





'''simple docstring'''







import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    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()





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			str,			SCREAMING_SNAKE_CASE__      :			str,			SCREAMING_SNAKE_CASE__      :			str,			SCREAMING_SNAKE_CASE__      :			PreTrainedTokenizer,			SCREAMING_SNAKE_CASE__      :			int,			SCREAMING_SNAKE_CASE__      :			Optional[int] = None,			) ->	Union[str, Any]:
						UpperCAmelCase_			:      List[str]     						=  {}

						if train_file is not None:
												UpperCAmelCase_			:      Optional[Any]     						=  [train_file]
						if eval_file is not None:
												UpperCAmelCase_			:      Any     						=  [eval_file]
						if test_file is not None:
												UpperCAmelCase_			:      int     						=  [test_file]

						UpperCAmelCase_			:      Dict     						=  datasets.load_dataset('''csv''',			data_files=SCREAMING_SNAKE_CASE__   )
						UpperCAmelCase_			:      List[str]     						=  list(ds[list(files.keys()   )[0]].features.keys()   )
						UpperCAmelCase_			:      int     						=  features_name.pop(SCREAMING_SNAKE_CASE__   )
						UpperCAmelCase_			:      str     						=  list(set(ds[list(files.keys()   )[0]][label_name]   )   )
						UpperCAmelCase_			:      int     						=  {label: i for i, label in enumerate(SCREAMING_SNAKE_CASE__   )}
						UpperCAmelCase_			:      List[str]     						=  tokenizer.model_input_names
						UpperCAmelCase_			:      Union[str, Any]     						=  {}

						if len(SCREAMING_SNAKE_CASE__   ) == 1:
												for k in files.keys():
																		UpperCAmelCase_			:      int     						=  ds[k].map(
																		    lambda SCREAMING_SNAKE_CASE__   : tokenizer.batch_encode_plus(
																		        example[features_name[0]],			truncation=SCREAMING_SNAKE_CASE__,			max_length=SCREAMING_SNAKE_CASE__,			padding='''max_length'''   ),			batched=SCREAMING_SNAKE_CASE__,			)
						elif len(SCREAMING_SNAKE_CASE__   ) == 2:
												for k in files.keys():
																		UpperCAmelCase_			:      List[Any]     						=  ds[k].map(
																		    lambda SCREAMING_SNAKE_CASE__   : tokenizer.batch_encode_plus(
																		        (example[features_name[0]], example[features_name[1]]),			truncation=SCREAMING_SNAKE_CASE__,			max_length=SCREAMING_SNAKE_CASE__,			padding='''max_length''',			),			batched=SCREAMING_SNAKE_CASE__,			)

						def gen_train():
												for ex in transformed_ds[datasets.Split.TRAIN]:
																		UpperCAmelCase_			:      Union[str, Any]     						=  {k: v for k, v in ex.items() if k in input_names}
																		UpperCAmelCase_			:      Dict     						=  labelaid[ex[label_name]]
																		yield (d, label)

						def gen_val():
												for ex in transformed_ds[datasets.Split.VALIDATION]:
																		UpperCAmelCase_			:      Dict     						=  {k: v for k, v in ex.items() if k in input_names}
																		UpperCAmelCase_			:      List[Any]     						=  labelaid[ex[label_name]]
																		yield (d, label)

						def gen_test():
												for ex in transformed_ds[datasets.Split.TEST]:
																		UpperCAmelCase_			:      List[str]     						=  {k: v for k, v in ex.items() if k in input_names}
																		UpperCAmelCase_			:      Optional[int]     						=  labelaid[ex[label_name]]
																		yield (d, label)

						UpperCAmelCase_			:      str     						=  (
						    tf.data.Dataset.from_generator(
						        SCREAMING_SNAKE_CASE__,			({k: tf.intaa for k in input_names}, tf.intaa),			({k: tf.TensorShape([None]   ) for k in input_names}, tf.TensorShape([]   )),			)
						    if datasets.Split.TRAIN in transformed_ds
						    else None
						)

						if train_ds is not None:
												UpperCAmelCase_			:      str     						=  train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]   )   )   )

						UpperCAmelCase_			:      Dict     						=  (
						    tf.data.Dataset.from_generator(
						        SCREAMING_SNAKE_CASE__,			({k: tf.intaa for k in input_names}, tf.intaa),			({k: tf.TensorShape([None]   ) for k in input_names}, tf.TensorShape([]   )),			)
						    if datasets.Split.VALIDATION in transformed_ds
						    else None
						)

						if val_ds is not None:
												UpperCAmelCase_			:      List[Any]     						=  val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]   )   )   )

