






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   ):





       """simple docstring"""







       def __init__(   self    ,     _lowerCamelCase    ,     _lowerCamelCase=7    ,     _lowerCamelCase=3    ,     _lowerCamelCase=1_8    ,     _lowerCamelCase=3_0    ,     _lowerCamelCase=4_0_0    ,     _lowerCamelCase=True    ,     _lowerCamelCase=3_2    ,     _lowerCamelCase=True    ,     ):
         UpperCamelCase_:   Any        =							parent
         UpperCamelCase_:   Optional[Any]        =							batch_size
         UpperCamelCase_:   List[str]        =							num_channels
         UpperCamelCase_:   Optional[Any]        =							image_size
         UpperCamelCase_:   Optional[int]        =							min_resolution
         UpperCamelCase_:   List[str]        =							max_resolution
         UpperCamelCase_:   Tuple        =							do_resize
         UpperCamelCase_:   Union[str, Any]        =							size_divisor
         UpperCamelCase_:   Optional[int]        =							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(			UpperCAmelCase_				,      unittest.TestCase   ):





       """simple docstring"""







       a							:						str										=GLPNImageProcessor if is_vision_available() else None





       def      _a       (   self				):
         UpperCamelCase_:   Tuple        =							GLPNImageProcessingTester(self				)





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





       def      _a       (   self				):
         UpperCamelCase_:   List[Any]        =							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
         UpperCamelCase_:   int        =							self.image_processing_class(**self.image_processor_dict				)
         # create random PIL images
         UpperCamelCase_:   List[Any]        =							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)
         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      _a       (   self				):
         # Initialize image_processing
         UpperCamelCase_:   Union[str, Any]        =							self.image_processing_class(**self.image_processor_dict				)
         # create random numpy tensors
         UpperCamelCase_:   Dict        =							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)
         UpperCamelCase_:   Tuple        =							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
         UpperCamelCase_:   Optional[Any]        =							self.image_processing_class(**self.image_processor_dict				)
         # create random PyTorch tensors
         UpperCamelCase_:   Optional[int]        =							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)
         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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase     :  List[str]											=    {
    """configuration_electra""": ["""ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ElectraConfig""", """ElectraOnnxConfig"""],
    """tokenization_electra""": ["""ElectraTokenizer"""],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase     :  Optional[Any]											=    ["""ElectraTokenizerFast"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase     :  str											=    [
           """ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """ElectraForCausalLM""",
           """ElectraForMaskedLM""",
           """ElectraForMultipleChoice""",
           """ElectraForPreTraining""",
           """ElectraForQuestionAnswering""",
           """ElectraForSequenceClassification""",
           """ElectraForTokenClassification""",
           """ElectraModel""",
           """ElectraPreTrainedModel""",
           """load_tf_weights_in_electra""",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase     :  Tuple											=    [
           """TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """TFElectraForMaskedLM""",
           """TFElectraForMultipleChoice""",
           """TFElectraForPreTraining""",
           """TFElectraForQuestionAnswering""",
           """TFElectraForSequenceClassification""",
           """TFElectraForTokenClassification""",
           """TFElectraModel""",
           """TFElectraPreTrainedModel""",
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase     :  Union[str, Any]											=    [
           """FlaxElectraForCausalLM""",
           """FlaxElectraForMaskedLM""",
           """FlaxElectraForMultipleChoice""",
           """FlaxElectraForPreTraining""",
           """FlaxElectraForQuestionAnswering""",
           """FlaxElectraForSequenceClassification""",
           """FlaxElectraForTokenClassification""",
           """FlaxElectraModel""",
           """FlaxElectraPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
       from .tokenization_electra import ElectraTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_electra_fast import ElectraTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_electra import (
                  ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  ElectraForCausalLM,
                  ElectraForMaskedLM,
                  ElectraForMultipleChoice,
                  ElectraForPreTraining,
                  ElectraForQuestionAnswering,
                  ElectraForSequenceClassification,
                  ElectraForTokenClassification,
                  ElectraModel,
                  ElectraPreTrainedModel,
                  load_tf_weights_in_electra,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_electra import (
                  TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFElectraForMaskedLM,
                  TFElectraForMultipleChoice,
                  TFElectraForPreTraining,
                  TFElectraForQuestionAnswering,
                  TFElectraForSequenceClassification,
                  TFElectraForTokenClassification,
                  TFElectraModel,
                  TFElectraPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_electra import (
                  FlaxElectraForCausalLM,
                  FlaxElectraForMaskedLM,
                  FlaxElectraForMultipleChoice,
                  FlaxElectraForPreTraining,
                  FlaxElectraForQuestionAnswering,
                  FlaxElectraForSequenceClassification,
                  FlaxElectraForTokenClassification,
                  FlaxElectraModel,
                  FlaxElectraPreTrainedModel,
              )

else:
       import sys

       lowerCAmelCase     :  int											=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

