





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 					A__   (       unittest.TestCase			):





      def __init__( self       ,							A_       ,							A_=7       ,							A_=3       ,							A_=18       ,							A_=30       ,							A_=400       ,							A_=True       ,							A_=32       ,							A_=True       ,							):
       '''simple docstring'''




       UpperCamelCase	:					str       	=   parent
       UpperCamelCase	:					Union[str, Any]       	=   batch_size
       UpperCamelCase	:					Tuple       	=   num_channels
       UpperCamelCase	:					Optional[Any]       	=   image_size
       UpperCamelCase	:					List[Any]       	=   min_resolution
       UpperCamelCase	:					Dict       	=   max_resolution
       UpperCamelCase	:					Optional[Any]       	=   do_resize
       UpperCamelCase	:					Dict       	=   size_divisor
       UpperCamelCase	:					Union[str, Any]       	=   do_rescale





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       return {
           "do_resize": self.do_resize,
           "size_divisor": self.size_divisor,
           "do_rescale": self.do_rescale,
       }



@require_torch
@require_vision
class 					A__   (       _UpperCamelCase       ,   unittest.TestCase			):
      _UpperCAmelCase	:str						    =   GLPNImageProcessor if is_vision_available() else None





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					List[Any]       	=   GLPNImageProcessingTester(self   )





      @property
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       return self.image_processor_tester.prepare_image_processor_dict()





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Dict       	=   self.image_processing_class(**self.image_processor_dict   )
       self.assertTrue(hasattr(_UpperCAmelCase       ,							"do_resize"   )   )
       self.assertTrue(hasattr(_UpperCAmelCase       ,							"size_divisor"   )   )
       self.assertTrue(hasattr(_UpperCAmelCase       ,							"resample"   )   )
       self.assertTrue(hasattr(_UpperCAmelCase       ,							"do_rescale"   )   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       pass





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Tuple       	=   self.image_processing_class(**self.image_processor_dict   )
       # create random PIL images
       UpperCamelCase	:					Union[str, Any]       	=   prepare_image_inputs(self.image_processor_tester       ,							equal_resolution=_UpperCAmelCase   )
       for image in image_inputs:
        self.assertIsInstance(_UpperCAmelCase       ,							Image.Image   )

       # Test not batched input (GLPNImageProcessor doesn't support batching)
       UpperCamelCase	:					List[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   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[Any]       	=   self.image_processing_class(**self.image_processor_dict   )
       # create random numpy tensors
       UpperCamelCase	:					Any       	=   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 (GLPNImageProcessor doesn't support batching)
       UpperCamelCase	:					str       	=   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   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[Any]       	=   self.image_processing_class(**self.image_processor_dict   )
       # create random PyTorch tensors
       UpperCamelCase	:					Any       	=   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 (GLPNImageProcessor doesn't support batching)
       UpperCamelCase	:					str       	=   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'''

# Copyright 2022 The HuggingFace Team and The OpenBMB Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__lowerCAmelCase									=   {
    '''configuration_cpmant''': ['''CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CpmAntConfig'''],
    '''tokenization_cpmant''': ['''CpmAntTokenizer'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase									=   [
							    '''CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''CpmAntForCausalLM''',
							    '''CpmAntModel''',
							    '''CpmAntPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
							from .tokenization_cpmant import CpmAntTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_cpmant import (
														    CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    CpmAntForCausalLM,
														    CpmAntModel,
														    CpmAntPreTrainedModel,
														)


else:
							import sys

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




