import argparse
import os

# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


SCREAMING_SNAKE_CASE__		:					Dict						=					16
SCREAMING_SNAKE_CASE__		:					Union[str, Any]						=					32







def     _a				(      lowercase__  :			int     ,  lowercase__  :			Any = 16 ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Dict			=		AutoTokenizer.from_pretrained('bert-base-cased' )
     SCREAMING_SNAKE_CASE__				:				List[str]			=		load_dataset('glue'     ,  'mrpc' )

     def tokenize_function(lowercase__  :			Any ):
          # max_length=None => use the model max length (it's actually the default)
          SCREAMING_SNAKE_CASE__				:				List[Any]			=		tokenizer(examples['sentence1']     ,  examples['sentence2']     ,  truncation=lowerCAmelCase_     ,  max_length=lowerCAmelCase_ )
          return outputs

     # Apply the method we just defined to all the examples in all the splits of the dataset
     # starting with the main process first:
     with accelerator.main_process_first():
          SCREAMING_SNAKE_CASE__				:				str			=		datasets.map(
              lowerCAmelCase_     ,  batched=lowerCAmelCase_     ,  remove_columns=['idx', 'sentence1', 'sentence2']     ,  )

     # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
     # transformers library
     SCREAMING_SNAKE_CASE__				:				int			=		tokenized_datasets.rename_column('label'     ,  'labels' )

     def collate_fn(lowercase__  :			int ):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          SCREAMING_SNAKE_CASE__				:				List[str]			=		1_28 if accelerator.distributed_type == DistributedType.TPU else None
          # When using mixed precision we want round multiples of 8/16
          if accelerator.mixed_precision == "fp8":
               SCREAMING_SNAKE_CASE__				:				Dict			=		16
          elif accelerator.mixed_precision != "no":
               SCREAMING_SNAKE_CASE__				:				Optional[int]			=		8
          else:
               SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		None

          return tokenizer.pad(
              lowerCAmelCase_     ,  padding='longest'     ,  max_length=lowerCAmelCase_     ,  pad_to_multiple_of=lowerCAmelCase_     ,  return_tensors='pt'     ,  )

     # Instantiate dataloaders.
     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		DataLoader(
         tokenized_datasets['train']     ,  shuffle=lowerCAmelCase_     ,  collate_fn=lowerCAmelCase_     ,  batch_size=lowerCAmelCase_ )
     SCREAMING_SNAKE_CASE__				:				str			=		DataLoader(
         tokenized_datasets['validation']     ,  shuffle=lowerCAmelCase_     ,  collate_fn=lowerCAmelCase_     ,  batch_size=lowerCAmelCase_ )

     return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
      from accelerate.test_utils.training import mocked_dataloaders

      SCREAMING_SNAKE_CASE__		:					List[Any]						=					mocked_dataloaders  # noqa: F811







def     _a				(      lowercase__  :			Union[str, Any]     ,  lowercase__  :			int ):







     '''simple docstring'''


     if os.environ.get('TESTING_MOCKED_DATALOADERS'     ,  lowerCAmelCase_ ) == "1":
          SCREAMING_SNAKE_CASE__				:				List[str]			=		2
     # Initialize accelerator
     SCREAMING_SNAKE_CASE__				:				List[Any]			=		Accelerator(cpu=args.cpu     ,  mixed_precision=args.mixed_precision )
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     SCREAMING_SNAKE_CASE__				:				Dict			=		config["lr"]
     SCREAMING_SNAKE_CASE__				:				Dict			=		int(config['num_epochs'] )
     SCREAMING_SNAKE_CASE__				:				int			=		int(config['seed'] )
     SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		int(config['batch_size'] )

     SCREAMING_SNAKE_CASE__				:				List[str]			=		evaluate.load('glue'     ,  'mrpc' )

