


"""simple docstring"""
import copy
import os
from typing import Dict, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings, logging
from ..auto import AutoConfig


SCREAMING_SNAKE_CASE					:       int							   =							logging.get_logger(__name__)


SCREAMING_SNAKE_CASE					:       Optional[Any]							   =							{
    """suno/bark-small""": """https://huggingface.co/suno/bark-small/resolve/main/config.json""",
    """suno/bark""": """https://huggingface.co/suno/bark/resolve/main/config.json""",
}

SCREAMING_SNAKE_CASE					:       int							   =							"""
    This is the configuration class to store the configuration of a [`{model}`]. It is used to instantiate the model
    according to the specified arguments, defining the model architecture. Instantiating a configuration with the
    defaults will yield a similar configuration to that of the Bark [suno/bark](https://huggingface.co/suno/bark)
    architecture.

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.

    Args:
        block_size (`int`, *optional*, defaults to 1024):
            The maximum sequence length that this model might ever be used with. Typically set this to something large
            just in case (e.g., 512 or 1024 or 2048).
        input_vocab_size (`int`, *optional*, defaults to 10_048):
            Vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`{model}`]. Defaults to 10_048 but should be carefully thought with
            regards to the chosen sub-model.
        output_vocab_size (`int`, *optional*, defaults to 10_048):
            Output vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented
            by the: `output_ids` when passing forward a [`{model}`]. Defaults to 10_048 but should be carefully thought
            with regards to the chosen sub-model.
        num_layers (`int`, *optional*, defaults to 12):
            Number of hidden layers in the given sub-model.
        num_heads (`int`, *optional*, defaults to 12):
            Number of attention heads for each attention layer in the Transformer architecture.
        hidden_size (`int`, *optional*, defaults to 768):
            Dimensionality of the \"intermediate\" (often named feed-forward) layer in the architecture.
        dropout (`float`, *optional*, defaults to 0.0):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        bias (`bool`, *optional*, defaults to `True`):
            Whether or not to use bias in the linear layers and layer norm layers.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models).
"""



class      _UpperCAmelCase					(				__snake_case							):






		'''simple docstring'''

		lowerCamelCase__           ='bark_module'
		lowerCamelCase__           =['past_key_values']

		lowerCamelCase__           ={
		    'num_attention_heads': 'num_heads',
		    'num_hidden_layers': 'num_layers',
		    'vocab_size': 'input_vocab_size',
		    'window_size': 'block_size',
		}



		def __init__(self							,			a_=10_24							,			a_=1_00_48							,			a_=1_00_48							,			a_=12							,			a_=12							,			a_=7_68							,			a_=0.0							,			a_=True							,			a_=0.02							,			a_=True							,			**a_							,			):





			'''simple docstring'''

			__snake_case      :		Union[str, Any]         =							block_size
			__snake_case      :		Any         =							input_vocab_size
			__snake_case      :		Dict         =							output_vocab_size
			__snake_case      :		Dict         =							num_layers
			__snake_case      :		List[str]         =							num_heads
			__snake_case      :		Dict         =							hidden_size
			__snake_case      :		str         =							dropout
			__snake_case      :		Optional[int]         =							bias
			__snake_case      :		Optional[Any]         =							use_cache
			__snake_case      :		Dict         =							initializer_range

			super().__init__(**a_   )



		@classmethod
		def 							SCREAMING_SNAKE_CASE    (cls							,			a_							,			a_ = None							,			a_ = False							,			a_ = False							,			a_ = None							,			a_ = "main"							,			**a_							,			):





			'''simple docstring'''

			__snake_case      :		Union[str, Any]         =							cache_dir
			__snake_case      :		Any         =							force_download
			__snake_case      :		Union[str, Any]         =							local_files_only
			__snake_case      :		Optional[int]         =							revision

			cls._set_token_in_kwargs(a_							,			a_   )

			__snake_case      ,				__snake_case      :		Union[str, Any]         =							cls.get_config_dict(a_							,			**a_   )

			# get the config dict if we are loading from Bark
			if config_dict.get('''model_type'''   ) == "bark":
				__snake_case      :		str         =							config_dict[f"""{cls.model_type}_config"""]

			if "model_type" in config_dict and hasattr(cls							,			'''model_type'''   ) and config_dict["model_type"] != cls.model_type:
				logger.warning(
				    f"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
				    f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""   )

			return cls.from_dict(a_							,			**a_   )



