
'''simple docstring'''
import unittest

import numpy as np

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


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import CLIPImageProcessor






class      snake_case__    (	unittest.TestCase ):







		"""simple docstring"""

		def __init__(	self							:       Optional[int]   ,     UpperCamelCase__							:       Tuple   ,     UpperCamelCase__							:       List[Any]=7   ,     UpperCamelCase__							:       List[Any]=3   ,     UpperCamelCase__							:       Optional[Any]=18   ,     UpperCamelCase__							:       Optional[Any]=30   ,     UpperCamelCase__							:       Optional[Any]=400   ,     UpperCamelCase__							:       int=True   ,     UpperCamelCase__							:       List[str]=None   ,     UpperCamelCase__							:       Any=True   ,     UpperCamelCase__							:       Union[str, Any]=None   ,     UpperCamelCase__							:       List[str]=True   ,     UpperCamelCase__							:       int=[0.48_145_466, 0.4_578_275, 0.40_821_073]   ,     UpperCamelCase__							:       Optional[Any]=[0.26_862_954, 0.26_130_258, 0.27_577_711]   ,     UpperCamelCase__							:       Optional[Any]=True   ,     )		->       Tuple:






				"""simple docstring"""







				snake_case   :    List[str]			       =		size if size is not None else {'''shortest_edge''': 20}
				snake_case   :    int			       =		crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
				snake_case   :    Optional[int]			       =		parent
				snake_case   :    Optional[Any]			       =		batch_size
				snake_case   :    str			       =		num_channels
				snake_case   :    Tuple			       =		image_size
				snake_case   :    Optional[int]			       =		min_resolution
				snake_case   :    List[str]			       =		max_resolution
				snake_case   :    Optional[Any]			       =		do_resize
				snake_case   :    List[Any]			       =		size
				snake_case   :    int			       =		do_center_crop
				snake_case   :    str			       =		crop_size
				snake_case   :    Optional[int]			       =		do_normalize
				snake_case   :    Union[str, Any]			       =		image_mean
				snake_case   :    Tuple			       =		image_std
				snake_case   :    Any			       =		do_convert_rgb



		def 	lowerCAmelCase							(	self							:       List[str]	)		->       Dict:






				"""simple docstring"""







				return {
				    "do_resize": self.do_resize,
				    "size": self.size,
				    "do_center_crop": self.do_center_crop,
				    "crop_size": self.crop_size,
				    "do_normalize": self.do_normalize,
				    "image_mean": self.image_mean,
				    "image_std": self.image_std,
				    "do_convert_rgb": self.do_convert_rgb,
				}



		def 	lowerCAmelCase							(	self							:       Dict   ,     UpperCamelCase__							:       str=False   ,     UpperCamelCase__							:       Optional[int]=False   ,     UpperCamelCase__							:       Tuple=False	)		->       List[Any]:






				"""simple docstring"""







				assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

				if equal_resolution:
						snake_case   :    Any			       =		[]
						for i in range(self.batch_size	):
								image_inputs.append(
								    np.random.randint(
								        255   ,     size=(self.num_channels, self.max_resolution, self.max_resolution)   ,     dtype=np.uinta	)	)
				else:
						snake_case   :    Optional[Any]			       =		[]
						for i in range(self.batch_size	):
								snake_case       ,snake_case   :    Any			       =		np.random.choice(np.arange(self.min_resolution   ,     self.max_resolution	)   ,     2	)
								image_inputs.append(np.random.randint(255   ,     size=(self.num_channels, width, height)   ,     dtype=np.uinta	)	)

				if not numpify and not torchify:
						# PIL expects the channel dimension as last dimension
						snake_case   :    str			       =		[Image.fromarray(np.moveaxis(UpperCamelCase__   ,     0   ,     -1	)	) for x in image_inputs]

				if torchify:
						snake_case   :    str			       =		[torch.from_numpy(UpperCamelCase__	) for x in image_inputs]

				return image_inputs







@require_torch
@require_vision
class      snake_case__    (	__SCREAMING_SNAKE_CASE  , unittest.TestCase ):







		"""simple docstring"""

		lowerCamelCase							=		CLIPImageProcessor if is_vision_available() else None



		def 	lowerCAmelCase							(	self							:       List[str]	)		->       Dict:






				"""simple docstring"""







				snake_case   :    Any			       =		CLIPImageProcessingTester(self	)



		@property
		def 	lowerCAmelCase							(	self							:       str	)		->       Any:






				"""simple docstring"""







				return self.image_processor_tester.prepare_image_processor_dict()



		def 	lowerCAmelCase							(	self							:       int	)		->       Optional[int]:






