



'''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


lowerCAmelCase_     :       Any								=						logging.get_logger(__name__)


if is_vision_available():
							import PIL
class 	lowerCamelCase_							(					snake_case_			):
						_lowerCAmelCase     :   int	     =				['pixel_values']







						def __init__(  self   :      Dict				,    lowerCAmelCase__   :      bool = True				,    lowerCAmelCase__   :      Dict[str, int] = None				,    lowerCAmelCase__   :      PILImageResampling = PILImageResampling.BICUBIC				,    lowerCAmelCase__   :      bool = True				,    lowerCAmelCase__   :      Dict[str, int] = None				,    lowerCAmelCase__   :      bool = True				,    lowerCAmelCase__   :      Union[int, float] = 1 / 2_55				,    lowerCAmelCase__   :      bool = True				,    lowerCAmelCase__   :      Optional[Union[float, List[float]]] = None				,    lowerCAmelCase__   :      Optional[Union[float, List[float]]] = None				,    lowerCAmelCase__   :      bool = True				,    **lowerCAmelCase__   :      Union[str, Any]				,    ):

													"""simple docstring"""
													super().__init__(**lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      str     					=			size if size is not None else {'''shortest_edge''': 2_24}
													SCREAMING_SNAKE_CASE					:      int     					=			get_size_dict(lowerCAmelCase__				,    default_to_square=lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      str     					=			crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
													SCREAMING_SNAKE_CASE					:      Tuple     					=			get_size_dict(lowerCAmelCase__				,    default_to_square=lowerCAmelCase__				,    param_name='''crop_size'''				)

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







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

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			get_size_dict(lowerCAmelCase__				,    default_to_square=lowerCAmelCase__				)
													if "shortest_edge" not in size:
																				raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""				)
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			get_resize_output_image_size(lowerCAmelCase__				,    size=size['''shortest_edge''']				,    default_to_square=lowerCAmelCase__				)
													return resize(lowerCAmelCase__				,    size=lowerCAmelCase__				,    resample=lowerCAmelCase__				,    data_format=lowerCAmelCase__				,    **lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Dict				,    lowerCAmelCase__   :      np.ndarray				,    lowerCAmelCase__   :      Dict[str, int]				,    lowerCAmelCase__   :      Optional[Union[str, ChannelDimension]] = None				,    **lowerCAmelCase__   :      Tuple				,    ):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			get_size_dict(lowerCAmelCase__				)
													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(lowerCAmelCase__				,    size=(size['''height'''], size['''width'''])				,    data_format=lowerCAmelCase__				,    **lowerCAmelCase__				)







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

													"""simple docstring"""
													return rescale(lowerCAmelCase__				,    scale=lowerCAmelCase__				,    data_format=lowerCAmelCase__				,    **lowerCAmelCase__				)







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

													"""simple docstring"""
													return normalize(lowerCAmelCase__				,    mean=lowerCAmelCase__				,    std=lowerCAmelCase__				,    data_format=lowerCAmelCase__				,    **lowerCAmelCase__				)







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

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      int     					=			do_resize if do_resize is not None else self.do_resize
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			size if size is not None else self.size
													SCREAMING_SNAKE_CASE					:      int     					=			get_size_dict(lowerCAmelCase__				,    param_name='''size'''				,    default_to_square=lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			resample if resample is not None else self.resample
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			do_center_crop if do_center_crop is not None else self.do_center_crop
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			crop_size if crop_size is not None else self.crop_size
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			get_size_dict(lowerCAmelCase__				,    param_name='''crop_size'''				,    default_to_square=lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      int     					=			do_rescale if do_rescale is not None else self.do_rescale
													SCREAMING_SNAKE_CASE					:      int     					=			rescale_factor if rescale_factor is not None else self.rescale_factor
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			do_normalize if do_normalize is not None else self.do_normalize
													SCREAMING_SNAKE_CASE					:      Dict     					=			image_mean if image_mean is not None else self.image_mean
													SCREAMING_SNAKE_CASE					:      str     					=			image_std if image_std is not None else self.image_std
													SCREAMING_SNAKE_CASE					:      Any     					=			do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

													SCREAMING_SNAKE_CASE					:      Any     					=			make_list_of_images(lowerCAmelCase__				)

													if not valid_images(lowerCAmelCase__				):
																				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:
																				SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			[convert_to_rgb(lowerCAmelCase__				) for image in images]

