






'''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 						SCREAMING_SNAKE_CASE       (       unittest.TestCase							):






			def __init__(			self   :      Optional[Any]	,    __SCREAMING_SNAKE_CASE   :      List[str]	,    __SCREAMING_SNAKE_CASE   :      Optional[Any]=7	,    __SCREAMING_SNAKE_CASE   :      str=3	,    __SCREAMING_SNAKE_CASE   :      Dict=30	,    __SCREAMING_SNAKE_CASE   :      Optional[Any]=400	,    __SCREAMING_SNAKE_CASE   :      Dict=True	,    __SCREAMING_SNAKE_CASE   :      Union[str, Any]=None	,    __SCREAMING_SNAKE_CASE   :      Tuple=0.9	,    __SCREAMING_SNAKE_CASE   :      str=None	,    __SCREAMING_SNAKE_CASE   :      List[str]=True	,    __SCREAMING_SNAKE_CASE   :      int=[0.5, 0.5, 0.5]	,    __SCREAMING_SNAKE_CASE   :      Any=[0.5, 0.5, 0.5]	,    )      ->      List[Any]:
							a_		:	List[str]									=   size if size is not None else {'''shortest_edge''': 30}
							a_		:	Optional[int]									=   crop_size if crop_size is not None else {'''height''': 30, '''width''': 30}
							a_		:	Optional[Any]									=   parent
							a_		:	List[Any]									=   batch_size
							a_		:	Dict									=   num_channels
							a_		:	List[str]									=   min_resolution
							a_		:	List[str]									=   max_resolution
							a_		:	List[str]									=   do_resize_and_center_crop
							a_		:	Optional[Any]									=   size
							a_		:	Tuple									=   crop_pct
							a_		:	Dict									=   crop_size
							a_		:	Tuple									=   do_normalize
							a_		:	List[str]									=   image_mean
							a_		:	Any									=   image_std







			def       SCREAMING_SNAKE_CASE   (			self   :      str       )      ->      List[str]:
							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 						SCREAMING_SNAKE_CASE       (       SCREAMING_SNAKE_CASE_						, unittest.TestCase							):
			snake_case__	=					PoolFormerImageProcessor if is_vision_available() else None






			def       SCREAMING_SNAKE_CASE   (			self   :      str       )      ->      int:
							a_		:	Any									=   PoolFormerImageProcessingTester(self       )






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






			def       SCREAMING_SNAKE_CASE   (			self   :      Any       )      ->      Optional[int]:
							a_		:	Optional[int]									=   self.image_processing_class(**self.image_processor_dict       )
							self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,    '''do_resize_and_center_crop'''       )       )
							self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,    '''size'''       )       )
							self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,    '''crop_pct'''       )       )
							self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,    '''do_normalize'''       )       )
							self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,    '''image_mean'''       )       )
							self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,    '''image_std'''       )       )






			def       SCREAMING_SNAKE_CASE   (			self   :      List[Any]       )      ->      Dict:
							a_		:	Dict									=   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}       )

							a_		:	Dict									=   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       SCREAMING_SNAKE_CASE   (			self   :      Tuple       )      ->      Dict:
							pass






			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[Any]       )      ->      Union[str, Any]:
							# Initialize image_processing
							a_		:	Optional[Any]									=   self.image_processing_class(**self.image_processor_dict       )
							# create random PIL images
							a_		:	str									=   prepare_image_inputs(self.image_processor_tester	,    equal_resolution=__SCREAMING_SNAKE_CASE       )
							for image in image_inputs:
											self.assertIsInstance(__SCREAMING_SNAKE_CASE	,    Image.Image       )

							# Test not batched input
							a_		:	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
							a_		:	Tuple									=   image_processing(__SCREAMING_SNAKE_CASE	,    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       SCREAMING_SNAKE_CASE   (			self   :      List[str]       )      ->      List[Any]:
							# Initialize image_processing
							a_		:	Union[str, Any]									=   self.image_processing_class(**self.image_processor_dict       )
							# create random numpy tensors
							a_		:	Union[str, Any]									=   prepare_image_inputs(self.image_processor_tester	,    equal_resolution=__SCREAMING_SNAKE_CASE	,    numpify=__SCREAMING_SNAKE_CASE       )
							for image in image_inputs:
											self.assertIsInstance(__SCREAMING_SNAKE_CASE	,    np.ndarray       )

							# Test not batched input
							a_		:	Any									=   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
							a_		:	Any									=   image_processing(__SCREAMING_SNAKE_CASE	,    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       SCREAMING_SNAKE_CASE   (			self   :      Any       )      ->      List[str]:
							# Initialize image_processing
							a_		:	Optional[Any]									=   self.image_processing_class(**self.image_processor_dict       )
							# create random PyTorch tensors
							a_		:	Optional[int]									=   prepare_image_inputs(self.image_processor_tester	,    equal_resolution=__SCREAMING_SNAKE_CASE	,    torchify=__SCREAMING_SNAKE_CASE       )
							for image in image_inputs:
											self.assertIsInstance(__SCREAMING_SNAKE_CASE	,    torch.Tensor       )

							# Test not batched input
							a_		:	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
							a_		:	str									=   image_processing(__SCREAMING_SNAKE_CASE	,    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'''],
							    )	,    )












'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCAmelCase									=   {'configuration_sew': ['SEW_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWConfig']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase									=   [
		    'SEW_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'SEWForCTC',
		    'SEWForSequenceClassification',
		    'SEWModel',
		    'SEWPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_sew import (
				    SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SEWForCTC,
				    SEWForSequenceClassification,
				    SEWModel,
				    SEWPreTrainedModel,
				)


else:
		import sys

		__lowerCAmelCase									=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