				"""simple docstring"""







				snake_case   :    str			       =		self.image_processing_class(**self.image_processor_dict	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''do_resize'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''size'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''do_center_crop'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''center_crop'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''do_normalize'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''image_mean'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''image_std'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''do_convert_rgb'''	)	)



		def 	lowerCAmelCase							(	self							:       List[str]	)		->       Optional[int]:






				"""simple docstring"""







				snake_case   :    Dict			       =		self.image_processing_class.from_dict(self.image_processor_dict	)
				self.assertEqual(image_processor.size   ,     {'''shortest_edge''': 20}	)
				self.assertEqual(image_processor.crop_size   ,     {'''height''': 18, '''width''': 18}	)

				snake_case   :    Any			       =		self.image_processing_class.from_dict(self.image_processor_dict   ,     size=42   ,     crop_size=84	)
				self.assertEqual(image_processor.size   ,     {'''shortest_edge''': 42}	)
				self.assertEqual(image_processor.crop_size   ,     {'''height''': 84, '''width''': 84}	)



		def 	lowerCAmelCase							(	self							:       Optional[Any]	)		->       List[Any]:






				"""simple docstring"""







				pass



		def 	lowerCAmelCase							(	self							:       List[Any]	)		->       Dict:






				"""simple docstring"""







				snake_case   :    int			       =		self.image_processing_class(**self.image_processor_dict	)
				# create random PIL images
				snake_case   :    List[str]			       =		self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase__	)
				for image in image_inputs:
						self.assertIsInstance(UpperCamelCase__   ,     Image.Image	)

				# Test not batched input
				snake_case   :    Dict			       =		image_processing(image_inputs[0]   ,     return_tensors='''pt'''	).pixel_values
				self.assertEqual(
				    encoded_images.shape   ,     (
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size['''height'''],
				        self.image_processor_tester.crop_size['''width'''],
				    )   ,     )

				# Test batched
				snake_case   :    Optional[Any]			       =		image_processing(UpperCamelCase__   ,     return_tensors='''pt'''	).pixel_values
				self.assertEqual(
				    encoded_images.shape   ,     (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size['''height'''],
				        self.image_processor_tester.crop_size['''width'''],
				    )   ,     )



		def 	lowerCAmelCase							(	self							:       str	)		->       Dict:






				"""simple docstring"""







				snake_case   :    str			       =		self.image_processing_class(**self.image_processor_dict	)
				# create random numpy tensors
				snake_case   :    Any			       =		self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase__   ,     numpify=UpperCamelCase__	)
				for image in image_inputs:
						self.assertIsInstance(UpperCamelCase__   ,     np.ndarray	)

				# Test not batched input
				snake_case   :    List[str]			       =		image_processing(image_inputs[0]   ,     return_tensors='''pt'''	).pixel_values
				self.assertEqual(
				    encoded_images.shape   ,     (
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size['''height'''],
				        self.image_processor_tester.crop_size['''width'''],
				    )   ,     )

				# Test batched
				snake_case   :    List[str]			       =		image_processing(UpperCamelCase__   ,     return_tensors='''pt'''	).pixel_values
				self.assertEqual(
				    encoded_images.shape   ,     (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size['''height'''],
				        self.image_processor_tester.crop_size['''width'''],
				    )   ,     )



		def 	lowerCAmelCase							(	self							:       Optional[int]	)		->       Union[str, Any]:






				"""simple docstring"""







				snake_case   :    List[Any]			       =		self.image_processing_class(**self.image_processor_dict	)
				# create random PyTorch tensors
				snake_case   :    int			       =		self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase__   ,     torchify=UpperCamelCase__	)
				for image in image_inputs:
						self.assertIsInstance(UpperCamelCase__   ,     torch.Tensor	)

				# Test not batched input
				snake_case   :    str			       =		image_processing(image_inputs[0]   ,     return_tensors='''pt'''	).pixel_values
				self.assertEqual(
				    encoded_images.shape   ,     (
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size['''height'''],
				        self.image_processor_tester.crop_size['''width'''],
				    )   ,     )

				# Test batched
				snake_case   :    Dict			       =		image_processing(UpperCamelCase__   ,     return_tensors='''pt'''	).pixel_values
				self.assertEqual(
				    encoded_images.shape   ,     (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size['''height'''],
				        self.image_processor_tester.crop_size['''width'''],
				    )   ,     )







@require_torch
@require_vision
class      snake_case__    (	__SCREAMING_SNAKE_CASE  , unittest.TestCase ):







		"""simple docstring"""

		lowerCamelCase							=		CLIPImageProcessor if is_vision_available() else None



		def 	lowerCAmelCase							(	self							:       Tuple	)		->       Any:






				"""simple docstring"""







				snake_case   :    Optional[Any]			       =		CLIPImageProcessingTester(self   ,     num_channels=4	)
				snake_case   :    Optional[Any]			       =		3



