


'''simple docstring'''



from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowercase     :						Optional[Any]	=     logging.get_logger(__name__)

__lowercase     :						int	=     {
    'microsoft/beit-base-patch16-224-pt22k': (
        'https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}





class 							__UpperCamelCase    (		lowerCAmelCase_ ):
	A_           =   "beit"




	def __init__(       self						,    __a=8192						,    __a=768						,    __a=12						,    __a=12						,    __a=3072						,    __a="gelu"						,    __a=0.0						,    __a=0.0						,    __a=0.02						,    __a=1E-1_2						,    __a=224						,    __a=16						,    __a=3						,    __a=False						,    __a=False						,    __a=False						,    __a=False						,    __a=0.1						,    __a=0.1						,    __a=True						,    __a=[3, 5, 7, 11]						,    __a=[1, 2, 3, 6]						,    __a=True						,    __a=0.4						,    __a=256						,    __a=1						,    __a=False						,    __a=255						,    **__a						,    ):

						'''simple docstring'''





						super().__init__(**__a							)

						__a      :	Any    =  vocab_size
						__a      :	Optional[Any]    =  hidden_size
						__a      :	Union[str, Any]    =  num_hidden_layers
						__a      :	Tuple    =  num_attention_heads
						__a      :	Tuple    =  intermediate_size
						__a      :	str    =  hidden_act
						__a      :	List[str]    =  hidden_dropout_prob
						__a      :	Tuple    =  attention_probs_dropout_prob
						__a      :	List[Any]    =  initializer_range
						__a      :	Optional[int]    =  layer_norm_eps

						__a      :	Any    =  image_size
						__a      :	Optional[Any]    =  patch_size
						__a      :	Dict    =  num_channels
						__a      :	List[str]    =  use_mask_token
						__a      :	Any    =  use_absolute_position_embeddings
						__a      :	List[str]    =  use_relative_position_bias
						__a      :	int    =  use_shared_relative_position_bias
						__a      :	int    =  layer_scale_init_value
						__a      :	Union[str, Any]    =  drop_path_rate
						__a      :	List[Any]    =  use_mean_pooling
						# decode head attributes (semantic segmentation)
						__a      :	List[str]    =  out_indices
						__a      :	Tuple    =  pool_scales
						# auxiliary head attributes (semantic segmentation)
						__a      :	Any    =  use_auxiliary_head
						__a      :	Union[str, Any]    =  auxiliary_loss_weight
						__a      :	List[Any]    =  auxiliary_channels
						__a      :	Union[str, Any]    =  auxiliary_num_convs
						__a      :	Dict    =  auxiliary_concat_input
						__a      :	Tuple    =  semantic_loss_ignore_index





class 							__UpperCamelCase    (		lowerCAmelCase_ ):
	A_           =   version.parse("1.11" )




	@property
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						return OrderedDict(
						    [
						        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
						    ]							)





	@property
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						return 1E-4







'''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 							__UpperCamelCase    (		unittest.TestCase ):




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Optional[Any]    =  'laion/clap-htsat-unfused'
						__a      :	Optional[Any]    =  tempfile.mkdtemp()




	def 				__UpperCAmelCase							(       self						,    **__a							):

						'''simple docstring'''





						return RobertaTokenizer.from_pretrained(self.checkpoint						,    **__a							)




	def 				__UpperCAmelCase							(       self						,    **__a							):

						'''simple docstring'''





						return ClapFeatureExtractor.from_pretrained(self.checkpoint						,    **__a							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						shutil.rmtree(self.tmpdirname							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Any    =  self.get_tokenizer()
						__a      :	List[str]    =  self.get_feature_extractor()

						__a      :	Any    =  ClapProcessor(tokenizer=__a						,    feature_extractor=__a							)

						processor.save_pretrained(self.tmpdirname							)
						__a      :	Tuple    =  ClapProcessor.from_pretrained(self.tmpdirname							)

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

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




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	str    =  ClapProcessor(tokenizer=self.get_tokenizer()						,    feature_extractor=self.get_feature_extractor()							)
						processor.save_pretrained(self.tmpdirname							)

						__a      :	int    =  self.get_tokenizer(bos_token='(BOS)'						,    eos_token='(EOS)'							)
						__a      :	List[str]    =  self.get_feature_extractor(do_normalize=__a						,    padding_value=1.0							)

						__a      :	Tuple    =  ClapProcessor.from_pretrained(
						    self.tmpdirname						,    bos_token='(BOS)'						,    eos_token='(EOS)'						,    do_normalize=__a						,    padding_value=1.0							)

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

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




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	str    =  self.get_feature_extractor()
						__a      :	int    =  self.get_tokenizer()

						__a      :	str    =  ClapProcessor(tokenizer=__a						,    feature_extractor=__a							)

						__a      :	int    =  floats_list((3, 1000)							)

						__a      :	str    =  feature_extractor(__a						,    return_tensors='np'							)
						__a      :	int    =  processor(audios=__a						,    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 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Union[str, Any]    =  self.get_feature_extractor()
						__a      :	Any    =  self.get_tokenizer()

						__a      :	Any    =  ClapProcessor(tokenizer=__a						,    feature_extractor=__a							)

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

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

						__a      :	Tuple    =  tokenizer(__a							)

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




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	str    =  self.get_feature_extractor()
						__a      :	str    =  self.get_tokenizer()

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

						__a      :	Dict    =  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						__a      :	Optional[int]    =  processor.batch_decode(__a							)
						__a      :	Optional[Any]    =  tokenizer.batch_decode(__a							)

						self.assertListEqual(__a						,    __a							)





	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Optional[Any]    =  self.get_feature_extractor()
						__a      :	Optional[int]    =  self.get_tokenizer()

						__a      :	int    =  ClapProcessor(tokenizer=__a						,    feature_extractor=__a							)

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



