





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    __A  (    unittest.TestCase     ):


				def __init__(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_=7 ,			UpperCAmelCase_=3 ,			UpperCAmelCase_=18 ,			UpperCAmelCase_=30 ,			UpperCAmelCase_=400 ,			UpperCAmelCase_=True ,			UpperCAmelCase_=None ,			UpperCAmelCase_=0.8_7_5 ,			UpperCAmelCase_=True ,			UpperCAmelCase_=[0.5, 0.5, 0.5] ,			UpperCAmelCase_=[0.5, 0.5, 0.5] ,			):
								lowerCamelCase									=size if size is not None else {"""shortest_edge""": 20}
								lowerCamelCase									=parent
								lowerCamelCase									=batch_size
								lowerCamelCase									=num_channels
								lowerCamelCase									=image_size
								lowerCamelCase									=min_resolution
								lowerCamelCase									=max_resolution
								lowerCamelCase									=do_resize
								lowerCamelCase									=size
								lowerCamelCase									=crop_pct
								lowerCamelCase									=do_normalize
								lowerCamelCase									=image_mean
								lowerCamelCase									=image_std


				def _snake_case							(		self				):
								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    __A  (    a     ,       unittest.TestCase     ):
				__A  =					ConvNextImageProcessor if is_vision_available() else None


				def _snake_case							(		self				):
								lowerCamelCase									=ConvNextImageProcessingTester(self				)


				@property
				def _snake_case							(		self				):
								return self.image_processor_tester.prepare_image_processor_dict()


				def _snake_case							(		self				):
								lowerCamelCase									=self.image_processing_class(**self.image_processor_dict				)
								self.assertTrue(hasattr(UpperCAmelCase_ ,			"""do_resize"""				)				)
								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 _snake_case							(		self				):
								lowerCamelCase									=self.image_processing_class.from_dict(self.image_processor_dict				)
								self.assertEqual(image_processor.size ,			{"""shortest_edge""": 20}				)

								lowerCamelCase									=self.image_processing_class.from_dict(self.image_processor_dict ,			size=42				)
								self.assertEqual(image_processor.size ,			{"""shortest_edge""": 42}				)


				def _snake_case							(		self				):
								pass


				def _snake_case							(		self				):
								# Initialize image_processing
								lowerCamelCase									=self.image_processing_class(**self.image_processor_dict				)
								# create random PIL images
								lowerCamelCase									=prepare_image_inputs(self.image_processor_tester ,			equal_resolution=UpperCAmelCase_				)
								for image in image_inputs:
												self.assertIsInstance(UpperCAmelCase_ ,			Image.Image				)

								# Test not batched input
								lowerCamelCase									=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
								lowerCamelCase									=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.size["""shortest_edge"""],
								        self.image_processor_tester.size["""shortest_edge"""],
								    ) ,			)


				def _snake_case							(		self				):
								# Initialize image_processing
								lowerCamelCase									=self.image_processing_class(**self.image_processor_dict				)
								# create random numpy tensors
								lowerCamelCase									=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
								lowerCamelCase									=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
								lowerCamelCase									=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.size["""shortest_edge"""],
								        self.image_processor_tester.size["""shortest_edge"""],
								    ) ,			)


				def _snake_case							(		self				):
								# Initialize image_processing
								lowerCamelCase									=self.image_processing_class(**self.image_processor_dict				)
								# create random PyTorch tensors
								lowerCamelCase									=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
								lowerCamelCase									=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
								lowerCamelCase									=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.size["""shortest_edge"""],
								        self.image_processor_tester.size["""shortest_edge"""],
								    ) ,			)












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


UpperCAmelCase__    :      Optional[Any]       					={
    '''configuration_rwkv''': ['''RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RwkvConfig''', '''RwkvOnnxConfig'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__    :      Optional[Any]       					=[
							    '''RWKV_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''RwkvForCausalLM''',
							    '''RwkvModel''',
							    '''RwkvPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_rwkv import RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP, RwkvConfig, RwkvOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_rwkv import (
														    RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
														    RwkvForCausalLM,
														    RwkvModel,
														    RwkvPreTrainedModel,
														)
else:
							import sys

							UpperCAmelCase__    :      int       					=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





