



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 ConvNextImageProcessor

class       UpperCamelCase  (					unittest.TestCase				):





     """simple docstring"""






     def __init__(					self :							int	,      _lowerCamelCase :							Dict	,      _lowerCamelCase :							Union[str, Any]=7	,      _lowerCamelCase :							Optional[int]=3	,      _lowerCamelCase :							Optional[int]=1_8	,      _lowerCamelCase :							Any=3_0	,      _lowerCamelCase :							List[str]=4_0_0	,      _lowerCamelCase :							Union[str, Any]=True	,      _lowerCamelCase :							int=None	,      _lowerCamelCase :							List[Any]=0.875	,      _lowerCamelCase :							Tuple=True	,      _lowerCamelCase :							str=[0.5, 0.5, 0.5]	,      _lowerCamelCase :							List[Any]=[0.5, 0.5, 0.5]	,      ):
          A__				=   size if size is not None else {'''shortest_edge''': 2_0}
          A__				=   parent
          A__				=   batch_size
          A__				=   num_channels
          A__				=   image_size
          A__				=   min_resolution
          A__				=   max_resolution
          A__				=   do_resize
          A__				=   size
          A__				=   crop_pct
          A__				=   do_normalize
          A__				=   image_mean
          A__				=   image_std





     def 						A__  (					self :							List[Any] ):
          return {
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_normalize": self.do_normalize,
              "do_resize": self.do_resize,
              "size": self.size,
              "crop_pct": self.crop_pct,
          }


@require_torch
@require_vision
class       UpperCamelCase  (					a      , unittest.TestCase				):





     """simple docstring"""






     _lowerCamelCase					: Any				   =ConvNextImageProcessor if is_vision_available() else None





     def 						A__  (					self :							Tuple ):
          A__				=   ConvNextImageProcessingTester(self )





     @property
     def 						A__  (					self :							Tuple ):
          return self.image_processor_tester.prepare_image_processor_dict()





     def 						A__  (					self :							Any ):
          A__				=   self.image_processing_class(**self.image_processor_dict )
          self.assertTrue(hasattr(_lowerCamelCase	,      '''do_resize''' ) )
          self.assertTrue(hasattr(_lowerCamelCase	,      '''size''' ) )
          self.assertTrue(hasattr(_lowerCamelCase	,      '''crop_pct''' ) )
          self.assertTrue(hasattr(_lowerCamelCase	,      '''do_normalize''' ) )
          self.assertTrue(hasattr(_lowerCamelCase	,      '''image_mean''' ) )
          self.assertTrue(hasattr(_lowerCamelCase	,      '''image_std''' ) )





     def 						A__  (					self :							List[Any] ):
          A__				=   self.image_processing_class.from_dict(self.image_processor_dict )
          self.assertEqual(image_processor.size	,      {'''shortest_edge''': 2_0} )

          A__				=   self.image_processing_class.from_dict(self.image_processor_dict	,      size=4_2 )
          self.assertEqual(image_processor.size	,      {'''shortest_edge''': 4_2} )





     def 						A__  (					self :							List[Any] ):
          pass





     def 						A__  (					self :							int ):
          # 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
          A__				=   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.size['''shortest_edge'''],
                  self.image_processor_tester.size['''shortest_edge'''],
              )	,      )

          # Test batched
          A__				=   image_processing(_lowerCamelCase	,      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.size['''shortest_edge'''],
                  self.image_processor_tester.size['''shortest_edge'''],
              )	,      )





     def 						A__  (					self :							Any ):
          # 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
          A__				=   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.size['''shortest_edge'''],
                  self.image_processor_tester.size['''shortest_edge'''],
              )	,      )

          # Test batched
          A__				=   image_processing(_lowerCamelCase	,      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.size['''shortest_edge'''],
                  self.image_processor_tester.size['''shortest_edge'''],
              )	,      )





     def 						A__  (					self :							Union[str, Any] ):
          # 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
          A__				=   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.size['''shortest_edge'''],
                  self.image_processor_tester.size['''shortest_edge'''],
              )	,      )

          # Test batched
          A__				=   image_processing(_lowerCamelCase	,      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.size['''shortest_edge'''],
                  self.image_processor_tester.size['''shortest_edge'''],
              )	,      )








"""simple docstring"""







from typing import TYPE_CHECKING

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


__snake_case	:				List[Any]  					=			{
    'configuration_unispeech_sat': ['UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechSatConfig'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __snake_case	:				str  					=			[
     'UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'UniSpeechSatForAudioFrameClassification',
     'UniSpeechSatForCTC',
     'UniSpeechSatForPreTraining',
     'UniSpeechSatForSequenceClassification',
     'UniSpeechSatForXVector',
     'UniSpeechSatModel',
     'UniSpeechSatPreTrainedModel',
 ]

if TYPE_CHECKING:
 from .configuration_unispeech_sat import UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechSatConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_unispeech_sat import (
      UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST,
      UniSpeechSatForAudioFrameClassification,
      UniSpeechSatForCTC,
      UniSpeechSatForPreTraining,
      UniSpeechSatForSequenceClassification,
      UniSpeechSatForXVector,
      UniSpeechSatModel,
      UniSpeechSatPreTrainedModel,
  )

else:
 import sys

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



