


'''simple docstring'''


import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
     import torch

if is_datasets_available():
     from datasets import load_dataset

__magic_name__			=      random.Random()




def      lowerCamelCase					(      lowerCamelCase  :      Tuple							,						lowerCamelCase  :      List[str]=1.0							,						lowerCamelCase  :      Optional[int]=None							,						lowerCamelCase  :      List[str]=None):
      if rng is None:
            A_						:  Dict          =				global_rng

      A_						:  List[str]          =				[]
      for batch_idx in range(shape[0]):
            values.append([])
            for _ in range(shape[1]):
                  values[-1].append(rng.random() * scale)

      return values



class 	__lowerCAmelCase		(							unittest.TestCase    ):


  '''simple docstring'''







  def __init__(							self :       List[Any]	,_a :       Optional[Any]	,_a :       str=7	,_a :       int=400	,_a :       List[str]=2000	,_a :       Any=2048	,_a :       Any=128	,_a :       int=1	,_a :       Optional[int]=512	,_a :       Union[str, Any]=30	,_a :       Any=44100	,):

        '''simple docstring'''




        A_						:  Optional[Any]          =				parent
        A_						:  str          =				batch_size
        A_						:  str          =				min_seq_length
        A_						:  Union[str, Any]          =				max_seq_length
        A_						:  List[Any]          =				(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
        A_						:  Union[str, Any]          =				spectrogram_length
        A_						:  Optional[int]          =				feature_size
        A_						:  Tuple          =				num_audio_channels
        A_						:  List[str]          =				hop_length
        A_						:  Union[str, Any]          =				chunk_length
        A_						:  Tuple          =				sampling_rate






  def        _a							(							self :       Dict ):

        '''simple docstring'''




        return {
            "spectrogram_length": self.spectrogram_length,
            "feature_size": self.feature_size,
            "num_audio_channels": self.num_audio_channels,
            "hop_length": self.hop_length,
            "chunk_length": self.chunk_length,
            "sampling_rate": self.sampling_rate,
        }






  def        _a							(							self :       int	,_a :       List[Any]=False	,_a :       List[str]=False ):

        '''simple docstring'''




        def _flatten(_a :       int ):
              return list(itertools.chain(*_a ) )

        if equal_length:
              A_						:  Union[str, Any]          =				[floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
        else:
              # make sure that inputs increase in size
              A_						:  str          =				[
                  floats_list((x, self.feature_size) )
                  for x in range(self.min_seq_length	,self.max_seq_length	,self.seq_length_diff )
              ]
        if numpify:
              A_						:  Tuple          =				[np.asarray(_a ) for x in speech_inputs]
        return speech_inputs







@require_torch
@require_torchaudio
class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE  , unittest.TestCase    ):


  '''simple docstring'''







  a_  					=							TvltFeatureExtractor






  def        _a							(							self :       Optional[int] ):

        '''simple docstring'''




        A_						:  Dict          =				TvltFeatureExtractionTester(self )






  def        _a							(							self :       Optional[Any] ):

        '''simple docstring'''




        A_						:  List[Any]          =				self.feature_extraction_class(**self.feat_extract_dict )
        self.assertTrue(hasattr(_a	,"""spectrogram_length""" ) )
        self.assertTrue(hasattr(_a	,"""feature_size""" ) )
        self.assertTrue(hasattr(_a	,"""num_audio_channels""" ) )
        self.assertTrue(hasattr(_a	,"""hop_length""" ) )
        self.assertTrue(hasattr(_a	,"""chunk_length""" ) )
        self.assertTrue(hasattr(_a	,"""sampling_rate""" ) )






  def        _a							(							self :       str ):

        '''simple docstring'''




        A_						:  Dict          =				self.feature_extraction_class(**self.feat_extract_dict )

        with tempfile.TemporaryDirectory() as tmpdirname:
              A_						:  List[Any]          =				feat_extract_first.save_pretrained(_a )[0]
              check_json_file_has_correct_format(_a )
              A_						:  Any          =				self.feature_extraction_class.from_pretrained(_a )

        A_						:  Any          =				feat_extract_first.to_dict()
        A_						:  List[str]          =				feat_extract_second.to_dict()
        A_						:  Union[str, Any]          =				dict_first.pop("""mel_filters""" )
        A_						:  str          =				dict_second.pop("""mel_filters""" )
        self.assertTrue(np.allclose(_a	,_a ) )
        self.assertEqual(_a	,_a )






  def        _a							(							self :       int ):

        '''simple docstring'''




        A_						:  Dict          =				self.feature_extraction_class(**self.feat_extract_dict )

        with tempfile.TemporaryDirectory() as tmpdirname:
              A_						:  Optional[Any]          =				os.path.join(_a	,"""feat_extract.json""" )
              feat_extract_first.to_json_file(_a )
              A_						:  Optional[Any]          =				self.feature_extraction_class.from_json_file(_a )

