




'''simple docstring'''


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       A   :







							def __init__(  self	:  List[str]							,      lowerCAmelCase_	:  Union[str, Any]							,      lowerCAmelCase_	:  Tuple=13							,      lowerCAmelCase_	:  Union[str, Any]=7							,      lowerCAmelCase_	:  str=True							,      lowerCAmelCase_	:  Optional[int]=True							,      lowerCAmelCase_	:  Tuple=False							,      lowerCAmelCase_	:  List[Any]=True							,      lowerCAmelCase_	:  Optional[Any]=99							,      lowerCAmelCase_	:  Any=32							,      lowerCAmelCase_	:  str=5							,      lowerCAmelCase_	:  Tuple=4							,      lowerCAmelCase_	:  List[Any]=37							,      lowerCAmelCase_	:  Dict="gelu"							,      lowerCAmelCase_	:  Dict=0.1							,      lowerCAmelCase_	:  List[Any]=0.1							,      lowerCAmelCase_	:  Tuple=5_12							,      lowerCAmelCase_	:  Optional[int]=16							,      lowerCAmelCase_	:  str=2							,      lowerCAmelCase_	:  Any=0.0_2							,      lowerCAmelCase_	:  int=3							,      lowerCAmelCase_	:  str=4							,      lowerCAmelCase_	:  int=None							,      )      ->							List[Any]:


														"""simple docstring"""


														_a     					=    parent
														_a     					=    batch_size
														_a     					=    seq_length
														_a     					=    is_training
														_a     					=    use_input_mask
														_a     					=    use_token_type_ids
														_a     					=    use_labels
														_a     					=    vocab_size
														_a     					=    hidden_size
														_a     					=    num_hidden_layers
														_a     					=    num_attention_heads
														_a     					=    intermediate_size
														_a     					=    hidden_act
														_a     					=    hidden_dropout_prob
														_a     					=    attention_probs_dropout_prob
														_a     					=    max_position_embeddings
														_a     					=    type_vocab_size
														_a     					=    type_sequence_label_size
														_a     					=    initializer_range
														_a     					=    num_labels
														_a     					=    num_choices
														_a     					=    scope







							def        __lowerCAmelCase      (  self	:  Tuple )      ->							str:


														"""simple docstring"""


														_a     					=    ids_tensor([self.batch_size, self.seq_length]							,      self.vocab_size )

														_a     					=    None
														if self.use_input_mask:
																					_a     					=    random_attention_mask([self.batch_size, self.seq_length] )

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

														_a     					=    None
														_a     					=    None
														_a     					=    None
														if self.use_labels:
																					_a     					=    ids_tensor([self.batch_size]							,      self.type_sequence_label_size )
																					_a     					=    ids_tensor([self.batch_size, self.seq_length]							,      self.num_labels )
																					_a     					=    ids_tensor([self.batch_size]							,      self.num_choices )

														_a     					=    self.get_config()

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







							def        __lowerCAmelCase      (  self	:  Any )      ->							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        __lowerCAmelCase      (  self	:  Optional[Any]							,      lowerCAmelCase_	:  Optional[int]							,      lowerCAmelCase_	:  Any							,      lowerCAmelCase_	:  int							,      lowerCAmelCase_	:  List[str]							,      lowerCAmelCase_	:  Tuple							,      lowerCAmelCase_	:  Tuple							,      lowerCAmelCase_	:  int )      ->							str:


														"""simple docstring"""


														_a     					=    BioGptModel(config=__A )
														model.to(__A )
														model.eval()
														_a     					=    model(__A							,      attention_mask=__A )
														_a     					=    model(__A )
														self.parent.assertEqual(result.last_hidden_state.shape							,      (self.batch_size, self.seq_length, self.hidden_size) )







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


														"""simple docstring"""


														_a     					=    BioGptForCausalLM(config=__A )
														model.to(__A )
														model.eval()
														_a     					=    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        __lowerCAmelCase      (  self	:  List[Any]							,      lowerCAmelCase_	:  str							,      lowerCAmelCase_	:  Optional[Any]							,      lowerCAmelCase_	:  str							,      lowerCAmelCase_	:  Dict							,      lowerCAmelCase_	:  List[Any]							,      *lowerCAmelCase_	:  int )      ->							Union[str, Any]:


														"""simple docstring"""


														_a     					=    BioGptModel(config=__A )
														model.to(__A )
														model.eval()

														# create attention mask
														_a     					=    torch.ones(input_ids.shape							,      dtype=torch.long							,      device=__A )
														_a     					=    self.seq_length // 2
														_a     					=    0

														# first forward pass
														_a     					=    model(__A							,      attention_mask=__A ).to_tuple()

														# create hypothetical next token and extent to next_input_ids
														_a     					=    ids_tensor((self.batch_size, 1)							,      config.vocab_size )

														# change a random masked slice from input_ids
														_a     					=    ids_tensor((1,)							,      __A ).item() + 1
														_a     					=    ids_tensor((self.batch_size, 1)							,      config.vocab_size ).squeeze(-1 )
														_a     					=    random_other_next_tokens

														# append to next input_ids and attn_mask
														_a     					=    torch.cat([input_ids, next_tokens]							,      dim=-1 )
														_a     					=    torch.cat(
														    [attn_mask, torch.ones((attn_mask.shape[0], 1)							,      dtype=torch.long							,      device=__A )]							,      dim=1							,      )

														# get two different outputs
														_a     					=    model(__A							,      attention_mask=__A )['''last_hidden_state''']
														_a     					=    model(__A							,      past_key_values=__A							,      attention_mask=__A )['''last_hidden_state''']

														# select random slice
														_a     					=    ids_tensor((1,)							,      output_from_past.shape[-1] ).item()
														_a     					=    output_from_no_past[:, -1, random_slice_idx].detach()
														_a     					=    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        __lowerCAmelCase      (  self	:  List[str]							,      lowerCAmelCase_	:  Dict							,      lowerCAmelCase_	:  Any							,      lowerCAmelCase_	:  Union[str, Any]							,      lowerCAmelCase_	:  Optional[int]							,      lowerCAmelCase_	:  Any							,      *lowerCAmelCase_	:  Any )      ->							Union[str, Any]:


														"""simple docstring"""


														_a     					=    BioGptModel(config=__A ).to(__A ).eval()

														_a     					=    torch.ones(input_ids.shape							,      dtype=torch.long							,      device=__A )

														# first forward pass
														_a     					=    model(__A							,      attention_mask=__A							,      use_cache=__A )

														_a     					=    outputs.to_tuple()

														# create hypothetical multiple next token and extent to next_input_ids
														_a     					=    ids_tensor((self.batch_size, 3)							,      config.vocab_size )
														_a     					=    ids_tensor((self.batch_size, 3)							,      2 )

														# append to next input_ids and
														_a     					=    torch.cat([input_ids, next_tokens]							,      dim=-1 )
														_a     					=    torch.cat([attention_mask, next_attn_mask]							,      dim=-1 )

														_a     					=    model(__A							,      attention_mask=__A )['''last_hidden_state''']
														_a     					=    model(__A							,      attention_mask=__A							,      past_key_values=__A )[
														    '''last_hidden_state'''
														]

														# select random slice
														_a     					=    ids_tensor((1,)							,      output_from_past.shape[-1] ).item()
														_a     					=    output_from_no_past[:, -3:, random_slice_idx].detach()
														_a     					=    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        __lowerCAmelCase      (  self	:  Union[str, Any]							,      lowerCAmelCase_	:  Union[str, Any]							,      lowerCAmelCase_	:  List[str]							,      lowerCAmelCase_	:  Optional[Any]							,      lowerCAmelCase_	:  int							,      lowerCAmelCase_	:  List[str]							,      *lowerCAmelCase_	:  List[Any]							,      lowerCAmelCase_	:  Any=False )      ->							List[Any]:


														"""simple docstring"""


														_a     					=    BioGptForCausalLM(__A )
														model.to(__A )
														if gradient_checkpointing:
																					model.gradient_checkpointing_enable()

