

"""simple docstring"""




import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
	from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

	from .test_feature_extraction_speechta import floats_list


__A	:       Optional[int]        =						get_tests_dir('fixtures/test_sentencepiece_bpe_char.model')





@require_torch
class      __UpperCamelCase					(  unittest.TestCase		):


		def    SCREAMING_SNAKE_CASE__  (self					:						str):
							A     =   tempfile.mkdtemp()

							A     =   SpeechTaTokenizer(__SCREAMING_SNAKE_CASE)
							tokenizer.save_pretrained(self.tmpdirname)

							A     =   {
							    "feature_size": 1,
							    "padding_value": 0.0,
							    "sampling_rate": 1_6_0_0_0,
							    "do_normalize": False,
							    "num_mel_bins": 8_0,
							    "hop_length": 1_6,
							    "win_length": 6_4,
							    "win_function": "hann_window",
							    "fmin": 8_0,
							    "fmax": 7_6_0_0,
							    "mel_floor": 1E-10,
							    "reduction_factor": 2,
							    "return_attention_mask": True,
							}

							A     =   os.path.join(self.tmpdirname	,	__SCREAMING_SNAKE_CASE)
							with open(self.feature_extraction_file	,	"w"	,	encoding="utf-8") as fp:
												fp.write(json.dumps(__SCREAMING_SNAKE_CASE) + "\n")


		def    SCREAMING_SNAKE_CASE__  (self					:						int	,	**__SCREAMING_SNAKE_CASE					:						int):
							return SpeechTaTokenizer.from_pretrained(self.tmpdirname	,	**__SCREAMING_SNAKE_CASE)


		def    SCREAMING_SNAKE_CASE__  (self					:						Union[str, Any]	,	**__SCREAMING_SNAKE_CASE					:						Optional[int]):
							return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname	,	**__SCREAMING_SNAKE_CASE)


		def    SCREAMING_SNAKE_CASE__  (self					:						List[Any]):
							shutil.rmtree(self.tmpdirname)


		def    SCREAMING_SNAKE_CASE__  (self					:						Tuple):
							A     =   self.get_tokenizer()
							A     =   self.get_feature_extractor()

							A     =   SpeechTaProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,	feature_extractor=__SCREAMING_SNAKE_CASE)

							processor.save_pretrained(self.tmpdirname)
							A     =   SpeechTaProcessor.from_pretrained(self.tmpdirname)

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

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


		def    SCREAMING_SNAKE_CASE__  (self					:						Dict):
							A     =   SpeechTaProcessor(tokenizer=self.get_tokenizer()	,	feature_extractor=self.get_feature_extractor())
							processor.save_pretrained(self.tmpdirname)

							A     =   self.get_tokenizer(bos_token="(BOS)"	,	eos_token="(EOS)")
							A     =   self.get_feature_extractor(do_normalize=__SCREAMING_SNAKE_CASE	,	padding_value=1.0)

							A     =   SpeechTaProcessor.from_pretrained(
							    self.tmpdirname	,	bos_token="(BOS)"	,	eos_token="(EOS)"	,	do_normalize=__SCREAMING_SNAKE_CASE	,	padding_value=1.0)

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

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


		def    SCREAMING_SNAKE_CASE__  (self					:						str):
							A     =   self.get_feature_extractor()
							A     =   self.get_tokenizer()

							A     =   SpeechTaProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,	feature_extractor=__SCREAMING_SNAKE_CASE)

							A     =   floats_list((3, 1_0_0_0))

							A     =   feature_extractor(audio=__SCREAMING_SNAKE_CASE	,	return_tensors="np")
							A     =   processor(audio=__SCREAMING_SNAKE_CASE	,	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    SCREAMING_SNAKE_CASE__  (self					:						Tuple):
							A     =   self.get_feature_extractor()
							A     =   self.get_tokenizer()

							A     =   SpeechTaProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,	feature_extractor=__SCREAMING_SNAKE_CASE)

							A     =   floats_list((3, 1_0_0_0))