     # New Code #
     # We now can define an inner training loop function. It should take a batch size as the only parameter,
     # and build the dataloaders in there.
     # It also gets our decorator
     @find_executable_batch_size(starting_batch_size=lowerCAmelCase_ )
     def inner_training_loop(lowercase__  :			List[Any] ):
          # And now just move everything below under this function
          # We need to bring in the Accelerator object from earlier
          nonlocal accelerator
          # And reset all of its attributes that could hold onto any memory:
          accelerator.free_memory()

          # Then we can declare the model, optimizer, and everything else:
          set_seed(lowerCAmelCase_ )

          # Instantiate the model (we build the model here so that the seed also control new weights initialization)
          SCREAMING_SNAKE_CASE__				:				List[Any]			=		AutoModelForSequenceClassification.from_pretrained('bert-base-cased'     ,  return_dict=lowerCAmelCase_ )

          # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
          # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
          # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
          SCREAMING_SNAKE_CASE__				:				int			=		model.to(accelerator.device )

          # Instantiate optimizer
          SCREAMING_SNAKE_CASE__				:				Dict			=		AdamW(params=model.parameters()     ,  lr=lowerCAmelCase_ )
          SCREAMING_SNAKE_CASE__				:				Dict			=		get_dataloaders(lowerCAmelCase_     ,  lowerCAmelCase_ )

          # Instantiate scheduler
          SCREAMING_SNAKE_CASE__				:				str			=		get_linear_schedule_with_warmup(
              optimizer=lowerCAmelCase_     ,  num_warmup_steps=1_00     ,  num_training_steps=(len(lowerCAmelCase_ ) * num_epochs)     ,  )

          # Prepare everything
          # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
          # prepare method.
          SCREAMING_SNAKE_CASE__				:				List[Any]			=		accelerator.prepare(
              lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_ )

          # Now we train the model
          for epoch in range(lowerCAmelCase_ ):
               model.train()
               for step, batch in enumerate(lowerCAmelCase_ ):
                    # We could avoid this line since we set the accelerator with `device_placement=True`.
                    batch.to(accelerator.device )
                    SCREAMING_SNAKE_CASE__				:				Tuple			=		model(**lowerCAmelCase_ )
                    SCREAMING_SNAKE_CASE__				:				str			=		outputs.loss
                    accelerator.backward(lowerCAmelCase_ )
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

               model.eval()
               for step, batch in enumerate(lowerCAmelCase_ ):
                    # We could avoid this line since we set the accelerator with `device_placement=True`.
                    batch.to(accelerator.device )
                    with torch.no_grad():
                         SCREAMING_SNAKE_CASE__				:				List[str]			=		model(**lowerCAmelCase_ )
                    SCREAMING_SNAKE_CASE__				:				str			=		outputs.logits.argmax(dim=-1 )
                    SCREAMING_SNAKE_CASE__				:				Optional[int]			=		accelerator.gather_for_metrics((predictions, batch['labels']) )
                    metric.add_batch(
                        predictions=lowerCAmelCase_     ,  references=lowerCAmelCase_     ,  )

               SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		metric.compute()
               # Use accelerator.print to print only on the main process.
               accelerator.print(f'''epoch {epoch}:'''     ,  lowerCAmelCase_ )

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
     inner_training_loop()







def     _a				(      ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		argparse.ArgumentParser(description='Simple example of training script.' )
     parser.add_argument(
         '--mixed_precision'     ,  type=lowerCAmelCase_     ,  default=lowerCAmelCase_     ,  choices=['no', 'fp16', 'bf16', 'fp8']     ,  help='Whether to use mixed precision. Choose'
         'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
         'and an Nvidia Ampere GPU.'     ,  )
     parser.add_argument('--cpu'     ,  action='store_true'     ,  help='If passed, will train on the CPU.' )
     SCREAMING_SNAKE_CASE__				:				List[Any]			=		parser.parse_args()
     SCREAMING_SNAKE_CASE__				:				int			=		{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
     training_function(lowerCAmelCase_     ,  lowerCAmelCase_ )


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_				)



