



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 ConvNextImageProcessor



class 						_snake_case (     unittest.TestCase			):





	"""simple docstring"""



	def __init__(  self		,      a		,      a=7		,      a=3		,      a=1_8		,      a=3_0		,      a=4_0_0		,      a=True		,      a=None		,      a=0.875		,      a=True		,      a=[0.5, 0.5, 0.5]		,      a=[0.5, 0.5, 0.5]		,      )		->							Any:




			"""simple docstring"""







			_A          =      size if size is not None else {'shortest_edge': 2_0}
			_A          =      parent
			_A          =      batch_size
			_A          =      num_channels
			_A          =      image_size
			_A          =      min_resolution
			_A          =      max_resolution
			_A          =      do_resize
			_A          =      size
			_A          =      crop_pct
			_A          =      do_normalize
			_A          =      image_mean
			_A          =      image_std



	def lowercase_						(  self   )		->							Optional[Any]:




			"""simple docstring"""







			return {
			    "image_mean": self.image_mean,
			    "image_std": self.image_std,
			    "do_normalize": self.do_normalize,
			    "do_resize": self.do_resize,
			    "size": self.size,
			    "crop_pct": self.crop_pct,
			}







@require_torch
@require_vision
class 						_snake_case (     UpperCamelCase_   ,unittest.TestCase			):





	"""simple docstring"""



	lowerCamelCase_       =     ConvNextImageProcessor if is_vision_available() else None
	def lowercase_						(  self   )		->							Tuple:




			"""simple docstring"""







			_A          =      ConvNextImageProcessingTester(self   )
	@property
	def lowercase_						(  self   )		->							Any:




			"""simple docstring"""







			return self.image_processor_tester.prepare_image_processor_dict()
	def lowercase_						(  self   )		->							Any:




			"""simple docstring"""







			_A          =      self.image_processing_class(**self.image_processor_dict   )
			self.assertTrue(hasattr(lowercase_		,      '''do_resize'''   )   )
			self.assertTrue(hasattr(lowercase_		,      '''size'''   )   )
			self.assertTrue(hasattr(lowercase_		,      '''crop_pct'''   )   )
			self.assertTrue(hasattr(lowercase_		,      '''do_normalize'''   )   )
			self.assertTrue(hasattr(lowercase_		,      '''image_mean'''   )   )
			self.assertTrue(hasattr(lowercase_		,      '''image_std'''   )   )
	def lowercase_						(  self   )		->							str:




			"""simple docstring"""







			_A          =      self.image_processing_class.from_dict(self.image_processor_dict   )
			self.assertEqual(image_processor.size		,      {'''shortest_edge''': 2_0}   )

			_A          =      self.image_processing_class.from_dict(self.image_processor_dict		,      size=4_2   )
			self.assertEqual(image_processor.size		,      {'''shortest_edge''': 4_2}   )
	def lowercase_						(  self   )		->							Union[str, Any]:




			"""simple docstring"""







			pass
	def lowercase_						(  self   )		->							str:




			"""simple docstring"""







			_A          =      self.image_processing_class(**self.image_processor_dict   )
			# create random PIL images
			_A          =      prepare_image_inputs(self.image_processor_tester		,      equal_resolution=lowercase_   )
			for image in image_inputs:
					self.assertIsInstance(lowercase_		,      Image.Image   )

			# Test not batched input
			_A          =      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.size['''shortest_edge'''],
			        self.image_processor_tester.size['''shortest_edge'''],
			    )		,      )

			# Test batched
			_A          =      image_processing(lowercase_		,      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.size['''shortest_edge'''],
			        self.image_processor_tester.size['''shortest_edge'''],
			    )		,      )
	def lowercase_						(  self   )		->							List[str]:




			"""simple docstring"""







			_A          =      self.image_processing_class(**self.image_processor_dict   )
			# create random numpy tensors
			_A          =      prepare_image_inputs(self.image_processor_tester		,      equal_resolution=lowercase_		,      numpify=lowercase_   )
			for image in image_inputs:
					self.assertIsInstance(lowercase_		,      np.ndarray   )

			# Test not batched input
			_A          =      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.size['''shortest_edge'''],
			        self.image_processor_tester.size['''shortest_edge'''],
			    )		,      )

			# Test batched
			_A          =      image_processing(lowercase_		,      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.size['''shortest_edge'''],
			        self.image_processor_tester.size['''shortest_edge'''],
			    )		,      )



	def lowercase_						(  self   )		->							List[str]:




			"""simple docstring"""







			_A          =      self.image_processing_class(**self.image_processor_dict   )
			# create random PyTorch tensors
			_A          =      prepare_image_inputs(self.image_processor_tester		,      equal_resolution=lowercase_		,      torchify=lowercase_   )
			for image in image_inputs:
					self.assertIsInstance(lowercase_		,      torch.Tensor   )

			# Test not batched input
			_A          =      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.size['''shortest_edge'''],
			        self.image_processor_tester.size['''shortest_edge'''],
			    )		,      )

			# Test batched
			_A          =      image_processing(lowercase_		,      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.size['''shortest_edge'''],
			        self.image_processor_tester.size['''shortest_edge'''],
			    )		,      )




import warnings

from .generation import FlaxGenerationMixin



class 						_snake_case (     lowerCamelCase			):





 """simple docstring"""



 warnings.warn(
     '''Importing `FlaxGenerationMixin` from `src/transformers/generation_flax_utils.py` is deprecated and will '''
     '''be removed in Transformers v5. Import as `from transformers import FlaxGenerationMixin` instead.'''   ,lowerCamelCase   ,)