


'''simple docstring'''







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 MobileNetVaImageProcessor


class 			__lowerCamelCase			(	unittest.TestCase	):
    """simple docstring"""


    def __init__(  self      :							Any				,							SCREAMING_SNAKE_CASE      :							Tuple				,							SCREAMING_SNAKE_CASE      :							Optional[Any]=7				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]=3				,							SCREAMING_SNAKE_CASE      :							Tuple=18				,							SCREAMING_SNAKE_CASE      :							Any=30				,							SCREAMING_SNAKE_CASE      :							Optional[int]=400				,							SCREAMING_SNAKE_CASE      :							int=True				,							SCREAMING_SNAKE_CASE      :							Dict=None				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]=True				,							SCREAMING_SNAKE_CASE      :							Optional[Any]=None				,							):
           _A					:     Dict     = size if size is not None else {'shortest_edge': 20}
           _A					:     int     = crop_size if crop_size is not None else {'height': 18, 'width': 18}
           _A					:     Dict     = parent
           _A					:     List[Any]     = batch_size
           _A					:     int     = num_channels
           _A					:     Dict     = image_size
           _A					:     Optional[int]     = min_resolution
           _A					:     int     = max_resolution
           _A					:     List[Any]     = do_resize
           _A					:     Optional[Any]     = size
           _A					:     Tuple     = do_center_crop
           _A					:     Optional[Any]     = crop_size


    def        A   (  self      :							Any):
           return {
               "do_resize": self.do_resize,
               "size": self.size,
               "do_center_crop": self.do_center_crop,
               "crop_size": self.crop_size,
           }




@require_torch
@require_vision
class 			__lowerCamelCase			(	a_    , unittest.TestCase	):
    """simple docstring"""
    a													=   MobileNetVaImageProcessor if is_vision_available() else None


    def        A   (  self      :							Union[str, Any]):
           _A					:     List[Any]     = MobileNetVaImageProcessingTester(self)


    @property
    def        A   (  self      :							List[str]):
           return self.image_processor_tester.prepare_image_processor_dict()


    def        A   (  self      :							List[str]):
           _A					:     Dict     = self.image_processing_class(**self.image_processor_dict)
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				,							'do_resize'))
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				,							'size'))
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				,							'do_center_crop'))
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				,							'crop_size'))


    def        A   (  self      :							Any):
           _A					:     List[Any]     = self.image_processing_class.from_dict(self.image_processor_dict)
           self.assertEqual(image_processor.size				,							{'shortest_edge': 20})
           self.assertEqual(image_processor.crop_size				,							{'height': 18, 'width': 18})

           _A					:     Optional[int]     = self.image_processing_class.from_dict(self.image_processor_dict				,							size=42				,							crop_size=84)
           self.assertEqual(image_processor.size				,							{'shortest_edge': 42})
           self.assertEqual(image_processor.crop_size				,							{'height': 84, 'width': 84})


    def        A   (  self      :							Any):
           pass


    def        A   (  self      :							List[str]):
           # Initialize image_processing
           _A					:     Union[str, Any]     = self.image_processing_class(**self.image_processor_dict)
           # create random PIL images
           _A					:     List[Any]     = prepare_image_inputs(self.image_processor_tester				,							equal_resolution=SCREAMING_SNAKE_CASE)
           for image in image_inputs:
                  self.assertIsInstance(SCREAMING_SNAKE_CASE				,							Image.Image)

           # Test not batched input
           _A					:     Optional[int]     = 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					:     Optional[int]     = image_processing(SCREAMING_SNAKE_CASE				,							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        A   (  self      :							Any):
           # Initialize image_processing
           _A					:     Optional[Any]     = self.image_processing_class(**self.image_processor_dict)
           # create random numpy tensors
           _A					:     str     = prepare_image_inputs(self.image_processor_tester				,							equal_resolution=SCREAMING_SNAKE_CASE				,							numpify=SCREAMING_SNAKE_CASE)
           for image in image_inputs:
                  self.assertIsInstance(SCREAMING_SNAKE_CASE				,							np.ndarray)

           # Test not batched input
           _A					:     Optional[int]     = 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					:     Tuple     = image_processing(SCREAMING_SNAKE_CASE				,							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        A   (  self      :							Optional[Any]):
           # Initialize image_processing
           _A					:     Tuple     = self.image_processing_class(**self.image_processor_dict)
           # create random PyTorch tensors
           _A					:     str     = prepare_image_inputs(self.image_processor_tester				,							equal_resolution=SCREAMING_SNAKE_CASE				,							torchify=SCREAMING_SNAKE_CASE)
           for image in image_inputs:
                  self.assertIsInstance(SCREAMING_SNAKE_CASE				,							torch.Tensor)

           # Test not batched input
           _A					:     str     = 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					:     Union[str, Any]     = image_processing(SCREAMING_SNAKE_CASE				,							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'],
               )				,							)










'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A				:					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:
   A				:					int    						=			['''ElectraTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					List[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:
   A				:					Dict    						=			[
       '''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:
   A				:					List[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

   A				:					Tuple    						=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






