

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 		UpperCamelCase      (lowercase_:				List[Any]					,   lowercase_:				int					,   lowercase_:				int      )  ->   Optional[int]:
	return [
	    int(1000 * (box[0] / width)      ),
	    int(1000 * (box[1] / height)      ),
	    int(1000 * (box[2] / width)      ),
	    int(1000 * (box[3] / height)      ),
	]

def 		UpperCamelCase      (lowercase_:				np.ndarray					,   lowercase_:				Optional[str]					,   lowercase_:				Optional[str]      )  ->   Optional[Any]:
	A__							:					int       =	to_pil_image(__A      )
	A__							:					Tuple       =	pil_image.size
	A__							:					Optional[Any]       =	pytesseract.image_to_data(__A					,   lang=__A					,   output_type="""dict"""					,   config=__A      )
	A__							:					Optional[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(__A      ) if not word.strip()]
	A__							:					str       =	[word for idx, word in enumerate(__A      ) if idx not in irrelevant_indices]
	A__							:					Optional[int]       =	[coord for idx, coord in enumerate(__A      ) if idx not in irrelevant_indices]
	A__							:					List[Any]       =	[coord for idx, coord in enumerate(__A      ) if idx not in irrelevant_indices]
	A__							:					str       =	[coord for idx, coord in enumerate(__A      ) if idx not in irrelevant_indices]
	A__							:					int       =	[coord for idx, coord in enumerate(__A      ) if idx not in irrelevant_indices]

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

	# finally, normalize the bounding boxes
	A__							:					List[str]       =	[]
	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 					_a		(__A       ):


					'''simple docstring'''






					UpperCAmelCase__:     int													= ['''pixel_values''']






					def __init__(				self					, A__ = True					, A__ = None					, A__ = PILImageResampling.BILINEAR					, A__ = True					, A__ = 1 / 255					, A__ = True					, A__ = None					, A__ = None					, A__ = True					, A__ = None					, A__ = ""					, **A__					, ):
						super().__init__(**A__    )
						A__							:					Tuple       =	size if size is not None else {'''height''': 224, '''width''': 224}
						A__							:					Optional[Any]       =	get_size_dict(A__    )

						A__							:					Optional[Any]       =	do_resize
						A__							:					List[Any]       =	size
						A__							:					List[str]       =	resample
						A__							:					Dict       =	do_rescale
						A__							:					str       =	rescale_value
						A__							:					Optional[int]       =	do_normalize
						A__							:					Any       =	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						A__							:					Union[str, Any]       =	image_std if image_std is not None else IMAGENET_STANDARD_STD
						A__							:					List[Any]       =	apply_ocr
						A__							:					Union[str, Any]       =	ocr_lang
						A__							:					str       =	tesseract_config






					def 						__A    (				self					, A__					, A__					, A__ = PILImageResampling.BILINEAR					, A__ = None					, **A__					, ):
						A__							:					Tuple       =	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()}"""    )
						A__							:					Optional[Any]       =	(size['''height'''], size['''width'''])
						return resize(A__					, size=A__					, resample=A__					, data_format=A__					, **A__    )






					def 						__A    (				self					, A__					, A__					, A__ = None					, **A__					, ):
						return rescale(A__					, scale=A__					, data_format=A__					, **A__    )






					def 						__A    (				self					, A__					, A__					, A__					, A__ = None					, **A__					, ):
						return normalize(A__					, mean=A__					, std=A__					, data_format=A__					, **A__    )