        A_						:  Optional[int]          =				feat_extract_first.to_dict()
        A_						:  int          =				feat_extract_second.to_dict()
        A_						:  Optional[Any]          =				dict_first.pop("""mel_filters""" )
        A_						:  List[str]          =				dict_second.pop("""mel_filters""" )
        self.assertTrue(np.allclose(_a	,_a ) )
        self.assertEqual(_a	,_a )






  def        _a							(							self :       List[Any] ):

        '''simple docstring'''




        A_						:  Any          =				self.feature_extraction_class(**self.feat_extract_dict )

        # create three inputs of length 800, 1000, and 1200
        A_						:  Optional[int]          =				[floats_list((1, x) )[0] for x in range(800	,1400	,200 )]
        A_						:  Optional[Any]          =				[np.asarray(_a ) for speech_input in speech_inputs]

        # Test not batched input
        A_						:  Union[str, Any]          =				feature_extractor(np_speech_inputs[0]	,return_tensors="""np"""	,sampling_rate=44100 ).audio_values

        self.assertTrue(encoded_audios.ndim == 4 )
        self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
        self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
        self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )

        # Test batched
        A_						:  Union[str, Any]          =				feature_extractor(_a	,return_tensors="""np"""	,sampling_rate=44100 ).audio_values

        self.assertTrue(encoded_audios.ndim == 4 )
        self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
        self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
        self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )

        # Test audio masking
        A_						:  str          =				feature_extractor(
            _a	,return_tensors="""np"""	,sampling_rate=44100	,mask_audio=_a ).audio_values

        self.assertTrue(encoded_audios.ndim == 4 )
        self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
        self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
        self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )

        # Test 2-D numpy arrays are batched.
        A_						:  List[str]          =				[floats_list((1, x) )[0] for x in (800, 800, 800)]
        A_						:  str          =				np.asarray(_a )
        A_						:  Tuple          =				feature_extractor(_a	,return_tensors="""np"""	,sampling_rate=44100 ).audio_values
        self.assertTrue(encoded_audios.ndim == 4 )
        self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
        self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
        self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )






  def        _a							(							self :       Tuple	,_a :       Union[str, Any] ):

        '''simple docstring'''




        A_						:  Union[str, Any]          =				load_dataset("""hf-internal-testing/librispeech_asr_dummy"""	,"""clean"""	,split="""validation""" )
        # automatic decoding with librispeech
        A_						:  Any          =				ds.sort("""id""" ).select(range(_a ) )[:num_samples]["""audio"""]

        return [x["array"] for x in speech_samples]






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

        '''simple docstring'''




        A_						:  List[Any]          =				self._load_datasamples(1 )
        A_						:  Dict          =				TvltFeatureExtractor()
        A_						:  int          =				feature_extractor(_a	,return_tensors="""pt""" ).audio_values

        self.assertEquals(audio_values.shape	,(1, 1, 192, 128) )

        A_						:  Any          =				torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]] )
        self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]	,_a	,atol=1e-4 ) )




'''simple docstring'''


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


__magic_name__			=      logging.get_logger(__name__)


if is_vision_available():
     import PIL



class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  a_  					=							["""pixel_values"""]






  def __init__(							self :       Optional[Any]	,_a :       bool = True	,_a :       Dict[str, int] = None	,_a :       PILImageResampling = PILImageResampling.BICUBIC	,_a :       bool = True	,_a :       Dict[str, int] = None	,_a :       bool = True	,_a :       Union[int, float] = 1 / 255	,_a :       bool = True	,_a :       Optional[Union[float, List[float]]] = None	,_a :       Optional[Union[float, List[float]]] = None	,_a :       bool = True	,**_a :       Dict	,):

        '''simple docstring'''




        super().__init__(**_a )
        A_						:  Tuple          =				size if size is not None else {"""shortest_edge""": 224}
        A_						:  Optional[Any]          =				get_size_dict(_a	,default_to_square=_a )
        A_						:  Tuple          =				crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
        A_						:  Optional[Any]          =				get_size_dict(_a	,default_to_square=_a	,param_name="""crop_size""" )

        A_						:  Any          =				do_resize
        A_						:  List[str]          =				size
        A_						:  Union[str, Any]          =				resample
        A_						:  Dict          =				do_center_crop
        A_						:  List[str]          =				crop_size
        A_						:  Any          =				do_rescale
        A_						:  Union[str, Any]          =				rescale_factor
        A_						:  Any          =				do_normalize
        A_						:  List[str]          =				image_mean if image_mean is not None else OPENAI_CLIP_MEAN
        A_						:  List[Any]          =				image_std if image_std is not None else OPENAI_CLIP_STD
        A_						:  Tuple          =				do_convert_rgb






  def        _a							(							self :       Optional[int]	,_a :       np.ndarray	,_a :       Dict[str, int]	,_a :       PILImageResampling = PILImageResampling.BICUBIC	,_a :       Optional[Union[str, ChannelDimension]] = None	,**_a :       Optional[Any]	,):

