

import unittest

import numpy as np
import requests

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

	from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
	lowerCAmelCase__		      =       False

if is_vision_available():
	from PIL import Image

	from transformers import PixaStructImageProcessor

class     a__  (    unittest.TestCase	):
			"""simple docstring"""







			def __init__(			self					,					lowercase					,					lowercase=7					,					lowercase=3					,					lowercase=18					,					lowercase=30					,					lowercase=400					,					lowercase=None					,					lowercase=True					,					lowercase=True					,					lowercase=None					,					)     ->  str:
							'''simple docstring'''






							A__						=	size if size is not None else {"height": 20, "width": 20}
							A__						=	parent
							A__						=	batch_size
							A__						=	num_channels
							A__						=	image_size
							A__						=	min_resolution
							A__						=	max_resolution
							A__						=	size
							A__						=	do_normalize
							A__						=	do_convert_rgb
							A__						=	[512, 1024, 2048, 4096]
							A__						=	patch_size if patch_size is not None else {"height": 16, "width": 16}


			def    UpperCamelCase						(			self				)     ->  Tuple:
							'''simple docstring'''






							return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}


			def    UpperCamelCase						(			self				)     ->  Dict:
							'''simple docstring'''






							A__						=	"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
							A__						=	Image.open(requests.get(lowercase					,					stream=lowercase				).raw				).convert("RGB"				)
							return raw_image

@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11							,		reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'							,		)
@require_torch
@require_vision
class     a__  (    lowercase_							,		unittest.TestCase	):
			"""simple docstring"""







			__lowerCamelCase       				=				PixaStructImageProcessor if is_vision_available() else None


			def    UpperCamelCase						(			self				)     ->  Optional[int]:
							'''simple docstring'''






							A__						=	PixaStructImageProcessingTester(self				)


			@property
			def    UpperCamelCase						(			self				)     ->  List[str]:
							'''simple docstring'''






							return self.image_processor_tester.prepare_image_processor_dict()


			def    UpperCamelCase						(			self				)     ->  str:
							'''simple docstring'''






							A__						=	self.image_processing_class(**self.image_processor_dict				)
							self.assertTrue(hasattr(lowercase					,					"do_normalize"				)				)
							self.assertTrue(hasattr(lowercase					,					"do_convert_rgb"				)				)


			def    UpperCamelCase						(			self				)     ->  str:
							'''simple docstring'''






							A__						=	self.image_processor_tester.prepare_dummy_image()

							A__						=	self.image_processing_class(**self.image_processor_dict				)
							A__						=	2048

							A__						=	image_processor(lowercase					,					return_tensors="pt"					,					max_patches=lowercase				)
							self.assertTrue(torch.allclose(inputs.flattened_patches.mean()					,					torch.tensor(0.0606				)					,					atol=1e-3					,					rtol=1e-3				)				)


			def    UpperCamelCase						(			self				)     ->  str:
							'''simple docstring'''






							A__						=	self.image_processing_class(**self.image_processor_dict				)
							# create random PIL images
							A__						=	prepare_image_inputs(self.image_processor_tester					,					equal_resolution=lowercase				)
							for image in image_inputs:
											self.assertIsInstance(lowercase					,					Image.Image				)

							# Test not batched input
							A__						=	(
							    (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
							    * self.image_processor_tester.num_channels
							) + 2

							for max_patch in self.image_processor_tester.max_patches:
											# Test not batched input
											A__						=	image_processor(
											    image_inputs[0]					,					return_tensors="pt"					,					max_patches=lowercase				).flattened_patches
											self.assertEqual(
											    encoded_images.shape					,					(1, max_patch, expected_hidden_dim)					,					)

											# Test batched
											A__						=	image_processor(
											    lowercase					,					return_tensors="pt"					,					max_patches=lowercase				).flattened_patches
											self.assertEqual(
											    encoded_images.shape					,					(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)					,					)


			def    UpperCamelCase						(			self				)     ->  str:
							'''simple docstring'''






							A__						=	self.image_processing_class(**self.image_processor_dict				)
							# create random PIL images
							A__						=	prepare_image_inputs(self.image_processor_tester					,					equal_resolution=lowercase				)
							for image in image_inputs:
											self.assertIsInstance(lowercase					,					Image.Image				)