					def 						__A    (				self					, A__					, A__ = None					, A__ = None					, A__=None					, A__ = None					, A__ = None					, A__ = None					, A__ = None					, A__ = None					, A__ = None					, A__ = None					, A__ = None					, A__ = None					, A__ = ChannelDimension.FIRST					, **A__					, ):
						A__							:					Optional[int]       =	do_resize if do_resize is not None else self.do_resize
						A__							:					Tuple       =	size if size is not None else self.size
						A__							:					Optional[int]       =	get_size_dict(A__    )
						A__							:					List[str]       =	resample if resample is not None else self.resample
						A__							:					List[Any]       =	do_rescale if do_rescale is not None else self.do_rescale
						A__							:					List[Any]       =	rescale_factor if rescale_factor is not None else self.rescale_factor
						A__							:					Optional[int]       =	do_normalize if do_normalize is not None else self.do_normalize
						A__							:					List[Any]       =	image_mean if image_mean is not None else self.image_mean
						A__							:					Optional[int]       =	image_std if image_std is not None else self.image_std
						A__							:					Any       =	apply_ocr if apply_ocr is not None else self.apply_ocr
						A__							:					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__							:					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."""    )

						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__							:					Tuple       =	[to_numpy_array(A__    ) for image in images]

						# Tesseract OCR to get words + normalized bounding boxes
						if apply_ocr:
							requires_backends(self					, """pytesseract"""    )
							A__							:					int       =	[]
							A__							:					Tuple       =	[]
							for image in images:
								A__							:					Dict       =	apply_tesseract(A__					, A__					, A__    )
								words_batch.append(A__    )
								boxes_batch.append(A__    )

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

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

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

						A__							:					Optional[Any]       =	[to_channel_dimension_format(A__					, A__    ) for image in images]

						A__							:					Dict       =	BatchFeature(data={"""pixel_values""": images}					, tensor_type=A__    )

						if apply_ocr:
							A__							:					int       =	words_batch
							A__							:					List[str]       =	boxes_batch
						return data






import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
	from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments



@require_torch
class 					_a		(unittest.TestCase       ):


					'''simple docstring'''






					def 						__A    (				self					, A__    ):
						for model_result in results.values():
							for batch_size, sequence_length in zip(model_result["""bs"""]					, model_result["""ss"""]    ):
								A__							:					str       =	model_result["""result"""][batch_size][sequence_length]
								self.assertIsNotNone(A__    )






					def 						__A    (				self    ):
						A__							:					Dict       =	"""sshleifer/tiny-gpt2"""
						A__							:					Tuple       =	PyTorchBenchmarkArguments(
						    models=[MODEL_ID]					, training=A__					, inference=A__					, sequence_lengths=[8]					, batch_sizes=[1]					, multi_process=A__					, )
						A__							:					int       =	PyTorchBenchmark(A__    )
						A__							:					List[Any]       =	benchmark.run()
						self.check_results_dict_not_empty(results.time_inference_result    )
						self.check_results_dict_not_empty(results.memory_inference_result    )






					def 						__A    (				self    ):
						A__							:					Dict       =	"""sgugger/tiny-distilbert-classification"""
						A__							:					Dict       =	PyTorchBenchmarkArguments(
						    models=[MODEL_ID]					, training=A__					, inference=A__					, sequence_lengths=[8]					, batch_sizes=[1]					, multi_process=A__					, only_pretrain_model=A__					, )
						A__							:					str       =	PyTorchBenchmark(A__    )
						A__							:					List[str]       =	benchmark.run()
						self.check_results_dict_not_empty(results.time_inference_result    )
						self.check_results_dict_not_empty(results.memory_inference_result    )






					def 						__A    (				self    ):
						A__							:					Any       =	"""sshleifer/tiny-gpt2"""
						A__							:					List[Any]       =	PyTorchBenchmarkArguments(
						    models=[MODEL_ID]					, training=A__					, inference=A__					, torchscript=A__					, sequence_lengths=[8]					, batch_sizes=[1]					, multi_process=A__					, )
						A__							:					Tuple       =	PyTorchBenchmark(A__    )
						A__							:					str       =	benchmark.run()
						self.check_results_dict_not_empty(results.time_inference_result    )
						self.check_results_dict_not_empty(results.memory_inference_result    )






