
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:
			__A				=False

if is_vision_available():
			from PIL import Image

			from transformers import PixaStructImageProcessor




class     _SCREAMING_SNAKE_CASE     (      unittest.TestCase						):



	def __init__(    self						,  lowercase						,  lowercase=7						,  lowercase=3						,  lowercase=18						,  lowercase=30						,  lowercase=400						,  lowercase=None						,  lowercase=True						,  lowercase=True						,  lowercase=None						,  )				->       Any:
					lowerCamelCase_						=			size if size is not None else {"height": 20, "width": 20}
					lowerCamelCase_						=			parent
					lowerCamelCase_						=			batch_size
					lowerCamelCase_						=			num_channels
					lowerCamelCase_						=			image_size
					lowerCamelCase_						=			min_resolution
					lowerCamelCase_						=			max_resolution
					lowerCamelCase_						=			size
					lowerCamelCase_						=			do_normalize
					lowerCamelCase_						=			do_convert_rgb
					lowerCamelCase_						=			[512, 1024, 2048, 4096]
					lowerCamelCase_						=			patch_size if patch_size is not None else {"height": 16, "width": 16}



	def     SCREAMING_SNAKE_CASE_(    self )				->       str:
					return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[int]:
					lowerCamelCase_						=			"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
					lowerCamelCase_						=			Image.open(requests.get(lowerCAmelCase__						,  stream=lowerCAmelCase__ ).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     _SCREAMING_SNAKE_CASE     (      lowerCamelCase_			,       unittest.TestCase						):
	lowerCAmelCase__         = PixaStructImageProcessor if is_vision_available() else None



	def     SCREAMING_SNAKE_CASE_(    self )				->       Any:
					lowerCamelCase_						=			PixaStructImageProcessingTester(self )



	@property
	def     SCREAMING_SNAKE_CASE_(    self )				->       Tuple:
					return self.image_processor_tester.prepare_image_processor_dict()



	def     SCREAMING_SNAKE_CASE_(    self )				->       Union[str, Any]:
					lowerCamelCase_						=			self.image_processing_class(**self.image_processor_dict )
					self.assertTrue(hasattr(lowerCAmelCase__						,  "do_normalize" ) )
					self.assertTrue(hasattr(lowerCAmelCase__						,  "do_convert_rgb" ) )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Dict:
					lowerCamelCase_						=			self.image_processor_tester.prepare_dummy_image()

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

					lowerCamelCase_						=			image_processor(lowerCAmelCase__						,  return_tensors="pt"						,  max_patches=lowerCAmelCase__ )
					self.assertTrue(torch.allclose(inputs.flattened_patches.mean()						,  torch.tensor(0.0_6_0_6 )						,  atol=1e-3						,  rtol=1e-3 ) )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Union[str, Any]:
					# Initialize image_processor
					lowerCamelCase_						=			self.image_processing_class(**self.image_processor_dict )
					# create random PIL images
					lowerCamelCase_						=			prepare_image_inputs(self.image_processor_tester						,  equal_resolution=lowerCAmelCase__ )
					for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase__						,  Image.Image )

					# Test not batched input
					lowerCamelCase_						=			(
					    (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
									lowerCamelCase_						=			image_processor(
									    image_inputs[0]						,  return_tensors="pt"						,  max_patches=lowerCAmelCase__ ).flattened_patches
									self.assertEqual(
									    encoded_images.shape						,  (1, max_patch, expected_hidden_dim)						,  )

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



	def     SCREAMING_SNAKE_CASE_(    self )				->       Any:
					# Initialize image_processor
					lowerCamelCase_						=			self.image_processing_class(**self.image_processor_dict )
					# create random PIL images
					lowerCamelCase_						=			prepare_image_inputs(self.image_processor_tester						,  equal_resolution=lowerCAmelCase__ )
					for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase__						,  Image.Image )

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

					lowerCamelCase_						=			True

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

									lowerCamelCase_						=			"Hello"

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

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



	def     SCREAMING_SNAKE_CASE_(    self )				->       Tuple:
					# Initialize image_processor
					lowerCamelCase_						=			self.image_processing_class(**self.image_processor_dict )
					# create random numpy tensors
					lowerCamelCase_						=			prepare_image_inputs(self.image_processor_tester						,  equal_resolution=lowerCAmelCase__						,  numpify=lowerCAmelCase__ )
					for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase__						,  np.ndarray )

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

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



	def     SCREAMING_SNAKE_CASE_(    self )				->       Any:
					# Initialize image_processor
					lowerCamelCase_						=			self.image_processing_class(**self.image_processor_dict )
					# create random PyTorch tensors
					lowerCamelCase_						=			prepare_image_inputs(self.image_processor_tester						,  equal_resolution=lowerCAmelCase__						,  torchify=lowerCAmelCase__ )
					for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase__						,  torch.Tensor )