														_a     					=    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        __lowerCAmelCase      (  self	:  Optional[Any]							,      lowerCAmelCase_	:  List[str]							,      *lowerCAmelCase_	:  Any )      ->							List[str]:


														"""simple docstring"""


														_a     					=    BioGptModel(__A )
														_a     					=    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 )							,      0.0_0_1 )
																												self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key] ) - 0.0 )							,      0.0_1 )







							def        __lowerCAmelCase      (  self	:  Optional[Any]							,      lowerCAmelCase_	:  Dict							,      lowerCAmelCase_	:  Any							,      lowerCAmelCase_	:  str							,      lowerCAmelCase_	:  List[Any]							,      lowerCAmelCase_	:  Optional[int]							,      *lowerCAmelCase_	:  Union[str, Any] )      ->							str:


														"""simple docstring"""


														_a     					=    self.num_labels
														_a     					=    BioGptForTokenClassification(__A )
														model.to(__A )
														model.eval()
														_a     					=    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        __lowerCAmelCase      (  self	:  Dict )      ->							Any:


														"""simple docstring"""


														_a     					=    self.prepare_config_and_inputs()
														(
														    _a						
														)     					=    config_and_inputs
														_a     					=    {'''input_ids''': input_ids, '''attention_mask''': input_mask}
														return config, inputs_dict



@require_torch
class       A   (		lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__					,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        __lowerCAmelCase      (  self	:  Dict )      ->							List[str]:


														"""simple docstring"""


														_a     					=    BioGptModelTester(self )
														_a     					=    ConfigTester(self							,      config_class=__A							,      hidden_size=37 )







							def        __lowerCAmelCase      (  self	:  Dict )      ->							Tuple:


														"""simple docstring"""


														self.config_tester.run_common_tests()







							def        __lowerCAmelCase      (  self	:  str )      ->							str:


														"""simple docstring"""


														_a     					=    self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*__A )







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


														"""simple docstring"""


														_a     					=    self.model_tester.prepare_config_and_inputs()
														for type in ["absolute", "relative_key", "relative_key_query"]:
																					_a     					=    type
																					self.model_tester.create_and_check_model(*__A )







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


														"""simple docstring"""


														_a     					=    self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_biogpt_model_attention_mask_past(*__A )







							def        __lowerCAmelCase      (  self	:  List[str] )      ->							Tuple:


														"""simple docstring"""


														_a     					=    self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_forward_and_backwards(*__A							,      gradient_checkpointing=__A )







							def        __lowerCAmelCase      (  self	:  Any )      ->							Dict:


														"""simple docstring"""


														_a     					=    self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_biogpt_model_past_large_inputs(*__A )







							def        __lowerCAmelCase      (  self	:  Tuple )      ->							Optional[int]:


														"""simple docstring"""


														_a     					=    self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_biogpt_weight_initialization(*__A )







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


														"""simple docstring"""


														_a     					=    self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_biogpt_for_token_classification(*__A )







							@slow
							def        __lowerCAmelCase      (  self	:  Dict )      ->							Optional[int]:


														"""simple docstring"""


														_a     					=    BioGptForCausalLM.from_pretrained('''microsoft/biogpt''' )
														model.to(__A )
														_a     					=    BioGptTokenizer.from_pretrained('''microsoft/biogpt''' )

														_a     					=    '''left'''

														# Define PAD Token = EOS Token = 50256
														_a     					=    tokenizer.eos_token
														_a     					=    model.config.eos_token_id

														# use different length sentences to test batching
														_a     					=    [
														    '''Hello, my dog is a little''',
														    '''Today, I''',
														]

														_a     					=    tokenizer(__A							,      return_tensors='''pt'''							,      padding=__A )
														_a     					=    inputs['''input_ids'''].to(__A )

														_a     					=    model.generate(
														    input_ids=__A							,      attention_mask=inputs['''attention_mask'''].to(__A )							,      )

														_a     					=    tokenizer(sentences[0]							,      return_tensors='''pt''' ).input_ids.to(__A )
														_a     					=    model.generate(input_ids=__A )