					@unittest.skipIf(torch_device == """cpu"""					, """Cant do half precision"""    )
					def 						__A    (				self    ):
						A__							:					Optional[Any]       =	"""sshleifer/tiny-gpt2"""
						A__							:					Optional[Any]       =	PyTorchBenchmarkArguments(
						    models=[MODEL_ID]					, training=A__					, inference=A__					, fpaa=A__					, sequence_lengths=[8]					, batch_sizes=[1]					, multi_process=A__					, )
						A__							:					str       =	PyTorchBenchmark(A__    )
						A__							:					Any       =	benchmark.run()
						self.check_results_dict_not_empty(results.time_inference_result    )
						self.check_results_dict_not_empty(results.memory_inference_result    )






					def 						__A    (				self    ):
						A__							:					Optional[Any]       =	"""sshleifer/tiny-gpt2"""
						A__							:					Tuple       =	AutoConfig.from_pretrained(A__    )
						# set architectures equal to `None`
						A__							:					List[Any]       =	None
						A__							:					str       =	PyTorchBenchmarkArguments(
						    models=[MODEL_ID]					, training=A__					, inference=A__					, sequence_lengths=[8]					, batch_sizes=[1]					, multi_process=A__					, )
						A__							:					List[str]       =	PyTorchBenchmark(A__					, configs=[config]    )
						A__							:					Any       =	benchmark.run()
						self.check_results_dict_not_empty(results.time_inference_result    )
						self.check_results_dict_not_empty(results.memory_inference_result    )






					def 						__A    (				self    ):
						A__							:					Optional[int]       =	"""sshleifer/tiny-gpt2"""
						A__							:					Optional[int]       =	PyTorchBenchmarkArguments(
						    models=[MODEL_ID]					, training=A__					, inference=A__					, sequence_lengths=[8]					, batch_sizes=[1]					, multi_process=A__					, )
						A__							:					Any       =	PyTorchBenchmark(A__    )
						A__							:					Dict       =	benchmark.run()
						self.check_results_dict_not_empty(results.time_train_result    )
						self.check_results_dict_not_empty(results.memory_train_result    )






					@unittest.skipIf(torch_device == """cpu"""					, """Can't do half precision"""    )
					def 						__A    (				self    ):
						A__							:					Optional[int]       =	"""sshleifer/tiny-gpt2"""
						A__							:					List[str]       =	PyTorchBenchmarkArguments(
						    models=[MODEL_ID]					, training=A__					, inference=A__					, sequence_lengths=[8]					, batch_sizes=[1]					, fpaa=A__					, multi_process=A__					, )
						A__							:					Dict       =	PyTorchBenchmark(A__    )
						A__							:					Optional[int]       =	benchmark.run()
						self.check_results_dict_not_empty(results.time_train_result    )
						self.check_results_dict_not_empty(results.memory_train_result    )






					def 						__A    (				self    ):
						A__							:					int       =	"""sshleifer/tiny-gpt2"""
						A__							:					Optional[int]       =	AutoConfig.from_pretrained(A__    )
						A__							:					str       =	PyTorchBenchmarkArguments(
						    models=[MODEL_ID]					, training=A__					, inference=A__					, sequence_lengths=[8]					, batch_sizes=[1]					, multi_process=A__					, )
						A__							:					int       =	PyTorchBenchmark(A__					, configs=[config]    )
						A__							:					Tuple       =	benchmark.run()
						self.check_results_dict_not_empty(results.time_inference_result    )
						self.check_results_dict_not_empty(results.memory_inference_result    )






					def 						__A    (				self    ):
						A__							:					List[str]       =	"""sshleifer/tinier_bart"""
						A__							:					List[str]       =	AutoConfig.from_pretrained(A__    )
						A__							:					List[str]       =	PyTorchBenchmarkArguments(
						    models=[MODEL_ID]					, training=A__					, inference=A__					, sequence_lengths=[8]					, batch_sizes=[1]					, multi_process=A__					, )
						A__							:					Union[str, Any]       =	PyTorchBenchmark(A__					, configs=[config]    )
						A__							:					str       =	benchmark.run()
						self.check_results_dict_not_empty(results.time_inference_result    )
						self.check_results_dict_not_empty(results.memory_inference_result    )






