



"""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      ):

			"""simple docstring"""


			def   snake_case__	(      self		)      ->	int:
				A__											=					"laion/clap-htsat-unfused"
				A__											=					tempfile.mkdtemp()


			def   snake_case__	(      self						,	**SCREAMING_SNAKE_CASE__		)      ->	List[str]:
				return RobertaTokenizer.from_pretrained(self.checkpoint						,	**SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self						,	**SCREAMING_SNAKE_CASE__		)      ->	Optional[Any]:
				return ClapFeatureExtractor.from_pretrained(self.checkpoint						,	**SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self		)      ->	Tuple:
				shutil.rmtree(self.tmpdirname		)


			def   snake_case__	(      self		)      ->	Optional[int]:
				A__											=					self.get_tokenizer()
				A__											=					self.get_feature_extractor()

				A__											=					ClapProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	feature_extractor=SCREAMING_SNAKE_CASE__		)

				processor.save_pretrained(self.tmpdirname		)
				A__											=					ClapProcessor.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   snake_case__	(      self		)      ->	int:
				A__											=					ClapProcessor(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__											=					ClapProcessor.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   snake_case__	(      self		)      ->	Optional[Any]:
				A__											=					self.get_feature_extractor()
				A__											=					self.get_tokenizer()

				A__											=					ClapProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	feature_extractor=SCREAMING_SNAKE_CASE__		)

				A__											=					floats_list((3, 1000)		)

				A__											=					feature_extractor(SCREAMING_SNAKE_CASE__						,	return_tensors="np"		)
				A__											=					processor(audios=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   snake_case__	(      self		)      ->	str:
				A__											=					self.get_feature_extractor()
				A__											=					self.get_tokenizer()

				A__											=					ClapProcessor(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   snake_case__	(      self		)      ->	int:
				A__											=					self.get_feature_extractor()
				A__											=					self.get_tokenizer()

				A__											=					ClapProcessor(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   snake_case__	(      self		)      ->	Dict:
				A__											=					self.get_feature_extractor()
				A__											=					self.get_tokenizer()

				A__											=					ClapProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	feature_extractor=SCREAMING_SNAKE_CASE__		)

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









"""simple docstring"""







import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin






class       UpperCamelCase__			(   _lowerCAmelCase       ,		_lowerCAmelCase       ,		_lowerCAmelCase      ):

			"""simple docstring"""


			@register_to_config
			def __init__(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = False						,	)      ->	Union[str, Any]:
				super().__init__()

				A__											=					nn.Embedding(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)

				A__											=					nn.Embedding(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)
				A__											=					False

				A__											=					nn.Dropout(p=SCREAMING_SNAKE_CASE__		)

				A__											=					TaConfig(
				    vocab_size=SCREAMING_SNAKE_CASE__						,	d_model=SCREAMING_SNAKE_CASE__						,	num_heads=SCREAMING_SNAKE_CASE__						,	d_kv=SCREAMING_SNAKE_CASE__						,	d_ff=SCREAMING_SNAKE_CASE__						,	dropout_rate=SCREAMING_SNAKE_CASE__						,	feed_forward_proj=SCREAMING_SNAKE_CASE__						,	is_decoder=SCREAMING_SNAKE_CASE__						,	is_encoder_decoder=SCREAMING_SNAKE_CASE__						,	)

				A__											=					nn.ModuleList()
				for lyr_num in range(SCREAMING_SNAKE_CASE__		):
					A__											=					TaBlock(SCREAMING_SNAKE_CASE__		)
					self.encoders.append(SCREAMING_SNAKE_CASE__		)

				A__											=					TaLayerNorm(SCREAMING_SNAKE_CASE__		)
				A__											=					nn.Dropout(p=SCREAMING_SNAKE_CASE__		)







			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	Any:
				A__											=					self.token_embedder(SCREAMING_SNAKE_CASE__		)

				A__											=					encoder_input_tokens.shape[1]
				A__											=					torch.arange(SCREAMING_SNAKE_CASE__						,	device=encoder_input_tokens.device		)
				x += self.position_encoding(SCREAMING_SNAKE_CASE__		)

				A__											=					self.dropout_pre(SCREAMING_SNAKE_CASE__		)

				# inverted the attention mask
				A__											=					encoder_input_tokens.size()
				A__											=					self.get_extended_attention_mask(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)

				for lyr in self.encoders:
					A__											=					lyr(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)[0]
				A__											=					self.layer_norm(SCREAMING_SNAKE_CASE__		)

				return self.dropout_post(SCREAMING_SNAKE_CASE__		), encoder_inputs_mask




