






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  UpperCamelCase_      (			unittest.TestCase				):



	'''simple docstring'''




	def __init__(       self       ,			a       ,			a=7       ,			a=3       ,			a=30       ,			a=4_00       ,			a=True       ,			a=None       ,			a=0.9       ,			a=None       ,			a=True       ,			a=[0.5, 0.5, 0.5]       ,			a=[0.5, 0.5, 0.5]       ,			)       ->    Optional[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 							_UpperCamelCase			(       self					)       ->    int:
								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  UpperCamelCase_      (			snake_case__       ,					unittest.TestCase				):



	'''simple docstring'''




	lowerCAmelCase													= PoolFormerImageProcessor if is_vision_available() else None




	def 							_UpperCamelCase			(       self					)       ->    Optional[Any]:
								snake_case_				=							PoolFormerImageProcessingTester(self					)




	@property
	def 							_UpperCamelCase			(       self					)       ->    Any:
								return self.image_processor_tester.prepare_image_processor_dict()




	def 							_UpperCamelCase			(       self					)       ->    List[Any]:
								snake_case_				=							self.image_processing_class(**self.image_processor_dict					)
								self.assertTrue(hasattr(_A       ,			'do_resize_and_center_crop'					)					)
								self.assertTrue(hasattr(_A       ,			'size'					)					)
								self.assertTrue(hasattr(_A       ,			'crop_pct'					)					)
								self.assertTrue(hasattr(_A       ,			'do_normalize'					)					)
								self.assertTrue(hasattr(_A       ,			'image_mean'					)					)
								self.assertTrue(hasattr(_A       ,			'image_std'					)					)




	def 							_UpperCamelCase			(       self					)       ->    str:
								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 							_UpperCamelCase			(       self					)       ->    List[str]:
								pass




	def 							_UpperCamelCase			(       self					)       ->    Union[str, Any]:
								# Initialize image_processing
								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=_A					)
								for image in image_inputs:
															self.assertIsInstance(_A       ,			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(_A       ,			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 							_UpperCamelCase			(       self					)       ->    Tuple:
								# Initialize image_processing
								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=_A       ,			numpify=_A					)
								for image in image_inputs:
															self.assertIsInstance(_A       ,			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(_A       ,			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 							_UpperCamelCase			(       self					)       ->    Optional[int]:
								# Initialize image_processing
								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=_A       ,			torchify=_A					)
								for image in image_inputs:
															self.assertIsInstance(_A       ,			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(_A       ,			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'],
								    )       ,			)












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCAmelCase						:   Any			  =     {
    '''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxConfig'''],
    '''tokenization_mvp''': ['''MvpTokenizer'''],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase						:   int			  =     ['''MvpTokenizerFast''']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase						:   str			  =     [
				    '''MVP_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''MvpForCausalLM''',
				    '''MvpForConditionalGeneration''',
				    '''MvpForQuestionAnswering''',
				    '''MvpForSequenceClassification''',
				    '''MvpModel''',
				    '''MvpPreTrainedModel''',
				]

if TYPE_CHECKING:
				from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
				from .tokenization_mvp import MvpTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_mvp_fast import MvpTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_mvp import (
								    MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MvpForCausalLM,
								    MvpForConditionalGeneration,
								    MvpForQuestionAnswering,
								    MvpForSequenceClassification,
								    MvpModel,
								    MvpPreTrainedModel,
								)

else:
				import sys

				UpperCAmelCase						:   List[str]			  =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




