


'''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  a     (     unittest.TestCase       ):
 def __init__( self      ,				__magic_name__      ,				__magic_name__=7      ,				__magic_name__=3      ,				__magic_name__=18      ,				__magic_name__=30      ,				__magic_name__=4_00      ,				__magic_name__=True      ,				__magic_name__=None      ,				__magic_name__=True      ,				__magic_name__=None      ,				)					->							Union[str, Any]:
      _a			      =							size if size is not None else {'shortest_edge': 20}
      _a			      =							crop_size if crop_size is not None else {'height': 18, 'width': 18}
      _a			      =							parent
      _a			      =							batch_size
      _a			      =							num_channels
      _a			      =							image_size
      _a			      =							min_resolution
      _a			      =							max_resolution
      _a			      =							do_resize
      _a			      =							size
      _a			      =							do_center_crop
      _a			      =							crop_size




 def 							__UpperCAmelCase		( self						)					->							Dict:
      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  a     (     _SCREAMING_SNAKE_CASE  ,				unittest.TestCase       ):
 _lowerCAmelCase 					=	MobileNetVaImageProcessor if is_vision_available() else None
 def 							__UpperCAmelCase		( self						)					->							Tuple:
      _a			      =							MobileNetVaImageProcessingTester(self						)
 @property
 def 							__UpperCAmelCase		( self						)					->							Union[str, Any]:
      return self.image_processor_tester.prepare_image_processor_dict()
 def 							__UpperCAmelCase		( self						)					->							Any:
      _a			      =							self.image_processing_class(**self.image_processor_dict						)
      self.assertTrue(hasattr(__magic_name__      ,				'do_resize'						)						)
      self.assertTrue(hasattr(__magic_name__      ,				'size'						)						)
      self.assertTrue(hasattr(__magic_name__      ,				'do_center_crop'						)						)
      self.assertTrue(hasattr(__magic_name__      ,				'crop_size'						)						)
 def 							__UpperCAmelCase		( self						)					->							int:
      _a			      =							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			      =							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 							__UpperCAmelCase		( self						)					->							Any:
      pass
 def 							__UpperCAmelCase		( 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=__magic_name__						)
      for image in image_inputs:
           self.assertIsInstance(__magic_name__      ,				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.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )      ,				)

      # Test batched
      _a			      =							image_processing(__magic_name__      ,				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 							__UpperCAmelCase		( self						)					->							Union[str, 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=__magic_name__      ,				numpify=__magic_name__						)
      for image in image_inputs:
           self.assertIsInstance(__magic_name__      ,				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.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )      ,				)

      # Test batched
      _a			      =							image_processing(__magic_name__      ,				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 							__UpperCAmelCase		( self						)					->							Optional[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=__magic_name__      ,				torchify=__magic_name__						)
      for image in image_inputs:
           self.assertIsInstance(__magic_name__      ,				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.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )      ,				)

      # Test batched
      _a			      =							image_processing(__magic_name__      ,				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 List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy




class  a     (     _SCREAMING_SNAKE_CASE       ):
 _lowerCAmelCase 					=	"""EncodecFeatureExtractor"""
 _lowerCAmelCase 					=	("""T5Tokenizer""", """T5TokenizerFast""")
 def __init__( self      ,				__magic_name__      ,				__magic_name__						)					->							Optional[Any]:
      super().__init__(__magic_name__      ,				__magic_name__						)
      _a			      =							self.feature_extractor
      _a			      =							False
 def 							__UpperCAmelCase		( self      ,				__magic_name__=None      ,				__magic_name__=None      ,				__magic_name__=True						)					->							Tuple:
      return self.tokenizer.get_decoder_prompt_ids(task=__magic_name__      ,				language=__magic_name__      ,				no_timestamps=__magic_name__						)
 def __call__( self      ,				*__magic_name__      ,				**__magic_name__						)					->							List[str]:
      # For backward compatibility
      if self._in_target_context_manager:
           return self.current_processor(*__magic_name__      ,				**__magic_name__						)

      _a			      =							kwargs.pop('audio'      ,				__magic_name__						)
      _a			      =							kwargs.pop('sampling_rate'      ,				__magic_name__						)
      _a			      =							kwargs.pop('text'      ,				__magic_name__						)
      if len(__magic_name__						) > 0:
           _a			      =							args[0]
           _a			      =							args[1:]

      if audio is None and text is None:
           raise ValueError('You need to specify either an `audio` or `text` input to process.'						)

      if text is not None:
           _a			      =							self.tokenizer(__magic_name__      ,				**__magic_name__						)

      if audio is not None:
           _a			      =							self.feature_extractor(__magic_name__      ,				*__magic_name__      ,				sampling_rate=__magic_name__      ,				**__magic_name__						)

      if audio is None:
           return inputs

      elif text is None:
           return audio_inputs

      else:
           _a			      =							audio_inputs['input_values']
           if "padding_mask" in audio_inputs:
                _a			      =							audio_inputs['padding_mask']
           return inputs
 def 							__UpperCAmelCase		( self      ,				*__magic_name__      ,				**__magic_name__						)					->							Tuple:
      _a			      =							kwargs.pop('audio'      ,				__magic_name__						)
      _a			      =							kwargs.pop('padding_mask'      ,				__magic_name__						)

      if len(__magic_name__						) > 0:
           _a			      =							args[0]
           _a			      =							args[1:]

      if audio_values is not None:
           return self._decode_audio(__magic_name__      ,				padding_mask=__magic_name__						)
      else:
           return self.tokenizer.batch_decode(*__magic_name__      ,				**__magic_name__						)
 def 							__UpperCAmelCase		( self      ,				*__magic_name__      ,				**__magic_name__						)					->							List[Any]:
      return self.tokenizer.decode(*__magic_name__      ,				**__magic_name__						)




 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__ = None						)					->							List[np.ndarray]:
      _a			      =							to_numpy(__magic_name__						)
      _a      ,   _a      ,   _a			      =							audio_values.shape

      if padding_mask is None:
           return list(__magic_name__						)

      _a			      =							to_numpy(__magic_name__						)

      # match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
      # token (so that the generated audio values are **not** treated as padded tokens)
      _a			      =							seq_len - padding_mask.shape[-1]
      _a			      =							1 - self.feature_extractor.padding_value
      _a			      =							np.pad(__magic_name__      ,				((0, 0), (0, difference))      ,				'constant'      ,				constant_values=__magic_name__						)

      _a			      =							audio_values.tolist()
      for i in range(__magic_name__						):
           _a			      =							np.asarray(audio_values[i]						)[
               padding_mask[i][None, :] != self.feature_extractor.padding_value
           ]
           _a			      =							sliced_audio.reshape(__magic_name__      ,				-1						)

      return audio_values

