






"""simple docstring"""







import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

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 DPTImageProcessor







class        _SCREAMING_SNAKE_CASE						(  unittest.TestCase							):





							"""simple docstring"""





							def __init__(   self:     Dict				,		__A:     List[str]				,		__A:     List[str]=7				,		__A:     Dict=3				,		__A:     List[str]=18				,		__A:     Tuple=30				,		__A:     Optional[Any]=400				,		__A:     Union[str, Any]=True				,		__A:     Union[str, Any]=None				,		__A:     str=True				,		__A:     Optional[int]=[0.5, 0.5, 0.5]				,		__A:     List[str]=[0.5, 0.5, 0.5]				,		):

												'''simple docstring'''

												a__          =  size if 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_normalize
												a__          =  image_mean
												a__          =  image_std





							def  lowercase		(   self:     str    ):

												'''simple docstring'''

												return {
												    "image_mean": self.image_mean,
												    "image_std": self.image_std,
												    "do_normalize": self.do_normalize,
												    "do_resize": self.do_resize,
												    "size": self.size,
												}







@require_torch
@require_vision
class        _SCREAMING_SNAKE_CASE						(  __snake_case       , unittest.TestCase							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    =DPTImageProcessor if is_vision_available() else None




							def  lowercase		(   self:     List[str]    ):

												'''simple docstring'''

												a__          =  DPTImageProcessingTester(self    )




							@property
							def  lowercase		(   self:     Union[str, Any]    ):

												'''simple docstring'''

												return self.image_processor_tester.prepare_image_processor_dict()




							def  lowercase		(   self:     Union[str, Any]    ):

												'''simple docstring'''

												a__          =  self.image_processing_class(**self.image_processor_dict    )
												self.assertTrue(hasattr(__A				,		'''image_mean'''    )    )
												self.assertTrue(hasattr(__A				,		'''image_std'''    )    )
												self.assertTrue(hasattr(__A				,		'''do_normalize'''    )    )
												self.assertTrue(hasattr(__A				,		'''do_resize'''    )    )
												self.assertTrue(hasattr(__A				,		'''size'''    )    )




							def  lowercase		(   self:     List[str]    ):

												'''simple docstring'''

												a__          =  self.image_processing_class.from_dict(self.image_processor_dict    )
												self.assertEqual(image_processor.size				,		{'''height''': 18, '''width''': 18}    )

												a__          =  self.image_processing_class.from_dict(self.image_processor_dict				,		size=42    )
												self.assertEqual(image_processor.size				,		{'''height''': 42, '''width''': 42}    )




							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												a__          =  self.image_processing_class(**self.image_processor_dict    )
												# create random PIL images
												a__          =  prepare_image_inputs(self.image_processor_tester				,		equal_resolution=__A    )
												for image in image_inputs:
																	self.assertIsInstance(__A				,		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['''height'''],
												        self.image_processor_tester.size['''width'''],
												    )				,		)

												# Test batched
												a__          =  image_processing(__A				,		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['''height'''],
												        self.image_processor_tester.size['''width'''],
												    )				,		)




							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												a__          =  self.image_processing_class(**self.image_processor_dict    )
												# create random numpy tensors
												a__          =  prepare_image_inputs(self.image_processor_tester				,		equal_resolution=__A				,		numpify=__A    )
												for image in image_inputs:
																	self.assertIsInstance(__A				,		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['''height'''],
												        self.image_processor_tester.size['''width'''],
												    )				,		)

												# Test batched
												a__          =  image_processing(__A				,		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['''height'''],
												        self.image_processor_tester.size['''width'''],
												    )				,		)





							def  lowercase		(   self:     int    ):

												'''simple docstring'''

												a__          =  self.image_processing_class(**self.image_processor_dict    )
												# create random PyTorch tensors
												a__          =  prepare_image_inputs(self.image_processor_tester				,		equal_resolution=__A				,		torchify=__A    )
												for image in image_inputs:
																	self.assertIsInstance(__A				,		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['''height'''],
												        self.image_processor_tester.size['''width'''],
												    )				,		)

												# Test batched
												a__          =  image_processing(__A				,		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['''height'''],
												        self.image_processor_tester.size['''width'''],
												    )				,		)










"""simple docstring"""







from typing import List, Optional

import numpy as np

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







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    ='EncodecFeatureExtractor'
							_SCREAMING_SNAKE_CASE    =('T5Tokenizer', 'T5TokenizerFast')




							def __init__(   self:     List[Any]				,		__A:     Any				,		__A:     Dict    ):

												'''simple docstring'''

												super().__init__(__A				,		__A    )
												a__          =  self.feature_extractor
												a__          =  False




							def  lowercase		(   self:     Union[str, Any]				,		__A:     List[Any]=None				,		__A:     Optional[Any]=None				,		__A:     List[Any]=True    ):

												'''simple docstring'''

												return self.tokenizer.get_decoder_prompt_ids(task=__A				,		language=__A				,		no_timestamps=__A    )




							def __call__(   self:     Union[str, Any]				,		*__A:     int				,		**__A:     Dict    ):

												'''simple docstring'''

												if self._in_target_context_manager:
																	return self.current_processor(*__A				,		**__A    )

												a__          =  kwargs.pop('''audio'''				,		__A    )
												a__          =  kwargs.pop('''sampling_rate'''				,		__A    )
												a__          =  kwargs.pop('''text'''				,		__A    )
												if len(__A    ) > 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(__A				,		**__A    )

												if audio is not None:
																	a__          =  self.feature_extractor(__A				,		*__A				,		sampling_rate=__A				,		**__A    )

												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  lowercase		(   self:     Union[str, Any]				,		*__A:     List[str]				,		**__A:     Tuple    ):

												'''simple docstring'''

												a__          =  kwargs.pop('''audio'''				,		__A    )
												a__          =  kwargs.pop('''padding_mask'''				,		__A    )

												if len(__A    ) > 0:
																	a__          =  args[0]
																	a__          =  args[1:]

												if audio_values is not None:
																	return self._decode_audio(__A				,		padding_mask=__A    )
												else:
																	return self.tokenizer.batch_decode(*__A				,		**__A    )




							def  lowercase		(   self:     Union[str, Any]				,		*__A:     Optional[int]				,		**__A:     Optional[Any]    ):

												'''simple docstring'''

												return self.tokenizer.decode(*__A				,		**__A    )





							def  lowercase		(   self:     Union[str, Any]				,		__A:     Dict				,		__A:     Optional = None    ):

												'''simple docstring'''

												a__          =  to_numpy(__A    )
												a__       ,a__       ,a__          =  audio_values.shape

												if padding_mask is None:
																	return list(__A    )

												a__          =  to_numpy(__A    )

												# 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(__A				,		((0, 0), (0, difference))				,		'''constant'''				,		constant_values=__A    )

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

												return audio_values


