


'''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()


__lowercase	:   Tuple   		=					logging.getLogger(__name__)





@dataclass
class _snake_case :
							"""simple docstring"""





							__a				       =  field(
							    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
							__a				       =  field(
							    default=snake_case_     , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
							__a				       =  field(
							    default='''NER'''     , metadata={'''help''': '''Task type to fine tune in training (e.g. NER, POS, etc)'''} )
							__a				       =  field(
							    default=snake_case_     , metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
							__a				       =  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.
							__a				       =  field(
							    default=snake_case_     , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}     , )





@dataclass
class _snake_case :
							"""simple docstring"""





							__a				       =  field(
							    metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''} )
							__a				       =  field(
							    metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''} )
							__a				       =  field(
							    default=1_2_8     , metadata={
							        '''help''': (
							            '''The maximum total input sequence length after tokenization. Sequences longer '''
							            '''than this will be truncated, sequences shorter will be padded.'''
							        )
							    }     , )
							__a				       =  field(
							    default=snake_case_     , metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )





def 				a_							(    )      ->Optional[int]:






				'''simple docstring'''

				__lowerCamelCase		:   Any           =       HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)     )
				__lowerCamelCase		:   Union[str, Any]           =       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."     )

				__lowerCamelCase		:   Any           =       import_module("tasks"     )

				try:
								__lowerCamelCase		:   Union[str, Any]           =       getattr(lowercase				,    model_args.task_type     )
								__lowerCamelCase		:   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"				,    lowercase     )

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

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

				__lowerCamelCase		:   Any           =       AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path				,    num_labels=lowercase				,    idalabel=lowercase				,    labelaid={label: i for i, label in enumerate(lowercase     )}				,    cache_dir=model_args.cache_dir				,    )
				__lowerCamelCase		:   List[Any]           =       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():
								__lowerCamelCase		:   int           =       TFAutoModelForTokenClassification.from_pretrained(
								    model_args.model_name_or_path				,    from_pt=bool(".bin" in model_args.model_name_or_path     )				,    config=lowercase				,    cache_dir=model_args.cache_dir				,    )

				# Get datasets
				__lowerCamelCase		:   List[str]           =       (
				    TFTokenClassificationDataset(
				        token_classification_task=lowercase				,    data_dir=data_args.data_dir				,    tokenizer=lowercase				,    labels=lowercase				,    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
				)
				__lowerCamelCase		:   Tuple           =       (
				    TFTokenClassificationDataset(
				        token_classification_task=lowercase				,    data_dir=data_args.data_dir				,    tokenizer=lowercase				,    labels=lowercase				,    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(lowercase				,    lowercase     ) -> Tuple[List[int], List[int]]:
								__lowerCamelCase		:   Dict           =       np.argmax(lowercase				,    axis=2     )
								__lowerCamelCase		:   List[Any]           =       preds.shape
								__lowerCamelCase		:   Union[str, Any]           =       [[] for _ in range(lowercase     )]
								__lowerCamelCase		:   List[str]           =       [[] for _ in range(lowercase     )]

								for i in range(lowercase     ):
												for j in range(lowercase     ):
																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(lowercase     ) -> Dict:
								__lowerCamelCase		:   Union[str, Any]           =       align_predictions(p.predictions				,    p.label_ids     )

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

				# Initialize our Trainer
				__lowerCamelCase		:   Any           =       TFTrainer(
				    model=lowercase				,    args=lowercase				,    train_dataset=train_dataset.get_dataset() if train_dataset else None				,    eval_dataset=eval_dataset.get_dataset() if eval_dataset else None				,    compute_metrics=lowercase				,    )

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

				# Evaluation
				__lowerCamelCase		:   List[Any]           =       {}
				if training_args.do_eval:
								logger.info("*** Evaluate ***"     )

								__lowerCamelCase		:   Dict           =       trainer.evaluate()
								__lowerCamelCase		:   int           =       os.path.join(training_args.output_dir				,    "eval_results.txt"     )

								with open(lowercase				,    "w"     ) as writer:
												logger.info("***** Eval results *****"     )

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

												results.update(lowercase     )

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

								__lowerCamelCase		:   List[Any]           =       trainer.predict(test_dataset.get_dataset()     )
								__lowerCamelCase		:   Union[str, Any]           =       align_predictions(lowercase				,    lowercase     )
								__lowerCamelCase		:   int           =       classification_report(lowercase				,    lowercase     )

								logger.info("\n%s"				,    lowercase     )

								__lowerCamelCase		:   str           =       os.path.join(training_args.output_dir				,    "test_results.txt"     )

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

								# Save predictions
								__lowerCamelCase		:   Optional[Any]           =       os.path.join(training_args.output_dir				,    "test_predictions.txt"     )

								with open(lowercase				,    "w"     ) as writer:
												with open(os.path.join(data_args.data_dir				,    "test.txt"     )				,    "r"     ) as f:
																__lowerCamelCase		:   str           =       0

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

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

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

				return results


if __name__ == "__main__":
				main()











"""simple docstring"""





import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


_lowercase						   =     logging.get_logger(__name__)

_lowercase						   =     {
    """facebook/musicgen-small""": """https://huggingface.co/facebook/musicgen-small/resolve/main/config.json""",
    # See all Musicgen models at https://huggingface.co/models?filter=musicgen
}

class    __lowercase							(      snake_case_						):


				'''simple docstring'''