@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config='BarkSemanticConfig',							model='BarkSemanticModel'							),							'\n    Example:\n\n    ```python\n    >>> from transformers import BarkSemanticConfig, BarkSemanticModel\n\n    >>> # Initializing a Bark sub-module style configuration\n    >>> configuration = BarkSemanticConfig()\n\n    >>> # Initializing a model (with random weights) from the suno/bark style configuration\n    >>> model = BarkSemanticModel(configuration)\n\n    >>> # Accessing the model configuration\n    >>> configuration = model.config\n    ```',							)
class      _UpperCAmelCase					(				__snake_case							):






		'''simple docstring'''

		lowerCamelCase__           ='semantic'



@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config='BarkCoarseConfig',							model='BarkCoarseModel'							),							'\n    Example:\n\n    ```python\n    >>> from transformers import BarkCoarseConfig, BarkCoarseModel\n\n    >>> # Initializing a Bark sub-module style configuration\n    >>> configuration = BarkCoarseConfig()\n\n    >>> # Initializing a model (with random weights) from the suno/bark style configuration\n    >>> model = BarkCoarseModel(configuration)\n\n    >>> # Accessing the model configuration\n    >>> configuration = model.config\n    ```',							)
class      _UpperCAmelCase					(				__snake_case							):






		'''simple docstring'''

		lowerCamelCase__           ='coarse_acoustics'



@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config='BarkFineConfig',							model='BarkFineModel'							),							'\n        n_codes_total (`int`, *optional*, defaults to 8):\n            The total number of audio codebooks predicted. Used in the fine acoustics sub-model.\n        n_codes_given (`int`, *optional*, defaults to 1):\n            The number of audio codebooks predicted in the coarse acoustics sub-model. Used in the acoustics\n            sub-models.\n    Example:\n\n    ```python\n    >>> from transformers import BarkFineConfig, BarkFineModel\n\n    >>> # Initializing a Bark sub-module style configuration\n    >>> configuration = BarkFineConfig()\n\n    >>> # Initializing a model (with random weights) from the suno/bark style configuration\n    >>> model = BarkFineModel(configuration)\n\n    >>> # Accessing the model configuration\n    >>> configuration = model.config\n    ```',							)
class      _UpperCAmelCase					(				__snake_case							):






		'''simple docstring'''

		lowerCamelCase__           ='fine_acoustics'



		def __init__(self							,			a_=True							,			a_=8							,			a_=1							,			**a_   ):





			'''simple docstring'''

			__snake_case      :		str         =							n_codes_total
			__snake_case      :		Dict         =							n_codes_given

			super().__init__(tie_word_embeddings=a_							,			**a_   )



class      _UpperCAmelCase					(				__snake_case							):






		'''simple docstring'''

		lowerCamelCase__           ='bark'
		lowerCamelCase__           =True



		def __init__(self							,			a_ = None							,			a_ = None							,			a_ = None							,			a_ = None							,			a_=0.02							,			**a_							,			):





			'''simple docstring'''

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

			if coarse_acoustics_config is None:
				__snake_case      :		Union[str, Any]         =							{}
				logger.info('''coarse_acoustics_config is None. initializing the coarse model with default values.'''   )

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

			if codec_config is None:
				__snake_case      :		Optional[int]         =							{}
				logger.info('''codec_config is None. initializing the codec model with default values.'''   )

			__snake_case      :		Any         =							BarkSemanticConfig(**a_   )
			__snake_case      :		List[Any]         =							BarkCoarseConfig(**a_   )
			__snake_case      :		Optional[int]         =							BarkFineConfig(**a_   )
			__snake_case      :		int         =							AutoConfig.for_model(**a_   )

			__snake_case      :		Tuple         =							initializer_range

			super().__init__(**a_   )



		@classmethod
		def 							SCREAMING_SNAKE_CASE    (cls							,			a_							,			a_							,			a_							,			a_							,			**a_							,			):





			'''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()							,			codec_config=codec_config.to_dict()							,			**a_							,			)



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		Tuple         =							copy.deepcopy(self.__dict__   )

			__snake_case      :		List[Any]         =							self.semantic_config.to_dict()
			__snake_case      :		Dict         =							self.coarse_acoustics_config.to_dict()
			__snake_case      :		str         =							self.fine_acoustics_config.to_dict()
			__snake_case      :		Tuple         =							self.codec_config.to_dict()

			__snake_case      :		Any         =							self.__class__.model_type
			return output









"""simple docstring"""
import logging
import os
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import classification_report, fa_score, precision_score, recall_score
from utils_ner import Split, TFTokenClassificationDataset, TokenClassificationTask

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    TFAutoModelForTokenClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()


SCREAMING_SNAKE_CASE					:       Optional[Any]							   =							logging.getLogger(__name__)



@dataclass
class      _UpperCAmelCase					:






		'''simple docstring'''

		lowerCamelCase__           =field(
		    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}							)
		lowerCamelCase__           =field(
		    default=__snake_case,							metadata={'help': 'Pretrained config name or path if not the same as model_name'}							)
		lowerCamelCase__           =field(
		    default='NER',							metadata={'help': 'Task type to fine tune in training (e.g. NER, POS, etc)'}							)
		lowerCamelCase__           =field(
		    default=__snake_case,							metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}							)
		lowerCamelCase__           =field(default=__snake_case,							metadata={'help': 'Set this flag to use fast tokenization.'}							)
		# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
		# or just modify its tokenizer_config.json.
		lowerCamelCase__           =field(
		    default=__snake_case,							metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'},							)



@dataclass
class      _UpperCAmelCase					:






		'''simple docstring'''

		lowerCamelCase__           =field(
		    metadata={'help': 'The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'}							)
		lowerCamelCase__           =field(
		    metadata={'help': 'Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'}							)
		lowerCamelCase__           =field(
		    default=128,							metadata={
		        'help': (
		            'The maximum total input sequence length after tokenization. Sequences longer '
		            'than this will be truncated, sequences shorter will be padded.'
		        )
		    },							)
		lowerCamelCase__           =field(
		    default=__snake_case,							metadata={'help': 'Overwrite the cached training and evaluation sets'}							)






def 						lowercase				(	)	->Dict:
	"""simple docstring"""





	__snake_case      :		str         =							HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)	)
	__snake_case      ,				__snake_case      ,				__snake_case      :		Dict         =							parser.parse_args_into_dataclasses()

	if (
	    os.path.exists(training_args.output_dir	)
	    and os.listdir(training_args.output_dir	)
	    and training_args.do_train
	    and not training_args.overwrite_output_dir
	):
		raise ValueError(
		    f"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
		    ''' --overwrite_output_dir to overcome.'''	)

	__snake_case      :		List[Any]         =							import_module('''tasks'''	)

	try:
		__snake_case      :		Optional[int]         =							getattr(_snake_case	,  model_args.task_type	)
		__snake_case      :		TokenClassificationTask         =							token_classification_task_clazz()
	except AttributeError:
		raise ValueError(
		    f"""Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
		    f"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}"""	)

	# Setup logging
	logging.basicConfig(
	    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''	,  datefmt='''%m/%d/%Y %H:%M:%S'''	,  level=logging.INFO	,  )
	logger.info(
	    '''n_replicas: %s, distributed training: %s, 16-bits training: %s'''	,  training_args.n_replicas	,  bool(training_args.n_replicas > 1	)	,  training_args.fpaa	,  )
	logger.info('''Training/evaluation parameters %s'''	,  _snake_case	)

	# Prepare Token Classification task
	__snake_case      :		Union[str, Any]         =							token_classification_task.get_labels(data_args.labels	)
	__snake_case      :		Dict[int, str]         =							dict(enumerate(_snake_case	)	)
	__snake_case      :		List[Any]         =							len(_snake_case	)

	# Load pretrained model and tokenizer
	#
	# Distributed training:
	# The .from_pretrained methods guarantee that only one local process can concurrently
	# download model & vocab.

	__snake_case      :		Any         =							AutoConfig.from_pretrained(
	    model_args.config_name if model_args.config_name else model_args.model_name_or_path	,  num_labels=_snake_case	,  idalabel=_snake_case	,  labelaid={label: i for i, label in enumerate(_snake_case	)}	,  cache_dir=model_args.cache_dir	,  )
	__snake_case      :		Tuple         =							AutoTokenizer.from_pretrained(
	    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path	,  cache_dir=model_args.cache_dir	,  use_fast=model_args.use_fast	,  )

	with training_args.strategy.scope():
		__snake_case      :		Optional[int]         =							TFAutoModelForTokenClassification.from_pretrained(
		    model_args.model_name_or_path	,  from_pt=bool('''.bin''' in model_args.model_name_or_path	)	,  config=_snake_case	,  cache_dir=model_args.cache_dir	,  )

