






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


_a      	=  ['''text''', '''image''', '''audio''']


def __A    (      __lowerCAmelCase	)->  Optional[int]:





						"""simple docstring"""



						_UpperCAmelCase									=			[]

						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(3_000	)	)
												elif isinstance(__lowerCAmelCase ,		__lowerCAmelCase	):
																		inputs.append(create_inputs(__lowerCAmelCase	)	)
												else:
																		raise ValueError(F"""Invalid type requested: {input_type}"""	)

						return inputs


def __A    (      __lowerCAmelCase	)->  Optional[Any]:





						"""simple docstring"""



						_UpperCAmelCase									=			[]

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

						return output_types





@is_tool_test
class __lowerCamelCase :





	"""simple docstring"""






	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







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

							_UpperCAmelCase									=			self.tool.inputs
							for _input in inputs:
													if isinstance(_input       ,    UpperCAmelCase    ):
																			for __input in _input:
																									self.assertTrue(__input in authorized_types    )
													else:
																			self.assertTrue(_input in authorized_types    )

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

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			create_inputs(self.tool.inputs    )
							_UpperCAmelCase									=			self.tool(*UpperCAmelCase    )

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

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

	def        UpperCamelCase    (							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        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			create_inputs(self.tool.inputs    )
							_UpperCAmelCase									=			self.tool(*UpperCAmelCase    )

							if not isinstance(UpperCAmelCase       ,    UpperCAmelCase    ):
													_UpperCAmelCase									=			[outputs]

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

							for output, output_type in zip(UpperCAmelCase       ,    self.tool.outputs    ):
													_UpperCAmelCase									=			AGENT_TYPE_MAPPING[output_type]
													self.assertTrue(isinstance(UpperCAmelCase       ,    UpperCAmelCase    )    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			create_inputs(self.tool.inputs    )

							_UpperCAmelCase									=			[]

							for _input, input_type in zip(UpperCAmelCase       ,    self.tool.inputs    ):
													if isinstance(UpperCAmelCase       ,    UpperCAmelCase    ):
																			_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
							_UpperCAmelCase									=			self.tool(*UpperCAmelCase    )

							if not isinstance(UpperCAmelCase       ,    UpperCAmelCase    ):
													_UpperCAmelCase									=			[outputs]

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




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       ):






				'''simple docstring'''



				def 		__magic_name__(       self			:Union[str, Any]	)		->			Tuple:
										__SCREAMING_SNAKE_CASE						:     str					=		'''ylacombe/bark-small'''
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		tempfile.mkdtemp()
										__SCREAMING_SNAKE_CASE						:     str					=		'''en_speaker_1'''
										__SCREAMING_SNAKE_CASE						:     Any					=		'''This is a test string'''
										__SCREAMING_SNAKE_CASE						:     int					=		'''speaker_embeddings_path.json'''
										__SCREAMING_SNAKE_CASE						:     int					=		'''speaker_embeddings'''


				def 		__magic_name__(       self			:List[str]			,			**lowerCAmelCase__			:Union[str, Any]	)		->			Any:
										return AutoTokenizer.from_pretrained(self.checkpoint			,			**lowerCAmelCase__	)


				def 		__magic_name__(       self			:List[str]	)		->			int:
										shutil.rmtree(self.tmpdirname	)


				def 		__magic_name__(       self			:Dict	)		->			str:
										__SCREAMING_SNAKE_CASE						:     Dict					=		self.get_tokenizer()

										__SCREAMING_SNAKE_CASE						:     Tuple					=		BarkProcessor(tokenizer=lowerCAmelCase__	)

										processor.save_pretrained(self.tmpdirname	)
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		BarkProcessor.from_pretrained(self.tmpdirname	)

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


				@slow
				def 		__magic_name__(       self			:Tuple	)		->			List[Any]:
										__SCREAMING_SNAKE_CASE						:     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			,			)

										__SCREAMING_SNAKE_CASE						:     Dict					=		self.get_tokenizer(bos_token='''(BOS)'''			,			eos_token='''(EOS)'''	)

										__SCREAMING_SNAKE_CASE						:     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 		__magic_name__(       self			:List[str]	)		->			Tuple:
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		BarkProcessor.from_pretrained(
										    pretrained_processor_name_or_path=self.checkpoint			,			speaker_embeddings_dict_path=self.speaker_embeddings_dict_path			,			)

										__SCREAMING_SNAKE_CASE						:     str					=		35
										__SCREAMING_SNAKE_CASE						:     str					=		2
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		8

										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		{
										    '''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
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		processor(text=self.input_string			,			voice_preset=lowerCAmelCase__	)

										__SCREAMING_SNAKE_CASE						:     List[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
										__SCREAMING_SNAKE_CASE						:     str					=		os.path.join(self.tmpdirname			,			'''file.npz'''	)
										np.savez(lowerCAmelCase__			,			**lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		processor(text=self.input_string			,			voice_preset=lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     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
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		processor(text=self.input_string			,			voice_preset=self.voice_preset	)



				def 		__magic_name__(       self			:Tuple	)		->			Optional[Any]:
										__SCREAMING_SNAKE_CASE						:     Tuple					=		self.get_tokenizer()

										__SCREAMING_SNAKE_CASE						:     Any					=		BarkProcessor(tokenizer=lowerCAmelCase__	)

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

										__SCREAMING_SNAKE_CASE						:     List[Any]					=		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()	)