		@property
		def 	lowerCAmelCase							(	self							:       Optional[int]	)		->       List[str]:






				"""simple docstring"""







				return self.image_processor_tester.prepare_image_processor_dict()



		def 	lowerCAmelCase							(	self							:       Optional[int]	)		->       Optional[int]:






				"""simple docstring"""







				snake_case   :    List[str]			       =		self.image_processing_class(**self.image_processor_dict	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''do_resize'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''size'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''do_center_crop'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''center_crop'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''do_normalize'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''image_mean'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''image_std'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''do_convert_rgb'''	)	)



		def 	lowerCAmelCase							(	self							:       List[Any]	)		->       int:






				"""simple docstring"""







				pass



		def 	lowerCAmelCase							(	self							:       Any	)		->       Dict:






				"""simple docstring"""







				snake_case   :    Optional[Any]			       =		self.image_processing_class(**self.image_processor_dict	)
				# create random PIL images
				snake_case   :    Any			       =		self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase__	)
				for image in image_inputs:
						self.assertIsInstance(UpperCamelCase__   ,     Image.Image	)

				# Test not batched input
				snake_case   :    List[Any]			       =		image_processing(image_inputs[0]   ,     return_tensors='''pt'''	).pixel_values
				self.assertEqual(
				    encoded_images.shape   ,     (
				        1,
				        self.expected_encoded_image_num_channels,
				        self.image_processor_tester.crop_size['''height'''],
				        self.image_processor_tester.crop_size['''width'''],
				    )   ,     )

				# Test batched
				snake_case   :    Dict			       =		image_processing(UpperCamelCase__   ,     return_tensors='''pt'''	).pixel_values
				self.assertEqual(
				    encoded_images.shape   ,     (
				        self.image_processor_tester.batch_size,
				        self.expected_encoded_image_num_channels,
				        self.image_processor_tester.crop_size['''height'''],
				        self.image_processor_tester.crop_size['''width'''],
				    )   ,     )





"""simple docstring"""




import textwrap

import pyarrow as pa
import pytest

from datasets import Features, Image
from datasets.packaged_modules.text.text import Text

from ..utils import require_pil
@pytest.fixture
def    __SCREAMING_SNAKE_CASE						(						lowercase__      ):
					"""simple docstring"""
					A			=					tmp_path / "text.txt"
					A			=					textwrap.dedent(
					    "\\n        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.\n        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.\n        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.\n        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.\n\n        Second paragraph:\n        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.\n        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.\n        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.\n        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.\n        "      )
					with open(lowercase__			,							"w"			,							encoding="utf-8"      ) as f:
										f.write(lowercase__      )
					return str(lowercase__      )
@pytest.fixture
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__      ):
					"""simple docstring"""
					A			=					tmp_path / "text_with_image.txt"
					with open(lowercase__			,							"w"			,							encoding="utf-8"      ) as f:
										f.write(lowercase__      )
					return str(lowercase__      )
@pytest.mark.parametrize("keep_linebreaks"			,							[True, False]      )
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__      ):
					"""simple docstring"""
					with open(lowercase__			,							encoding="utf-8"      ) as f:
										A			=					f.read().splitlines(keepends=lowercase__      )
					A			=					Text(keep_linebreaks=lowercase__			,							encoding="utf-8"      )
					A			=					text._generate_tables([[text_file]]      )
					A			=					pa.concat_tables([table for _, table in generator]      ).to_pydict()["text"]
					assert generated_content == expected_content
@require_pil
def    __SCREAMING_SNAKE_CASE						(						lowercase__      ):
					"""simple docstring"""
					with open(lowercase__			,							encoding="utf-8"      ) as f:
										A			=					f.read().splitlines()[0]
					A			=					Text(encoding="utf-8"			,							features=Features({"image": Image()}      )      )
					A			=					text._generate_tables([[text_file_with_image]]      )
					A			=					pa.concat_tables([table for _, table in generator]      )
					assert pa_table.schema.field("image"      ).type == Image()()
					A			=					pa_table.to_pydict()["image"]
					assert generated_content == [{"path": image_file, "bytes": None}]
@pytest.mark.parametrize("sample_by"			,							["line", "paragraph", "document"]      )
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__      ):
					"""simple docstring"""
					with open(lowercase__			,							encoding="utf-8"      ) as f:
										A			=					f.read()
					if sample_by == "line":
										A			=					expected_content.splitlines()
					elif sample_by == "paragraph":
										A			=					expected_content.split("\n\n"      )
					elif sample_by == "document":
										A			=					[expected_content]
					A			=					Text(sample_by=lowercase__			,							encoding="utf-8"			,							chunksize=100      )
					A			=					text._generate_tables([[text_file]]      )
					A			=					pa.concat_tables([table for _, table in generator]      ).to_pydict()["text"]
					assert generated_content == expected_content