	# Get datasets
	__snake_case      :		Any         =							(
	    TFTokenClassificationDataset(
	        token_classification_task=_snake_case	,  data_dir=data_args.data_dir	,  tokenizer=_snake_case	,  labels=_snake_case	,  model_type=config.model_type	,  max_seq_length=data_args.max_seq_length	,  overwrite_cache=data_args.overwrite_cache	,  mode=Split.train	,  )
	    if training_args.do_train
	    else None
	)
	__snake_case      :		List[Any]         =							(
	    TFTokenClassificationDataset(
	        token_classification_task=_snake_case	,  data_dir=data_args.data_dir	,  tokenizer=_snake_case	,  labels=_snake_case	,  model_type=config.model_type	,  max_seq_length=data_args.max_seq_length	,  overwrite_cache=data_args.overwrite_cache	,  mode=Split.dev	,  )
	    if training_args.do_eval
	    else None
	)

	def align_predictions(_snake_case		: np.ndarray	,  _snake_case		: np.ndarray	) -> Tuple[List[int], List[int]]:
		__snake_case      :		List[Any]         =							np.argmax(_snake_case	,  axis=2	)
		__snake_case      ,				__snake_case      :		Dict         =							preds.shape
		__snake_case      :		Union[str, Any]         =							[[] for _ in range(_snake_case	)]
		__snake_case      :		str         =							[[] for _ in range(_snake_case	)]

		for i in range(_snake_case	):
			for j in range(_snake_case	):
				if label_ids[i, j] != -100:
					out_label_list[i].append(label_map[label_ids[i][j]]	)
					preds_list[i].append(label_map[preds[i][j]]	)

		return preds_list, out_label_list

	def compute_metrics(_snake_case		: EvalPrediction	) -> Dict:
		__snake_case      ,				__snake_case      :		Optional[int]         =							align_predictions(p.predictions	,  p.label_ids	)

		return {
		    "precision": precision_score(_snake_case	,  _snake_case	),
		    "recall": recall_score(_snake_case	,  _snake_case	),
		    "f1": fa_score(_snake_case	,  _snake_case	),
		}

	# Initialize our Trainer
	__snake_case      :		Optional[Any]         =							TFTrainer(
	    model=_snake_case	,  args=_snake_case	,  train_dataset=train_dataset.get_dataset() if train_dataset else None	,  eval_dataset=eval_dataset.get_dataset() if eval_dataset else None	,  compute_metrics=_snake_case	,  )

	# Training
	if training_args.do_train:
		trainer.train()
		trainer.save_model()
		tokenizer.save_pretrained(training_args.output_dir	)

	# Evaluation
	__snake_case      :		Union[str, Any]         =							{}
	if training_args.do_eval:
		logger.info('''*** Evaluate ***'''	)

		__snake_case      :		int         =							trainer.evaluate()
		__snake_case      :		List[Any]         =							os.path.join(training_args.output_dir	,  '''eval_results.txt'''	)

		with open(_snake_case	,  '''w'''	) as writer:
			logger.info('''***** Eval results *****'''	)

			for key, value in result.items():
				logger.info('''  %s = %s'''	,  _snake_case	,  _snake_case	)
				writer.write('''%s = %s\n''' % (key, value)	)

			results.update(_snake_case	)

    # Predict
	if training_args.do_predict:
		__snake_case      :		Optional[Any]         =							TFTokenClassificationDataset(
		    token_classification_task=_snake_case	,  data_dir=data_args.data_dir	,  tokenizer=_snake_case	,  labels=_snake_case	,  model_type=config.model_type	,  max_seq_length=data_args.max_seq_length	,  overwrite_cache=data_args.overwrite_cache	,  mode=Split.test	,  )

		__snake_case      ,				__snake_case      ,				__snake_case      :		str         =							trainer.predict(test_dataset.get_dataset()	)
		__snake_case      ,				__snake_case      :		Optional[int]         =							align_predictions(_snake_case	,  _snake_case	)
		__snake_case      :		Dict         =							classification_report(_snake_case	,  _snake_case	)

		logger.info('''\n%s'''	,  _snake_case	)

		__snake_case      :		List[str]         =							os.path.join(training_args.output_dir	,  '''test_results.txt'''	)

		with open(_snake_case	,  '''w'''	) as writer:
			writer.write('''%s\n''' % report	)

		# Save predictions
		__snake_case      :		List[str]         =							os.path.join(training_args.output_dir	,  '''test_predictions.txt'''	)

		with open(_snake_case	,  '''w'''	) as writer:
			with open(os.path.join(data_args.data_dir	,  '''test.txt'''	)	,  '''r'''	) as f:
				__snake_case      :		Union[str, Any]         =							0

				for line in f:
					if line.startswith('''-DOCSTART-'''	) or line == "" or line == "\n":
						writer.write(_snake_case	)

						if not preds_list[example_id]:
							example_id += 1
					elif preds_list[example_id]:
						__snake_case      :		List[str]         =							line.split()[0] + ''' ''' + preds_list[example_id].pop(0	) + '''\n'''

						writer.write(_snake_case	)
					else:
						logger.warning('''Maximum sequence length exceeded: No prediction for \'%s\'.'''	,  line.split()[0]	)

	return results


if __name__ == "__main__":
			main()