					# Test not batched input
					lowerCamelCase_						=			(
					    (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
									lowerCamelCase_						=			image_processor(
									    image_inputs[0]						,  return_tensors="pt"						,  max_patches=lowerCAmelCase__ ).flattened_patches
									self.assertEqual(
									    encoded_images.shape						,  (1, max_patch, expected_hidden_dim)						,  )

									# Test batched
									lowerCamelCase_						=			image_processor(
									    lowerCAmelCase__						,  return_tensors="pt"						,  max_patches=lowerCAmelCase__ ).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     _SCREAMING_SNAKE_CASE     (      lowerCamelCase_			,       unittest.TestCase						):
	lowerCAmelCase__         = PixaStructImageProcessor if is_vision_available() else None



	def     SCREAMING_SNAKE_CASE_(    self )				->       Dict:
					lowerCamelCase_						=			PixaStructImageProcessingTester(self						,  num_channels=4 )
					lowerCamelCase_						=			3



	@property
	def     SCREAMING_SNAKE_CASE_(    self )				->       List[str]:
					return self.image_processor_tester.prepare_image_processor_dict()



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[int]:
					lowerCamelCase_						=			self.image_processing_class(**self.image_processor_dict )
					self.assertTrue(hasattr(lowerCAmelCase__						,  "do_normalize" ) )
					self.assertTrue(hasattr(lowerCAmelCase__						,  "do_convert_rgb" ) )



	def     SCREAMING_SNAKE_CASE_(    self )				->       int:
					# Initialize image_processor
					lowerCamelCase_						=			self.image_processing_class(**self.image_processor_dict )
					# create random PIL images
					lowerCamelCase_						=			prepare_image_inputs(self.image_processor_tester						,  equal_resolution=lowerCAmelCase__ )
					for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase__						,  Image.Image )

					# Test not batched input
					lowerCamelCase_						=			(
					    (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
									lowerCamelCase_						=			image_processor(
									    image_inputs[0]						,  return_tensors="pt"						,  max_patches=lowerCAmelCase__ ).flattened_patches
									self.assertEqual(
									    encoded_images.shape						,  (1, max_patch, expected_hidden_dim)						,  )

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






"""simple docstring"""




import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch

import datasets
import datasets.config

from .utils import require_beam
class 					lowerCAmelCase	(					datasets.BeamBasedBuilder    ):




		'''simple docstring'''







		def 							__A						(	self				)     ->							Dict:
								return datasets.DatasetInfo(
								    features=datasets.Features({'content': datasets.Value('string'				)}				)						,       supervised_keys=lowerCAmelCase__						,       )







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							str:
								return [datasets.SplitGenerator(name=datasets.Split.TRAIN						,       gen_kwargs={'examples': get_test_dummy_examples()}				)]







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							Optional[int]:
								import apache_beam as beam

								return pipeline | "Load Examples" >> beam.Create(lowerCAmelCase__				)





class 					lowerCAmelCase	(					datasets.BeamBasedBuilder    ):




		'''simple docstring'''







		def 							__A						(	self				)     ->							int:
								return datasets.DatasetInfo(
								    features=datasets.Features({'a': datasets.Sequence({'b': datasets.Value('string'				)}				)}				)						,       supervised_keys=lowerCAmelCase__						,       )







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							Dict:
								return [
								    datasets.SplitGenerator(name=datasets.Split.TRAIN						,       gen_kwargs={'examples': get_test_nested_examples()}				)
								]







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							Optional[Any]:
								import apache_beam as beam

								return pipeline | "Load Examples" >> beam.Create(lowerCAmelCase__				)





def     lowercase			()   ->						str:
						return [(i, {"content": content}) for i, content in enumerate(['foo', 'bar', 'foobar']  )]


def     lowercase			()   ->						Optional[Any]:
						return [(i, {"a": {"b": [content]}}) for i, content in enumerate(['foo', 'bar', 'foobar']  )]


class 					lowerCAmelCase	(					lowerCamelCase_    ):




		'''simple docstring'''







