
import copy
from typing import Dict

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


lowercase__						:List[str]					    =       logging.get_logger(__name__)







class 							lowercase						(   lowerCamelCase_						):
							lowercase_			:			Tuple			   ='''semantic'''




							def __init__(							self  ,A__=1_0_0_0_0  ,A__=True  ,A__=7_6_8  ,A__=False  ,A__=False  ,A__=False  ,A__=False  ,A__=0.7  ,A__=True  ,A__=1_0_0_4_8  ,A__=1_2_9_5_9_5  ,A__=1_2_9_5_9_9  ,A__=1_0_0_0_0  ,A__=2_5_6  ,A__=49.9  ,**A__  ,):
												super().__init__(
												    temperature=_lowercase  ,do_sample=_lowercase  ,eos_token_id=_lowercase  ,renormalize_logits=_lowercase  ,max_new_tokens=_lowercase  ,output_scores=_lowercase  ,return_dict_in_generate=_lowercase  ,output_hidden_states=_lowercase  ,output_attentions=_lowercase  ,**_lowercase  ,)

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







class 							lowercase						(   lowerCamelCase_						):
							lowercase_			:			str			   ='''coarse_acoustics'''




							def __init__(							self  ,A__=True  ,A__=False  ,A__=False  ,A__=False  ,A__=False  ,A__=0.7  ,A__=True  ,A__=1_2_0_4_8  ,A__=7_5  ,A__=2  ,A__=1_2_0_5_0  ,A__=2_5_6  ,A__ = 6_3_0  ,A__ = 6_0  ,**A__  ,):
												super().__init__(
												    temperature=_lowercase  ,do_sample=_lowercase  ,renormalize_logits=_lowercase  ,output_scores=_lowercase  ,return_dict_in_generate=_lowercase  ,output_hidden_states=_lowercase  ,output_attentions=_lowercase  ,**_lowercase  ,)

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







class 							lowercase						(   lowerCamelCase_						):
							lowercase_			:			Tuple			   ='''fine_acoustics'''




							def __init__(							self  ,A__=0.5  ,A__=5_1_2  ,A__=1_0_2_4  ,A__=8  ,**A__  ,):
												super().__init__(temperature=_lowercase)

												lowercase		      =		max_fine_history_length
												lowercase		      =		max_fine_input_length
												lowercase		      =		n_fine_codebooks







class 							lowercase						(   lowerCamelCase_						):
							lowercase_			:			Dict			   ='''bark'''
							lowercase_			:			Any			   =True




							def __init__(							self  ,A__ = None  ,A__ = None  ,A__ = None  ,A__=2_4_0_0_0  ,A__=1_0_2_4  ,**A__  ,):






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

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

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

												lowercase		      =		BarkSemanticGenerationConfig(**_lowercase)
												lowercase		      =		BarkCoarseGenerationConfig(**_lowercase)
												lowercase		      =		BarkFineGenerationConfig(**_lowercase)

												lowercase		      =		sample_rate
												lowercase		      =		codebook_size




							@classmethod
							def 					A__ (							cls  ,A__  ,A__  ,A__  ,**A__  ,):
												return cls(
												    semantic_config=semantic_config.to_dict()  ,coarse_acoustics_config=coarse_acoustics_config.to_dict()  ,fine_acoustics_config=fine_acoustics_config.to_dict()  ,**_lowercase  ,)





							def 					A__ (							self):
												lowercase		      =		copy.deepcopy(self.__dict__)

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

												lowercase		      =		self.__class__.model_type
												return output





# This test is meant to be run in on an instance with TPUs like this:
#
#   python examples/pytorch/xla_spawn.py --num_cores=8 tests/test_trainer_tpu.py
#
# Replace 8 with the number of TPU cores you have.
#

import sys
from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.utils import logging


