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

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

import transformers
from transformers import (
    AutoConfig,
    AutoFeatureExtractor,
    AutoModelForAudioClassification,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    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


SCREAMING_SNAKE_CASE__		:					int						=					logging.getLogger(__name__)

# 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>=1.14.0", "To fix: pip install -r examples/pytorch/audio-classification/requirements.txt")







def     _a				(      lowercase__  :			Union[str, Any]     ,  lowercase__  :			List[str]     ,  lowercase__  :			List[Any] = 1_60_00 ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Any			=		int(round(sample_rate * max_length ) )
     if len(__snake_case ) <= sample_length:
          return wav
     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		randint(0     ,  len(__snake_case ) - sample_length - 1 )
     return wav[random_offset : random_offset + sample_length]

@dataclass
class       snake_case					:
  lowercase_			=     field(default=UpperCamelCase_   ,						metadata={'help': 'Name of a dataset from the datasets package'}						)
  lowercase_			=     field(
      default=UpperCamelCase_   ,						metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}						)
  lowercase_			=     field(
      default=UpperCamelCase_   ,						metadata={'help': 'A file containing the training audio paths and labels.'}						)
  lowercase_			=     field(
      default=UpperCamelCase_   ,						metadata={'help': 'A file containing the validation audio paths and labels.'}						)
  lowercase_			=     field(
      default='train'   ,						metadata={
          'help': 'The name of the training data set split to use (via the datasets library). Defaults to \'train\''
      }   ,						)
  lowercase_			=     field(
      default='validation'   ,						metadata={
          'help': (
              'The name of the training data set split to use (via the datasets library). Defaults to \'validation\''
          )
      }   ,						)
  lowercase_			=     field(
      default='audio'   ,						metadata={'help': 'The name of the dataset column containing the audio data. Defaults to \'audio\''}   ,						)
  lowercase_			=     field(
      default='label'   ,						metadata={'help': 'The name of the dataset column containing the labels. Defaults to \'label\''}						)
  lowercase_			=     field(
      default=UpperCamelCase_   ,						metadata={
          'help': (
              'For debugging purposes or quicker training, truncate the number of training examples to this '
              'value if set.'
          )
      }   ,						)
  lowercase_			=     field(
      default=UpperCamelCase_   ,						metadata={
          'help': (
              'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
              'value if set.'
          )
      }   ,						)
  lowercase_			=     field(
      default=20   ,						metadata={'help': 'Audio clips will be randomly cut to this length during training if the value is set.'}   ,						)




@dataclass
class       snake_case					:
  lowercase_			=     field(
      default='facebook/wav2vec2-base'   ,						metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}   ,						)
  lowercase_			=     field(
      default=UpperCamelCase_   ,						metadata={'help': 'Pretrained config name or path if not the same as model_name'}						)
  lowercase_			=     field(
      default=UpperCamelCase_   ,						metadata={'help': 'Where do you want to store the pretrained models downloaded from the Hub'}						)
  lowercase_			=     field(
      default='main'   ,						metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}   ,						)
  lowercase_			=     field(
      default=UpperCamelCase_   ,						metadata={'help': 'Name or path of preprocessor config.'}						)
  lowercase_			=     field(
      default=UpperCamelCase_   ,						metadata={'help': 'Whether to freeze the feature encoder layers of the model.'}						)
  lowercase_			=     field(
      default=UpperCamelCase_   ,						metadata={'help': 'Whether to generate an attention mask in the feature extractor.'}						)
  lowercase_			=     field(
      default=UpperCamelCase_   ,						metadata={
          'help': (
              'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
              'with private models).'
          )
      }   ,						)
  lowercase_			=     field(
      default=UpperCamelCase_   ,						metadata={'help': 'Whether to freeze the feature extractor layers of the model.'}						)
  lowercase_			=     field(
      default=UpperCamelCase_   ,						metadata={'help': 'Will enable to load a pretrained model whose head dimensions are different.'}   ,						)




  def 	__lowercase(      self     :	Tuple				)->       Union[str, Any]:




       """simple docstring"""







       if not self.freeze_feature_extractor and self.freeze_feature_encoder:
            warnings.warn(
                'The argument `--freeze_feature_extractor` is deprecated and '
                'will be removed in a future version. Use `--freeze_feature_encoder`'
                'instead. Setting `freeze_feature_encoder==True`.'     , __lowerCAmelCase     , )
       if self.freeze_feature_extractor and not self.freeze_feature_encoder:
            raise ValueError(
                'The argument `--freeze_feature_extractor` is deprecated and '
                'should not be used in combination with `--freeze_feature_encoder`.'
                'Only make use of `--freeze_feature_encoder`.'				)







def     _a				(      ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				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.
          SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				Optional[int]			=		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
     else:
          SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		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_audio_classification'     ,  __snake_case     ,  __snake_case )

     # 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()

     SCREAMING_SNAKE_CASE__				:				List[str]			=		training_args.get_process_log_level()
     logger.setLevel(__snake_case )
     transformers.utils.logging.set_verbosity(__snake_case )
     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}''' )

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

     # Detecting last checkpoint.
     SCREAMING_SNAKE_CASE__				:				Any			=		None
     if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
          SCREAMING_SNAKE_CASE__				:				List[Any]			=		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 train from scratch.' )
          elif last_checkpoint is not None and training_args.resume_from_checkpoint is 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.' )

    # Initialize our dataset and prepare it for the audio classification task.
     SCREAMING_SNAKE_CASE__				:				List[Any]			=		DatasetDict()
     SCREAMING_SNAKE_CASE__				:				str			=		load_dataset(
         data_args.dataset_name     ,  data_args.dataset_config_name     ,  split=data_args.train_split_name     ,  use_auth_token=True if model_args.use_auth_token else None     ,  )
     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		load_dataset(
         data_args.dataset_name     ,  data_args.dataset_config_name     ,  split=data_args.eval_split_name     ,  use_auth_token=True if model_args.use_auth_token else None     ,  )

     if data_args.audio_column_name not in raw_datasets["train"].column_names:
          raise ValueError(
              f'''--audio_column_name {data_args.audio_column_name} not found in dataset \'{data_args.dataset_name}\'. '''
              'Make sure to set `--audio_column_name` to the correct audio column - one of '
              f'''{', '.join(raw_datasets['train'].column_names )}.''' )

     if data_args.label_column_name not in raw_datasets["train"].column_names:
          raise ValueError(
              f'''--label_column_name {data_args.label_column_name} not found in dataset \'{data_args.dataset_name}\'. '''
              'Make sure to set `--label_column_name` to the correct text column - one of '
              f'''{', '.join(raw_datasets['train'].column_names )}.''' )

     # Setting `return_attention_mask=True` is the way to get a correctly masked mean-pooling over
     # transformer outputs in the classifier, but it doesn't always lead to better accuracy
     SCREAMING_SNAKE_CASE__				:				List[Any]			=		AutoFeatureExtractor.from_pretrained(
         model_args.feature_extractor_name or model_args.model_name_or_path     ,  return_attention_mask=model_args.attention_mask     ,  cache_dir=model_args.cache_dir     ,  revision=model_args.model_revision     ,  use_auth_token=True if model_args.use_auth_token else None     ,  )

     # `datasets` takes care of automatically loading and resampling the audio,
     # so we just need to set the correct target sampling rate.
     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		raw_datasets.cast_column(
         data_args.audio_column_name     ,  datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate ) )

     SCREAMING_SNAKE_CASE__				:				int			=		feature_extractor.model_input_names[0]

     def train_transforms(lowercase__  :			Union[str, Any] ):
          SCREAMING_SNAKE_CASE__				:				Dict			=		[]
          for audio in batch[data_args.audio_column_name]:
               SCREAMING_SNAKE_CASE__				:				Any			=		random_subsample(
                   audio['array']     ,  max_length=data_args.max_length_seconds     ,  sample_rate=feature_extractor.sampling_rate )
               subsampled_wavs.append(__snake_case )
          SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		feature_extractor(__snake_case     ,  sampling_rate=feature_extractor.sampling_rate )
          SCREAMING_SNAKE_CASE__				:				Dict			=		{model_input_name: inputs.get(__snake_case )}
          SCREAMING_SNAKE_CASE__				:				str			=		list(batch[data_args.label_column_name] )