		@require_beam
		def 							__A						(	self				)     ->							Optional[int]:
								SCREAMING_SNAKE_CASE													=					len(get_test_dummy_examples()				)
								with tempfile.TemporaryDirectory() as tmp_cache_dir:
														SCREAMING_SNAKE_CASE													=					DummyBeamDataset(cache_dir=lowerCAmelCase__						,       beam_runner='DirectRunner'				)
														builder.download_and_prepare()
														self.assertTrue(
														    os.path.exists(
														        os.path.join(lowerCAmelCase__						,       builder.name						,       'default'						,       '0.0.0'						,       F'{builder.name}-train.arrow'				)				)				)
														self.assertDictEqual(builder.info.features						,       datasets.Features({'content': datasets.Value('string'				)}				)				)
														SCREAMING_SNAKE_CASE													=					builder.as_dataset()
														self.assertEqual(dset['train'].num_rows						,       lowerCAmelCase__				)
														self.assertEqual(dset['train'].info.splits['train'].num_examples						,       lowerCAmelCase__				)
														self.assertDictEqual(dset['train'][0]						,       get_test_dummy_examples()[0][1]				)
														self.assertDictEqual(
														    dset['train'][expected_num_examples - 1]						,       get_test_dummy_examples()[expected_num_examples - 1][1]				)
														self.assertTrue(
														    os.path.exists(os.path.join(lowerCAmelCase__						,       builder.name						,       'default'						,       '0.0.0'						,       'dataset_info.json'				)				)				)
														del dset







		@require_beam
		def 							__A						(	self				)     ->							int:
								import apache_beam as beam

								SCREAMING_SNAKE_CASE													=					beam.io.parquetio.WriteToParquet

								SCREAMING_SNAKE_CASE													=					len(get_test_dummy_examples()				)
								with tempfile.TemporaryDirectory() as tmp_cache_dir:
														SCREAMING_SNAKE_CASE													=					DummyBeamDataset(cache_dir=lowerCAmelCase__						,       beam_runner='DirectRunner'				)
														with patch('apache_beam.io.parquetio.WriteToParquet'				) as write_parquet_mock:
																				SCREAMING_SNAKE_CASE													=					partial(lowerCAmelCase__						,       num_shards=2				)
																				builder.download_and_prepare()
														self.assertTrue(
														    os.path.exists(
														        os.path.join(
														            lowerCAmelCase__						,       builder.name						,       'default'						,       '0.0.0'						,       F'{builder.name}-train-00000-of-00002.arrow'				)				)				)
														self.assertTrue(
														    os.path.exists(
														        os.path.join(
														            lowerCAmelCase__						,       builder.name						,       'default'						,       '0.0.0'						,       F'{builder.name}-train-00000-of-00002.arrow'				)				)				)
														self.assertDictEqual(builder.info.features						,       datasets.Features({'content': datasets.Value('string'				)}				)				)
														SCREAMING_SNAKE_CASE													=					builder.as_dataset()
														self.assertEqual(dset['train'].num_rows						,       lowerCAmelCase__				)
														self.assertEqual(dset['train'].info.splits['train'].num_examples						,       lowerCAmelCase__				)
														# Order is not preserved when sharding, so we just check that all the elements are there
														self.assertListEqual(sorted(dset['train']['content']				)						,       sorted(['foo', 'bar', 'foobar']				)				)
														self.assertTrue(
														    os.path.exists(os.path.join(lowerCAmelCase__						,       builder.name						,       'default'						,       '0.0.0'						,       'dataset_info.json'				)				)				)
														del dset







		@require_beam
		def 							__A						(	self				)     ->							Optional[Any]:
								with tempfile.TemporaryDirectory() as tmp_cache_dir:
														SCREAMING_SNAKE_CASE													=					DummyBeamDataset(cache_dir=lowerCAmelCase__				)
														self.assertRaises(datasets.builder.MissingBeamOptions						,       builder.download_and_prepare				)







		@require_beam
		def 							__A						(	self				)     ->							int:
								SCREAMING_SNAKE_CASE													=					len(get_test_nested_examples()				)
								with tempfile.TemporaryDirectory() as tmp_cache_dir:
														SCREAMING_SNAKE_CASE													=					NestedBeamDataset(cache_dir=lowerCAmelCase__						,       beam_runner='DirectRunner'				)
														builder.download_and_prepare()
														self.assertTrue(
														    os.path.exists(
														        os.path.join(lowerCAmelCase__						,       builder.name						,       'default'						,       '0.0.0'						,       F'{builder.name}-train.arrow'				)				)				)
														self.assertDictEqual(
														    builder.info.features						,       datasets.Features({'a': datasets.Sequence({'b': datasets.Value('string'				)}				)}				)				)
														SCREAMING_SNAKE_CASE													=					builder.as_dataset()
														self.assertEqual(dset['train'].num_rows						,       lowerCAmelCase__				)
														self.assertEqual(dset['train'].info.splits['train'].num_examples						,       lowerCAmelCase__				)
														self.assertDictEqual(dset['train'][0]						,       get_test_nested_examples()[0][1]				)
														self.assertDictEqual(
														    dset['train'][expected_num_examples - 1]						,       get_test_nested_examples()[expected_num_examples - 1][1]				)
														self.assertTrue(
														    os.path.exists(os.path.join(lowerCAmelCase__						,       builder.name						,       'default'						,       '0.0.0'						,       'dataset_info.json'				)				)				)
														del dset


