





'''simple docstring'''







import logging
import os
import sys
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 accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


snake_case_     :       str						=  logging.getLogger(__name__)


@dataclass
class 					__a      :
							__a					:   Tuple									=       field(
							    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}     )
							__a					:   Any									=       field(
							    default=UpperCamelCase_       ,		metadata={"help": "Pretrained config name or path if not the same as model_name"}     )
							__a					:   Dict									=       field(
							    default="NER"       ,		metadata={"help": "Task type to fine tune in training (e.g. NER, POS, etc)"}     )
							__a					:   Tuple									=       field(
							    default=UpperCamelCase_       ,		metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}     )
							__a					:   Union[str, Any]									=       field(default=UpperCamelCase_       ,		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					:   List[str]									=       field(
							    default=UpperCamelCase_       ,		metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}       ,		)





@dataclass
class 					__a      :
							__a					:   str									=       field(
							    metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."}     )
							__a					:   Union[str, Any]									=       field(
							    default=UpperCamelCase_       ,		metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."}       ,		)
							__a					:   Optional[int]									=       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."
							        )
							    }       ,		)
							__a					:   Any									=       field(
							    default=UpperCamelCase_       ,		metadata={"help": "Overwrite the cached training and evaluation sets"}     )





def        lowerCamelCase_      (  ) ->	int:

						'''simple docstring'''





						UpperCAmelCase_			:      List[Any]     						=  HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)   )
						if len(sys.argv   ) == 2 and sys.argv[1].endswith('''.json'''   ):
												# If we pass only one argument to the script and it's the path to a json file,
												# let's parse it to get our arguments.
												UpperCAmelCase_			:      List[str]     						=  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
						else:
												UpperCAmelCase_			:      int     						=  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.'''   )

						UpperCAmelCase_			:      List[str]     						=  import_module('''tasks'''   )
						try:
												UpperCAmelCase_			:      Any     						=  getattr(SCREAMING_SNAKE_CASE__,			model_args.task_type   )
												UpperCAmelCase_			:      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 if training_args.local_rank in [-1, 0] else logging.WARN,			)
						logger.warning(
						    '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s''',			training_args.local_rank,			training_args.device,			training_args.n_gpu,			bool(training_args.local_rank != -1   ),			training_args.fpaa,			)
						# Set the verbosity to info of the Transformers logger (on main process only):
						if is_main_process(training_args.local_rank   ):
												transformers.utils.logging.set_verbosity_info()
												transformers.utils.logging.enable_default_handler()
												transformers.utils.logging.enable_explicit_format()
						logger.info('''Training/evaluation parameters %s''',			SCREAMING_SNAKE_CASE__   )

						# Set seed
						set_seed(training_args.seed   )

						# Prepare CONLL-2003 task
						UpperCAmelCase_			:      Optional[Any]     						=  token_classification_task.get_labels(data_args.labels   )
						UpperCAmelCase_			:      Dict[int, str]     						=  dict(enumerate(SCREAMING_SNAKE_CASE__   )   )
						UpperCAmelCase_			:      Optional[Any]     						=  len(SCREAMING_SNAKE_CASE__   )

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

						UpperCAmelCase_			:      Any     						=  AutoConfig.from_pretrained(
						    model_args.config_name if model_args.config_name else model_args.model_name_or_path,			num_labels=SCREAMING_SNAKE_CASE__,			idalabel=SCREAMING_SNAKE_CASE__,			labelaid={label: i for i, label in enumerate(SCREAMING_SNAKE_CASE__   )},			cache_dir=model_args.cache_dir,			)
						UpperCAmelCase_			:      List[str]     						=  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,			)
						UpperCAmelCase_			:      Optional[int]     						=  AutoModelForTokenClassification.from_pretrained(
						    model_args.model_name_or_path,			from_tf=bool('''.ckpt''' in model_args.model_name_or_path   ),			config=SCREAMING_SNAKE_CASE__,			cache_dir=model_args.cache_dir,			)

						# Get datasets
						UpperCAmelCase_			:      List[Any]     						=  (
						    TokenClassificationDataset(
						        token_classification_task=SCREAMING_SNAKE_CASE__,			data_dir=data_args.data_dir,			tokenizer=SCREAMING_SNAKE_CASE__,			labels=SCREAMING_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
						)
						UpperCAmelCase_			:      int     						=  (
						    TokenClassificationDataset(
						        token_classification_task=SCREAMING_SNAKE_CASE__,			data_dir=data_args.data_dir,			tokenizer=SCREAMING_SNAKE_CASE__,			labels=SCREAMING_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(SCREAMING_SNAKE_CASE__      :			np.ndarray,			SCREAMING_SNAKE_CASE__      :			np.ndarray   ) -> Tuple[List[int], List[int]]:
												UpperCAmelCase_			:      str     						=  np.argmax(SCREAMING_SNAKE_CASE__,			axis=2   )