          return output_batch

     def val_transforms(lowercase__  :			Union[str, Any] ):
          SCREAMING_SNAKE_CASE__				:				Tuple			=		[audio['array'] for audio in batch[data_args.audio_column_name]]
          SCREAMING_SNAKE_CASE__				:				List[Any]			=		feature_extractor(__snake_case     ,  sampling_rate=feature_extractor.sampling_rate )
          SCREAMING_SNAKE_CASE__				:				int			=		{model_input_name: inputs.get(__snake_case )}
          SCREAMING_SNAKE_CASE__				:				str			=		list(batch[data_args.label_column_name] )

          return output_batch

     # Prepare label mappings.
     # We'll include these in the model's config to get human readable labels in the Inference API.
     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		raw_datasets['train'].features[data_args.label_column_name].names
     SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				int			=		{}, {}
     for i, label in enumerate(__snake_case ):
          SCREAMING_SNAKE_CASE__				:				str			=		str(__snake_case )
          SCREAMING_SNAKE_CASE__				:				Dict			=		label

     # Load the accuracy metric from the datasets package
     SCREAMING_SNAKE_CASE__				:				Dict			=		evaluate.load('accuracy' )

     # Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with
     # `predictions` and `label_ids` fields) and has to return a dictionary string to float.
     def compute_metrics(lowercase__  :			Any ):
          SCREAMING_SNAKE_CASE__				:				int			=		np.argmax(eval_pred.predictions     ,  axis=1 )
          return metric.compute(predictions=__snake_case     ,  references=eval_pred.label_ids )

     SCREAMING_SNAKE_CASE__				:				List[str]			=		AutoConfig.from_pretrained(
         model_args.config_name or model_args.model_name_or_path     ,  num_labels=len(__snake_case )     ,  labelaid=__snake_case     ,  idalabel=__snake_case     ,  finetuning_task='audio-classification'     ,  cache_dir=model_args.cache_dir     ,  revision=model_args.model_revision     ,  use_auth_token=True if model_args.use_auth_token else None     ,  )
     SCREAMING_SNAKE_CASE__				:				Any			=		AutoModelForAudioClassification.from_pretrained(
         model_args.model_name_or_path     ,  from_tf=bool('.ckpt' in model_args.model_name_or_path )     ,  config=__snake_case     ,  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     ,  )

     # freeze the convolutional waveform encoder
     if model_args.freeze_feature_encoder:
          model.freeze_feature_encoder()

     if training_args.do_train:
          if data_args.max_train_samples is not None:
               SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		(
                   raw_datasets['train'].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
               )
          # Set the training transforms
          raw_datasets["train"].set_transform(__snake_case     ,  output_all_columns=__snake_case )

     if training_args.do_eval:
          if data_args.max_eval_samples is not None:
               SCREAMING_SNAKE_CASE__				:				List[str]			=		(
                   raw_datasets['eval'].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
               )
          # Set the validation transforms
          raw_datasets["eval"].set_transform(__snake_case     ,  output_all_columns=__snake_case )

     # Initialize our trainer
     SCREAMING_SNAKE_CASE__				:				int			=		Trainer(
         model=__snake_case     ,  args=__snake_case     ,  train_dataset=raw_datasets['train'] if training_args.do_train else None     ,  eval_dataset=raw_datasets['eval'] if training_args.do_eval else None     ,  compute_metrics=__snake_case     ,  tokenizer=__snake_case     ,  )

     # Training
     if training_args.do_train:
          SCREAMING_SNAKE_CASE__				:				List[str]			=		None
          if training_args.resume_from_checkpoint is not None:
               SCREAMING_SNAKE_CASE__				:				Tuple			=		training_args.resume_from_checkpoint
          elif last_checkpoint is not None:
               SCREAMING_SNAKE_CASE__				:				int			=		last_checkpoint
          SCREAMING_SNAKE_CASE__				:				int			=		trainer.train(resume_from_checkpoint=__snake_case )
          trainer.save_model()
          trainer.log_metrics('train'     ,  train_result.metrics )
          trainer.save_metrics('train'     ,  train_result.metrics )
          trainer.save_state()