														_a     					=    inputs_non_padded.shape[-1] - inputs['''attention_mask'''][-1].long().sum().cpu().item()
														_a     					=    tokenizer(sentences[1]							,      return_tensors='''pt''' ).input_ids.to(__A )
														_a     					=    model.generate(input_ids=__A							,      max_length=model.config.max_length - num_paddings )

														_a     					=    tokenizer.batch_decode(__A							,      skip_special_tokens=__A )
														_a     					=    tokenizer.decode(output_non_padded[0]							,      skip_special_tokens=__A )
														_a     					=    tokenizer.decode(output_padded[0]							,      skip_special_tokens=__A )

														_a     					=    [
														    '''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        __lowerCAmelCase      (  self	:  int )      ->							Tuple:


														"""simple docstring"""


														for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					_a     					=    BioGptModel.from_pretrained(__A )
																					self.assertIsNotNone(__A )







							def        __lowerCAmelCase      (  self	:  int )      ->							List[Any]:


														"""simple docstring"""


														_a     					=    self.model_tester.prepare_config_and_inputs_for_common()
														_a     					=    3
														_a     					=    input_dict['''input_ids''']
														_a     					=    input_ids.ne(1 ).to(__A )
														_a     					=    ids_tensor([self.model_tester.batch_size]							,      self.model_tester.type_sequence_label_size )
														_a     					=    BioGptForSequenceClassification(__A )
														model.to(__A )
														model.eval()
														_a     					=    model(__A							,      attention_mask=__A							,      labels=__A )
														self.assertEqual(result.logits.shape							,      (self.model_tester.batch_size, self.model_tester.num_labels) )







							def        __lowerCAmelCase      (  self	:  Optional[Any] )      ->							Optional[Any]:


														"""simple docstring"""


														_a     					=    self.model_tester.prepare_config_and_inputs_for_common()
														_a     					=    3
														_a     					=    '''multi_label_classification'''
														_a     					=    input_dict['''input_ids''']
														_a     					=    input_ids.ne(1 ).to(__A )
														_a     					=    ids_tensor(
														    [self.model_tester.batch_size, config.num_labels]							,      self.model_tester.type_sequence_label_size ).to(torch.float )
														_a     					=    BioGptForSequenceClassification(__A )
														model.to(__A )
														model.eval()
														_a     					=    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       A   (		unittest.TestCase   ):







							@slow
							def        __lowerCAmelCase      (  self	:  Optional[Any] )      ->							Optional[Any]:


														"""simple docstring"""


														_a     					=    BioGptForCausalLM.from_pretrained('''microsoft/biogpt''' )
														_a     					=    torch.tensor([[2, 48_05, 9, 6_56, 21]] )
														_a     					=    model(__A )[0]

														_a     					=    4_23_84

														_a     					=    torch.Size((1, 5, vocab_size) )
														self.assertEqual(output.shape							,      __A )

														_a     					=    torch.tensor(
														    [[[-9.5_2_3_6, -9.8_9_1_8, 10.45_57], [-11.04_69, -9.6_4_2_3, 8.1_0_2_2], [-8.8_6_6_4, -7.8_8_2_6, 5.5_3_2_5]]] )

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







							@slow
							def        __lowerCAmelCase      (  self	:  Optional[Any] )      ->							Union[str, Any]:


														"""simple docstring"""


														_a     					=    BioGptTokenizer.from_pretrained('''microsoft/biogpt''' )
														_a     					=    BioGptForCausalLM.from_pretrained('''microsoft/biogpt''' )
														model.to(__A )

														torch.manual_seed(0 )
														_a     					=    tokenizer('''COVID-19 is'''							,      return_tensors='''pt''' ).to(__A )
														_a     					=    model.generate(
														    **__A							,      min_length=1_00							,      max_length=10_24							,      num_beams=5							,      early_stopping=__A							,      )
														_a     					=    tokenizer.decode(output_ids[0]							,      skip_special_tokens=__A )

														_a     					=    (
														    '''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 )







