



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     _UpperCAmelCase			(       unittest.TestCase     ):




     def __init__(				self ,						a__ ,						a__=7 ,						a__=3 ,						a__=1_8 ,						a__=3_0 ,						a__=4_0_0 ,						a__=True ,						a__=3_2 ,						a__=True ,						):
         A__		 =					parent
         A__		 =					batch_size
         A__		 =					num_channels
         A__		 =					image_size
         A__		 =					min_resolution
         A__		 =					max_resolution
         A__		 =					do_resize
         A__		 =					size_divisor
         A__		 =					do_rescale




     def 		snake_case_		(				self):
         return {
             "do_resize": self.do_resize,
             "size_divisor": self.size_divisor,
             "do_rescale": self.do_rescale,
         }







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




     def 		snake_case_		(				self):
         A__		 =					GLPNImageProcessingTester(self)




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




     def 		snake_case_		(				self):
         A__		 =					self.image_processing_class(**self.image_processor_dict)
         self.assertTrue(hasattr(a__ ,						'''do_resize'''))
         self.assertTrue(hasattr(a__ ,						'''size_divisor'''))
         self.assertTrue(hasattr(a__ ,						'''resample'''))
         self.assertTrue(hasattr(a__ ,						'''do_rescale'''))




     def 		snake_case_		(				self):
         pass




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

         # Test not batched input (GLPNImageProcessor doesn't support batching)
         A__		 =					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 		snake_case_		(				self):
         # Initialize image_processing
         A__		 =					self.image_processing_class(**self.image_processor_dict)
         # create random numpy tensors
         A__		 =					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 (GLPNImageProcessor doesn't support batching)
         A__		 =					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 		snake_case_		(				self):
         # Initialize image_processing
         A__		 =					self.image_processing_class(**self.image_processor_dict)
         # create random PyTorch tensors
         A__		 =					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 (GLPNImageProcessor doesn't support batching)
         A__		 =					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)





# 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


_lowercase						  =		{
    "configuration_cpmant": ["CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CpmAntConfig"],
    "tokenization_cpmant": ["CpmAntTokenizer"],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowercase						  =		[
       "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

   _lowercase						  =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