     # Evaluation
     if training_args.do_eval:
          SCREAMING_SNAKE_CASE__				:				Optional[int]			=		trainer.evaluate()
          trainer.log_metrics('eval'     ,  __snake_case )
          trainer.save_metrics('eval'     ,  __snake_case )

     # Write model card and (optionally) push to hub
     SCREAMING_SNAKE_CASE__				:				str			=		{
         'finetuned_from': model_args.model_name_or_path,
         'tasks': 'audio-classification',
         'dataset': data_args.dataset_name,
         'tags': ['audio-classification'],
     }
     if training_args.push_to_hub:
          trainer.push_to_hub(**__snake_case )
     else:
          trainer.create_model_card(**__snake_case )


if __name__ == "__main__":
      main()




import math
import unittest

from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import (
          BioGptForCausalLM,
          BioGptForSequenceClassification,
          BioGptForTokenClassification,
          BioGptModel,
          BioGptTokenizer,
      )
      from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST

class       snake_case					:




  def __init__(      self     :	List[Any]     , a_     :	Dict     , a_     :	Any=13     , a_     :	Any=7     , a_     :	Tuple=True     , a_     :	Tuple=True     , a_     :	Optional[int]=False     , a_     :	Dict=True     , a_     :	Optional[Any]=99     , a_     :	Any=32     , a_     :	Dict=5     , a_     :	Tuple=4     , a_     :	List[str]=37     , a_     :	Union[str, Any]="gelu"     , a_     :	Dict=0.1     , a_     :	Tuple=0.1     , a_     :	List[str]=512     , a_     :	List[str]=16     , a_     :	List[str]=2     , a_     :	Optional[int]=0.02     , a_     :	List[str]=3     , a_     :	Union[str, Any]=4     , a_     :	Optional[Any]=None     , )->       Optional[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Dict			=		parent
       SCREAMING_SNAKE_CASE__				:				Dict			=		batch_size
       SCREAMING_SNAKE_CASE__				:				Dict			=		seq_length
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		is_training
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		use_input_mask
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		use_token_type_ids
       SCREAMING_SNAKE_CASE__				:				int			=		use_labels
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		vocab_size
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		hidden_size
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		num_hidden_layers
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		num_attention_heads
       SCREAMING_SNAKE_CASE__				:				Dict			=		intermediate_size
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		hidden_act
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		hidden_dropout_prob
       SCREAMING_SNAKE_CASE__				:				int			=		attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		max_position_embeddings
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		type_vocab_size
       SCREAMING_SNAKE_CASE__				:				Any			=		type_sequence_label_size
       SCREAMING_SNAKE_CASE__				:				Tuple			=		initializer_range
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		num_labels
       SCREAMING_SNAKE_CASE__				:				Dict			=		num_choices
       SCREAMING_SNAKE_CASE__				:				str			=		scope




  def 	__lowercase(      self     :	Tuple				)->       Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		ids_tensor([self.batch_size, self.seq_length]     , self.vocab_size				)

       SCREAMING_SNAKE_CASE__				:				Tuple			=		None
       if self.use_input_mask:
            SCREAMING_SNAKE_CASE__				:				List[str]			=		random_attention_mask([self.batch_size, self.seq_length]				)

       SCREAMING_SNAKE_CASE__				:				str			=		None
       if self.use_token_type_ids:
            SCREAMING_SNAKE_CASE__				:				Tuple			=		ids_tensor([self.batch_size, self.seq_length]     , self.type_vocab_size				)

       SCREAMING_SNAKE_CASE__				:				List[str]			=		None
       SCREAMING_SNAKE_CASE__				:				str			=		None
       SCREAMING_SNAKE_CASE__				:				List[str]			=		None
       if self.use_labels:
            SCREAMING_SNAKE_CASE__				:				Dict			=		ids_tensor([self.batch_size]     , self.type_sequence_label_size				)
            SCREAMING_SNAKE_CASE__				:				List[str]			=		ids_tensor([self.batch_size, self.seq_length]     , self.num_labels				)
            SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		ids_tensor([self.batch_size]     , self.num_choices				)

       SCREAMING_SNAKE_CASE__				:				List[Any]			=		self.get_config()

       return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




  def 	__lowercase(      self     :	Dict				)->       Tuple:




       """simple docstring"""
       return BioGptConfig(
           vocab_size=self.vocab_size     , hidden_size=self.hidden_size     , num_hidden_layers=self.num_hidden_layers     , num_attention_heads=self.num_attention_heads     , intermediate_size=self.intermediate_size     , hidden_act=self.hidden_act     , hidden_dropout_prob=self.hidden_dropout_prob     , attention_probs_dropout_prob=self.attention_probs_dropout_prob     , max_position_embeddings=self.max_position_embeddings     , type_vocab_size=self.type_vocab_size     , is_decoder=a_     , initializer_range=self.initializer_range     , )




  def 	__lowercase(      self     :	Any     , a_     :	str     , a_     :	Tuple     , a_     :	Dict     , a_     :	Optional[int]     , a_     :	List[Any]     , a_     :	Union[str, Any]     , a_     :	Tuple				)->       Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		BioGptModel(config=a_				)
       model.to(a_				)
       model.eval()
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		model(a_     , attention_mask=a_				)
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		model(a_				)
       self.parent.assertEqual(result.last_hidden_state.shape     , (self.batch_size, self.seq_length, self.hidden_size)				)




  def 	__lowercase(      self     :	List[Any]     , a_     :	Union[str, Any]     , a_     :	Optional[int]     , a_     :	Tuple     , a_     :	Optional[Any]     , a_     :	int     , a_     :	Optional[int]     , a_     :	int     , a_     :	str     , a_     :	Optional[Any]     , )->       Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		BioGptForCausalLM(config=a_				)
       model.to(a_				)
       model.eval()
       SCREAMING_SNAKE_CASE__				:				Tuple			=		model(a_     , attention_mask=a_     , token_type_ids=a_     , labels=a_				)
       self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.seq_length, self.vocab_size)				)




  def 	__lowercase(      self     :	Tuple     , a_     :	Optional[int]     , a_     :	Union[str, Any]     , a_     :	Any     , a_     :	Any     , a_     :	Optional[int]     , *a_     :	Tuple				)->       Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Dict			=		BioGptModel(config=a_				)
       model.to(a_				)
       model.eval()

       # create attention mask
       SCREAMING_SNAKE_CASE__				:				Any			=		torch.ones(input_ids.shape     , dtype=torch.long     , device=a_				)
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		self.seq_length // 2
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		0

       # first forward pass
       SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		model(a_     , attention_mask=a_				).to_tuple()

       # create hypothetical next token and extent to next_input_ids
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		ids_tensor((self.batch_size, 1)     , config.vocab_size				)

       # change a random masked slice from input_ids
       SCREAMING_SNAKE_CASE__				:				str			=		ids_tensor((1,)     , a_				).item() + 1
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		ids_tensor((self.batch_size, 1)     , config.vocab_size				).squeeze(-1				)
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		random_other_next_tokens

       # append to next input_ids and attn_mask
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		torch.cat([input_ids, next_tokens]     , dim=-1				)
       SCREAMING_SNAKE_CASE__				:				Dict			=		torch.cat(
           [attn_mask, torch.ones((attn_mask.shape[0], 1)     , dtype=torch.long     , device=a_				)]     , dim=1     , )

       # get two different outputs
       SCREAMING_SNAKE_CASE__				:				str			=		model(a_     , attention_mask=a_				)['last_hidden_state']
       SCREAMING_SNAKE_CASE__				:				List[str]			=		model(a_     , past_key_values=a_     , attention_mask=a_				)['last_hidden_state']

       # select random slice
       SCREAMING_SNAKE_CASE__				:				Any			=		ids_tensor((1,)     , output_from_past.shape[-1]				).item()
       SCREAMING_SNAKE_CASE__				:				List[str]			=		output_from_no_past[:, -1, random_slice_idx].detach()
       SCREAMING_SNAKE_CASE__				:				List[str]			=		output_from_past[:, 0, random_slice_idx].detach()

       # test that outputs are equal for slice
       self.parent.assertTrue(torch.allclose(a_     , a_     , atol=1e-3				)				)




  def 	__lowercase(      self     :	str     , a_     :	List[Any]     , a_     :	str     , a_     :	Union[str, Any]     , a_     :	Union[str, Any]     , a_     :	Optional[Any]     , *a_     :	List[str]				)->       Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		BioGptModel(config=a_				).to(a_				).eval()