        '''simple docstring'''




        A_						:  Optional[Any]          =				get_size_dict(_a	,default_to_square=_a )
        if "shortest_edge" not in size:
              raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}' )
        A_						:  Tuple          =				get_resize_output_image_size(_a	,size=size["""shortest_edge"""]	,default_to_square=_a )
        return resize(_a	,size=_a	,resample=_a	,data_format=_a	,**_a )






  def        _a							(							self :       List[Any]	,_a :       np.ndarray	,_a :       Dict[str, int]	,_a :       Optional[Union[str, ChannelDimension]] = None	,**_a :       Optional[int]	,):

        '''simple docstring'''




        A_						:  Optional[int]          =				get_size_dict(_a )
        if "height" not in size or "width" not in size:
              raise ValueError(f'The `size` parameter must contain the keys (height, width). Got {size.keys()}' )
        return center_crop(_a	,size=(size["""height"""], size["""width"""])	,data_format=_a	,**_a )






  def        _a							(							self :       Any	,_a :       np.ndarray	,_a :       Union[int, float]	,_a :       Optional[Union[str, ChannelDimension]] = None	,**_a :       Any	,):

        '''simple docstring'''




        return rescale(_a	,scale=_a	,data_format=_a	,**_a )






  def        _a							(							self :       Any	,_a :       np.ndarray	,_a :       Union[float, List[float]]	,_a :       Union[float, List[float]]	,_a :       Optional[Union[str, ChannelDimension]] = None	,**_a :       List[str]	,):

        '''simple docstring'''




        return normalize(_a	,mean=_a	,std=_a	,data_format=_a	,**_a )






  def        _a							(							self :       Optional[Any]	,_a :       ImageInput	,_a :       bool = None	,_a :       Dict[str, int] = None	,_a :       PILImageResampling = None	,_a :       bool = None	,_a :       int = None	,_a :       bool = None	,_a :       float = None	,_a :       bool = None	,_a :       Optional[Union[float, List[float]]] = None	,_a :       Optional[Union[float, List[float]]] = None	,_a :       bool = None	,_a :       Optional[Union[str, TensorType]] = None	,_a :       Optional[ChannelDimension] = ChannelDimension.FIRST	,**_a :       int	,):

        '''simple docstring'''




        A_						:  Union[str, Any]          =				do_resize if do_resize is not None else self.do_resize
        A_						:  Tuple          =				size if size is not None else self.size
        A_						:  Optional[int]          =				get_size_dict(_a	,param_name="""size"""	,default_to_square=_a )
        A_						:  List[str]          =				resample if resample is not None else self.resample
        A_						:  int          =				do_center_crop if do_center_crop is not None else self.do_center_crop
        A_						:  Any          =				crop_size if crop_size is not None else self.crop_size
        A_						:  int          =				get_size_dict(_a	,param_name="""crop_size"""	,default_to_square=_a )
        A_						:  List[Any]          =				do_rescale if do_rescale is not None else self.do_rescale
        A_						:  int          =				rescale_factor if rescale_factor is not None else self.rescale_factor
        A_						:  Any          =				do_normalize if do_normalize is not None else self.do_normalize
        A_						:  int          =				image_mean if image_mean is not None else self.image_mean
        A_						:  int          =				image_std if image_std is not None else self.image_std
        A_						:  List[str]          =				do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

        A_						:  int          =				make_list_of_images(_a )

        if not valid_images(_a ):
              raise ValueError(
                  """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
                  """torch.Tensor, tf.Tensor or jax.ndarray.""" )

        if do_resize and size is None:
              raise ValueError("""Size must be specified if do_resize is True.""" )

        if do_center_crop and crop_size is None:
              raise ValueError("""Crop size must be specified if do_center_crop is True.""" )

        if do_rescale and rescale_factor is None:
              raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )

        if do_normalize and (image_mean is None or image_std is None):
              raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )

        # PIL RGBA images are converted to RGB
        if do_convert_rgb:
              A_						:  Optional[int]          =				[convert_to_rgb(_a ) for image in images]

        # All transformations expect numpy arrays.
        A_						:  Dict          =				[to_numpy_array(_a ) for image in images]

        if do_resize:
              A_						:  int          =				[self.resize(image=_a	,size=_a	,resample=_a ) for image in images]

        if do_center_crop:
              A_						:  Tuple          =				[self.center_crop(image=_a	,size=_a ) for image in images]

        if do_rescale:
              A_						:  List[str]          =				[self.rescale(image=_a	,scale=_a ) for image in images]

        if do_normalize:
              A_						:  Any          =				[self.normalize(image=_a	,mean=_a	,std=_a ) for image in images]

        A_						:  List[str]          =				[to_channel_dimension_format(_a	,_a ) for image in images]

        A_						:  List[str]          =				{"""pixel_values""": images}
        return BatchFeature(data=_a	,tensor_type=_a )
