





from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
    import PIL

_UpperCAmelCase       : List[Any]			     =       logging.get_logger(__name__)


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase				)    ->  List[List[ImageInput]]:
    if isinstance(_UpperCAmelCase		,			(list, tuple)				) and isinstance(videos[0]		,			(list, tuple)				) and is_valid_image(videos[0][0]				):
        return videos

    elif isinstance(_UpperCAmelCase		,			(list, tuple)				) and is_valid_image(videos[0]				):
        return [videos]

    elif is_valid_image(_UpperCAmelCase				):
        return [[videos]]

    raise ValueError(F"""Could not make batched video from {videos}"""				)


class      lowerCAmelCase							(      __UpperCamelCase    ):
 UpperCAmelCase__									=	["""pixel_values"""]
 def __init__(   self						:   int    ,  UpperCAmelCase						:   bool = True    ,  UpperCAmelCase						:   Dict[str, int] = None    ,  UpperCAmelCase						:   PILImageResampling = PILImageResampling.BILINEAR    ,  UpperCAmelCase						:   bool = True    ,  UpperCAmelCase						:   Dict[str, int] = None    ,  UpperCAmelCase						:   bool = True    ,  UpperCAmelCase						:   Union[int, float] = 1 / 255    ,  UpperCAmelCase						:   bool = True    ,  UpperCAmelCase						:   bool = True    ,  UpperCAmelCase						:   Optional[Union[float, List[float]]] = None    ,  UpperCAmelCase						:   Optional[Union[float, List[float]]] = None    ,  **UpperCAmelCase						:   Any    ,  )    ->			None:
     super().__init__(**UpperCAmelCase			)
     lowerCamelCase__					:   Any      				=	size if size is not None else {'shortest_edge': 256}
     lowerCamelCase__					:   str      				=	get_size_dict(UpperCAmelCase    ,  default_to_square=UpperCAmelCase			)
     lowerCamelCase__					:   Tuple      				=	crop_size if crop_size is not None else {'height': 224, 'width': 224}
     lowerCamelCase__					:   List[str]      				=	get_size_dict(UpperCAmelCase    ,  param_name='crop_size'			)