       SCREAMING_SNAKE_CASE__				:				Dict			=		torch.ones(input_ids.shape     , dtype=torch.long     , device=a_				)

       # first forward pass
       SCREAMING_SNAKE_CASE__				:				Any			=		model(a_     , attention_mask=a_     , use_cache=a_				)

       SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				Any			=		outputs.to_tuple()

       # create hypothetical multiple next token and extent to next_input_ids
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		ids_tensor((self.batch_size, 3)     , config.vocab_size				)
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		ids_tensor((self.batch_size, 3)     , 2				)

       # append to next input_ids and
       SCREAMING_SNAKE_CASE__				:				int			=		torch.cat([input_ids, next_tokens]     , dim=-1				)
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		torch.cat([attention_mask, next_attn_mask]     , dim=-1				)

       SCREAMING_SNAKE_CASE__				:				int			=		model(a_     , attention_mask=a_				)['last_hidden_state']
       SCREAMING_SNAKE_CASE__				:				List[str]			=		model(a_     , attention_mask=a_     , past_key_values=a_				)[
           'last_hidden_state'
       ]

       # select random slice
       SCREAMING_SNAKE_CASE__				:				int			=		ids_tensor((1,)     , output_from_past.shape[-1]				).item()
       SCREAMING_SNAKE_CASE__				:				Any			=		output_from_no_past[:, -3:, random_slice_idx].detach()
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		output_from_past[:, :, random_slice_idx].detach()

       self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]				)

       # test that outputs are equal for slice
       self.parent.assertTrue(torch.allclose(a_     , a_     , atol=1e-3				)				)




  def 	__lowercase(      self     :	Any     , a_     :	List[str]     , a_     :	Optional[int]     , a_     :	Any     , a_     :	Tuple     , a_     :	Any     , *a_     :	List[Any]     , a_     :	Union[str, Any]=False				)->       Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[str]			=		BioGptForCausalLM(a_				)
       model.to(a_				)
       if gradient_checkpointing:
            model.gradient_checkpointing_enable()

       SCREAMING_SNAKE_CASE__				:				Tuple			=		model(a_     , labels=a_				)
       self.parent.assertEqual(result.loss.shape     , ()				)
       self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.seq_length, self.vocab_size)				)
       result.loss.backward()




  def 	__lowercase(      self     :	Union[str, Any]     , a_     :	List[str]     , *a_     :	Optional[int]				)->       Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Dict			=		BioGptModel(a_				)
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers				)
       for key in model.state_dict().keys():
            if "c_proj" in key and "weight" in key:
                 self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key]				) - model_std				)     , 0.001				)
                 self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]				) - 0.0				)     , 0.01				)




  def 	__lowercase(      self     :	Dict     , a_     :	Tuple     , a_     :	Tuple     , a_     :	List[str]     , a_     :	Any     , a_     :	str     , *a_     :	str				)->       Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Any			=		self.num_labels
       SCREAMING_SNAKE_CASE__				:				str			=		BioGptForTokenClassification(a_				)
       model.to(a_				)
       model.eval()
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		model(a_     , attention_mask=a_     , token_type_ids=a_				)
       self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.seq_length, self.num_labels)				)




  def 	__lowercase(      self     :	Any				)->       Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				str			=		self.prepare_config_and_inputs()
       (
           (
           SCREAMING_SNAKE_CASE__
       )			,							(
           SCREAMING_SNAKE_CASE__
       )			,							(
           SCREAMING_SNAKE_CASE__
       )			,							(
           SCREAMING_SNAKE_CASE__
       )			,							(
           SCREAMING_SNAKE_CASE__
       )			,							(
           SCREAMING_SNAKE_CASE__
       )			,							(
           SCREAMING_SNAKE_CASE__
       )			,							
       )				:				Tuple			=		config_and_inputs
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		{'input_ids': input_ids, 'attention_mask': input_mask}
       return config, inputs_dict




