





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      lowerCAmelCase						(					unittest.TestCase			):



     def __init__(					self				,      lowerCAmelCase__				,      lowerCAmelCase__=7				,      lowerCAmelCase__=3				,      lowerCAmelCase__=18				,      lowerCAmelCase__=30				,      lowerCAmelCase__=400				,      lowerCAmelCase__=True				,      lowerCAmelCase__=32				,      lowerCAmelCase__=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 			a__  (					self       ):
            return {
                "do_resize": self.do_resize,
                "size_divisor": self.size_divisor,
                "do_rescale": self.do_rescale,
            }





@require_torch
@require_vision
class      lowerCAmelCase						(					_a			,		unittest.TestCase			):
     _SCREAMING_SNAKE_CASE     :     int		=GLPNImageProcessor if is_vision_available() else None



     def 			a__  (					self       ):
            _A=	GLPNImageProcessingTester(self       )



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



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



     def 			a__  (					self       ):
            pass



     def 			a__  (					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=lowerCAmelCase__       )
            for image in image_inputs:
                   self.assertIsInstance(lowerCAmelCase__				,      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 			a__  (					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=lowerCAmelCase__				,      numpify=lowerCAmelCase__       )
            for image in image_inputs:
                   self.assertIsInstance(lowerCAmelCase__				,      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 			a__  (					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=lowerCAmelCase__				,      torchify=lowerCAmelCase__       )
            for image in image_inputs:
                   self.assertIsInstance(lowerCAmelCase__				,      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       )
from typing import TYPE_CHECKING

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


UpperCAmelCase_								=							{
    '''configuration_ernie''': ['''ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ErnieConfig''', '''ErnieOnnxConfig'''],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_								=							[
          '''ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''ErnieForCausalLM''',
          '''ErnieForMaskedLM''',
          '''ErnieForMultipleChoice''',
          '''ErnieForNextSentencePrediction''',
          '''ErnieForPreTraining''',
          '''ErnieForQuestionAnswering''',
          '''ErnieForSequenceClassification''',
          '''ErnieForTokenClassification''',
          '''ErnieModel''',
          '''ErniePreTrainedModel''',
      ]

if TYPE_CHECKING:
      from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_ernie import (
                ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
                ErnieForCausalLM,
                ErnieForMaskedLM,
                ErnieForMultipleChoice,
                ErnieForNextSentencePrediction,
                ErnieForPreTraining,
                ErnieForQuestionAnswering,
                ErnieForSequenceClassification,
                ErnieForTokenClassification,
                ErnieModel,
                ErniePreTrainedModel,
            )

else:
      import sys

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