



'''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 				lowercase__      (				unittest.TestCase  ):





	def       UpperCamelCase_    (					self   :							Any					):







							'''simple docstring'''
							_UpperCamelCase   :						Union[str, Any]												=  'ylacombe/bark-small'
							_UpperCamelCase   :						str												=  tempfile.mkdtemp()
							_UpperCamelCase   :						int												=  'en_speaker_1'
							_UpperCamelCase   :						str												=  'This is a test string'
							_UpperCamelCase   :						str												=  'speaker_embeddings_path.json'
							_UpperCamelCase   :						Union[str, Any]												=  'speaker_embeddings'





	def       UpperCamelCase_    (					self   :							Optional[Any]					,**lowerCamelCase__   :							List[Any]					):







							'''simple docstring'''
							return AutoTokenizer.from_pretrained(self.checkpoint					,**lowerCamelCase__					)





	def       UpperCamelCase_    (					self   :							int					):







							'''simple docstring'''
							shutil.rmtree(self.tmpdirname					)





	def       UpperCamelCase_    (					self   :							Union[str, Any]					):







							'''simple docstring'''
							_UpperCamelCase   :						Tuple												=  self.get_tokenizer()

							_UpperCamelCase   :						Any												=  BarkProcessor(tokenizer=lowerCamelCase__					)

							processor.save_pretrained(self.tmpdirname					)
							_UpperCamelCase   :						Tuple												=  BarkProcessor.from_pretrained(self.tmpdirname					)

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





	@slow
	def       UpperCamelCase_    (					self   :							str					):







							'''simple docstring'''
							_UpperCamelCase   :						Optional[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					,)

							_UpperCamelCase   :						Tuple												=  self.get_tokenizer(bos_token='(BOS)'					,eos_token='(EOS)'					)

							_UpperCamelCase   :						List[Any]												=  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   :							int					):







							'''simple docstring'''
							_UpperCamelCase   :						int												=  BarkProcessor.from_pretrained(
							    pretrained_processor_name_or_path=self.checkpoint					,speaker_embeddings_dict_path=self.speaker_embeddings_dict_path					,)

							_UpperCamelCase   :						str												=  35
							_UpperCamelCase   :						Optional[int]												=  2
							_UpperCamelCase   :						Optional[Any]												=  8

							_UpperCamelCase   :						Dict												=  {
							    '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
							_UpperCamelCase   :						List[str]												=  processor(text=self.input_string					,voice_preset=lowerCamelCase__					)

							_UpperCamelCase   :						str												=  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
							_UpperCamelCase   :						Optional[int]												=  os.path.join(self.tmpdirname					,'file.npz'					)
							np.savez(lowerCamelCase__					,**lowerCamelCase__					)
							_UpperCamelCase   :						Any												=  processor(text=self.input_string					,voice_preset=lowerCamelCase__					)
							_UpperCamelCase   :						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
							_UpperCamelCase   :						str												=  processor(text=self.input_string					,voice_preset=self.voice_preset					)





	def       UpperCamelCase_    (					self   :							Union[str, Any]					):







							'''simple docstring'''
							_UpperCamelCase   :						int												=  self.get_tokenizer()

							_UpperCamelCase   :						List[str]												=  BarkProcessor(tokenizer=lowerCamelCase__					)

							_UpperCamelCase   :						Dict												=  processor(text=self.input_string					)

							_UpperCamelCase   :						str												=  tokenizer(
							    self.input_string					,padding='max_length'					,max_length=256					,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()					)







'''simple docstring'''
import os
import tempfile
import unittest
import uuid
from pathlib import Path

from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_torch_available, is_vision_available


if is_torch_available():
					import torch

if is_soundfile_availble():
					import soundfile as sf

if is_vision_available():
					from PIL import Image






def A__       (	UpperCAmelCase_=""				):
						_UpperCamelCase   :						Any												=  tempfile.mkdtemp()
						return os.path.join(UpperCAmelCase_    ,    str(uuid.uuida()				) + suffix				)






@require_soundfile
@require_torch
class 				lowercase__      (				unittest.TestCase  ):





	def       UpperCamelCase_    (					self   :							int					):







							'''simple docstring'''
							_UpperCamelCase   :						List[str]												=  torch.rand(12					,dtype=torch.floataa					) - 0.5
							_UpperCamelCase   :						Optional[int]												=  AgentAudio(lowerCamelCase__					)
							_UpperCamelCase   :						List[str]												=  str(agent_type.to_string()					)