@require_torch
class       snake_case					(      UpperCamelCase_   ,						UpperCamelCase_   ,						UpperCamelCase_   ,						unittest.TestCase						):
  lowercase_			=     (
      (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
      if is_torch_available()
      else ()
  )
  lowercase_			=     (BioGptForCausalLM,) if is_torch_available() else ()
  lowercase_			=     (
      {
          'feature-extraction': BioGptModel,
          'text-classification': BioGptForSequenceClassification,
          'text-generation': BioGptForCausalLM,
          'token-classification': BioGptForTokenClassification,
          'zero-shot': BioGptForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )
  lowercase_			=     False




  def 	__lowercase(      self     :	str				)->       Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		BioGptModelTester(self				)
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		ConfigTester(self     , config_class=a_     , hidden_size=37				)




  def 	__lowercase(      self     :	Tuple				)->       int:




       """simple docstring"""
       self.config_tester.run_common_tests()




  def 	__lowercase(      self     :	Optional[Any]				)->       Dict:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Tuple			=		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*a_				)




  def 	__lowercase(      self     :	Union[str, Any]				)->       Union[str, Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[str]			=		self.model_tester.prepare_config_and_inputs()
       for type in ["absolute", "relative_key", "relative_key_query"]:
            SCREAMING_SNAKE_CASE__				:				List[str]			=		type
            self.model_tester.create_and_check_model(*a_				)




  def 	__lowercase(      self     :	int				)->       Dict:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Dict			=		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_biogpt_model_attention_mask_past(*a_				)




  def 	__lowercase(      self     :	Optional[Any]				)->       List[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_forward_and_backwards(*a_     , gradient_checkpointing=a_				)




  def 	__lowercase(      self     :	Union[str, Any]				)->       Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_biogpt_model_past_large_inputs(*a_				)




  def 	__lowercase(      self     :	Any				)->       Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Dict			=		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_biogpt_weight_initialization(*a_				)




  def 	__lowercase(      self     :	str				)->       Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_biogpt_for_token_classification(*a_				)




  @slow
  def 	__lowercase(      self     :	List[str]				)->       Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				str			=		BioGptForCausalLM.from_pretrained('microsoft/biogpt'				)
       model.to(a_				)
       SCREAMING_SNAKE_CASE__				:				Dict			=		BioGptTokenizer.from_pretrained('microsoft/biogpt'				)

       SCREAMING_SNAKE_CASE__				:				List[str]			=		'left'

       # Define PAD Token = EOS Token = 50256
       SCREAMING_SNAKE_CASE__				:				Any			=		tokenizer.eos_token
       SCREAMING_SNAKE_CASE__				:				Tuple			=		model.config.eos_token_id

       # use different length sentences to test batching
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		[
           'Hello, my dog is a little',
           'Today, I',
       ]

       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		tokenizer(a_     , return_tensors='pt'     , padding=a_				)
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		inputs['input_ids'].to(a_				)

       SCREAMING_SNAKE_CASE__				:				List[str]			=		model.generate(
           input_ids=a_     , attention_mask=inputs['attention_mask'].to(a_				)     , )

       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		tokenizer(sentences[0]     , return_tensors='pt'				).input_ids.to(a_				)
       SCREAMING_SNAKE_CASE__				:				Dict			=		model.generate(input_ids=a_				)

       SCREAMING_SNAKE_CASE__				:				Tuple			=		inputs_non_padded.shape[-1] - inputs['attention_mask'][-1].long().sum().cpu().item()
       SCREAMING_SNAKE_CASE__				:				Dict			=		tokenizer(sentences[1]     , return_tensors='pt'				).input_ids.to(a_				)
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		model.generate(input_ids=a_     , max_length=model.config.max_length - num_paddings				)

       SCREAMING_SNAKE_CASE__				:				Any			=		tokenizer.batch_decode(a_     , skip_special_tokens=a_				)
       SCREAMING_SNAKE_CASE__				:				Tuple			=		tokenizer.decode(output_non_padded[0]     , skip_special_tokens=a_				)
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		tokenizer.decode(output_padded[0]     , skip_special_tokens=a_				)

       SCREAMING_SNAKE_CASE__				:				List[Any]			=		[
           'Hello, my dog is a little bit bigger than a little bit.',
           'Today, I have a good idea of how to use the information',
       ]
       self.assertListEqual(a_     , a_				)
       self.assertListEqual(a_     , [non_padded_sentence, padded_sentence]				)




