





import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import datasets
import evaluate
import numpy as np
from datasets import load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version


# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")

require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")

lowerCAmelCase_				= logging.getLogger(__name__)

@dataclass
class 					A     :
      _SCREAMING_SNAKE_CASE       =				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."""
              )
          }							,)
      _SCREAMING_SNAKE_CASE       =				field(
          default=SCREAMING_SNAKE_CASE_							,metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""}					)
      _SCREAMING_SNAKE_CASE       =				field(
          default=SCREAMING_SNAKE_CASE_							,metadata={
              """help""": (
                  """Whether to pad all samples to `max_seq_length`. """
                  """If False, will pad the samples dynamically when batching to the maximum length in the batch."""
              )
          }							,)
      _SCREAMING_SNAKE_CASE       =				field(
          default=SCREAMING_SNAKE_CASE_							,metadata={
              """help""": (
                  """For debugging purposes or quicker training, truncate the number of training examples to this """
                  """value if set."""
              )
          }							,)
      _SCREAMING_SNAKE_CASE       =				field(
          default=SCREAMING_SNAKE_CASE_							,metadata={
              """help""": (
                  """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
                  """value if set."""
              )
          }							,)
      _SCREAMING_SNAKE_CASE       =				field(
          default=SCREAMING_SNAKE_CASE_							,metadata={
              """help""": (
                  """For debugging purposes or quicker training, truncate the number of prediction examples to this """
                  """value if set."""
              )
          }							,)


@dataclass
class 					A     :
      _SCREAMING_SNAKE_CASE       =				field(
          default=SCREAMING_SNAKE_CASE_							,metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}					)
      _SCREAMING_SNAKE_CASE       =				field(
          default=SCREAMING_SNAKE_CASE_							,metadata={"""help""": """Evaluation language. Also train language if `train_language` is set to None."""}					)
      _SCREAMING_SNAKE_CASE       =				field(
          default=SCREAMING_SNAKE_CASE_							,metadata={"""help""": """Train language if it is different from the evaluation language."""}					)
      _SCREAMING_SNAKE_CASE       =				field(
          default=SCREAMING_SNAKE_CASE_							,metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}					)
      _SCREAMING_SNAKE_CASE       =				field(
          default=SCREAMING_SNAKE_CASE_							,metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}					)
      _SCREAMING_SNAKE_CASE       =				field(
          default=SCREAMING_SNAKE_CASE_							,metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}							,)
      _SCREAMING_SNAKE_CASE       =				field(
          default=SCREAMING_SNAKE_CASE_							,metadata={"""help""": """arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()"""}							,)
      _SCREAMING_SNAKE_CASE       =				field(
          default=SCREAMING_SNAKE_CASE_							,metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""}							,)
      _SCREAMING_SNAKE_CASE       =				field(
          default="""main"""							,metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}							,)
      _SCREAMING_SNAKE_CASE       =				field(
          default=SCREAMING_SNAKE_CASE_							,metadata={
              """help""": (
                  """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
                  """with private models)."""
              )
          }							,)
      _SCREAMING_SNAKE_CASE       =				field(
          default=SCREAMING_SNAKE_CASE_							,metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""}							,)




def     A_      (	)      ->    Union[str, Any]:
       # 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.

       _snake_case   :				Dict  					=    HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)	)
       _snake_case   :				Optional[int]  					=    parser.parse_args_into_dataclasses()

       # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
       # information sent is the one passed as arguments along with your Python/PyTorch versions.
       send_example_telemetry('''run_xnli'''   ,		__A	)

       # Setup logging
       logging.basicConfig(
           format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''   ,		datefmt='''%m/%d/%Y %H:%M:%S'''   ,		handlers=[logging.StreamHandler(sys.stdout	)]   ,		)

       if training_args.should_log:
              # The default of training_args.log_level is passive, so we set log level at info here to have that default.
              transformers.utils.logging.set_verbosity_info()

