



"""simple docstring"""
import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

__SCREAMING_SNAKE_CASE  :						Dict    							=      logging.get_logger(__name__)


__SCREAMING_SNAKE_CASE  :						Tuple    							=      {name: getattr(transformers, name + '''Fast''') for name in SLOW_TO_FAST_CONVERTERS}



def        lowerCAmelCase_(   lowercase_     :							Dict						,   lowercase_     :							Tuple						,   lowercase_     :							Dict						,   lowercase_     :							List[Any]							)	->       int:

				if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES:
								raise ValueError(F"""Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys()							)}."""							)

				if tokenizer_name is None:
								_lowerCamelCase        =				TOKENIZER_CLASSES
				else:
								_lowerCamelCase        =				{tokenizer_name: getattr(_A						,   tokenizer_name + '''Fast'''							)}

				logger.info(F"""Loading tokenizer classes: {tokenizer_names}"""							)

				for tokenizer_name in tokenizer_names:
								_lowerCamelCase        =				TOKENIZER_CLASSES[tokenizer_name]

								_lowerCamelCase        =				True
								if checkpoint_name is None:
												_lowerCamelCase        =				list(tokenizer_class.max_model_input_sizes.keys()							)
								else:
												_lowerCamelCase        =				[checkpoint_name]

								logger.info(F"""For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}"""							)

								for checkpoint in checkpoint_names:
												logger.info(F"""Loading {tokenizer_class.__class__.__name__} {checkpoint}"""							)

												# Load tokenizer
												_lowerCamelCase        =				tokenizer_class.from_pretrained(_A						,   force_download=_A							)

												# Save fast tokenizer
												logger.info(F"""Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}"""							)

												# For organization names we create sub-directories
												if "/" in checkpoint:
																_lowerCamelCase  ,     _lowerCamelCase        =				checkpoint.split('''/'''							)
																_lowerCamelCase        =				os.path.join(_A						,   _A							)
												elif add_prefix:
																_lowerCamelCase        =				checkpoint
																_lowerCamelCase        =				dump_path
												else:
																_lowerCamelCase        =				None
																_lowerCamelCase        =				dump_path

												logger.info(F"""=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}"""							)

												if checkpoint in list(tokenizer.pretrained_vocab_files_map.values()							)[0]:
																_lowerCamelCase        =				list(tokenizer.pretrained_vocab_files_map.values()							)[0][checkpoint]
																_lowerCamelCase        =				file_path.split(_A							)[-1][0]
																if next_char == "/":
																				_lowerCamelCase        =				os.path.join(_A						,   _A							)
																				_lowerCamelCase        =				None

																logger.info(F"""=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}"""							)

												_lowerCamelCase        =				tokenizer.save_pretrained(
												    _A						,   legacy_format=_A						,   filename_prefix=_A							)
												logger.info(F"""=> File names {file_names}"""							)

												for file_name in file_names:
																if not file_name.endswith('''tokenizer.json'''							):
																				os.remove(_A							)
																				logger.info(F"""=> removing {file_name}"""							)


if __name__ == "__main__":
						__SCREAMING_SNAKE_CASE  :						str    							=      argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--dump_path''', default=None, type=str, required=True, help='''Path to output generated fast tokenizer files.'''
						)
						parser.add_argument(
						    '''--tokenizer_name''',
						    default=None,
						    type=str,
						    help=(
						        F"""Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will """
						        '''download and convert all the checkpoints from AWS.'''
						    ),
						)
						parser.add_argument(
						    '''--checkpoint_name''',
						    default=None,
						    type=str,
						    help='''Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.''',
						)
						parser.add_argument(
						    '''--force_download''',
						    action='''store_true''',
						    help='''Re-download checkpoints.''',
						)
						__SCREAMING_SNAKE_CASE  :						Optional[int]    							=      parser.parse_args()

						convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)





"""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 							lowerCamelCase_(   A__    ):





					'''simple docstring'''



					def __init__(    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__=1_0_2_4				,       lowerCamelCase__=1_0_2_4				,       lowerCamelCase__=3.6   ):
									_lowerCamelCase        =				tokenizer
									_lowerCamelCase        =				tokenizer.bos_token_id
									_lowerCamelCase        =				dataset
									_lowerCamelCase        =				seq_length
									_lowerCamelCase        =				seq_length * chars_per_token * num_of_sequences





					def __iter__(    self   ):
									_lowerCamelCase        =				iter(self.dataset   )
									_lowerCamelCase        =				True
									while more_examples:
													_lowerCamelCase  ,     _lowerCamelCase        =				[], 0
													while True:
																	if buffer_len >= self.input_characters:
																					break
																	try:
																					buffer.append(next(lowerCamelCase__   )['''content''']   )
																					buffer_len += len(buffer[-1]   )
																	except StopIteration:
																					_lowerCamelCase        =				False
																					break
													_lowerCamelCase        =				tokenizer(lowerCamelCase__				,       truncation=lowerCamelCase__   )['''input_ids''']
													_lowerCamelCase        =				[]
													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   ):
																	_lowerCamelCase        =				all_token_ids[i : i + self.seq_length]
																	if len(lowerCamelCase__   ) == self.seq_length:
																					yield torch.tensor(lowerCamelCase__   )



def        lowerCAmelCase_(   lowercase_     :							Any							)	->       Optional[Any]:
				_lowerCamelCase        =				{'''streaming''': True}
				_lowerCamelCase        =				load_dataset(args.dataset_name						,   split='''train'''						,   **lowercase_							)
				_lowerCamelCase        =				ConstantLengthDataset(lowercase_						,   lowercase_						,   seq_length=args.seq_length							)
				_lowerCamelCase        =				DataLoader(lowercase_						,   batch_size=args.batch_size							)
				return eval_dataloader



def        lowerCAmelCase_(   lowercase_     :							Tuple							)	->       str:
				model.eval()
				_lowerCamelCase        =				[]
				for step, batch in enumerate(lowercase_							):
								with torch.no_grad():
												_lowerCamelCase        =				model(lowercase_						,   labels=lowercase_							)
								_lowerCamelCase        =				outputs.loss.repeat(args.batch_size							)
								losses.append(accelerator.gather(lowercase_							)							)

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


# Setup Accelerator
__SCREAMING_SNAKE_CASE  :						Dict    							=      Accelerator()

# Parse configuration
__SCREAMING_SNAKE_CASE  :						Tuple    							=      HfArgumentParser(EvaluationArguments)
__SCREAMING_SNAKE_CASE  :						Optional[int]    							=      parser.parse_args()
set_seed(args.seed)

# Logging
__SCREAMING_SNAKE_CASE  :						Optional[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
__SCREAMING_SNAKE_CASE  :						str    							=      AutoModelForCausalLM.from_pretrained(args.model_ckpt)
__SCREAMING_SNAKE_CASE  :						Union[str, Any]    							=      AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
__SCREAMING_SNAKE_CASE  :						str    							=      create_dataloader(args)

# Prepare everything with our `accelerator`.
__SCREAMING_SNAKE_CASE							,					__SCREAMING_SNAKE_CASE  :						Any    							=      accelerator.prepare(model, eval_dataloader)

# Evaluate and save the last checkpoint
logger.info('''Evaluating and saving model after training''')
__SCREAMING_SNAKE_CASE							,					__SCREAMING_SNAKE_CASE  :						List[str]    							=      evaluate(args)




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