     lowerCamelCase__					:   Tuple      				=	do_resize
     lowerCamelCase__					:   Union[str, Any]      				=	size
     lowerCamelCase__					:   Union[str, Any]      				=	do_center_crop
     lowerCamelCase__					:   Dict      				=	crop_size
     lowerCamelCase__					:   List[Any]      				=	resample
     lowerCamelCase__					:   Tuple      				=	do_rescale
     lowerCamelCase__					:   Optional[int]      				=	rescale_factor
     lowerCamelCase__					:   int      				=	offset
     lowerCamelCase__					:   Tuple      				=	do_normalize
     lowerCamelCase__					:   Union[str, Any]      				=	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     lowerCamelCase__					:   str      				=	image_std if image_std is not None else IMAGENET_STANDARD_STD
 def   A_			(   self						:   Union[str, Any]    ,  UpperCAmelCase						:   np.ndarray    ,  UpperCAmelCase						:   Dict[str, int]    ,  UpperCAmelCase						:   PILImageResampling = PILImageResampling.BILINEAR    ,  UpperCAmelCase						:   Optional[Union[str, ChannelDimension]] = None    ,  **UpperCAmelCase						:   Tuple    ,  )    ->			np.ndarray:
     lowerCamelCase__					:   int      				=	get_size_dict(UpperCAmelCase    ,  default_to_square=UpperCAmelCase			)
     if "shortest_edge" in size:
         lowerCamelCase__					:   Optional[int]      				=	get_resize_output_image_size(UpperCAmelCase    ,  size['shortest_edge']    ,  default_to_square=UpperCAmelCase			)
     elif "height" in size and "width" in size:
         lowerCamelCase__					:   Dict      				=	(size['height'], size['width'])
     else:
         raise ValueError(F"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""			)
     return resize(UpperCAmelCase    ,  size=UpperCAmelCase    ,  resample=UpperCAmelCase    ,  data_format=UpperCAmelCase    ,  **UpperCAmelCase			)
 def   A_			(   self						:   str    ,  UpperCAmelCase						:   np.ndarray    ,  UpperCAmelCase						:   Dict[str, int]    ,  UpperCAmelCase						:   Optional[Union[str, ChannelDimension]] = None    ,  **UpperCAmelCase						:   int    ,  )    ->			np.ndarray:
     lowerCamelCase__					:   Optional[Any]      				=	get_size_dict(UpperCAmelCase			)
     if "height" not in size or "width" not in size:
         raise ValueError(F"""Size must have 'height' and 'width' as keys. Got {size.keys()}"""			)
     return center_crop(UpperCAmelCase    ,  size=(size['height'], size['width'])    ,  data_format=UpperCAmelCase    ,  **UpperCAmelCase			)
 def   A_			(   self						:   str    ,  UpperCAmelCase						:   np.ndarray    ,  UpperCAmelCase						:   Union[int, float]    ,  UpperCAmelCase						:   bool = True    ,  UpperCAmelCase						:   Optional[Union[str, ChannelDimension]] = None    ,  **UpperCAmelCase						:   str    ,  )    ->			List[str]:
     lowerCamelCase__					:   Optional[int]      				=	image.astype(np.floataa			)
     if offset:
         lowerCamelCase__					:   Optional[int]      				=	image - (scale / 2)
     return rescale(UpperCAmelCase    ,  scale=UpperCAmelCase    ,  data_format=UpperCAmelCase    ,  **UpperCAmelCase			)
 def   A_			(   self						:   Union[str, Any]    ,  UpperCAmelCase						:   np.ndarray    ,  UpperCAmelCase						:   Union[float, List[float]]    ,  UpperCAmelCase						:   Union[float, List[float]]    ,  UpperCAmelCase						:   Optional[Union[str, ChannelDimension]] = None    ,  **UpperCAmelCase						:   List[Any]    ,  )    ->			np.ndarray:
     return normalize(UpperCAmelCase    ,  mean=UpperCAmelCase    ,  std=UpperCAmelCase    ,  data_format=UpperCAmelCase    ,  **UpperCAmelCase			)
 def   A_			(   self						:   Any    ,  UpperCAmelCase						:   ImageInput    ,  UpperCAmelCase						:   bool = None    ,  UpperCAmelCase						:   Dict[str, int] = None    ,  UpperCAmelCase						:   PILImageResampling = None    ,  UpperCAmelCase						:   bool = None    ,  UpperCAmelCase						:   Dict[str, int] = None    ,  UpperCAmelCase						:   bool = None    ,  UpperCAmelCase						:   float = None    ,  UpperCAmelCase						:   bool = None    ,  UpperCAmelCase						:   bool = None    ,  UpperCAmelCase						:   Optional[Union[float, List[float]]] = None    ,  UpperCAmelCase						:   Optional[Union[float, List[float]]] = None    ,  UpperCAmelCase						:   Optional[ChannelDimension] = ChannelDimension.FIRST    ,  )    ->			np.ndarray:
     if do_resize and size is None or resample is None:
         raise ValueError('Size and resample 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.'			)

     if offset and not do_rescale:
         raise ValueError('For offset, do_rescale must also be set to True.'			)

     # All transformations expect numpy arrays.
     lowerCamelCase__					:   Tuple      				=	to_numpy_array(UpperCAmelCase			)

     if do_resize:
         lowerCamelCase__					:   int      				=	self.resize(image=UpperCAmelCase    ,  size=UpperCAmelCase    ,  resample=UpperCAmelCase			)

     if do_center_crop:
         lowerCamelCase__					:   str      				=	self.center_crop(UpperCAmelCase    ,  size=UpperCAmelCase			)

     if do_rescale:
         lowerCamelCase__					:   str      				=	self.rescale(image=UpperCAmelCase    ,  scale=UpperCAmelCase    ,  offset=UpperCAmelCase			)

     if do_normalize:
         lowerCamelCase__					:   Dict      				=	self.normalize(image=UpperCAmelCase    ,  mean=UpperCAmelCase    ,  std=UpperCAmelCase			)

     lowerCamelCase__					:   List[Any]      				=	to_channel_dimension_format(UpperCAmelCase    ,  UpperCAmelCase			)
     return image