													# All transformations expect numpy arrays.
													SCREAMING_SNAKE_CASE					:      Any     					=			[to_numpy_array(lowerCAmelCase__				) for image in images]

													if do_resize:
																				SCREAMING_SNAKE_CASE					:      Dict     					=			[self.resize(image=lowerCAmelCase__				,    size=lowerCAmelCase__				,    resample=lowerCAmelCase__				) for image in images]

													if do_center_crop:
																				SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			[self.center_crop(image=lowerCAmelCase__				,    size=lowerCAmelCase__				) for image in images]

													if do_rescale:
																				SCREAMING_SNAKE_CASE					:      Dict     					=			[self.rescale(image=lowerCAmelCase__				,    scale=lowerCAmelCase__				) for image in images]

													if do_normalize:
																				SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			[self.normalize(image=lowerCAmelCase__				,    mean=lowerCAmelCase__				,    std=lowerCAmelCase__				) for image in images]

													SCREAMING_SNAKE_CASE					:      Dict     					=			[to_channel_dimension_format(lowerCAmelCase__				,    lowerCAmelCase__				) for image in images]

													SCREAMING_SNAKE_CASE					:      List[str]     					=			{'''pixel_values''': images}
													return BatchFeature(data=lowerCAmelCase__				,    tensor_type=lowerCAmelCase__				)









'''simple docstring'''



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 					__lowercase    (  self   :      Union[str, Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      int     					=			'''laion/clap-htsat-unfused'''
													SCREAMING_SNAKE_CASE					:      Tuple     					=			tempfile.mkdtemp()







						def 					__lowercase    (  self   :      Tuple				,    **lowerCAmelCase__   :      Dict				):

													"""simple docstring"""
													return RobertaTokenizer.from_pretrained(self.checkpoint				,    **lowerCAmelCase__				)







						def 					__lowercase    (  self   :      int				,    **lowerCAmelCase__   :      str				):

													"""simple docstring"""
													return ClapFeatureExtractor.from_pretrained(self.checkpoint				,    **lowerCAmelCase__				)







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

													"""simple docstring"""
													shutil.rmtree(self.tmpdirname				)







						def 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[str]     					=			self.get_tokenizer()
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			self.get_feature_extractor()

													SCREAMING_SNAKE_CASE					:      List[str]     					=			ClapProcessor(tokenizer=lowerCAmelCase__				,    feature_extractor=lowerCAmelCase__				)

													processor.save_pretrained(self.tmpdirname				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			ClapProcessor.from_pretrained(self.tmpdirname				)

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

													self.assertEqual(processor.feature_extractor.to_json_string()				,    feature_extractor.to_json_string()				)
													self.assertIsInstance(processor.feature_extractor				,    lowerCAmelCase__				)







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

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			ClapProcessor(tokenizer=self.get_tokenizer()				,    feature_extractor=self.get_feature_extractor()				)
													processor.save_pretrained(self.tmpdirname				)

													SCREAMING_SNAKE_CASE					:      List[Any]     					=			self.get_tokenizer(bos_token='''(BOS)'''				,    eos_token='''(EOS)'''				)
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			self.get_feature_extractor(do_normalize=lowerCAmelCase__				,    padding_value=1.0				)

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

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

													self.assertEqual(processor.feature_extractor.to_json_string()				,    feature_extractor_add_kwargs.to_json_string()				)
													self.assertIsInstance(processor.feature_extractor				,    lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			self.get_feature_extractor()
													SCREAMING_SNAKE_CASE					:      Tuple     					=			self.get_tokenizer()

													SCREAMING_SNAKE_CASE					:      Tuple     					=			ClapProcessor(tokenizer=lowerCAmelCase__				,    feature_extractor=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			floats_list((3, 10_00)				)

													SCREAMING_SNAKE_CASE					:      Any     					=			feature_extractor(lowerCAmelCase__				,    return_tensors='''np'''				)
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			processor(audios=lowerCAmelCase__				,    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 					__lowercase    (  self   :      Any				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Tuple     					=			self.get_feature_extractor()
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self.get_tokenizer()

													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			ClapProcessor(tokenizer=lowerCAmelCase__				,    feature_extractor=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      int     					=			'''This is a test string'''

													SCREAMING_SNAKE_CASE					:      int     					=			processor(text=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			tokenizer(lowerCAmelCase__				)

													for key in encoded_tok.keys():
																				self.assertListEqual(encoded_tok[key]				,    encoded_processor[key]				)







						def 					__lowercase    (  self   :      Optional[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      str     					=			self.get_feature_extractor()
													SCREAMING_SNAKE_CASE					:      List[str]     					=			self.get_tokenizer()

													SCREAMING_SNAKE_CASE					:      Any     					=			ClapProcessor(tokenizer=lowerCAmelCase__				,    feature_extractor=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

													SCREAMING_SNAKE_CASE					:      Tuple     					=			processor.batch_decode(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			tokenizer.batch_decode(lowerCAmelCase__				)

													self.assertListEqual(lowerCAmelCase__				,    lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Optional[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self.get_feature_extractor()
													SCREAMING_SNAKE_CASE					:      str     					=			self.get_tokenizer()

													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			ClapProcessor(tokenizer=lowerCAmelCase__				,    feature_extractor=lowerCAmelCase__				)

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




