


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 				snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						def __init__(				self   ,						__lowercase   ,						__lowercase   ,						__lowercase=1_0_2_4   ,						__lowercase=1_0_2_4   ,						__lowercase=3.6							)					->    Any:
										lowerCAmelCase_ :      str    				=				tokenizer
										lowerCAmelCase_ :      Optional[int]    				=				tokenizer.bos_token_id
										lowerCAmelCase_ :      Union[str, Any]    				=				dataset
										lowerCAmelCase_ :      List[str]    				=				seq_length
										lowerCAmelCase_ :      int    				=				seq_length * chars_per_token * num_of_sequences




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






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



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

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


# Setup Accelerator
_UpperCAmelCase			:  Union[str, Any]     =Accelerator()

# Parse configuration
_UpperCAmelCase			:  Union[str, Any]     =HfArgumentParser(EvaluationArguments)
_UpperCAmelCase			:  Any     =parser.parse_args()
set_seed(args.seed)

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

# Load dataset and dataloader
_UpperCAmelCase			:  Any     =create_dataloader(args)

# Prepare everything with our `accelerator`.
_UpperCAmelCase     ,					_UpperCAmelCase			:  List[str]     =accelerator.prepare(model, eval_dataloader)

# Evaluate and save the last checkpoint
logger.info("""Evaluating and saving model after training""")
_UpperCAmelCase     ,					_UpperCAmelCase			:  Union[str, Any]     =evaluate(args)




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



import inspect
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
_UpperCAmelCase			:  Any     ="""src/transformers"""


# This is to make sure the transformers module imported is the one in the repo.
_UpperCAmelCase			:  Optional[Any]     =direct_transformers_import(PATH_TO_TRANSFORMERS)

_UpperCAmelCase			:  List[str]     =transformers.models.auto.configuration_auto.CONFIG_MAPPING

# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
_UpperCAmelCase			:  Dict     =re.compile(R"""\[(.+?)\]\((https://huggingface\.co/.+?)\)""")


_UpperCAmelCase			:  Any     ={
    """DecisionTransformerConfig""",
    """EncoderDecoderConfig""",
    """MusicgenConfig""",
    """RagConfig""",
    """SpeechEncoderDecoderConfig""",
    """TimmBackboneConfig""",
    """VisionEncoderDecoderConfig""",
    """VisionTextDualEncoderConfig""",
    """LlamaConfig""",
}



def 			lowerCAmelCase (		lowerCAmelCase_ )-> str:
				lowerCAmelCase_ :      Any    				=				None

				# source code of `config_class`
				lowerCAmelCase_ :      Optional[int]    				=				inspect.getsource(lowerCAmelCase_ )
				lowerCAmelCase_ :      str    				=				_re_checkpoint.findall(lowerCAmelCase_ )

				# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
				# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
				for ckpt_name, ckpt_link in checkpoints:
								# allow the link to end with `/`
								if ckpt_link.endswith('''/''' ):
												lowerCAmelCase_ :      Optional[Any]    				=				ckpt_link[:-1]

								# verify the checkpoint name corresponds to the checkpoint link
								lowerCAmelCase_ :      Tuple    				=				f"""https://huggingface.co/{ckpt_name}"""
								if ckpt_link == ckpt_link_from_name:
												lowerCAmelCase_ :      List[str]    				=				ckpt_name
												break

				return checkpoint



def 			lowerCAmelCase (		)-> Optional[Any]:
				lowerCAmelCase_ :      Tuple    				=				[]

				for config_class in list(CONFIG_MAPPING.values() ):
								# Skip deprecated models
								if "models.deprecated" in config_class.__module__:
												continue
								lowerCAmelCase_ :      int    				=				get_checkpoint_from_config_class(lowerCAmelCase_ )

								lowerCAmelCase_ :      Tuple    				=				config_class.__name__
								if checkpoint is None and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
												configs_without_checkpoint.append(lowerCAmelCase_ )

				if len(lowerCAmelCase_ ) > 0:
								lowerCAmelCase_ :      List[Any]    				=				'''\n'''.join(sorted(lowerCAmelCase_ ) )
								raise ValueError(f"""The following configurations don't contain any valid checkpoint:\n{message}""" )


if __name__ == "__main__":
						check_config_docstrings_have_checkpoints()