							# Test not batched input
							A__						=	(
							    (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
							    * self.image_processor_tester.num_channels
							) + 2

							A__						=	True

							for max_patch in self.image_processor_tester.max_patches:
											# Test not batched input
											with self.assertRaises(lowercase				):
															A__						=	image_processor(
															    image_inputs[0]					,					return_tensors="pt"					,					max_patches=lowercase				).flattened_patches

											A__						=	"Hello"

											A__						=	image_processor(
											    image_inputs[0]					,					return_tensors="pt"					,					max_patches=lowercase					,					header_text=lowercase				).flattened_patches
											self.assertEqual(
											    encoded_images.shape					,					(1, max_patch, expected_hidden_dim)					,					)

											# Test batched
											A__						=	image_processor(
											    lowercase					,					return_tensors="pt"					,					max_patches=lowercase					,					header_text=lowercase				).flattened_patches
											self.assertEqual(
											    encoded_images.shape					,					(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)					,					)


			def    UpperCamelCase						(			self				)     ->  Any:
							'''simple docstring'''






							A__						=	self.image_processing_class(**self.image_processor_dict				)
							# create random numpy tensors
							A__						=	prepare_image_inputs(self.image_processor_tester					,					equal_resolution=lowercase					,					numpify=lowercase				)
							for image in image_inputs:
											self.assertIsInstance(lowercase					,					np.ndarray				)

							A__						=	(
							    (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
							    * self.image_processor_tester.num_channels
							) + 2

							for max_patch in self.image_processor_tester.max_patches:
											# Test not batched input
											A__						=	image_processor(
											    image_inputs[0]					,					return_tensors="pt"					,					max_patches=lowercase				).flattened_patches
											self.assertEqual(
											    encoded_images.shape					,					(1, max_patch, expected_hidden_dim)					,					)

											# Test batched
											A__						=	image_processor(
											    lowercase					,					return_tensors="pt"					,					max_patches=lowercase				).flattened_patches
											self.assertEqual(
											    encoded_images.shape					,					(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)					,					)


			def    UpperCamelCase						(			self				)     ->  Union[str, Any]:
							'''simple docstring'''






							A__						=	self.image_processing_class(**self.image_processor_dict				)
							# create random PyTorch tensors
							A__						=	prepare_image_inputs(self.image_processor_tester					,					equal_resolution=lowercase					,					torchify=lowercase				)
							for image in image_inputs:
											self.assertIsInstance(lowercase					,					torch.Tensor				)

							# Test not batched input
							A__						=	(
							    (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
							    * self.image_processor_tester.num_channels
							) + 2

							for max_patch in self.image_processor_tester.max_patches:
											# Test not batched input
											A__						=	image_processor(
											    image_inputs[0]					,					return_tensors="pt"					,					max_patches=lowercase				).flattened_patches
											self.assertEqual(
											    encoded_images.shape					,					(1, max_patch, expected_hidden_dim)					,					)

											# Test batched
											A__						=	image_processor(
											    lowercase					,					return_tensors="pt"					,					max_patches=lowercase				).flattened_patches
											self.assertEqual(
											    encoded_images.shape					,					(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)					,					)

@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11							,		reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'							,		)
@require_torch
@require_vision
class     a__  (    lowercase_							,		unittest.TestCase	):
			"""simple docstring"""







			__lowerCamelCase       				=				PixaStructImageProcessor if is_vision_available() else None


			def    UpperCamelCase						(			self				)     ->  Dict:
							'''simple docstring'''






							A__						=	PixaStructImageProcessingTester(self					,					num_channels=4				)
							A__						=	3


			@property
			def    UpperCamelCase						(			self				)     ->  List[Any]:
							'''simple docstring'''






							return self.image_processor_tester.prepare_image_processor_dict()


			def    UpperCamelCase						(			self				)     ->  Optional[int]:
							'''simple docstring'''






							A__						=	self.image_processing_class(**self.image_processor_dict				)
							self.assertTrue(hasattr(lowercase					,					"do_normalize"				)				)
							self.assertTrue(hasattr(lowercase					,					"do_convert_rgb"				)				)


			def    UpperCamelCase						(			self				)     ->  List[Any]:
							'''simple docstring'''






