





"""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 				__lowercase			(			_UpperCamelCase							):







					'''simple docstring'''




					def __init__(						self	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase=1024	,			_UpperCAmelCase=1024	,			_UpperCAmelCase=3.6     ):
									__a :						Union[str, Any]			       =  tokenizer
									__a :						str			       =  tokenizer.bos_token_id
									__a :						List[Any]			       =  dataset
									__a :						Union[str, Any]			       =  seq_length
									__a :						List[str]			       =  seq_length * chars_per_token * num_of_sequences







					def __iter__(						self     ):
									__a :						int			       =  iter(self.dataset     )
									__a :						List[Any]			       =  True
									while more_examples:
													__a       ,						__a :						Tuple			       =  [], 0
													while True:
																	if buffer_len >= self.input_characters:
																					break
																	try:
																					buffer.append(next(_UpperCAmelCase     )['''content''']     )
																					buffer_len += len(buffer[-1]     )
																	except StopIteration:
																					__a :						Union[str, Any]			       =  False
																					break
													__a :						Any			       =  tokenizer(_UpperCAmelCase	,			truncation=_UpperCAmelCase     )['''input_ids''']
													__a :						Dict			       =  []
													for tokenized_input in tokenized_inputs:
																	all_token_ids.extend(tokenized_input + [self.concat_token_id]     )
													for i in range(0	,			len(_UpperCAmelCase     )	,			self.seq_length     ):
																	__a :						Union[str, Any]			       =  all_token_ids[i : i + self.seq_length]
																	if len(_UpperCAmelCase     ) == self.seq_length:
																					yield torch.tensor(_UpperCAmelCase     )






def     __A      (			a_						:Tuple)		->		Optional[Any]:
				__a :						int			       =  {'''streaming''': True}
				__a :						int			       =  load_dataset(args.dataset_name   ,       split='''train'''   ,       **a_)
				__a :						int			       =  ConstantLengthDataset(a_   ,       a_   ,       seq_length=args.seq_length)
				__a :						Optional[int]			       =  DataLoader(a_   ,       batch_size=args.batch_size)
				return eval_dataloader

def     __A      (			a_						:Optional[int])		->		List[str]:
				model.eval()
				__a :						str			       =  []
				for step, batch in enumerate(a_):
								with torch.no_grad():
												__a :						Dict			       =  model(a_   ,       labels=a_)
								__a :						Optional[Any]			       =  outputs.loss.repeat(args.batch_size)
								losses.append(accelerator.gather(a_))

								if args.max_eval_steps > 0 and step >= args.max_eval_steps:
												break
				__a :						Union[str, Any]			       =  torch.mean(torch.cat(a_))
				try:
								__a :						Tuple			       =  torch.exp(a_)
				except OverflowError:
								__a :						Dict			       =  float('''inf''')
				return loss.item(), perplexity.item()


# Setup Accelerator
A 			=  Accelerator()

# Parse configuration
A 			=  HfArgumentParser(EvaluationArguments)
A 			=  parser.parse_args()
set_seed(args.seed)

# Logging
A 			=  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
A 			=  AutoModelForCausalLM.from_pretrained(args.model_ckpt)
A 			=  AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
A 			=  create_dataloader(args)

# Prepare everything with our `accelerator`.
A			,   A 			=  accelerator.prepare(model, eval_dataloader)

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






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






"""simple docstring"""






import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class 				__lowercase			(			_UpperCamelCase   ,      unittest.TestCase							):







					'''simple docstring'''




					__lowerCAmelCase  =  OpenAIGPTTokenizer
					__lowerCAmelCase  =  OpenAIGPTTokenizerFast
					__lowerCAmelCase  =  True
					__lowerCAmelCase  =  False







					def  _lowerCamelCase		(						self     ):
									super().setUp()

									# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
									__a :						List[str]			       =  [
									    '''l''',
									    '''o''',
									    '''w''',
									    '''e''',
									    '''r''',
									    '''s''',
									    '''t''',
									    '''i''',
									    '''d''',
									    '''n''',
									    '''w</w>''',
									    '''r</w>''',
									    '''t</w>''',
									    '''lo''',
									    '''low''',
									    '''er</w>''',
									    '''low</w>''',
									    '''lowest</w>''',
									    '''newer</w>''',
									    '''wider</w>''',
									    '''<unk>''',
									]
									__a :						str			       =  dict(zip(_UpperCAmelCase	,			range(len(_UpperCAmelCase     )     )     )     )
									__a :						List[Any]			       =  ['''#version: 0.2''', '''l o''', '''lo w''', '''e r</w>''', '''''']

									__a :						Union[str, Any]			       =  os.path.join(self.tmpdirname	,			VOCAB_FILES_NAMES['''vocab_file''']     )
									__a :						Dict			       =  os.path.join(self.tmpdirname	,			VOCAB_FILES_NAMES['''merges_file''']     )
									with open(self.vocab_file	,			'''w'''     ) as fp:
													fp.write(json.dumps(_UpperCAmelCase     )     )
									with open(self.merges_file	,			'''w'''     ) as fp:
													fp.write('''\n'''.join(_UpperCAmelCase     )     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase     ):
									return "lower newer", "lower newer"







					def  _lowerCamelCase		(						self     ):
									__a :						Tuple			       =  OpenAIGPTTokenizer(self.vocab_file	,			self.merges_file     )

									__a :						Tuple			       =  '''lower'''
									__a :						Union[str, Any]			       =  ['''low''', '''er</w>''']
									__a :						str			       =  tokenizer.tokenize(_UpperCAmelCase     )
									self.assertListEqual(_UpperCAmelCase	,			_UpperCAmelCase     )

									__a :						List[Any]			       =  tokens + ['''<unk>''']
									__a :						Tuple			       =  [14, 15, 20]
									self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase     )	,			_UpperCAmelCase     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase=15     ):
									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""     ):
																	__a :						Any			       =  self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase	,			**_UpperCAmelCase     )

																	# Simple input
																	__a :						str			       =  '''This is a simple input'''
																	__a :						Dict			       =  ['''This is a simple input 1''', '''This is a simple input 2''']
																	__a :						List[Any]			       =  ('''This is a simple input''', '''This is a pair''')
																	__a :						Optional[Any]			       =  [
																	    ('''This is a simple input 1''', '''This is a simple input 2'''),
																	    ('''This is a simple pair 1''', '''This is a simple pair 2'''),
																	]

																	# Simple input tests
																	self.assertRaises(_UpperCAmelCase	,			tokenizer_r.encode	,			_UpperCAmelCase	,			max_length=_UpperCAmelCase	,			padding='''max_length'''     )

																	# Simple input
																	self.assertRaises(_UpperCAmelCase	,			tokenizer_r.encode_plus	,			_UpperCAmelCase	,			max_length=_UpperCAmelCase	,			padding='''max_length'''     )

																	# Simple input
																	self.assertRaises(
																	    _UpperCAmelCase	,			tokenizer_r.batch_encode_plus	,			_UpperCAmelCase	,			max_length=_UpperCAmelCase	,			padding='''max_length'''	,			)

																	# Pair input
																	self.assertRaises(_UpperCAmelCase	,			tokenizer_r.encode	,			_UpperCAmelCase	,			max_length=_UpperCAmelCase	,			padding='''max_length'''     )

																	# Pair input
																	self.assertRaises(_UpperCAmelCase	,			tokenizer_r.encode_plus	,			_UpperCAmelCase	,			max_length=_UpperCAmelCase	,			padding='''max_length'''     )

																	# Pair input
																	self.assertRaises(
																	    _UpperCAmelCase	,			tokenizer_r.batch_encode_plus	,			_UpperCAmelCase	,			max_length=_UpperCAmelCase	,			padding='''max_length'''	,			)







					def  _lowerCamelCase		(						self     ):
									pass






@require_ftfy
@require_spacy
@require_tokenizers
class 				__lowercase			(			_UpperCamelCase							):







					'''simple docstring'''




					pass