





'''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 GLPNImageProcessor




class 					UpperCAmelCase	(     unittest.TestCase  ):




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




      def 							lowercase__       (     self     :					int    )							-> Tuple:
            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  ):
      _lowercase:     List[str]			=  GLPNImageProcessor if is_vision_available() else None




      def 							lowercase__       (     self     :					Any    )							-> Tuple:
            _lowerCAmelCase					=				GLPNImageProcessingTester(self    )




      @property
      def 							lowercase__       (     self     :					int    )							-> Union[str, Any]:
            return self.image_processor_tester.prepare_image_processor_dict()




      def 							lowercase__       (     self     :					List[Any]    )							-> Optional[int]:
            _lowerCAmelCase					=				self.image_processing_class(**self.image_processor_dict    )
            self.assertTrue(hasattr(__snake_case     ,     """do_resize"""    )    )
            self.assertTrue(hasattr(__snake_case     ,     """size_divisor"""    )    )
            self.assertTrue(hasattr(__snake_case     ,     """resample"""    )    )
            self.assertTrue(hasattr(__snake_case     ,     """do_rescale"""    )    )




      def 							lowercase__       (     self     :					Optional[Any]    )							-> List[Any]:
            pass




      def 							lowercase__       (     self     :					str    )							-> Any:
            # Initialize image_processing
            _lowerCAmelCase					=				self.image_processing_class(**self.image_processor_dict    )
            # create random PIL images
            _lowerCAmelCase					=				prepare_image_inputs(self.image_processor_tester     ,     equal_resolution=__snake_case    )
            for image in image_inputs:
                  self.assertIsInstance(__snake_case     ,     Image.Image    )

            # Test not batched input (GLPNImageProcessor doesn't support batching)
            _lowerCAmelCase					=				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 							lowercase__       (     self     :					int    )							-> Union[str, Any]:
            # Initialize image_processing
            _lowerCAmelCase					=				self.image_processing_class(**self.image_processor_dict    )
            # create random numpy tensors
            _lowerCAmelCase					=				prepare_image_inputs(self.image_processor_tester     ,     equal_resolution=__snake_case     ,     numpify=__snake_case    )
            for image in image_inputs:
                  self.assertIsInstance(__snake_case     ,     np.ndarray    )

            # Test not batched input (GLPNImageProcessor doesn't support batching)
            _lowerCAmelCase					=				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 							lowercase__       (     self     :					Tuple    )							-> List[Any]:
            # Initialize image_processing
            _lowerCAmelCase					=				self.image_processing_class(**self.image_processor_dict    )
            # create random PyTorch tensors
            _lowerCAmelCase					=				prepare_image_inputs(self.image_processor_tester     ,     equal_resolution=__snake_case     ,     torchify=__snake_case    )
            for image in image_inputs:
                  self.assertIsInstance(__snake_case     ,     torch.Tensor    )

            # Test not batched input (GLPNImageProcessor doesn't support batching)
            _lowerCAmelCase					=				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_torch_available,
)


A__							:  int    ={
    '''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Wav2Vec2Config'''],
    '''feature_extraction_wav2vec2''': ['''Wav2Vec2FeatureExtractor'''],
    '''processing_wav2vec2''': ['''Wav2Vec2Processor'''],
    '''tokenization_wav2vec2''': ['''Wav2Vec2CTCTokenizer''', '''Wav2Vec2Tokenizer'''],
}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__							:  str    =[
          '''WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''Wav2Vec2ForAudioFrameClassification''',
          '''Wav2Vec2ForCTC''',
          '''Wav2Vec2ForMaskedLM''',
          '''Wav2Vec2ForPreTraining''',
          '''Wav2Vec2ForSequenceClassification''',
          '''Wav2Vec2ForXVector''',
          '''Wav2Vec2Model''',
          '''Wav2Vec2PreTrainedModel''',
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__							:  Union[str, Any]    =[
          '''TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''TFWav2Vec2ForCTC''',
          '''TFWav2Vec2Model''',
          '''TFWav2Vec2PreTrainedModel''',
          '''TFWav2Vec2ForSequenceClassification''',
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__							:  List[str]    =[
          '''FlaxWav2Vec2ForCTC''',
          '''FlaxWav2Vec2ForPreTraining''',
          '''FlaxWav2Vec2Model''',
          '''FlaxWav2Vec2PreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
      from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
      from .processing_wavaveca import WavaVecaProcessor
      from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_wavaveca import (
                WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
                WavaVecaForAudioFrameClassification,
                WavaVecaForCTC,
                WavaVecaForMaskedLM,
                WavaVecaForPreTraining,
                WavaVecaForSequenceClassification,
                WavaVecaForXVector,
                WavaVecaModel,
                WavaVecaPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_wavaveca import (
                TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFWavaVecaForCTC,
                TFWavaVecaForSequenceClassification,
                TFWavaVecaModel,
                TFWavaVecaPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_wavaveca import (
                FlaxWavaVecaForCTC,
                FlaxWavaVecaForPreTraining,
                FlaxWavaVecaModel,
                FlaxWavaVecaPreTrainedModel,
            )


else:
      import sys

      A__							:  Any    =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






