
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 ):




							'''simple docstring'''

							def __init__(					self  ,    UpperCamelCase  ,    UpperCamelCase=7  ,    UpperCamelCase=3  ,    UpperCamelCase=18  ,    UpperCamelCase=30  ,    UpperCamelCase=4_00  ,    UpperCamelCase=True  ,    UpperCamelCase=32  ,    UpperCamelCase=True  ,    )			->						int:
										UpperCamelCase__     :		Optional[int]	 =					parent
										UpperCamelCase__     :		int	 =					batch_size
										UpperCamelCase__     :		int	 =					num_channels
										UpperCamelCase__     :		Optional[int]	 =					image_size
										UpperCamelCase__     :		Any	 =					min_resolution
										UpperCamelCase__     :		int	 =					max_resolution
										UpperCamelCase__     :		int	 =					do_resize
										UpperCamelCase__     :		int	 =					size_divisor
										UpperCamelCase__     :		int	 =					do_rescale






							def 							lowerCAmelCase__			(					self)			->						Optional[int]:
										return {
										    "do_resize": self.do_resize,
										    "size_divisor": self.size_divisor,
										    "do_rescale": self.do_rescale,
										}



@require_torch
@require_vision
class       UpperCamelCase_      (    snake_case_						,  unittest.TestCase ):




							'''simple docstring'''

							UpperCamelCase_        =				GLPNImageProcessor if is_vision_available() else None
							def 							lowerCAmelCase__			(					self)			->						List[str]:
										UpperCamelCase__     :		Optional[Any]	 =					GLPNImageProcessingTester(self)
							@property
							def 							lowerCAmelCase__			(					self)			->						Optional[Any]:
										return self.image_processor_tester.prepare_image_processor_dict()
							def 							lowerCAmelCase__			(					self)			->						List[Any]:
										UpperCamelCase__     :		Optional[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 							lowerCAmelCase__			(					self)			->						List[Any]:
										pass
							def 							lowerCAmelCase__			(					self)			->						Any:
										UpperCamelCase__     :		List[str]	 =					self.image_processing_class(**self.image_processor_dict)
										# create random PIL images
										UpperCamelCase__     :		List[str]	 =					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__     :		List[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 							lowerCAmelCase__			(					self)			->						Dict:
										UpperCamelCase__     :		int	 =					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__     :		Union[str, 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 							lowerCAmelCase__			(					self)			->						Tuple:
										UpperCamelCase__     :		str	 =					self.image_processing_class(**self.image_processor_dict)
										# create random PyTorch tensors
										UpperCamelCase__     :		Tuple	 =					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__     :		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'''
from typing import TYPE_CHECKING

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


UpperCAmelCase_			:  Optional[Any]										=   {
    'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
    'tokenization_transfo_xl': ['TransfoXLCorpus', 'TransfoXLTokenizer'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase_			:  Any										=   [
     'TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
     'AdaptiveEmbedding',
     'TransfoXLForSequenceClassification',
     'TransfoXLLMHeadModel',
     'TransfoXLModel',
     'TransfoXLPreTrainedModel',
     'load_tf_weights_in_transfo_xl',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase_			:  Optional[Any]										=   [
     'TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFAdaptiveEmbedding',
     'TFTransfoXLForSequenceClassification',
     'TFTransfoXLLMHeadModel',
     'TFTransfoXLMainLayer',
     'TFTransfoXLModel',
     'TFTransfoXLPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
 from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_transfo_xl import (
      TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
      AdaptiveEmbedding,
      TransfoXLForSequenceClassification,
      TransfoXLLMHeadModel,
      TransfoXLModel,
      TransfoXLPreTrainedModel,
      load_tf_weights_in_transfo_xl,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_transfo_xl import (
      TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFAdaptiveEmbedding,
      TFTransfoXLForSequenceClassification,
      TFTransfoXLLMHeadModel,
      TFTransfoXLMainLayer,
      TFTransfoXLModel,
      TFTransfoXLPreTrainedModel,
  )

else:
 import sys

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