												UpperCAmelCase_			:      int     						=  preds.shape

												UpperCAmelCase_			:      Dict     						=  [[] for _ in range(SCREAMING_SNAKE_CASE__   )]
												UpperCAmelCase_			:      Union[str, Any]     						=  [[] for _ in range(SCREAMING_SNAKE_CASE__   )]

												for i in range(SCREAMING_SNAKE_CASE__   ):
																		for j in range(SCREAMING_SNAKE_CASE__   ):
																								if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
																														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(SCREAMING_SNAKE_CASE__      :			EvalPrediction   ) -> Dict:
												UpperCAmelCase_			:      Any     						=  align_predictions(p.predictions,			p.label_ids   )
												return {
												    "accuracy_score": accuracy_score(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   ),
												    "precision": precision_score(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   ),
												    "recall": recall_score(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   ),
												    "f1": fa_score(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   ),
												}

						# Data collator
						UpperCAmelCase_			:      Optional[int]     						=  DataCollatorWithPadding(SCREAMING_SNAKE_CASE__,			pad_to_multiple_of=8   ) if training_args.fpaa else None

						# Initialize our Trainer
						UpperCAmelCase_			:      Optional[Any]     						=  Trainer(
						    model=SCREAMING_SNAKE_CASE__,			args=SCREAMING_SNAKE_CASE__,			train_dataset=SCREAMING_SNAKE_CASE__,			eval_dataset=SCREAMING_SNAKE_CASE__,			compute_metrics=SCREAMING_SNAKE_CASE__,			data_collator=SCREAMING_SNAKE_CASE__,			)

						# Training
						if training_args.do_train:
												trainer.train(
												    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path   ) else None   )
												trainer.save_model()
												# For convenience, we also re-save the tokenizer to the same directory,
												# so that you can share your model easily on huggingface.co/models =)
												if trainer.is_world_process_zero():
																		tokenizer.save_pretrained(training_args.output_dir   )

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

												UpperCAmelCase_			:      List[str]     						=  trainer.evaluate()

												UpperCAmelCase_			:      Tuple     						=  os.path.join(training_args.output_dir,			'''eval_results.txt'''   )
												if trainer.is_world_process_zero():
																		with open(SCREAMING_SNAKE_CASE__,			'''w'''   ) as writer:
																								logger.info('''***** Eval results *****'''   )
																								for key, value in result.items():
																														logger.info('''  %s = %s''',			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )
																														writer.write('''%s = %s\n''' % (key, value)   )

																		results.update(SCREAMING_SNAKE_CASE__   )

    # Predict
						if training_args.do_predict:
												UpperCAmelCase_			:      str     						=  TokenClassificationDataset(
												    token_classification_task=SCREAMING_SNAKE_CASE__,			data_dir=data_args.data_dir,			tokenizer=SCREAMING_SNAKE_CASE__,			labels=SCREAMING_SNAKE_CASE__,			model_type=config.model_type,			max_seq_length=data_args.max_seq_length,			overwrite_cache=data_args.overwrite_cache,			mode=Split.test,			)

												UpperCAmelCase_			:      str     						=  trainer.predict(SCREAMING_SNAKE_CASE__   )
												UpperCAmelCase_			:      List[str]     						=  align_predictions(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )

												UpperCAmelCase_			:      Optional[int]     						=  os.path.join(training_args.output_dir,			'''test_results.txt'''   )
												if trainer.is_world_process_zero():
																		with open(SCREAMING_SNAKE_CASE__,			'''w'''   ) as writer:
																								for key, value in metrics.items():
																														logger.info('''  %s = %s''',			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )
																														writer.write('''%s = %s\n''' % (key, value)   )

        # Save predictions
												UpperCAmelCase_			:      List[str]     						=  os.path.join(training_args.output_dir,			'''test_predictions.txt'''   )
												if trainer.is_world_process_zero():
																		with open(SCREAMING_SNAKE_CASE__,			'''w'''   ) as writer:
																								with open(os.path.join(data_args.data_dir,			'''test.txt'''   ),			'''r'''   ) as f:
																														token_classification_task.write_predictions_to_file(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )

						return results





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			int   ) ->	Any:

						'''simple docstring'''





						main()


if __name__ == "__main__":
					main()












'''simple docstring'''







# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.whisper import WhisperForConditionalGeneration, WhisperProcessor
from .base import PipelineTool


class 					__a      (lowerCamelCase     ):
							__a					:   List[Any]									=       "openai/whisper-base"
							__a					:   Optional[Any]									=       (
							    "This is a tool that transcribes an audio into text. It takes an input named `audio` and returns the "
							    "transcribed text."
							)
							__a					:   Any									=       "transcriber"
							__a					:   str									=       WhisperProcessor
							__a					:   List[Any]									=       WhisperForConditionalGeneration

							__a					:   int									=       ["audio"]
							__a					:   Optional[Any]									=       ["text"]

							def     UpperCAmelCase__	(							self							:     Dict       ,     __magic_name__							:     List[str]   )					->       Optional[int]:







													"""simple docstring"""
													return self.pre_processor(__magic_name__       ,     return_tensors='''pt'''   ).input_features

							def     UpperCAmelCase__	(							self							:     Dict       ,     __magic_name__							:     Dict   )					->       Tuple:







													"""simple docstring"""
													return self.model.generate(inputs=__magic_name__   )



							def     UpperCAmelCase__	(							self							:     List[str]       ,     __magic_name__							:     Dict   )					->       str:







													"""simple docstring"""
													return self.pre_processor.batch_decode(__magic_name__       ,     skip_special_tokens=__magic_name__   )[0]