 def   A_			(   self						:   int    ,  UpperCAmelCase						:   ImageInput    ,  UpperCAmelCase						:   bool = None    ,  UpperCAmelCase						:   Dict[str, int] = None    ,  UpperCAmelCase						:   PILImageResampling = None    ,  UpperCAmelCase						:   bool = None    ,  UpperCAmelCase						:   Dict[str, int] = None    ,  UpperCAmelCase						:   bool = None    ,  UpperCAmelCase						:   float = None    ,  UpperCAmelCase						:   bool = None    ,  UpperCAmelCase						:   bool = None    ,  UpperCAmelCase						:   Optional[Union[float, List[float]]] = None    ,  UpperCAmelCase						:   Optional[Union[float, List[float]]] = None    ,  UpperCAmelCase						:   Optional[Union[str, TensorType]] = None    ,  UpperCAmelCase						:   ChannelDimension = ChannelDimension.FIRST    ,  **UpperCAmelCase						:   int    ,  )    ->			PIL.Image.Image:
     lowerCamelCase__					:   List[str]      				=	do_resize if do_resize is not None else self.do_resize
     lowerCamelCase__					:   List[Any]      				=	resample if resample is not None else self.resample
     lowerCamelCase__					:   Dict      				=	do_center_crop if do_center_crop is not None else self.do_center_crop
     lowerCamelCase__					:   int      				=	do_rescale if do_rescale is not None else self.do_rescale
     lowerCamelCase__					:   str      				=	rescale_factor if rescale_factor is not None else self.rescale_factor
     lowerCamelCase__					:   Union[str, Any]      				=	offset if offset is not None else self.offset
     lowerCamelCase__					:   Optional[Any]      				=	do_normalize if do_normalize is not None else self.do_normalize
     lowerCamelCase__					:   Tuple      				=	image_mean if image_mean is not None else self.image_mean
     lowerCamelCase__					:   List[str]      				=	image_std if image_std is not None else self.image_std

     lowerCamelCase__					:   Union[str, Any]      				=	size if size is not None else self.size
     lowerCamelCase__					:   int      				=	get_size_dict(UpperCAmelCase    ,  default_to_square=UpperCAmelCase			)
     lowerCamelCase__					:   Optional[int]      				=	crop_size if crop_size is not None else self.crop_size
     lowerCamelCase__					:   List[Any]      				=	get_size_dict(UpperCAmelCase    ,  param_name='crop_size'			)

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

     lowerCamelCase__					:   Any      				=	make_batched(UpperCAmelCase			)

     lowerCamelCase__					:   List[Any]      				=	[
         [
             self._preprocess_image(
                 image=UpperCAmelCase    ,  do_resize=UpperCAmelCase    ,  size=UpperCAmelCase    ,  resample=UpperCAmelCase    ,  do_center_crop=UpperCAmelCase    ,  crop_size=UpperCAmelCase    ,  do_rescale=UpperCAmelCase    ,  rescale_factor=UpperCAmelCase    ,  offset=UpperCAmelCase    ,  do_normalize=UpperCAmelCase    ,  image_mean=UpperCAmelCase    ,  image_std=UpperCAmelCase    ,  data_format=UpperCAmelCase    ,  )
             for img in video
         ]
         for video in videos
     ]

     lowerCamelCase__					:   Union[str, Any]      				=	{'pixel_values': videos}
     return BatchFeature(data=UpperCAmelCase    ,  tensor_type=UpperCAmelCase			)












import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list

@require_torchaudio
@require_sentencepiece
class      lowerCAmelCase							(      unittest.TestCase    ):
 def   A_			(   self						:   str			)    ->			Union[str, Any]:
     lowerCamelCase__					:   Optional[int]      				=	'laion/clap-htsat-unfused'
     lowerCamelCase__					:   List[Any]      				=	tempfile.mkdtemp()
 def   A_			(   self						:   Optional[int]    ,  **UpperCAmelCase						:   int			)    ->			Optional[Any]:
     return RobertaTokenizer.from_pretrained(self.checkpoint    ,  **UpperCAmelCase			)
 def   A_			(   self						:   Union[str, Any]    ,  **UpperCAmelCase						:   Any			)    ->			List[str]:
     return ClapFeatureExtractor.from_pretrained(self.checkpoint    ,  **UpperCAmelCase			)
 def   A_			(   self						:   List[str]			)    ->			Dict:
     shutil.rmtree(self.tmpdirname			)
 def   A_			(   self						:   Optional[Any]			)    ->			Optional[Any]:
     lowerCamelCase__					:   Tuple      				=	self.get_tokenizer()
     lowerCamelCase__					:   Dict      				=	self.get_feature_extractor()

     lowerCamelCase__					:   Dict      				=	ClapProcessor(tokenizer=UpperCAmelCase    ,  feature_extractor=UpperCAmelCase			)

     processor.save_pretrained(self.tmpdirname			)
     lowerCamelCase__					:   List[Any]      				=	ClapProcessor.from_pretrained(self.tmpdirname			)

     self.assertEqual(processor.tokenizer.get_vocab()    ,  tokenizer.get_vocab()			)
     self.assertIsInstance(processor.tokenizer    ,  UpperCAmelCase			)