							A     =   feature_extractor(audio_target=__SCREAMING_SNAKE_CASE	,	return_tensors="np")
							A     =   processor(audio_target=__SCREAMING_SNAKE_CASE	,	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    SCREAMING_SNAKE_CASE__  (self					:						List[str]):
							A     =   self.get_feature_extractor()
							A     =   self.get_tokenizer()

							A     =   SpeechTaProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,	feature_extractor=__SCREAMING_SNAKE_CASE)

							A     =   "This is a test string"

							A     =   processor(text=__SCREAMING_SNAKE_CASE)
							A     =   tokenizer(__SCREAMING_SNAKE_CASE)

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


		def    SCREAMING_SNAKE_CASE__  (self					:						int):
							A     =   self.get_feature_extractor()
							A     =   self.get_tokenizer()

							A     =   SpeechTaProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,	feature_extractor=__SCREAMING_SNAKE_CASE)

							A     =   "This is a test string"

							A     =   processor(text_target=__SCREAMING_SNAKE_CASE)
							A     =   tokenizer(__SCREAMING_SNAKE_CASE)

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


		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[int]):
							A     =   self.get_feature_extractor()
							A     =   self.get_tokenizer()

							A     =   SpeechTaProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,	feature_extractor=__SCREAMING_SNAKE_CASE)

							A     =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							A     =   processor.batch_decode(__SCREAMING_SNAKE_CASE)
							A     =   tokenizer.batch_decode(__SCREAMING_SNAKE_CASE)

							self.assertListEqual(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE)


		def    SCREAMING_SNAKE_CASE__  (self					:						List[str]):
							A     =   self.get_feature_extractor()
							A     =   self.get_tokenizer()

							A     =   SpeechTaProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,	feature_extractor=__SCREAMING_SNAKE_CASE)

							self.assertListEqual(
							    processor.model_input_names	,	feature_extractor.model_input_names	,	msg="`processor` and `feature_extractor` model input names do not match"	,	)










from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case     :    Any						=    logging.get_logger(__name__)

snake_case     :    List[str]						=    {
    'MCG-NJU/videomae-base': 'https://huggingface.co/MCG-NJU/videomae-base/resolve/main/config.json',
}




class 				lowerCAmelCase__	(						UpperCamelCase    ):
					__A  :     str     =    'videomae'







					def __init__(							self   :  Optional[Any]      ,		_A   :  str=224      ,		_A   :  Union[str, Any]=16      ,		_A   :  int=3      ,		_A   :  Tuple=16      ,		_A   :  int=2      ,		_A   :  List[Any]=768      ,		_A   :  List[Any]=12      ,		_A   :  Tuple=12      ,		_A   :  List[Any]=3072      ,		_A   :  Union[str, Any]="gelu"      ,		_A   :  Any=0.0      ,		_A   :  str=0.0      ,		_A   :  Tuple=0.02      ,		_A   :  Optional[Any]=1e-12      ,		_A   :  Tuple=True      ,		_A   :  Dict=True      ,		_A   :  str=6      ,		_A   :  Optional[int]=384      ,		_A   :  Any=4      ,		_A   :  Any=1536      ,		_A   :  Optional[int]=True      ,		**_A   :  Tuple      ,		):
											super().__init__(**_A)

											A__						:				List[str]         =			image_size
											A__						:				Union[str, Any]         =			patch_size
											A__						:				Optional[int]         =			num_channels
											A__						:				int         =			num_frames
											A__						:				List[Any]         =			tubelet_size

											A__						:				str         =			hidden_size
											A__						:				Optional[Any]         =			num_hidden_layers
											A__						:				int         =			num_attention_heads
											A__						:				List[Any]         =			intermediate_size
											A__						:				Optional[int]         =			hidden_act
											A__						:				Any         =			hidden_dropout_prob
											A__						:				str         =			attention_probs_dropout_prob
											A__						:				List[Any]         =			initializer_range
											A__						:				str         =			layer_norm_eps
											A__						:				Any         =			qkv_bias
											A__						:				Union[str, Any]         =			use_mean_pooling

											A__						:				int         =			decoder_num_attention_heads
											A__						:				Tuple         =			decoder_hidden_size
											A__						:				int         =			decoder_num_hidden_layers
											A__						:				int         =			decoder_intermediate_size
											A__						:				Any         =			norm_pix_loss