__lowerCamelCase     :					List[str]				  =						logging.get_logger(__name__)


if is_torch_available():
   import torch
   from torch import nn
   from torch.utils.data import Dataset

   from transformers import Trainer







   class __snake_case							(		lowerCamelCase_						):




      def __init__(       self     :							Tuple			,      _lowercase     :							int = 1_01 ):



        """simple docstring"""






        SCREAMING_SNAKE_CASE__     				= length




      def __len__(       self     :							str ):



        """simple docstring"""






        return self.length




      def __getitem__(       self     :							Optional[Any]			,      _lowercase     :							Union[str, Any] ):



        """simple docstring"""






        return i







   class __snake_case							:




      def __call__(       self     :							Dict			,      _lowercase     :							Dict ):



        """simple docstring"""






        return {"input_ids": torch.tensor(_lowercase ), "labels": torch.tensor(_lowercase )}







   class __snake_case							(		nn.Module						):




      def __init__(       self     :							Tuple ):



        """simple docstring"""






        super().__init__()
        # Add some (unused) params otherwise DDP will complain.
        SCREAMING_SNAKE_CASE__     				= nn.Linear(1_20			,      80 )




      def       __a		(       self     :							Any			,      _lowercase     :							Union[str, Any]			,      _lowercase     :							Dict=None ):



        """simple docstring"""






        if labels is not None:
          return torch.tensor(0.0			,      device=input_ids.device ), input_ids
        else:
          return input_ids



def       __SCREAMING_SNAKE_CASE    (   )				->   Optional[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= HfArgumentParser((TrainingArguments,)      )
  sys.argv += ["--output_dir", "./examples"]
  SCREAMING_SNAKE_CASE__     				= parser.parse_args_into_dataclasses()[0]

  logger.warning(
      f"""Process rank: {training_args.local_rank}, device: {training_args.device}, """
      f"""tpu_num_cores: {training_args.tpu_num_cores}"""					,	)

  # Essentially, what we want to verify in the distributed case is
  # that we get all samples back, in the right order.
  # (this is crucial for prediction for instance)
  for dataset_length in [10_01, 2_56, 15]:
    SCREAMING_SNAKE_CASE__     				= DummyDataset(__UpperCamelCase      )

    def compute_metrics(__UpperCamelCase       :			EvalPrediction      ) -> Dict:
      SCREAMING_SNAKE_CASE__     				= list(range(len(__UpperCamelCase      )      )      )
      SCREAMING_SNAKE_CASE__     				= p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
      return {"success": success}

    SCREAMING_SNAKE_CASE__     				= Trainer(
        model=DummyModel()					,	args=__UpperCamelCase					,	data_collator=DummyDataCollator()					,	eval_dataset=__UpperCamelCase					,	compute_metrics=__UpperCamelCase					,	)
    SCREAMING_SNAKE_CASE__     				= trainer.evaluate()
    logger.info(__UpperCamelCase      )
    if metrics["eval_success"] is not True:
      logger.error(__UpperCamelCase      )
      exit(1      )

    SCREAMING_SNAKE_CASE__     				= trainer.predict(__UpperCamelCase      )
    logger.info(p.metrics      )
    if p.metrics["test_success"] is not True:
      logger.error(p.metrics      )
      exit(1      )

    SCREAMING_SNAKE_CASE__     				= 2

    SCREAMING_SNAKE_CASE__     				= trainer.evaluate()
    logger.info(__UpperCamelCase      )
    if metrics["eval_success"] is not True:
      logger.error(__UpperCamelCase      )
      exit(1      )

    SCREAMING_SNAKE_CASE__     				= trainer.predict(__UpperCamelCase      )
    logger.info(p.metrics      )
    if p.metrics["test_success"] is not True:
      logger.error(p.metrics      )
      exit(1      )

    SCREAMING_SNAKE_CASE__     				= None

  logger.info("""🔥 All distributed tests successful"""      )



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			Union[str, Any]      )				->   Dict:







  """simple docstring"""





  main()


if __name__ == "__main__":
   main()
