


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       lowercase_					(unittest.TestCase   ):





				def 						__UpperCamelCase    (				self)  -> List[str]:
											a__      ='ylacombe/bark-small'
											a__      =tempfile.mkdtemp()
											a__      ='en_speaker_1'
											a__      ='This is a test string'
											a__      ='speaker_embeddings_path.json'
											a__      ='speaker_embeddings'





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





				def 						__UpperCamelCase    (				self)  -> str:
											shutil.rmtree(self.tmpdirname)





				def 						__UpperCamelCase    (				self)  -> Union[str, Any]:
											a__      =self.get_tokenizer()

											a__      =BarkProcessor(tokenizer=lowercase_)

											processor.save_pretrained(self.tmpdirname)
											a__      =BarkProcessor.from_pretrained(self.tmpdirname)

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





				@slow
				def 						__UpperCamelCase    (				self)  -> List[str]:
											a__      =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__      =self.get_tokenizer(bos_token='(BOS)'       ,  eos_token='(EOS)')

											a__      =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 						__UpperCamelCase    (				self)  -> List[Any]:
											a__      =BarkProcessor.from_pretrained(
											    pretrained_processor_name_or_path=self.checkpoint       ,  speaker_embeddings_dict_path=self.speaker_embeddings_dict_path       ,  )

											a__      =35
											a__      =2
											a__      =8

											a__      ={
											    'semantic_prompt': np.ones(lowercase_),
											    '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__      =processor(text=self.input_string       ,  voice_preset=lowercase_)

											a__      =inputs['history_prompt']
											for key in voice_preset:
																		self.assertListEqual(voice_preset[key].tolist()       ,  processed_voice_preset.get(lowercase_       ,  np.array([])).tolist())

											# test loading voice preset from npz file
											a__      =os.path.join(self.tmpdirname       ,  'file.npz')
											np.savez(lowercase_       ,  **lowercase_)
											a__      =processor(text=self.input_string       ,  voice_preset=lowercase_)
											a__      =inputs['history_prompt']

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

											# test loading voice preset from the hub
											a__      =processor(text=self.input_string       ,  voice_preset=self.voice_preset)





				def 						__UpperCamelCase    (				self)  -> Dict:
											a__      =self.get_tokenizer()

											a__      =BarkProcessor(tokenizer=lowercase_)

											a__      =processor(text=self.input_string)

											a__      =tokenizer(
											    self.input_string       ,  padding='max_length'       ,  max_length=256       ,  add_special_tokens=lowercase_       ,  return_attention_mask=lowercase_       ,  return_token_type_ids=lowercase_       ,  )

											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, torch_device


if is_torch_available():
	from transformers import AutoModelForSeqaSeqLM, AutoTokenizer




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





				@slow
				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											a__      =AutoModelForSeqaSeqLM.from_pretrained('google/mt5-small'       ,  return_dict=lowercase_).to(lowercase_)
											a__      =AutoTokenizer.from_pretrained('google/mt5-small')

											a__      =tokenizer('Hello there'       ,  return_tensors='pt').input_ids
											a__      =tokenizer('Hi I am'       ,  return_tensors='pt').input_ids

											a__      =model(input_ids.to(lowercase_)       ,  labels=labels.to(lowercase_)).loss
											a__      =-(labels.shape[-1] * loss.item())

											a__      =-84.91_27
											self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)