				_lowerCAmelCase						      ="musicgen_decoder"
				_lowerCAmelCase						      =["past_key_values"]

				def __init__( self :  List[str]   ,SCREAMING_SNAKE_CASE_ :  Dict=2_0_4_8   ,SCREAMING_SNAKE_CASE_ :  str=2_0_4_8   ,SCREAMING_SNAKE_CASE_ :  List[str]=2_4   ,SCREAMING_SNAKE_CASE_ :  int=4_0_9_6   ,SCREAMING_SNAKE_CASE_ :  List[Any]=1_6   ,SCREAMING_SNAKE_CASE_ :  Dict=0.0   ,SCREAMING_SNAKE_CASE_ :  Any=True   ,SCREAMING_SNAKE_CASE_ :  Any="gelu"   ,SCREAMING_SNAKE_CASE_ :  Tuple=1_0_2_4   ,SCREAMING_SNAKE_CASE_ :  Any=0.1   ,SCREAMING_SNAKE_CASE_ :  List[str]=0.0   ,SCREAMING_SNAKE_CASE_ :  Optional[Any]=0.0   ,SCREAMING_SNAKE_CASE_ :  Tuple=0.02   ,SCREAMING_SNAKE_CASE_ :  Optional[Any]=False   ,SCREAMING_SNAKE_CASE_ :  List[str]=4   ,SCREAMING_SNAKE_CASE_ :  str=2_0_4_8   ,SCREAMING_SNAKE_CASE_ :  str=2_0_4_8   ,SCREAMING_SNAKE_CASE_ :  Optional[int]=None   ,SCREAMING_SNAKE_CASE_ :  Tuple=False   ,**SCREAMING_SNAKE_CASE_ :  Union[str, Any]   ,):
										snake_case__							:       List[str]				      =       vocab_size
										snake_case__							:       List[str]				      =       max_position_embeddings
										snake_case__							:       List[Any]				      =       hidden_size
										snake_case__							:       Dict				      =       ffn_dim
										snake_case__							:       Union[str, Any]				      =       num_hidden_layers
										snake_case__							:       List[str]				      =       num_attention_heads
										snake_case__							:       Optional[Any]				      =       dropout
										snake_case__							:       Dict				      =       attention_dropout
										snake_case__							:       Union[str, Any]				      =       activation_dropout
										snake_case__							:       Dict				      =       activation_function
										snake_case__							:       Union[str, Any]				      =       initializer_factor
										snake_case__							:       List[str]				      =       layerdrop
										snake_case__							:       Any				      =       use_cache
										snake_case__							:       Tuple				      =       scale_embedding  # scale factor will be sqrt(d_model) if True
										snake_case__							:       Any				      =       num_codebooks
										super().__init__(
										    pad_token_id=SCREAMING_SNAKE_CASE_   ,bos_token_id=SCREAMING_SNAKE_CASE_   ,eos_token_id=SCREAMING_SNAKE_CASE_   ,tie_word_embeddings=SCREAMING_SNAKE_CASE_   ,**SCREAMING_SNAKE_CASE_   ,)







class    __lowercase							(      snake_case_						):


				'''simple docstring'''


				_lowerCAmelCase						      ="musicgen"
				_lowerCAmelCase						      =True

				def __init__( self :  Optional[Any]   ,**SCREAMING_SNAKE_CASE_ :  Dict   ):
										super().__init__(**SCREAMING_SNAKE_CASE_   )
										if "text_encoder" not in kwargs or "audio_encoder" not in kwargs or "decoder" not in kwargs:
																raise ValueError('''Config has to be initialized with text_encoder, audio_encoder and decoder config'''   )

										snake_case__							:       Optional[int]				      =       kwargs.pop('''text_encoder'''   )
										snake_case__							:       Optional[Any]				      =       text_encoder_config.pop('''model_type'''   )

										snake_case__							:       Union[str, Any]				      =       kwargs.pop('''audio_encoder'''   )
										snake_case__							:       Dict				      =       audio_encoder_config.pop('''model_type'''   )

										snake_case__							:       Optional[Any]				      =       kwargs.pop('''decoder'''   )

										snake_case__							:       Union[str, Any]				      =       AutoConfig.for_model(SCREAMING_SNAKE_CASE_   ,**SCREAMING_SNAKE_CASE_   )
										snake_case__							:       List[Any]				      =       AutoConfig.for_model(SCREAMING_SNAKE_CASE_   ,**SCREAMING_SNAKE_CASE_   )
										snake_case__							:       List[str]				      =       MusicgenDecoderConfig(**SCREAMING_SNAKE_CASE_   )
										snake_case__							:       Union[str, Any]				      =       True

				@classmethod
				def    _lowerCamelCase       ( cls :  Dict   ,SCREAMING_SNAKE_CASE_ :  PretrainedConfig   ,SCREAMING_SNAKE_CASE_ :  PretrainedConfig   ,SCREAMING_SNAKE_CASE_ :  MusicgenDecoderConfig   ,**SCREAMING_SNAKE_CASE_ :  str   ,):
										return cls(
										    text_encoder=text_encoder_config.to_dict()   ,audio_encoder=audio_encoder_config.to_dict()   ,decoder=decoder_config.to_dict()   ,**SCREAMING_SNAKE_CASE_   ,)





				def    _lowerCamelCase       ( self :  Optional[Any]   ):
										snake_case__							:       Optional[Any]				      =       copy.deepcopy(self.__dict__   )
										snake_case__							:       Any				      =       self.text_encoder.to_dict()
										snake_case__							:       Dict				      =       self.audio_encoder.to_dict()
										snake_case__							:       Dict				      =       self.decoder.to_dict()
										snake_case__							:       Optional[int]				      =       self.__class__.model_type
										return output