     self.assertEqual(processor.feature_extractor.to_json_string()    ,  feature_extractor.to_json_string()			)
     self.assertIsInstance(processor.feature_extractor    ,  UpperCAmelCase			)
 def   A_			(   self						:   str			)    ->			int:
     lowerCamelCase__					:   Optional[Any]      				=	ClapProcessor(tokenizer=self.get_tokenizer()    ,  feature_extractor=self.get_feature_extractor()			)
     processor.save_pretrained(self.tmpdirname			)

     lowerCamelCase__					:   Dict      				=	self.get_tokenizer(bos_token='(BOS)'    ,  eos_token='(EOS)'			)
     lowerCamelCase__					:   Dict      				=	self.get_feature_extractor(do_normalize=UpperCAmelCase    ,  padding_value=1.0			)

     lowerCamelCase__					:   str      				=	ClapProcessor.from_pretrained(
         self.tmpdirname    ,  bos_token='(BOS)'    ,  eos_token='(EOS)'    ,  do_normalize=UpperCAmelCase    ,  padding_value=1.0			)

     self.assertEqual(processor.tokenizer.get_vocab()    ,  tokenizer_add_kwargs.get_vocab()			)
     self.assertIsInstance(processor.tokenizer    ,  UpperCAmelCase			)

     self.assertEqual(processor.feature_extractor.to_json_string()    ,  feature_extractor_add_kwargs.to_json_string()			)
     self.assertIsInstance(processor.feature_extractor    ,  UpperCAmelCase			)
 def   A_			(   self						:   List[Any]			)    ->			int:
     lowerCamelCase__					:   Any      				=	self.get_feature_extractor()
     lowerCamelCase__					:   str      				=	self.get_tokenizer()

     lowerCamelCase__					:   Optional[Any]      				=	ClapProcessor(tokenizer=UpperCAmelCase    ,  feature_extractor=UpperCAmelCase			)

     lowerCamelCase__					:   Optional[Any]      				=	floats_list((3, 1000)			)

     lowerCamelCase__					:   Tuple      				=	feature_extractor(UpperCAmelCase    ,  return_tensors='np'			)
     lowerCamelCase__					:   Tuple      				=	processor(audios=UpperCAmelCase    ,  return_tensors='np'			)

     for key in input_feat_extract.keys():
         self.assertAlmostEqual(input_feat_extract[key].sum()    ,  input_processor[key].sum()    ,  delta=1e-2			)
 def   A_			(   self						:   List[str]			)    ->			Tuple:
     lowerCamelCase__					:   Dict      				=	self.get_feature_extractor()
     lowerCamelCase__					:   List[Any]      				=	self.get_tokenizer()

     lowerCamelCase__					:   Any      				=	ClapProcessor(tokenizer=UpperCAmelCase    ,  feature_extractor=UpperCAmelCase			)

     lowerCamelCase__					:   Union[str, Any]      				=	'This is a test string'

     lowerCamelCase__					:   Union[str, Any]      				=	processor(text=UpperCAmelCase			)

     lowerCamelCase__					:   Any      				=	tokenizer(UpperCAmelCase			)

     for key in encoded_tok.keys():
         self.assertListEqual(encoded_tok[key]    ,  encoded_processor[key]			)
 def   A_			(   self						:   Optional[int]			)    ->			Tuple:
     lowerCamelCase__					:   Tuple      				=	self.get_feature_extractor()
     lowerCamelCase__					:   List[str]      				=	self.get_tokenizer()

     lowerCamelCase__					:   Optional[int]      				=	ClapProcessor(tokenizer=UpperCAmelCase    ,  feature_extractor=UpperCAmelCase			)

     lowerCamelCase__					:   int      				=	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     lowerCamelCase__					:   List[Any]      				=	processor.batch_decode(UpperCAmelCase			)
     lowerCamelCase__					:   Tuple      				=	tokenizer.batch_decode(UpperCAmelCase			)

     self.assertListEqual(UpperCAmelCase    ,  UpperCAmelCase			)







 def   A_			(   self						:   Optional[Any]			)    ->			int:
     lowerCamelCase__					:   str      				=	self.get_feature_extractor()
     lowerCamelCase__					:   str      				=	self.get_tokenizer()

     lowerCamelCase__					:   Dict      				=	ClapProcessor(tokenizer=UpperCAmelCase    ,  feature_extractor=UpperCAmelCase			)

     self.assertListEqual(
         processor.model_input_names[2:]    ,  feature_extractor.model_input_names    ,  msg='`processor` and `feature_extractor` model input names do not match'    ,  )