'''simple docstring'''


import logging

import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader

from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed

class       A   (		_a   ):







							def __init__(  self	:  Optional[int]							,      lowerCAmelCase_	:  Optional[Any]							,      lowerCAmelCase_	:  Union[str, Any]							,      lowerCAmelCase_	:  List[str]=10_24							,      lowerCAmelCase_	:  Optional[Any]=10_24							,      lowerCAmelCase_	:  Tuple=3.6 )      ->							List[Any]:


														"""simple docstring"""


														_a     					=    tokenizer
														_a     					=    tokenizer.bos_token_id
														_a     					=    dataset
														_a     					=    seq_length
														_a     					=    seq_length * chars_per_token * num_of_sequences







							def __iter__(  self	:  Any )      ->							int:


														"""simple docstring"""


														_a     					=    iter(self.dataset )
														_a     					=    True
														while more_examples:
																					_a					,							_a     					=    [], 0
																					while True:
																												if buffer_len >= self.input_characters:
																																			break
																												try:
																																			buffer.append(next(lowerCAmelCase_ )['''content'''] )
																																			buffer_len += len(buffer[-1] )
																												except StopIteration:
																																			_a     					=    False
																																			break
																					_a     					=    tokenizer(lowerCAmelCase_							,      truncation=lowerCAmelCase_ )['''input_ids''']
																					_a     					=    []
																					for tokenized_input in tokenized_inputs:
																												all_token_ids.extend(tokenized_input + [self.concat_token_id] )
																					for i in range(0							,      len(lowerCAmelCase_ )							,      self.seq_length ):
																												_a     					=    all_token_ids[i : i + self.seq_length]
																												if len(lowerCAmelCase_ ) == self.seq_length:
																																			yield torch.tensor(lowerCAmelCase_ )



def 		snake_case_   (UpperCamelCase	:  int				):





							'''simple docstring'''


							_a     					=    {'''streaming''': True}
							_a     					=    load_dataset(args.dataset_name			,						split='''train'''			,						**UpperCamelCase				)
							_a     					=    ConstantLengthDataset(UpperCamelCase			,						UpperCamelCase			,						seq_length=args.seq_length				)
							_a     					=    DataLoader(UpperCamelCase			,						batch_size=args.batch_size				)
							return eval_dataloader
def 		snake_case_   (UpperCamelCase	:  int				):





							'''simple docstring'''


							model.eval()
							_a     					=    []
							for step, batch in enumerate(UpperCamelCase				):
														with torch.no_grad():
																					_a     					=    model(UpperCamelCase			,						labels=UpperCamelCase				)
														_a     					=    outputs.loss.repeat(args.batch_size				)
														losses.append(accelerator.gather(UpperCamelCase				)				)

														if args.max_eval_steps > 0 and step >= args.max_eval_steps:
																					break
							_a     					=    torch.mean(torch.cat(UpperCamelCase				)				)
							try:
														_a     					=    torch.exp(UpperCamelCase				)
							except OverflowError:
														_a     					=    float('''inf'''				)
							return loss.item(), perplexity.item()


# Setup Accelerator
_snake_case				:				List[str]       							=		Accelerator()

# Parse configuration
_snake_case				:				List[str]       							=		HfArgumentParser(EvaluationArguments)
_snake_case				:				Optional[int]       							=		parser.parse_args()
set_seed(args.seed)

# Logging
_snake_case				:				Any       							=		logging.getLogger(__name__)
logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)

# Load model and tokenizer
_snake_case				:				Dict       							=		AutoModelForCausalLM.from_pretrained(args.model_ckpt)
_snake_case				:				Optional[Any]       							=		AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
_snake_case				:				List[str]       							=		create_dataloader(args)

# Prepare everything with our `accelerator`.
_snake_case							,    _snake_case				:				Optional[int]       							=		accelerator.prepare(model, eval_dataloader)

# Evaluate and save the last checkpoint
logger.info('Evaluating and saving model after training')
_snake_case							,    _snake_case				:				int       							=		evaluate(args)

logger.info(F'''loss/eval: {eval_loss}, perplexity: {perplexity}''')

