

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

A									=  logging.get_logger(__name__)





def a(lowercase__      ,			lowercase__      ,			lowercase__						):





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





def a(lowercase__      ,			lowercase__      ,			lowercase__						):





	'''simple docstring'''
	snake_case_   		=			to_pil_image(lowercase__						)
	snake_case_		,							snake_case_   		=			pil_image.size
	snake_case_   		=			pytesseract.image_to_data(lowercase__      ,			lang=lowercase__      ,			output_type='dict'      ,			config=lowercase__						)
	snake_case_		,							snake_case_		,							snake_case_		,							snake_case_		,							snake_case_   		=			data['text'], data['left'], data['top'], data['width'], data['height']

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

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

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

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

	return words, normalized_boxes






class SCREAMING_SNAKE_CASE	(		__snake_case				):






			"""simple docstring"""




			__A    	=	["""pixel_values"""]


			def __init__(     self			,					__UpperCamelCase = True			,					__UpperCamelCase = None			,					__UpperCamelCase = PILImageResampling.BILINEAR			,					__UpperCamelCase = True			,					__UpperCamelCase = 1 / 2_55			,					__UpperCamelCase = True			,					__UpperCamelCase = None			,					__UpperCamelCase = None			,					__UpperCamelCase = True			,					__UpperCamelCase = None			,					__UpperCamelCase = ""			,					**__UpperCamelCase			,					):

				"""simple docstring"""


				super().__init__(**__UpperCamelCase				)
				snake_case_   		=			size if size is not None else {'height': 2_24, 'width': 2_24}
				snake_case_   		=			get_size_dict(__UpperCamelCase				)

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


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase = PILImageResampling.BILINEAR			,					__UpperCamelCase = None			,					**__UpperCamelCase			,					):

				"""simple docstring"""


				snake_case_   		=			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()}"""				)
				snake_case_   		=			(size['height'], size['width'])
				return resize(__UpperCamelCase			,					size=__UpperCamelCase			,					resample=__UpperCamelCase			,					data_format=__UpperCamelCase			,					**__UpperCamelCase				)


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase = None			,					**__UpperCamelCase			,					):

				"""simple docstring"""


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


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase = None			,					**__UpperCamelCase			,					):

				"""simple docstring"""


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







			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase = None			,					__UpperCamelCase = None			,					__UpperCamelCase=None			,					__UpperCamelCase = None			,					__UpperCamelCase = None			,					__UpperCamelCase = None			,					__UpperCamelCase = None			,					__UpperCamelCase = None			,					__UpperCamelCase = None			,					__UpperCamelCase = None			,					__UpperCamelCase = None			,					__UpperCamelCase = None			,					__UpperCamelCase = ChannelDimension.FIRST			,					**__UpperCamelCase			,					):

				"""simple docstring"""


				snake_case_   		=			do_resize if do_resize is not None else self.do_resize
				snake_case_   		=			size if size is not None else self.size
				snake_case_   		=			get_size_dict(__UpperCamelCase				)
				snake_case_   		=			resample if resample is not None else self.resample
				snake_case_   		=			do_rescale if do_rescale is not None else self.do_rescale
				snake_case_   		=			rescale_factor if rescale_factor is not None else self.rescale_factor
				snake_case_   		=			do_normalize if do_normalize is not None else self.do_normalize
				snake_case_   		=			image_mean if image_mean is not None else self.image_mean
				snake_case_   		=			image_std if image_std is not None else self.image_std
				snake_case_   		=			apply_ocr if apply_ocr is not None else self.apply_ocr
				snake_case_   		=			ocr_lang if ocr_lang is not None else self.ocr_lang
				snake_case_   		=			tesseract_config if tesseract_config is not None else self.tesseract_config

				snake_case_   		=			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.
				snake_case_   		=			[to_numpy_array(__UpperCamelCase				) for image in images]