							# Ensure that the tensor and the agent_type's tensor are the same
							self.assertTrue(torch.allclose(lowerCamelCase__					,agent_type.to_raw()					,atol=1E-4					)					)

							del agent_type

							# Ensure the path remains even after the object deletion
							self.assertTrue(os.path.exists(lowerCamelCase__					)					)

							# Ensure that the file contains the same value as the original tensor
							_UpperCamelCase							,     _UpperCamelCase   :						Union[str, Any]												=  sf.read(lowerCamelCase__					)
							self.assertTrue(torch.allclose(lowerCamelCase__					,torch.tensor(lowerCamelCase__					)					,atol=1E-4					)					)





	def       UpperCamelCase_    (					self   :							Dict					):







							'''simple docstring'''
							_UpperCamelCase   :						Optional[Any]												=  torch.rand(12					,dtype=torch.floataa					) - 0.5
							_UpperCamelCase   :						Any												=  get_new_path(suffix='.wav'					)
							sf.write(lowerCamelCase__					,lowerCamelCase__					,16000					)

							_UpperCamelCase   :						List[Any]												=  AgentAudio(lowerCamelCase__					)

							self.assertTrue(torch.allclose(lowerCamelCase__					,agent_type.to_raw()					,atol=1E-4					)					)
							self.assertEqual(agent_type.to_string()					,lowerCamelCase__					)






@require_vision
@require_torch
class 				lowercase__      (				unittest.TestCase  ):





	def       UpperCamelCase_    (					self   :							List[Any]					):







							'''simple docstring'''
							_UpperCamelCase   :						int												=  torch.randint(0					,256					,(64, 64, 3)					)
							_UpperCamelCase   :						Optional[Any]												=  AgentImage(lowerCamelCase__					)
							_UpperCamelCase   :						List[Any]												=  str(agent_type.to_string()					)

							# Ensure that the tensor and the agent_type's tensor are the same
							self.assertTrue(torch.allclose(lowerCamelCase__					,agent_type._tensor					,atol=1E-4					)					)

							self.assertIsInstance(agent_type.to_raw()					,Image.Image					)

							# Ensure the path remains even after the object deletion
							del agent_type
							self.assertTrue(os.path.exists(lowerCamelCase__					)					)





	def       UpperCamelCase_    (					self   :							Dict					):







							'''simple docstring'''
							_UpperCamelCase   :						str												=  Path(get_tests_dir('fixtures/tests_samples/COCO'					)					) / '000000039769.png'
							_UpperCamelCase   :						Tuple												=  Image.open(lowerCamelCase__					)
							_UpperCamelCase   :						Optional[Any]												=  AgentImage(lowerCamelCase__					)

							self.assertTrue(path.samefile(agent_type.to_string()					)					)
							self.assertTrue(image == agent_type.to_raw()					)

							# Ensure the path remains even after the object deletion
							del agent_type
							self.assertTrue(os.path.exists(lowerCamelCase__					)					)





	def       UpperCamelCase_    (					self   :							Optional[int]					):







							'''simple docstring'''
							_UpperCamelCase   :						List[Any]												=  Path(get_tests_dir('fixtures/tests_samples/COCO'					)					) / '000000039769.png'
							_UpperCamelCase   :						Union[str, Any]												=  Image.open(lowerCamelCase__					)
							_UpperCamelCase   :						List[Any]												=  AgentImage(lowerCamelCase__					)

							self.assertFalse(path.samefile(agent_type.to_string()					)					)
							self.assertTrue(image == agent_type.to_raw()					)

							# Ensure the path remains even after the object deletion
							del agent_type
							self.assertTrue(os.path.exists(lowerCamelCase__					)					)






class 				lowercase__      (				unittest.TestCase  ):





	def       UpperCamelCase_    (					self   :							Any					):







							'''simple docstring'''
							_UpperCamelCase   :						List[Any]												=  'Hey!'
							_UpperCamelCase   :						Optional[int]												=  AgentText(lowerCamelCase__					)

							self.assertEqual(lowerCamelCase__					,agent_type.to_string()					)
							self.assertEqual(lowerCamelCase__					,agent_type.to_raw()					)
							self.assertEqual(lowerCamelCase__					,lowerCamelCase__					)


