



'''simple docstring'''





import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow

@require_torch
class 	lowerCamelCase_    ( unittest.TestCase						):






						'''simple docstring'''







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict )      ->				Dict:
									A							:					Any              =						"ylacombe/bark-small"
									A							:					Dict              =						tempfile.mkdtemp()
									A							:					Dict              =						"en_speaker_1"
									A							:					List[str]              =						"This is a test string"
									A							:					Any              =						"speaker_embeddings_path.json"
									A							:					int              =						"speaker_embeddings"







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any]	,	**__lowerCamelCase      :			Any )      ->				Any:
									return AutoTokenizer.from_pretrained(self.checkpoint	,	**__lowerCamelCase )







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







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple )      ->				int:
									A							:					Any              =						self.get_tokenizer()

									A							:					Optional[int]              =						BarkProcessor(tokenizer=__lowerCamelCase )

									processor.save_pretrained(self.tmpdirname )
									A							:					str              =						BarkProcessor.from_pretrained(self.tmpdirname )

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







						@slow
						def 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				Optional[Any]:
									A							:					Any              =						BarkProcessor.from_pretrained(
									    pretrained_processor_name_or_path=self.checkpoint	,	speaker_embeddings_dict_path=self.speaker_embeddings_dict_path	,	)
									processor.save_pretrained(
									    self.tmpdirname	,	speaker_embeddings_dict_path=self.speaker_embeddings_dict_path	,	speaker_embeddings_directory=self.speaker_embeddings_directory	,	)

									A							:					Optional[Any]              =						self.get_tokenizer(bos_token="(BOS)"	,	eos_token="(EOS)" )

									A							:					Optional[int]              =						BarkProcessor.from_pretrained(
									    self.tmpdirname	,	self.speaker_embeddings_dict_path	,	bos_token="(BOS)"	,	eos_token="(EOS)"	,	)

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







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any] )      ->				str:
									A							:					Any              =						BarkProcessor.from_pretrained(
									    pretrained_processor_name_or_path=self.checkpoint	,	speaker_embeddings_dict_path=self.speaker_embeddings_dict_path	,	)

									A							:					Tuple              =						35
									A							:					Dict              =						2
									A							:					Dict              =						8

									A							:					str              =						{
									    "semantic_prompt": np.ones(__lowerCamelCase ),
									    "coarse_prompt": np.ones((nb_codebooks_coarse, seq_len) ),
									    "fine_prompt": np.ones((nb_codebooks_total, seq_len) ),
									}

									# test providing already loaded voice_preset
									A							:					int              =						processor(text=self.input_string	,	voice_preset=__lowerCamelCase )

									A							:					List[Any]              =						inputs["history_prompt"]
									for key in voice_preset:
												self.assertListEqual(voice_preset[key].tolist()	,	processed_voice_preset.get(__lowerCamelCase	,	np.array([] ) ).tolist() )

									# test loading voice preset from npz file
									A							:					Union[str, Any]              =						os.path.join(self.tmpdirname	,	"file.npz" )
									np.savez(__lowerCamelCase	,	**__lowerCamelCase )
									A							:					Optional[int]              =						processor(text=self.input_string	,	voice_preset=__lowerCamelCase )
									A							:					Union[str, Any]              =						inputs["history_prompt"]

									for key in voice_preset:
												self.assertListEqual(voice_preset[key].tolist()	,	processed_voice_preset.get(__lowerCamelCase	,	np.array([] ) ).tolist() )

									# test loading voice preset from the hub
									A							:					List[Any]              =						processor(text=self.input_string	,	voice_preset=self.voice_preset )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				Tuple:
									A							:					str              =						self.get_tokenizer()

									A							:					Optional[int]              =						BarkProcessor(tokenizer=__lowerCamelCase )

									A							:					List[Any]              =						processor(text=self.input_string )

									A							:					List[Any]              =						tokenizer(
									    self.input_string	,	padding="max_length"	,	max_length=2_56	,	add_special_tokens=__lowerCamelCase	,	return_attention_mask=__lowerCamelCase	,	return_token_type_ids=__lowerCamelCase	,	)

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




import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
							import torch

							from transformers import XLMRobertaModel

@require_sentencepiece
@require_tokenizers
@require_torch
class 	lowerCamelCase_    ( unittest.TestCase						):






						'''simple docstring'''







						@slow
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				Union[str, Any]:
									A							:					Union[str, Any]              =						XLMRobertaModel.from_pretrained("xlm-roberta-base" )
									A							:					Tuple              =						torch.tensor([[0, 5_81, 1_02_69, 83, 9_99_42, 1_36, 6_07_42, 23, 70, 8_05_83, 1_82_76, 2]] )
									# The dog is cute and lives in the garden house

									A							:					Tuple              =						torch.Size((1, 12, 7_68) )  # batch_size, sequence_length, embedding_vector_dim
									A							:					List[str]              =						torch.tensor(
									    [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]] )
									#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
									#  xlmr.eval()
									#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
									with torch.no_grad():
												A							:					Tuple              =						model(__lowerCamelCase )["last_hidden_state"].detach()
									self.assertEqual(output.shape	,	__lowerCamelCase )
									# compare the actual values for a slice of last dim
									self.assertTrue(torch.allclose(output[:, :, -1]	,	__lowerCamelCase	,	atol=1e-3 ) )







						@slow
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int] )      ->				int:
									A							:					str              =						XLMRobertaModel.from_pretrained("xlm-roberta-large" )
									A							:					List[Any]              =						torch.tensor([[0, 5_81, 1_02_69, 83, 9_99_42, 1_36, 6_07_42, 23, 70, 8_05_83, 1_82_76, 2]] )
									# The dog is cute and lives in the garden house

									A							:					Optional[Any]              =						torch.Size((1, 12, 10_24) )  # batch_size, sequence_length, embedding_vector_dim
									A							:					List[Any]              =						torch.tensor(
									    [[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]] )
									#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
									#  xlmr.eval()
									#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
									with torch.no_grad():
												A							:					Optional[int]              =						model(__lowerCamelCase )["last_hidden_state"].detach()
									self.assertEqual(output.shape	,	__lowerCamelCase )
									# compare the actual values for a slice of last dim
									self.assertTrue(torch.allclose(output[:, :, -1]	,	__lowerCamelCase	,	atol=1e-3 ) )