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

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

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

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

				snake_case_   		=			[to_channel_dimension_format(__UpperCamelCase			,					__UpperCamelCase				) for image in images]

				snake_case_   		=			BatchFeature(data={'pixel_values': images}			,					tensor_type=__UpperCamelCase				)

				if apply_ocr:
					snake_case_   		=			words_batch
					snake_case_   		=			boxes_batch
				return data



import json
import logging
import os
import sys
from pathlib import Path

import finetune_rag

from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    require_ray,
    require_torch_gpu,
    require_torch_multi_gpu,
)


logging.basicConfig(level=logging.DEBUG)
A									=  logging.getLogger()

A									=  logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)


class SCREAMING_SNAKE_CASE	(		__snake_case				):






			"""simple docstring"""




			def       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				os.makedirs(__UpperCamelCase			,					exist_ok=__UpperCamelCase				)
				snake_case_   		=			{'source': 'What is love ?', 'target': 'life'}
				snake_case_   		=			{'train': 12, 'val': 2, 'test': 2}
				for split in ["train", "test", "val"]:
					for field in ["source", "target"]:
						snake_case_   		=			'\n'.join([contents[field]] * n_lines[split]				)
						with open(os.path.join(__UpperCamelCase			,					f"""{split}.{field}"""				)			,					'w'				) as f:
							f.write(__UpperCamelCase				)


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase = "pytorch"				):

				"""simple docstring"""


				snake_case_   		=			self.get_auto_remove_tmp_dir()
				snake_case_   		=			os.path.join(__UpperCamelCase			,					'output'				)
				snake_case_   		=			os.path.join(__UpperCamelCase			,					'data'				)
				self._create_dummy_data(data_dir=__UpperCamelCase				)

				snake_case_   		=			f"""
                --data_dir {data_dir} \
                --output_dir {output_dir} \
                --model_name_or_path facebook/rag-sequence-base \
                --model_type rag_sequence \
                --do_train \
                --do_predict \
                --n_val -1 \
                --val_check_interval 1.0 \
                --train_batch_size 2 \
                --eval_batch_size 1 \
                --max_source_length 25 \
                --max_target_length 25 \
                --val_max_target_length 25 \
                --test_max_target_length 25 \
                --label_smoothing 0.1 \
                --dropout 0.1 \
                --attention_dropout 0.1 \
                --weight_decay 0.001 \
                --adam_epsilon 1e-08 \
                --max_grad_norm 0.1 \
                --lr_scheduler polynomial \
                --learning_rate 3e-04 \
                --num_train_epochs 1 \
                --warmup_steps 4 \
                --gradient_accumulation_steps 1 \
                --distributed-port 8787 \
                --use_dummy_dataset 1 \
                --distributed_retriever {distributed_retriever} \
            """.split()

				if gpus > 0:
					testargs.append(f"""--gpus={gpus}"""				)
					if is_apex_available():
						testargs.append('--fp16'				)
				else:
					testargs.append('--gpus=0'				)
					testargs.append('--distributed_backend=ddp_cpu'				)
					testargs.append('--num_processes=2'				)

				snake_case_   		=			[sys.executable, str(Path(finetune_rag.__file__				).resolve()				)] + testargs
				execute_subprocess_async(__UpperCamelCase			,					env=self.get_env()				)

				snake_case_   		=			os.path.join(__UpperCamelCase			,					'metrics.json'				)
				with open(__UpperCamelCase				) as f:
					snake_case_   		=			json.load(__UpperCamelCase				)
				return result


			@require_torch_gpu
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self._run_finetune(gpus=1				)
				self.assertGreaterEqual(result['test'][0]['test_avg_em']			,					0.2				)


			@require_torch_multi_gpu
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self._run_finetune(gpus=2				)
				self.assertGreaterEqual(result['test'][0]['test_avg_em']			,					0.2				)


			@require_torch_gpu
			@require_ray
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self._run_finetune(gpus=1			,					distributed_retriever='ray'				)
				self.assertGreaterEqual(result['test'][0]['test_avg_em']			,					0.2				)







			@require_torch_multi_gpu
			@require_ray
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self._run_finetune(gpus=1			,					distributed_retriever='ray'				)
				self.assertGreaterEqual(result['test'][0]['test_avg_em']			,					0.2				)
