
'''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     ):






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Optional[int]													=       '''ylacombe/bark-small'''
											lowercase_	:     Any													=       tempfile.mkdtemp()
											lowercase_	:     Tuple													=       '''en_speaker_1'''
											lowercase_	:     Dict													=       '''This is a test string'''
											lowercase_	:     List[str]													=       '''speaker_embeddings_path.json'''
											lowercase_	:     Optional[Any]													=       '''speaker_embeddings'''






					def 	_snake_case    (   self				,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											return AutoTokenizer.from_pretrained(self.checkpoint				,							**__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											shutil.rmtree(self.tmpdirname		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Any													=       self.get_tokenizer()

											lowercase_	:     str													=       BarkProcessor(tokenizer=__SCREAMING_SNAKE_CASE		)

											processor.save_pretrained(self.tmpdirname		)
											lowercase_	:     List[str]													=       BarkProcessor.from_pretrained(self.tmpdirname		)

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






					@slow
					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Dict													=       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				,							)

											lowercase_	:     Union[str, Any]													=       self.get_tokenizer(bos_token='''(BOS)'''				,							eos_token='''(EOS)'''		)

											lowercase_	:     Union[str, 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 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Any													=       BarkProcessor.from_pretrained(
											    pretrained_processor_name_or_path=self.checkpoint				,							speaker_embeddings_dict_path=self.speaker_embeddings_dict_path				,							)

											lowercase_	:     List[Any]													=       35
											lowercase_	:     Union[str, Any]													=       2
											lowercase_	:     List[Any]													=       8

											lowercase_	:     int													=       {
											    '''semantic_prompt''': np.ones(__SCREAMING_SNAKE_CASE		),
											    '''coarse_prompt''': np.ones((nb_codebooks_coarse, seq_len)		),
											    '''fine_prompt''': np.ones((nb_codebooks_total, seq_len)		),
											}

											# test providing already loaded voice_preset
											lowercase_	:     Tuple													=       processor(text=self.input_string				,							voice_preset=__SCREAMING_SNAKE_CASE		)

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

											# test loading voice preset from npz file
											lowercase_	:     Dict													=       os.path.join(self.tmpdirname				,							'''file.npz'''		)
											np.savez(__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Union[str, Any]													=       processor(text=self.input_string				,							voice_preset=__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Tuple													=       inputs['''history_prompt''']

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

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






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Tuple													=       self.get_tokenizer()

											lowercase_	:     int													=       BarkProcessor(tokenizer=__SCREAMING_SNAKE_CASE		)

											lowercase_	:     Optional[Any]													=       processor(text=self.input_string		)

											lowercase_	:     List[Any]													=       tokenizer(
											    self.input_string				,							padding='''max_length'''				,							max_length=2_56				,							add_special_tokens=__SCREAMING_SNAKE_CASE				,							return_attention_mask=__SCREAMING_SNAKE_CASE				,							return_token_type_ids=__SCREAMING_SNAKE_CASE				,							)

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





'''simple docstring'''







from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image


_lowercase :  List[str]    							=       ["text", "image", "audio"]
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: List[str]							):



						"""simple docstring"""





						lowercase_	:     int													=       []

						for input_type in input_types:
												if input_type == "text":
																		inputs.append('''Text input'''							)
												elif input_type == "image":
																		inputs.append(
																		    Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''							)							) / '''000000039769.png'''							).resize((512, 512)							)							)
												elif input_type == "audio":
																		inputs.append(torch.ones(3000							)							)
												elif isinstance(__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							):
																		inputs.append(create_inputs(__SCREAMING_SNAKE_CASE							)							)
												else:
																		raise ValueError(F'''Invalid type requested: {input_type}'''							)

						return inputs
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: List							):



						"""simple docstring"""





						lowercase_	:     Optional[Any]													=       []

						for output in outputs:
												if isinstance(__SCREAMING_SNAKE_CASE      ,      (str, AgentText)							):
																		output_types.append('''text'''							)
												elif isinstance(__SCREAMING_SNAKE_CASE      ,      (Image.Image, AgentImage)							):
																		output_types.append('''image'''							)
												elif isinstance(__SCREAMING_SNAKE_CASE      ,      (torch.Tensor, AgentAudio)							):
																		output_types.append('''audio'''							)
												else:
																		raise ValueError(F'''Invalid output: {output}'''							)

						return output_types






@is_tool_test
class  lowerCAmelCase__   :






					def 	_snake_case    (   self		):


											"""simple docstring"""





											self.assertTrue(hasattr(self.tool				,							'''inputs'''		)		)
											self.assertTrue(hasattr(self.tool				,							'''outputs'''		)		)

											lowercase_	:     Optional[Any]													=       self.tool.inputs
											for _input in inputs:
																	if isinstance(_input				,							__SCREAMING_SNAKE_CASE		):
																							for __input in _input:
																													self.assertTrue(__input in authorized_types		)
																	else:
																							self.assertTrue(_input in authorized_types		)

											lowercase_	:     int													=       self.tool.outputs
											for _output in outputs:
																	self.assertTrue(_output in authorized_types		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     int													=       create_inputs(self.tool.inputs		)
											lowercase_	:     Tuple													=       self.tool(*__SCREAMING_SNAKE_CASE		)

											# There is a single output
											if len(self.tool.outputs		) == 1:
																	lowercase_	:     Any													=       [outputs]

											self.assertListEqual(output_types(__SCREAMING_SNAKE_CASE		)				,							self.tool.outputs		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											self.assertTrue(hasattr(self.tool				,							'''description'''		)		)
											self.assertTrue(hasattr(self.tool				,							'''default_checkpoint'''		)		)
											self.assertTrue(self.tool.description.startswith('''This is a tool that'''		)		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     int													=       create_inputs(self.tool.inputs		)
											lowercase_	:     int													=       self.tool(*__SCREAMING_SNAKE_CASE		)

											if not isinstance(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):
																	lowercase_	:     Optional[Any]													=       [outputs]

											self.assertEqual(len(__SCREAMING_SNAKE_CASE		)				,							len(self.tool.outputs		)		)

											for output, output_type in zip(__SCREAMING_SNAKE_CASE				,							self.tool.outputs		):
																	lowercase_	:     Optional[int]													=       AGENT_TYPE_MAPPING[output_type]
																	self.assertTrue(isinstance(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Dict													=       create_inputs(self.tool.inputs		)

											lowercase_	:     int													=       []

											for _input, input_type in zip(__SCREAMING_SNAKE_CASE				,							self.tool.inputs		):
																	if isinstance(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):
																							_inputs.append([AGENT_TYPE_MAPPING[_input_type](_input		) for _input_type in input_type]		)
																	else:
																							_inputs.append(AGENT_TYPE_MAPPING[input_type](_input		)		)

        # Should not raise an error
											lowercase_	:     Optional[Any]													=       self.tool(*__SCREAMING_SNAKE_CASE		)

											if not isinstance(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):
																	lowercase_	:     Dict													=       [outputs]

											self.assertEqual(len(__SCREAMING_SNAKE_CASE		)				,							len(self.tool.outputs		)		)