							A__						=	self.image_processing_class(**self.image_processor_dict				)
							# create random PIL images
							A__						=	prepare_image_inputs(self.image_processor_tester					,					equal_resolution=lowercase				)
							for image in image_inputs:
											self.assertIsInstance(lowercase					,					Image.Image				)

							# Test not batched input
							A__						=	(
							    (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
							    * (self.image_processor_tester.num_channels - 1)
							) + 2

							for max_patch in self.image_processor_tester.max_patches:
											# Test not batched input
											A__						=	image_processor(
											    image_inputs[0]					,					return_tensors="pt"					,					max_patches=lowercase				).flattened_patches
											self.assertEqual(
											    encoded_images.shape					,					(1, max_patch, expected_hidden_dim)					,					)

											# Test batched
											A__						=	image_processor(
											    lowercase					,					return_tensors="pt"					,					max_patches=lowercase				).flattened_patches
											self.assertEqual(
											    encoded_images.shape					,					(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)					,					)




import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_UpperCAmelCase		:   Optional[int]										=       5_0000
_UpperCAmelCase		:   Dict										=       5000

_UpperCAmelCase				,						_UpperCAmelCase		:   Optional[int]										=       os.path.split(__file__)
_UpperCAmelCase		:   List[str]										=       os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))





@get_duration
def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							for i in range(UpperCamelCase__      ):
														snake_case_							=    dataset[i]





@get_duration
def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							for i in range(0   ,							len(UpperCamelCase__      )   ,							UpperCamelCase__      ):
														snake_case_							=    dataset[i : i + batch_size]





@get_duration
def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							with dataset.formatted_as(type=UpperCamelCase__      ):
														for i in range(UpperCamelCase__      ):
																					snake_case_							=    dataset[i]





@get_duration
def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							with dataset.formatted_as(type=UpperCamelCase__      ):
														for i in range(0   ,							UpperCamelCase__   ,							UpperCamelCase__      ):
																					snake_case_							=    dataset[i : i + batch_size]





def 					__lowerCamelCase	( ):







							'''simple docstring'''






							snake_case_							=    {'num examples': SPEED_TEST_N_EXAMPLES}
							snake_case_							=    [
							    (read, {'length': SMALL_TEST}),
							    (read, {'length': SPEED_TEST_N_EXAMPLES}),
							    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
							    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 100}),
							    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1000}),
							    (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
							    (read_formatted, {'type': 'pandas', 'length': SMALL_TEST}),
							    (read_formatted, {'type': 'torch', 'length': SMALL_TEST}),
							    (read_formatted, {'type': 'tensorflow', 'length': SMALL_TEST}),
							    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
							    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 1000}),
							]

							snake_case_							=    [
							    (read, {'length': SMALL_TEST}),
							    (read, {'length': SPEED_TEST_N_EXAMPLES}),
							    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
							    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 100}),
							    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1000}),
							    (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
							    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
							    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 1000}),
							]
							with tempfile.TemporaryDirectory() as tmp_dir:
														print('generating dataset'      )
														snake_case_							=    datasets.Features(
														    {'list': datasets.Sequence(datasets.Value('float32'      )      ), 'numbers': datasets.Value('float32'      )}      )
														snake_case_							=    generate_example_dataset(
														    os.path.join(UpperCamelCase__   ,							'dataset.arrow'      )   ,							UpperCamelCase__   ,							num_examples=UpperCamelCase__   ,							seq_shapes={'list': (100,)}   ,							)
														print('first set of iterations'      )
														for func, kwargs in functions:
																					print(func.__name__   ,							str(UpperCamelCase__      )      )
																					snake_case_							=    func(UpperCamelCase__   ,							**UpperCamelCase__      )

														print('shuffling dataset'      )
														snake_case_							=    dataset.shuffle()
														print('Second set of iterations (after shuffling'      )
														for func, kwargs in functions_shuffled:
																					print('shuffled '   ,							func.__name__   ,							str(UpperCamelCase__      )      )
																					snake_case_							=    func(
																					    UpperCamelCase__   ,							**UpperCamelCase__      )

							with open(UpperCamelCase__   ,							'wb'      ) as f:
														f.write(json.dumps(UpperCamelCase__      ).encode('utf-8'      )      )


if __name__ == "__main__":  # useful to run the profiler
					benchmark_iterating()