					def 						__A    (				self    ):
						A__							:					Optional[int]       =	"""sshleifer/tiny-gpt2"""
						A__							:					Union[str, Any]       =	AutoConfig.from_pretrained(A__    )
						A__							:					Tuple       =	PyTorchBenchmarkArguments(
						    models=[MODEL_ID]					, training=A__					, inference=A__					, sequence_lengths=[8]					, batch_sizes=[1]					, multi_process=A__					, )
						A__							:					int       =	PyTorchBenchmark(A__					, configs=[config]    )
						A__							:					Tuple       =	benchmark.run()
						self.check_results_dict_not_empty(results.time_train_result    )
						self.check_results_dict_not_empty(results.memory_train_result    )






					def 						__A    (				self    ):
						A__							:					Dict       =	"""sshleifer/tinier_bart"""
						A__							:					int       =	AutoConfig.from_pretrained(A__    )
						A__							:					Union[str, Any]       =	PyTorchBenchmarkArguments(
						    models=[MODEL_ID]					, training=A__					, inference=A__					, sequence_lengths=[8]					, batch_sizes=[1]					, multi_process=A__					, )
						A__							:					List[Any]       =	PyTorchBenchmark(A__					, configs=[config]    )
						A__							:					Optional[Any]       =	benchmark.run()
						self.check_results_dict_not_empty(results.time_train_result    )
						self.check_results_dict_not_empty(results.memory_train_result    )






					def 						__A    (				self    ):
						A__							:					int       =	"""sshleifer/tiny-gpt2"""
						with tempfile.TemporaryDirectory() as tmp_dir:
							A__							:					Dict       =	PyTorchBenchmarkArguments(
							    models=[MODEL_ID]					, training=A__					, inference=A__					, save_to_csv=A__					, sequence_lengths=[8]					, batch_sizes=[1]					, inference_time_csv_file=os.path.join(A__					, """inf_time.csv"""    )					, train_memory_csv_file=os.path.join(A__					, """train_mem.csv"""    )					, inference_memory_csv_file=os.path.join(A__					, """inf_mem.csv"""    )					, train_time_csv_file=os.path.join(A__					, """train_time.csv"""    )					, env_info_csv_file=os.path.join(A__					, """env.csv"""    )					, multi_process=A__					, )
							A__							:					Optional[Any]       =	PyTorchBenchmark(A__    )
							benchmark.run()
							self.assertTrue(Path(os.path.join(A__					, """inf_time.csv"""    )    ).exists()    )
							self.assertTrue(Path(os.path.join(A__					, """train_time.csv"""    )    ).exists()    )
							self.assertTrue(Path(os.path.join(A__					, """inf_mem.csv"""    )    ).exists()    )
							self.assertTrue(Path(os.path.join(A__					, """train_mem.csv"""    )    ).exists()    )
							self.assertTrue(Path(os.path.join(A__					, """env.csv"""    )    ).exists()    )






					def 						__A    (				self    ):
						A__							:					Optional[int]       =	"""sshleifer/tiny-gpt2"""

						def _check_summary_is_not_empty(A__    ):
							self.assertTrue(hasattr(A__					, """sequential"""    )    )
							self.assertTrue(hasattr(A__					, """cumulative"""    )    )
							self.assertTrue(hasattr(A__					, """current"""    )    )
							self.assertTrue(hasattr(A__					, """total"""    )    )

						with tempfile.TemporaryDirectory() as tmp_dir:
							A__							:					Dict       =	PyTorchBenchmarkArguments(
							    models=[MODEL_ID]					, training=A__					, inference=A__					, sequence_lengths=[8]					, batch_sizes=[1]					, log_filename=os.path.join(A__					, """log.txt"""    )					, log_print=A__					, trace_memory_line_by_line=A__					, multi_process=A__					, )
							A__							:					Dict       =	PyTorchBenchmark(A__    )
							A__							:					str       =	benchmark.run()
							_check_summary_is_not_empty(result.inference_summary    )
							_check_summary_is_not_empty(result.train_summary    )
							self.assertTrue(Path(os.path.join(A__					, """log.txt"""    )    ).exists()    )