  @slow
  def 	__lowercase(      self     :	Any				)->       List[Any]:




       """simple docstring"""
       for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            SCREAMING_SNAKE_CASE__				:				List[Any]			=		BioGptModel.from_pretrained(a_				)
            self.assertIsNotNone(a_				)




  def 	__lowercase(      self     :	Optional[int]				)->       Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				List[str]			=		self.model_tester.prepare_config_and_inputs_for_common()
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		3
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		input_dict['input_ids']
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		input_ids.ne(1				).to(a_				)
       SCREAMING_SNAKE_CASE__				:				str			=		ids_tensor([self.model_tester.batch_size]     , self.model_tester.type_sequence_label_size				)
       SCREAMING_SNAKE_CASE__				:				int			=		BioGptForSequenceClassification(a_				)
       model.to(a_				)
       model.eval()
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		model(a_     , attention_mask=a_     , labels=a_				)
       self.assertEqual(result.logits.shape     , (self.model_tester.batch_size, self.model_tester.num_labels)				)




  def 	__lowercase(      self     :	str				)->       Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				str			=		self.model_tester.prepare_config_and_inputs_for_common()
       SCREAMING_SNAKE_CASE__				:				str			=		3
       SCREAMING_SNAKE_CASE__				:				Any			=		'multi_label_classification'
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		input_dict['input_ids']
       SCREAMING_SNAKE_CASE__				:				Any			=		input_ids.ne(1				).to(a_				)
       SCREAMING_SNAKE_CASE__				:				str			=		ids_tensor(
           [self.model_tester.batch_size, config.num_labels]     , self.model_tester.type_sequence_label_size				).to(torch.float				)
       SCREAMING_SNAKE_CASE__				:				Dict			=		BioGptForSequenceClassification(a_				)
       model.to(a_				)
       model.eval()
       SCREAMING_SNAKE_CASE__				:				Dict			=		model(a_     , attention_mask=a_     , labels=a_				)
       self.assertEqual(result.logits.shape     , (self.model_tester.batch_size, self.model_tester.num_labels)				)




@require_torch
class       snake_case					(      unittest.TestCase						):




  @slow
  def 	__lowercase(      self     :	Union[str, Any]				)->       List[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		BioGptForCausalLM.from_pretrained('microsoft/biogpt'				)
       SCREAMING_SNAKE_CASE__				:				List[str]			=		torch.tensor([[2, 4805, 9, 656, 21]]				)
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		model(a_				)[0]

       SCREAMING_SNAKE_CASE__				:				List[str]			=		4_2384

       SCREAMING_SNAKE_CASE__				:				Dict			=		torch.Size((1, 5, vocab_size)				)
       self.assertEqual(output.shape     , a_				)

       SCREAMING_SNAKE_CASE__				:				int			=		torch.tensor(
           [[[-9.5236, -9.8918, 10.4557], [-11.0469, -9.6423, 8.1022], [-8.8664, -7.8826, 5.5325]]]				)

       self.assertTrue(torch.allclose(output[:, :3, :3]     , a_     , atol=1e-4				)				)




  @slow
  def 	__lowercase(      self     :	Union[str, Any]				)->       Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		BioGptTokenizer.from_pretrained('microsoft/biogpt'				)
       SCREAMING_SNAKE_CASE__				:				Dict			=		BioGptForCausalLM.from_pretrained('microsoft/biogpt'				)
       model.to(a_				)

       torch.manual_seed(0				)
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		tokenizer('COVID-19 is'     , return_tensors='pt'				).to(a_				)
       SCREAMING_SNAKE_CASE__				:				int			=		model.generate(
           **a_     , min_length=100     , max_length=1024     , num_beams=5     , early_stopping=a_     , )
       SCREAMING_SNAKE_CASE__				:				int			=		tokenizer.decode(output_ids[0]     , skip_special_tokens=a_				)

       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		(
           'COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the'
           ' causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and'
           ' territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),'
           ' and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and'
           ' more than 800,000 deaths.'
       )
       self.assertEqual(a_     , a_				)



