
'''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, prepare_image_inputs


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import PoolFormerImageProcessor


class      _lowerCAmelCase       (       unittest.TestCase ):







							'''simple docstring'''

							def __init__(self    ,    UpperCAmelCase    ,    UpperCAmelCase=7    ,    UpperCAmelCase=3    ,    UpperCAmelCase=30    ,    UpperCAmelCase=400    ,    UpperCAmelCase=True    ,    UpperCAmelCase=None    ,    UpperCAmelCase=0.9    ,    UpperCAmelCase=None    ,    UpperCAmelCase=True    ,    UpperCAmelCase=[0.5, 0.5, 0.5]    ,    UpperCAmelCase=[0.5, 0.5, 0.5]    ,    )	->  int:
														_snake_case  =    size if size is not None else {"""shortest_edge""": 30}
														_snake_case  =    crop_size if crop_size is not None else {"""height""": 30, """width""": 30}
														_snake_case  =    parent
														_snake_case  =    batch_size
														_snake_case  =    num_channels
														_snake_case  =    min_resolution
														_snake_case  =    max_resolution
														_snake_case  =    do_resize_and_center_crop
														_snake_case  =    size
														_snake_case  =    crop_pct
														_snake_case  =    crop_size
														_snake_case  =    do_normalize
														_snake_case  =    image_mean
														_snake_case  =    image_std





							def 			lowercase				(self    )	->  Union[str, Any]:
														return {
														    "size": self.size,
														    "do_resize_and_center_crop": self.do_resize_and_center_crop,
														    "crop_pct": self.crop_pct,
														    "crop_size": self.crop_size,
														    "do_normalize": self.do_normalize,
														    "image_mean": self.image_mean,
														    "image_std": self.image_std,
														}


@require_torch
@require_vision
class      _lowerCAmelCase       (       __lowercase	,				unittest.TestCase ):







							'''simple docstring'''

							lowerCAmelCase_   		=		PoolFormerImageProcessor if is_vision_available() else None

							def 			lowercase				(self    )	->  List[str]:
														_snake_case  =    PoolFormerImageProcessingTester(self    )

							@property
							def 			lowercase				(self    )	->  Optional[Any]:
														return self.image_processor_tester.prepare_image_processor_dict()

							def 			lowercase				(self    )	->  Dict:
														_snake_case  =    self.image_processing_class(**self.image_processor_dict    )
														self.assertTrue(hasattr(UpperCAmelCase    ,    """do_resize_and_center_crop"""    )    )
														self.assertTrue(hasattr(UpperCAmelCase    ,    """size"""    )    )
														self.assertTrue(hasattr(UpperCAmelCase    ,    """crop_pct"""    )    )
														self.assertTrue(hasattr(UpperCAmelCase    ,    """do_normalize"""    )    )
														self.assertTrue(hasattr(UpperCAmelCase    ,    """image_mean"""    )    )
														self.assertTrue(hasattr(UpperCAmelCase    ,    """image_std"""    )    )

							def 			lowercase				(self    )	->  Union[str, Any]:
														_snake_case  =    self.image_processing_class.from_dict(self.image_processor_dict    )
														self.assertEqual(image_processor.size    ,    {"""shortest_edge""": 30}    )
														self.assertEqual(image_processor.crop_size    ,    {"""height""": 30, """width""": 30}    )

														_snake_case  =    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 			lowercase				(self    )	->  List[str]:
														pass

							def 			lowercase				(self    )	->  Dict:
														_snake_case  =    self.image_processing_class(**self.image_processor_dict    )
														# create random PIL images
														_snake_case  =    prepare_image_inputs(self.image_processor_tester    ,    equal_resolution=UpperCAmelCase    )
														for image in image_inputs:
																					self.assertIsInstance(UpperCAmelCase    ,    Image.Image    )

														# Test not batched input
														_snake_case  =    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  =    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 			lowercase				(self    )	->  Optional[Any]:
														_snake_case  =    self.image_processing_class(**self.image_processor_dict    )
														# create random numpy tensors
														_snake_case  =    prepare_image_inputs(self.image_processor_tester    ,    equal_resolution=UpperCAmelCase    ,    numpify=UpperCAmelCase    )
														for image in image_inputs:
																					self.assertIsInstance(UpperCAmelCase    ,    np.ndarray    )

														# Test not batched input
														_snake_case  =    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  =    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 			lowercase				(self    )	->  Tuple:
														_snake_case  =    self.image_processing_class(**self.image_processor_dict    )
														# create random PyTorch tensors
														_snake_case  =    prepare_image_inputs(self.image_processor_tester    ,    equal_resolution=UpperCAmelCase    ,    torchify=UpperCAmelCase    )
														for image in image_inputs:
																					self.assertIsInstance(UpperCAmelCase    ,    torch.Tensor    )

														# Test not batched input
														_snake_case  =    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  =    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"""],
														    )    ,    )

import warnings

from .generation import TFGenerationMixin







class 							lowerCAmelCase__   ( __lowercase						):
						# warning at import time
						warnings.warn(
						    "Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will "
						    "be removed in Transformers v5. Import as `from transformers import TFGenerationMixin` instead."		,						__lowercase		,						)