       _snake_case   :				Dict  					=    training_args.get_process_log_level()
       logger.setLevel(__A	)
       datasets.utils.logging.set_verbosity(__A	)
       transformers.utils.logging.set_verbosity(__A	)
       transformers.utils.logging.enable_default_handler()
       transformers.utils.logging.enable_explicit_format()

       # Log on each process the small summary:
       logger.warning(
           f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
           + f'''distributed training: {bool(training_args.local_rank != -1	)}, 16-bits training: {training_args.fpaa}'''	)
       logger.info(f'''Training/evaluation parameters {training_args}'''	)

       # Detecting last checkpoint.
       _snake_case   :				Optional[int]  					=    None
       if os.path.isdir(training_args.output_dir	) and training_args.do_train and not training_args.overwrite_output_dir:
              _snake_case   :				Dict  					=    get_last_checkpoint(training_args.output_dir	)
              if last_checkpoint is None and len(os.listdir(training_args.output_dir	)	) > 0:
                     raise ValueError(
                         f'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
                         '''Use --overwrite_output_dir to overcome.'''	)
              elif last_checkpoint is not None:
                     logger.info(
                         f'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
                         '''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'''	)

    # Set seed before initializing model.
       set_seed(training_args.seed	)

       # In distributed training, the load_dataset function guarantees that only one local process can concurrently
       # download the dataset.
       # Downloading and loading xnli dataset from the hub.
       if training_args.do_train:
              if model_args.train_language is None:
                     _snake_case   :				List[str]  					=    load_dataset(
                         '''xnli'''   ,		model_args.language   ,		split='''train'''   ,		cache_dir=model_args.cache_dir   ,		use_auth_token=True if model_args.use_auth_token else None   ,		)
              else:
                     _snake_case   :				str  					=    load_dataset(
                         '''xnli'''   ,		model_args.train_language   ,		split='''train'''   ,		cache_dir=model_args.cache_dir   ,		use_auth_token=True if model_args.use_auth_token else None   ,		)
              _snake_case   :				Any  					=    train_dataset.features['''label'''].names

       if training_args.do_eval:
              _snake_case   :				Union[str, Any]  					=    load_dataset(
                  '''xnli'''   ,		model_args.language   ,		split='''validation'''   ,		cache_dir=model_args.cache_dir   ,		use_auth_token=True if model_args.use_auth_token else None   ,		)
              _snake_case   :				Dict  					=    eval_dataset.features['''label'''].names

       if training_args.do_predict:
              _snake_case   :				Union[str, Any]  					=    load_dataset(
                  '''xnli'''   ,		model_args.language   ,		split='''test'''   ,		cache_dir=model_args.cache_dir   ,		use_auth_token=True if model_args.use_auth_token else None   ,		)
              _snake_case   :				Optional[Any]  					=    predict_dataset.features['''label'''].names

       # Labels
       _snake_case   :				List[Any]  					=    len(__A	)

       # Load pretrained model and tokenizer
       # In 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=__A   ,		idalabel={str(__A	): label for i, label in enumerate(__A	)}   ,		labelaid={label: i for i, label in enumerate(__A	)}   ,		finetuning_task='''xnli'''   ,		cache_dir=model_args.cache_dir   ,		revision=model_args.model_revision   ,		use_auth_token=True if model_args.use_auth_token else None   ,		)
       _snake_case   :				Tuple  					=    AutoTokenizer.from_pretrained(
           model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path   ,		do_lower_case=model_args.do_lower_case   ,		cache_dir=model_args.cache_dir   ,		use_fast=model_args.use_fast_tokenizer   ,		revision=model_args.model_revision   ,		use_auth_token=True if model_args.use_auth_token else None   ,		)
       _snake_case   :				int  					=    AutoModelForSequenceClassification.from_pretrained(
           model_args.model_name_or_path   ,		from_tf=bool('''.ckpt''' in model_args.model_name_or_path	)   ,		config=__A   ,		cache_dir=model_args.cache_dir   ,		revision=model_args.model_revision   ,		use_auth_token=True if model_args.use_auth_token else None   ,		ignore_mismatched_sizes=model_args.ignore_mismatched_sizes   ,		)

