



import unittest

import numpy as np
from datasets import load_dataset

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 BeitImageProcessor



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__=None ,						a__=True ,						a__=None ,						a__=True ,						a__=[0.5, 0.5, 0.5] ,						a__=[0.5, 0.5, 0.5] ,						a__=False ,						):
         A__		 =					size if size is not None else {'''height''': 2_0, '''width''': 2_0}
         A__		 =					crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}
         A__		 =					parent
         A__		 =					batch_size
         A__		 =					num_channels
         A__		 =					image_size
         A__		 =					min_resolution
         A__		 =					max_resolution
         A__		 =					do_resize
         A__		 =					size
         A__		 =					do_center_crop
         A__		 =					crop_size
         A__		 =					do_normalize
         A__		 =					image_mean
         A__		 =					image_std
         A__		 =					do_reduce_labels




     def 		snake_case_		(				self):
         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_center_crop": self.do_center_crop,
             "crop_size": self.crop_size,
             "do_normalize": self.do_normalize,
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_reduce_labels": self.do_reduce_labels,
         }







def 		lowerCAmelCase__    (					)->  Dict:
    A__		 =					load_dataset('''hf-internal-testing/fixtures_ade20k''' ,       split='''test'''	)

    A__		 =					Image.open(dataset[0]['''file''']	)
    A__		 =					Image.open(dataset[1]['''file''']	)

    return image, map



def 		lowerCAmelCase__    (					)->  Any:
    A__		 =					load_dataset('''hf-internal-testing/fixtures_ade20k''' ,       split='''test'''	)

    A__		 =					Image.open(ds[0]['''file''']	)
    A__		 =					Image.open(ds[1]['''file''']	)
    A__		 =					Image.open(ds[2]['''file''']	)
    A__		 =					Image.open(ds[3]['''file''']	)

    return [imagea, imagea], [mapa, mapa]



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




     def 		snake_case_		(				self):
         A__		 =					BeitImageProcessingTester(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'''))
         self.assertTrue(hasattr(a__ ,						'''do_center_crop'''))
         self.assertTrue(hasattr(a__ ,						'''center_crop'''))
         self.assertTrue(hasattr(a__ ,						'''do_normalize'''))
         self.assertTrue(hasattr(a__ ,						'''image_mean'''))
         self.assertTrue(hasattr(a__ ,						'''image_std'''))




     def 		snake_case_		(				self):
         A__		 =					self.image_processing_class.from_dict(self.image_processor_dict)
         self.assertEqual(image_processor.size ,						{'''height''': 2_0, '''width''': 2_0})
         self.assertEqual(image_processor.crop_size ,						{'''height''': 1_8, '''width''': 1_8})
         self.assertEqual(image_processor.do_reduce_labels ,						a__)

         A__		 =					self.image_processing_class.from_dict(
             self.image_processor_dict ,						size=4_2 ,						crop_size=8_4 ,						reduce_labels=a__)
         self.assertEqual(image_processor.size ,						{'''height''': 4_2, '''width''': 4_2})
         self.assertEqual(image_processor.crop_size ,						{'''height''': 8_4, '''width''': 8_4})
         self.assertEqual(image_processor.do_reduce_labels ,						a__)




     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
         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.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             ) ,						)

         # Test batched
         A__		 =					image_processing(a__ ,						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.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             ) ,						)




     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
         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.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             ) ,						)

         # Test batched
         A__		 =					image_processing(a__ ,						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.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             ) ,						)




     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
         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.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             ) ,						)

         # Test batched
         A__		 =					image_processing(a__ ,						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.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             ) ,						)




     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__)
         A__		 =					[]
         for image in image_inputs:
             self.assertIsInstance(a__ ,						torch.Tensor)
             maps.append(torch.zeros(image.shape[-2:]).long())

         # Test not batched input
         A__		 =					image_processing(image_inputs[0] ,						maps[0] ,						return_tensors='''pt''')
         self.assertEqual(
             encoding['''pixel_values'''].shape ,						(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             ) ,						)
         self.assertEqual(
             encoding['''labels'''].shape ,						(
                 1,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             ) ,						)
         self.assertEqual(encoding['''labels'''].dtype ,						torch.long)
         self.assertTrue(encoding['''labels'''].min().item() >= 0)
         self.assertTrue(encoding['''labels'''].max().item() <= 2_5_5)

         # Test batched
         A__		 =					image_processing(a__ ,						a__ ,						return_tensors='''pt''')
         self.assertEqual(
             encoding['''pixel_values'''].shape ,						(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             ) ,						)
         self.assertEqual(
             encoding['''labels'''].shape ,						(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             ) ,						)
         self.assertEqual(encoding['''labels'''].dtype ,						torch.long)
         self.assertTrue(encoding['''labels'''].min().item() >= 0)
         self.assertTrue(encoding['''labels'''].max().item() <= 2_5_5)

         # Test not batched input (PIL images)
         A__ ,	A__		 =					prepare_semantic_single_inputs()

         A__		 =					image_processing(a__ ,						a__ ,						return_tensors='''pt''')
         self.assertEqual(
             encoding['''pixel_values'''].shape ,						(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             ) ,						)
         self.assertEqual(
             encoding['''labels'''].shape ,						(
                 1,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             ) ,						)
         self.assertEqual(encoding['''labels'''].dtype ,						torch.long)
         self.assertTrue(encoding['''labels'''].min().item() >= 0)
         self.assertTrue(encoding['''labels'''].max().item() <= 2_5_5)

         # Test batched input (PIL images)
         A__ ,	A__		 =					prepare_semantic_batch_inputs()

         A__		 =					image_processing(a__ ,						a__ ,						return_tensors='''pt''')
         self.assertEqual(
             encoding['''pixel_values'''].shape ,						(
                 2,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             ) ,						)
         self.assertEqual(
             encoding['''labels'''].shape ,						(
                 2,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             ) ,						)
         self.assertEqual(encoding['''labels'''].dtype ,						torch.long)
         self.assertTrue(encoding['''labels'''].min().item() >= 0)
         self.assertTrue(encoding['''labels'''].max().item() <= 2_5_5)




     def 		snake_case_		(				self):
         # Initialize image_processing
         A__		 =					self.image_processing_class(**self.image_processor_dict)

         # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
         A__ ,	A__		 =					prepare_semantic_single_inputs()
         A__		 =					image_processing(a__ ,						a__ ,						return_tensors='''pt''')
         self.assertTrue(encoding['''labels'''].min().item() >= 0)
         self.assertTrue(encoding['''labels'''].max().item() <= 1_5_0)

         A__		 =					True
         A__		 =					image_processing(a__ ,						a__ ,						return_tensors='''pt''')
         self.assertTrue(encoding['''labels'''].min().item() >= 0)
         self.assertTrue(encoding['''labels'''].max().item() <= 2_5_5)





# 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__)