						UpperCAmelCase_			:      List[str]     						=  (
						    tf.data.Dataset.from_generator(
						        SCREAMING_SNAKE_CASE__,			({k: tf.intaa for k in input_names}, tf.intaa),			({k: tf.TensorShape([None]   ) for k in input_names}, tf.TensorShape([]   )),			)
						    if datasets.Split.TEST in transformed_ds
						    else None
						)

						if test_ds is not None:
												UpperCAmelCase_			:      Optional[Any]     						=  test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]   )   )   )

						return train_ds, val_ds, test_ds, labelaid


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


@dataclass
class 					__a      :
							__a					:   int									=       field(metadata={"help": "Which column contains the label"}     )
							__a					:   str									=       field(default=lowerCamelCase       ,		metadata={"help": "The path of the training file"}     )
							__a					:   Optional[str]									=       field(default=lowerCamelCase       ,		metadata={"help": "The path of the development file"}     )
							__a					:   Optional[str]									=       field(default=lowerCamelCase       ,		metadata={"help": "The path of the test file"}     )
							__a					:   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					:   bool									=       field(
							    default=lowerCamelCase       ,		metadata={"help": "Overwrite the cached training and evaluation sets"}     )





@dataclass
class 					__a      :
							__a					:   str									=       field(
							    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}     )
							__a					:   Optional[str]									=       field(
							    default=lowerCamelCase       ,		metadata={"help": "Pretrained config name or path if not the same as model_name"}     )
							__a					:   Optional[str]									=       field(
							    default=lowerCamelCase       ,		metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}     )
							__a					:   bool									=       field(default=lowerCamelCase       ,		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					:   Optional[str]									=       field(
							    default=lowerCamelCase       ,		metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}       ,		)





def        lowerCamelCase_      (  ) ->	Dict:
						# See all possible arguments in src/transformers/training_args.py
						# or by passing the --help flag to this script.
						# We now keep distinct sets of args, for a cleaner separation of concerns.
						UpperCAmelCase_			:      Optional[Any]     						=  HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)   )
						UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      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.'''   )

						# 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(
						    F"""n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1   )}, """
						    F"""16-bits training: {training_args.fpaa}"""   )
						logger.info(F"""Training/evaluation parameters {training_args}"""   )

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

						UpperCAmelCase_			:      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,			)

						UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      Optional[Any]     						=  get_tfds(
						    train_file=data_args.train_file,			eval_file=data_args.dev_file,			test_file=data_args.test_file,			tokenizer=SCREAMING_SNAKE_CASE__,			label_column_id=data_args.label_column_id,			max_seq_length=data_args.max_seq_length,			)

						UpperCAmelCase_			:      Union[str, Any]     						=  AutoConfig.from_pretrained(
						    model_args.config_name if model_args.config_name else model_args.model_name_or_path,			num_labels=len(SCREAMING_SNAKE_CASE__   ),			labelaid=SCREAMING_SNAKE_CASE__,			idalabel={id: label for label, id in labelaid.items()},			finetuning_task='''text-classification''',			cache_dir=model_args.cache_dir,			)

						with training_args.strategy.scope():
												UpperCAmelCase_			:      str     						=  TFAutoModelForSequenceClassification.from_pretrained(
												    model_args.model_name_or_path,			from_pt=bool('''.bin''' in model_args.model_name_or_path   ),			config=SCREAMING_SNAKE_CASE__,			cache_dir=model_args.cache_dir,			)

						def compute_metrics(SCREAMING_SNAKE_CASE__      :			EvalPrediction   ) -> Dict:
												UpperCAmelCase_			:      List[Any]     						=  np.argmax(p.predictions,			axis=1   )

												return {"acc": (preds == p.label_ids).mean()}

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

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

						# Evaluation
						UpperCAmelCase_			:      Optional[int]     						=  {}
						if training_args.do_eval:
												logger.info('''*** Evaluate ***'''   )
												UpperCAmelCase_			:      Tuple     						=  trainer.evaluate()
												UpperCAmelCase_			:      int     						=  os.path.join(training_args.output_dir,			'''eval_results.txt'''   )

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

																		for key, value in result.items():
																								logger.info(F"""  {key} = {value}"""   )
																								writer.write(F"""{key} = {value}\n"""   )

																		results.update(SCREAMING_SNAKE_CASE__   )

						return results


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]