       # Preprocessing the datasets
       # Padding strategy
       if data_args.pad_to_max_length:
              _snake_case   :				Union[str, Any]  					=    '''max_length'''
       else:
              # We will pad later, dynamically at batch creation, to the max sequence length in each batch
              _snake_case   :				str  					=    False

       def preprocess_function(lowercase_	):
              # Tokenize the texts
              return tokenizer(
                  examples['''premise''']   ,		examples['''hypothesis''']   ,		padding=__A   ,		max_length=data_args.max_seq_length   ,		truncation=__A   ,		)

       if training_args.do_train:
              if data_args.max_train_samples is not None:
                     _snake_case   :				Optional[int]  					=    min(len(__A	)   ,		data_args.max_train_samples	)
                     _snake_case   :				Optional[int]  					=    train_dataset.select(range(__A	)	)
              with training_args.main_process_first(desc='''train dataset map pre-processing'''	):
                     _snake_case   :				Optional[int]  					=    train_dataset.map(
                         __A   ,		batched=__A   ,		load_from_cache_file=not data_args.overwrite_cache   ,		desc='''Running tokenizer on train dataset'''   ,		)
              # Log a few random samples from the training set:
              for index in random.sample(range(len(__A	)	)   ,		3	):
                     logger.info(f'''Sample {index} of the training set: {train_dataset[index]}.'''	)

       if training_args.do_eval:
              if data_args.max_eval_samples is not None:
                     _snake_case   :				Optional[Any]  					=    min(len(__A	)   ,		data_args.max_eval_samples	)
                     _snake_case   :				Optional[Any]  					=    eval_dataset.select(range(__A	)	)
              with training_args.main_process_first(desc='''validation dataset map pre-processing'''	):
                     _snake_case   :				Optional[int]  					=    eval_dataset.map(
                         __A   ,		batched=__A   ,		load_from_cache_file=not data_args.overwrite_cache   ,		desc='''Running tokenizer on validation dataset'''   ,		)

       if training_args.do_predict:
              if data_args.max_predict_samples is not None:
                     _snake_case   :				int  					=    min(len(__A	)   ,		data_args.max_predict_samples	)
                     _snake_case   :				Optional[int]  					=    predict_dataset.select(range(__A	)	)
              with training_args.main_process_first(desc='''prediction dataset map pre-processing'''	):
                     _snake_case   :				Tuple  					=    predict_dataset.map(
                         __A   ,		batched=__A   ,		load_from_cache_file=not data_args.overwrite_cache   ,		desc='''Running tokenizer on prediction dataset'''   ,		)

    # Get the metric function
       _snake_case   :				List[Any]  					=    evaluate.load('''xnli'''	)

       # You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
       # predictions and label_ids field) and has to return a dictionary string to float.
       def compute_metrics(lowercase_	):
              _snake_case   :				str  					=    p.predictions[0] if isinstance(p.predictions   ,		__A	) else p.predictions
              _snake_case   :				Optional[Any]  					=    np.argmax(__A   ,		axis=1	)
              return metric.compute(predictions=__A   ,		references=p.label_ids	)

       # Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
       if data_args.pad_to_max_length:
              _snake_case   :				Dict  					=    default_data_collator
       elif training_args.fpaa:
              _snake_case   :				str  					=    DataCollatorWithPadding(__A   ,		pad_to_multiple_of=8	)
       else:
              _snake_case   :				List[Any]  					=    None

       # Initialize our Trainer
       _snake_case   :				Optional[int]  					=    Trainer(
           model=__A   ,		args=__A   ,		train_dataset=train_dataset if training_args.do_train else None   ,		eval_dataset=eval_dataset if training_args.do_eval else None   ,		compute_metrics=__A   ,		tokenizer=__A   ,		data_collator=__A   ,		)

