



'''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 GLPNImageProcessor






class 							lowercase__     (							unittest.TestCase       ):





 def __init__(			self :    Any			,lowerCamelCase__ :    Dict			,lowerCamelCase__ :    int=7			,lowerCamelCase__ :    Any=3			,lowerCamelCase__ :    Union[str, Any]=18			,lowerCamelCase__ :    Dict=30			,lowerCamelCase__ :    Optional[Any]=400			,lowerCamelCase__ :    Tuple=True			,lowerCamelCase__ :    Dict=32			,lowerCamelCase__ :    List[Any]=True			,):







       '''simple docstring'''
       _UpperCamelCase :		Optional[Any]			    =       parent
       _UpperCamelCase :		str			    =       batch_size
       _UpperCamelCase :		Dict			    =       num_channels
       _UpperCamelCase :		Optional[Any]			    =       image_size
       _UpperCamelCase :		List[Any]			    =       min_resolution
       _UpperCamelCase :		List[Any]			    =       max_resolution
       _UpperCamelCase :		Dict			    =       do_resize
       _UpperCamelCase :		Union[str, Any]			    =       size_divisor
       _UpperCamelCase :		Dict			    =       do_rescale





 def      UpperCamelCase_  (			self :    int			):







       '''simple docstring'''
       return {
           "do_resize": self.do_resize,
           "size_divisor": self.size_divisor,
           "do_rescale": self.do_rescale,
       }






@require_torch
@require_vision
class 							lowercase__     (							lowercase						, unittest.TestCase       ):
 lowercase__      =      GLPNImageProcessor if is_vision_available() else None





 def      UpperCamelCase_  (			self :    Union[str, Any]			):







       '''simple docstring'''
       _UpperCamelCase :		Union[str, Any]			    =       GLPNImageProcessingTester(self			)





 @property
 def      UpperCamelCase_  (			self :    Any			):







       '''simple docstring'''
       return self.image_processor_tester.prepare_image_processor_dict()





 def      UpperCamelCase_  (			self :    Union[str, Any]			):







       '''simple docstring'''
       _UpperCamelCase :		Any			    =       self.image_processing_class(**self.image_processor_dict			)
       self.assertTrue(hasattr(_lowercase			,'do_resize'			)			)
       self.assertTrue(hasattr(_lowercase			,'size_divisor'			)			)
       self.assertTrue(hasattr(_lowercase			,'resample'			)			)
       self.assertTrue(hasattr(_lowercase			,'do_rescale'			)			)





 def      UpperCamelCase_  (			self :    Optional[int]			):







       '''simple docstring'''
       pass





 def      UpperCamelCase_  (			self :    Union[str, Any]			):







       '''simple docstring'''
       # Initialize image_processing
       _UpperCamelCase :		List[Any]			    =       self.image_processing_class(**self.image_processor_dict			)
       # create random PIL images
       _UpperCamelCase :		Dict			    =       prepare_image_inputs(self.image_processor_tester			,equal_resolution=_lowercase			)
       for image in image_inputs:
             self.assertIsInstance(_lowercase			,Image.Image			)

       # Test not batched input (GLPNImageProcessor doesn't support batching)
       _UpperCamelCase :		Tuple			    =       image_processing(image_inputs[0]			,return_tensors='pt'			).pixel_values
       self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0			)
       self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0			)





 def      UpperCamelCase_  (			self :    List[Any]			):







       '''simple docstring'''
       # Initialize image_processing
       _UpperCamelCase :		Optional[Any]			    =       self.image_processing_class(**self.image_processor_dict			)
       # create random numpy tensors
       _UpperCamelCase :		Tuple			    =       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 (GLPNImageProcessor doesn't support batching)
       _UpperCamelCase :		Any			    =       image_processing(image_inputs[0]			,return_tensors='pt'			).pixel_values
       self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0			)
       self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0			)





 def      UpperCamelCase_  (			self :    int			):







       '''simple docstring'''
       # Initialize image_processing
       _UpperCamelCase :		int			    =       self.image_processing_class(**self.image_processor_dict			)
       # create random PyTorch tensors
       _UpperCamelCase :		Union[str, Any]			    =       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 (GLPNImageProcessor doesn't support batching)
       _UpperCamelCase :		int			    =       image_processing(image_inputs[0]			,return_tensors='pt'			).pixel_values
       self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0			)
       self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0			)







'''simple docstring'''
from typing import TYPE_CHECKING

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


snake_case_			:     Any  	=  {
    'configuration_gpt_neox_japanese': ['GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoXJapaneseConfig'],
    'tokenization_gpt_neox_japanese': ['GPTNeoXJapaneseTokenizer'],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_			:     Optional[int]  	=  [
					    'GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'GPTNeoXJapaneseForCausalLM',
					    'GPTNeoXJapaneseLayer',
					    'GPTNeoXJapaneseModel',
					    'GPTNeoXJapanesePreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
					from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_gpt_neox_japanese import (
										    GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GPTNeoXJapaneseForCausalLM,
										    GPTNeoXJapaneseLayer,
										    GPTNeoXJapaneseModel,
										    GPTNeoXJapanesePreTrainedModel,
										)


else:
					import sys

					snake_case_			:     Any  	=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


