





import copy
from typing import Dict

from ...generation.configuration_utils import GenerationConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_		  =			logging.get_logger(__name__)

class 						UpperCamelCase__					( __lowercase  ):



     '''simple docstring'''

     __snake_case       :      Any						      =     'semantic'






     def __init__(				self		:       List[str]		,lowerCamelCase__		:       Dict=10000		,lowerCamelCase__		:       Any=True		,lowerCamelCase__		:       Tuple=768		,lowerCamelCase__		:       str=False		,lowerCamelCase__		:       str=False		,lowerCamelCase__		:       Optional[int]=False		,lowerCamelCase__		:       Any=False		,lowerCamelCase__		:       Optional[int]=0.7		,lowerCamelCase__		:       int=True		,lowerCamelCase__		:       int=10048		,lowerCamelCase__		:       Optional[int]=129595		,lowerCamelCase__		:       int=129599		,lowerCamelCase__		:       str=10000		,lowerCamelCase__		:       Optional[int]=256		,lowerCamelCase__		:       List[Any]=49.9		,**lowerCamelCase__		:       Any		,)	->						Any:




           '''simple docstring'''


           super().__init__(
               temperature=__A		,do_sample=__A		,eos_token_id=__A		,renormalize_logits=__A		,max_new_tokens=__A		,output_scores=__A		,return_dict_in_generate=__A		,output_hidden_states=__A		,output_attentions=__A		,**__A		,)

           SCREAMING_SNAKE_CASE						=		text_encoding_offset
           SCREAMING_SNAKE_CASE						=		text_pad_token
           SCREAMING_SNAKE_CASE						=		eos_token_id
           SCREAMING_SNAKE_CASE						=		semantic_infer_token
           SCREAMING_SNAKE_CASE						=		semantic_vocab_size
           SCREAMING_SNAKE_CASE						=		max_input_semantic_length
           SCREAMING_SNAKE_CASE						=		semantic_rate_hz

class 						UpperCamelCase__					( __lowercase  ):



     '''simple docstring'''

     __snake_case       :      Any						      =     'coarse_acoustics'






     def __init__(				self		:       Any		,lowerCamelCase__		:       List[Any]=True		,lowerCamelCase__		:       Any=False		,lowerCamelCase__		:       Optional[Any]=False		,lowerCamelCase__		:       List[Any]=False		,lowerCamelCase__		:       Any=False		,lowerCamelCase__		:       Any=0.7		,lowerCamelCase__		:       int=True		,lowerCamelCase__		:       Tuple=12048		,lowerCamelCase__		:       List[Any]=75		,lowerCamelCase__		:       int=2		,lowerCamelCase__		:       Tuple=12050		,lowerCamelCase__		:       List[Any]=256		,lowerCamelCase__		:       str = 630		,lowerCamelCase__		:       str = 60		,**lowerCamelCase__		:       Dict		,)	->						Union[str, Any]:




           '''simple docstring'''


           super().__init__(
               temperature=__A		,do_sample=__A		,renormalize_logits=__A		,output_scores=__A		,return_dict_in_generate=__A		,output_hidden_states=__A		,output_attentions=__A		,**__A		,)

           SCREAMING_SNAKE_CASE						=		coarse_semantic_pad_token
           SCREAMING_SNAKE_CASE						=		coarse_rate_hz
           SCREAMING_SNAKE_CASE						=		n_coarse_codebooks
           SCREAMING_SNAKE_CASE						=		coarse_infer_token
           SCREAMING_SNAKE_CASE						=		max_coarse_input_length
           SCREAMING_SNAKE_CASE						=		max_coarse_history
           SCREAMING_SNAKE_CASE						=		sliding_window_len

class 						UpperCamelCase__					( __lowercase  ):



     '''simple docstring'''

     __snake_case       :      str						      =     'fine_acoustics'






     def __init__(				self		:       str		,lowerCamelCase__		:       List[str]=0.5		,lowerCamelCase__		:       Tuple=512		,lowerCamelCase__		:       Any=1024		,lowerCamelCase__		:       List[Any]=8		,**lowerCamelCase__		:       Optional[Any]		,)	->						int:




           '''simple docstring'''


           super().__init__(temperature=__A			)

           SCREAMING_SNAKE_CASE						=		max_fine_history_length
           SCREAMING_SNAKE_CASE						=		max_fine_input_length
           SCREAMING_SNAKE_CASE						=		n_fine_codebooks

class 						UpperCamelCase__					( __lowercase  ):



     '''simple docstring'''

     __snake_case       :      int						      =     'bark'
     __snake_case       :      Optional[Any]						      =     True






     def __init__(				self		:       Any		,lowerCamelCase__		:       Tuple = None		,lowerCamelCase__		:       Any = None		,lowerCamelCase__		:       Any = None		,lowerCamelCase__		:       int=24000		,lowerCamelCase__		:       List[Any]=1024		,**lowerCamelCase__		:       Tuple		,)	->						Optional[int]:




           '''simple docstring'''


           if semantic_config is None:
                 SCREAMING_SNAKE_CASE						=		{}
                 logger.info("""semantic_config is None. initializing the semantic model with default values."""			)

           if coarse_acoustics_config is None:
                 SCREAMING_SNAKE_CASE						=		{}
                 logger.info("""coarse_acoustics_config is None. initializing the coarse model with default values."""			)

           if fine_acoustics_config is None:
                 SCREAMING_SNAKE_CASE						=		{}
                 logger.info("""fine_acoustics_config is None. initializing the fine model with default values."""			)

           SCREAMING_SNAKE_CASE						=		BarkSemanticGenerationConfig(**__A			)
           SCREAMING_SNAKE_CASE						=		BarkCoarseGenerationConfig(**__A			)
           SCREAMING_SNAKE_CASE						=		BarkFineGenerationConfig(**__A			)

           SCREAMING_SNAKE_CASE						=		sample_rate
           SCREAMING_SNAKE_CASE						=		codebook_size






     @classmethod
     def 							SCREAMING_SNAKE_CASE__    (				cls		:       int		,lowerCamelCase__		:       Dict		,lowerCamelCase__		:       Dict		,lowerCamelCase__		:       str		,**lowerCamelCase__		:       Optional[Any]		,)	->						List[Any]:




           '''simple docstring'''


           return cls(
               semantic_config=semantic_config.to_dict()		,coarse_acoustics_config=coarse_acoustics_config.to_dict()		,fine_acoustics_config=fine_acoustics_config.to_dict()		,**__A		,)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Any			)	->						Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		copy.deepcopy(self.__dict__			)

           SCREAMING_SNAKE_CASE						=		self.semantic_config.to_dict()
           SCREAMING_SNAKE_CASE						=		self.coarse_acoustics_config.to_dict()
           SCREAMING_SNAKE_CASE						=		self.fine_acoustics_config.to_dict()

           SCREAMING_SNAKE_CASE						=		self.__class__.model_type
           return output



'''simple docstring'''

import os
import pickle
import unittest
from collections import UserDict, namedtuple

import torch

from accelerate.test_utils.testing import require_cuda, require_torch_min_version
from accelerate.test_utils.training import RegressionModel
from accelerate.utils import (
    convert_outputs_to_fpaa,
    extract_model_from_parallel,
    find_device,
    listify,
    patch_environment,
    recursively_apply,
    send_to_device,
)


lowercase_  		=			namedtuple('''ExampleNamedTuple''', '''a b c''')
class 	SCREAMING_SNAKE_CASE      (    unittest.TestCase):







		"""simple docstring"""






		def 		UpperCamelCase__							( self					)			->  int:
								_lowerCAmelCase					    =torch.randn(5		,    2					)
								_lowerCAmelCase					    =torch.device('cuda'					) if torch.cuda.is_available() else torch.device('cpu'					)

								_lowerCAmelCase					    =send_to_device(__A		,    __A					)
								self.assertTrue(torch.equal(resulta.cpu()		,    __A					)					)

								_lowerCAmelCase					    =send_to_device((tensor, [tensor, tensor], 1)		,    __A					)
								self.assertIsInstance(__A		,    __A					)
								self.assertTrue(torch.equal(resulta[0].cpu()		,    __A					)					)
								self.assertIsInstance(resulta[1]		,    __A					)
								self.assertTrue(torch.equal(resulta[1][0].cpu()		,    __A					)					)
								self.assertTrue(torch.equal(resulta[1][1].cpu()		,    __A					)					)
								self.assertEqual(resulta[2]		,    1					)

								_lowerCAmelCase					    =send_to_device({'a': tensor, 'b': [tensor, tensor], 'c': 1}		,    __A					)
								self.assertIsInstance(__A		,    __A					)
								self.assertTrue(torch.equal(resulta['a'].cpu()		,    __A					)					)
								self.assertIsInstance(resulta['b']		,    __A					)
								self.assertTrue(torch.equal(resulta['b'][0].cpu()		,    __A					)					)
								self.assertTrue(torch.equal(resulta['b'][1].cpu()		,    __A					)					)
								self.assertEqual(resulta['c']		,    1					)

								_lowerCAmelCase					    =send_to_device(ExampleNamedTuple(a=__A		,    b=[tensor, tensor]		,    c=1					)		,    __A					)
								self.assertIsInstance(__A		,    __A					)
								self.assertTrue(torch.equal(resulta.a.cpu()		,    __A					)					)
								self.assertIsInstance(resulta.b		,    __A					)
								self.assertTrue(torch.equal(resulta.b[0].cpu()		,    __A					)					)
								self.assertTrue(torch.equal(resulta.b[1].cpu()		,    __A					)					)
								self.assertEqual(resulta.c		,    1					)

