:   int							=		accelerator.state.num_processes - accelerator.state.process_index - 1
							if not torch.equal(tensora[index:], tensor):
														error_msg += "Tensors have different values."
							if not torch.all(tensora[:index] == 0):
														error_msg += "Padding was not done with the right value (0)."

							# Raise error at the end to make sure we don't stop at the first failure.
							if len(error_msg) > 0:
														raise ValueError(error_msg)