       # Training
       if training_args.do_train:
              _snake_case   :				Any  					=    None
              if training_args.resume_from_checkpoint is not None:
                     _snake_case   :				Optional[Any]  					=    training_args.resume_from_checkpoint
              elif last_checkpoint is not None:
                     _snake_case   :				Dict  					=    last_checkpoint
              _snake_case   :				Optional[Any]  					=    trainer.train(resume_from_checkpoint=__A	)
              _snake_case   :				Any  					=    train_result.metrics
              _snake_case   :				Any  					=    (
                  data_args.max_train_samples if data_args.max_train_samples is not None else len(__A	)
              )
              _snake_case   :				int  					=    min(__A   ,		len(__A	)	)

              trainer.save_model()  # Saves the tokenizer too for easy upload

              trainer.log_metrics('''train'''   ,		__A	)
              trainer.save_metrics('''train'''   ,		__A	)
              trainer.save_state()

       # Evaluation
       if training_args.do_eval:
              logger.info('''*** Evaluate ***'''	)
              _snake_case   :				Any  					=    trainer.evaluate(eval_dataset=__A	)

              _snake_case   :				Dict  					=    data_args.max_eval_samples if data_args.max_eval_samples is not None else len(__A	)
              _snake_case   :				int  					=    min(__A   ,		len(__A	)	)

              trainer.log_metrics('''eval'''   ,		__A	)
              trainer.save_metrics('''eval'''   ,		__A	)

       # Prediction
       if training_args.do_predict:
              logger.info('''*** Predict ***'''	)
              _snake_case   :				Dict  					=    trainer.predict(__A   ,		metric_key_prefix='''predict'''	)

              _snake_case   :				Optional[Any]  					=    (
                  data_args.max_predict_samples if data_args.max_predict_samples is not None else len(__A	)
              )
              _snake_case   :				Any  					=    min(__A   ,		len(__A	)	)

              trainer.log_metrics('''predict'''   ,		__A	)
              trainer.save_metrics('''predict'''   ,		__A	)

              _snake_case   :				Dict  					=    np.argmax(__A   ,		axis=1	)
              _snake_case   :				int  					=    os.path.join(training_args.output_dir   ,		'''predictions.txt'''	)
              if trainer.is_world_process_zero():
                     with open(__A   ,		'''w'''	) as writer:
                            writer.write('''index\tprediction\n'''	)
                            for index, item in enumerate(__A	):
                                   _snake_case   :				Union[str, Any]  					=    label_list[item]
                                   writer.write(f'''{index}\t{item}\n'''	)


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 						SCREAMING_SNAKE_CASE       (       SCREAMING_SNAKE_CASE_							):
			snake_case__	=					"openai/whisper-base"
			snake_case__	=					(
			    "This is a tool that transcribes an audio into text. It takes an input named `audio` and returns the "
			    "transcribed text."
			)
			snake_case__	=					"transcriber"
			snake_case__	=					WhisperProcessor
			snake_case__	=					WhisperForConditionalGeneration

			snake_case__	=					["audio"]
			snake_case__	=					["text"]






			def       SCREAMING_SNAKE_CASE   (			self   :      str	,    __SCREAMING_SNAKE_CASE   :      Tuple       )      ->      str:
							return self.pre_processor(__SCREAMING_SNAKE_CASE	,    return_tensors='''pt'''       ).input_features






			def       SCREAMING_SNAKE_CASE   (			self   :      List[Any]	,    __SCREAMING_SNAKE_CASE   :      Optional[Any]       )      ->      List[str]:
							return self.model.generate(inputs=__SCREAMING_SNAKE_CASE       )







			def       SCREAMING_SNAKE_CASE   (			self   :      Dict	,    __SCREAMING_SNAKE_CASE   :      Optional[int]       )      ->      Optional[Any]:
							return self.pre_processor.batch_decode(__SCREAMING_SNAKE_CASE	,    skip_special_tokens=__SCREAMING_SNAKE_CASE       )[0]