								_lowerCAmelCase					    =send_to_device(UserDict({'a': tensor, 'b': [tensor, tensor], 'c': 1}					)		,    __A					)
								self.assertIsInstance(__A		,    __A					)
								self.assertTrue(torch.equal(resulta['a'].cpu()		,    __A					)					)
								self.assertIsInstance(resulta['b']		,    __A					)
								self.assertTrue(torch.equal(resulta['b'][0].cpu()		,    __A					)					)
								self.assertTrue(torch.equal(resulta['b'][1].cpu()		,    __A					)					)
								self.assertEqual(resulta['c']		,    1					)






		def 		UpperCamelCase__							( self					)			->  Optional[int]:
								with self.assertRaises(__A					) as cm:
														_lowerCAmelCase					    =recursively_apply(torch.tensor		,    (torch.tensor(1					), 1)		,    error_on_other_type=__A					)
								self.assertEqual(
								    str(cm.exception					)		,    'Unsupported types (<class \'int\'>) passed to `tensor`. Only nested list/tuple/dicts of objects that are valid for `is_torch_tensor` should be passed.'		,    )






		def 		UpperCamelCase__							( self					)			->  int:
								_lowerCAmelCase					    =torch.tensor([1, 2, 3, 4, 5]					)
								self.assertEqual(listify(__A					)		,    [1, 2, 3, 4, 5]					)

								_lowerCAmelCase					    =torch.tensor([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]					)
								self.assertEqual(listify(__A					)		,    [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]					)

								_lowerCAmelCase					    =torch.tensor([[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]]					)
								self.assertEqual(
								    listify(__A					)		,    [[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]]					)






		def 		UpperCamelCase__							( self					)			->  List[Any]:
								with patch_environment(aa=1		,    BB=2					):
														self.assertEqual(os.environ.get('AA'					)		,    '1'					)
														self.assertEqual(os.environ.get('BB'					)		,    '2'					)

								self.assertNotIn('AA'		,    os.environ					)
								self.assertNotIn('BB'		,    os.environ					)






		def 		UpperCamelCase__							( self					)			->  Union[str, Any]:
								_lowerCAmelCase					    =RegressionModel()
								_lowerCAmelCase					    =model.forward
								_lowerCAmelCase					    =convert_outputs_to_fpaa(model.forward					)
								_lowerCAmelCase					    =extract_model_from_parallel(__A		,    keep_fpaa_wrapper=__A					)
								_lowerCAmelCase					    =pickle.dumps(__A					)






		@require_cuda
		def 		UpperCamelCase__							( self					)			->  Dict:
								_lowerCAmelCase					    =RegressionModel()
								_lowerCAmelCase					    =model.forward
								_lowerCAmelCase					    =torch.cuda.amp.autocast(dtype=torch.floataa					)(model.forward					)
								_lowerCAmelCase					    =convert_outputs_to_fpaa(model.forward					)
								_lowerCAmelCase					    =extract_model_from_parallel(__A		,    keep_fpaa_wrapper=__A					)
								_lowerCAmelCase					    =pickle.dumps(__A					)






		@require_cuda
		@require_torch_min_version(version='2.0'					)
		def 		UpperCamelCase__							( self					)			->  int:
								_lowerCAmelCase					    =RegressionModel()
								_lowerCAmelCase					    =model.forward
								_lowerCAmelCase					    =torch.cuda.amp.autocast(dtype=torch.floataa					)(model.forward					)
								_lowerCAmelCase					    =convert_outputs_to_fpaa(model.forward					)
								_lowerCAmelCase					    =torch.compile(model.forward		,    backend='inductor'					)
								_lowerCAmelCase					    =torch.randn(4		,    10					).cuda()
								_lowerCAmelCase					    =model(__A					)






		def 		UpperCamelCase__							( self					)			->  Optional[int]:
								_lowerCAmelCase					    =RegressionModel()
								# could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
								_lowerCAmelCase					    =torch.nn.parallel.DataParallel(__A					)
								_lowerCAmelCase					    =extract_model_from_parallel(__A					)

								self.assertEqual(__A		,    __A					)






		@require_torch_min_version(version='2.0'					)
		def 		UpperCamelCase__							( self					)			->  str:
								_lowerCAmelCase					    =RegressionModel()
								_lowerCAmelCase					    =torch.compile(__A					)

								# could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
								_lowerCAmelCase					    =torch.nn.parallel.DataParallel(__A					)
								_lowerCAmelCase					    =torch.compile(__A					)
								_lowerCAmelCase					    =extract_model_from_parallel(__A					)

								self.assertEqual(compiled_model._orig_mod		,    compiled_model_unwrapped._orig_mod					)






		def 		UpperCamelCase__							( self					)			->  Tuple:
								self.assertEqual(find_device([1, 'a', torch.tensor([1, 2, 3]					)]					)		,    torch.device('cpu'					)					)
								self.assertEqual(find_device({'a': 1, 'b': torch.tensor([1, 2, 3]					)}					)		,    torch.device('cpu'					)					)
								self.assertIsNone(find_device([1, 'a']					)					)





