


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    (A__  ):





							"""simple docstring"""







							def __init__(      self			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__=1_0_2_4			,						lowerCAmelCase__=1_0_2_4			,						lowerCAmelCase__=3.6       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[Any]					=					tokenizer
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					tokenizer.bos_token_id
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					dataset
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					seq_length
										SCREAMING_SNAKE_CASE_		:     str					=					seq_length * chars_per_token * num_of_sequences






							def __iter__(      self       ):





										"""simple docstring"""




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







def 						a__			(				A__				):
			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					{'streaming': True}
			SCREAMING_SNAKE_CASE_		:     List[str]					=					load_dataset(args.dataset_name,      split='train',      **_lowerCAmelCase				)
			SCREAMING_SNAKE_CASE_		:     List[str]					=					ConstantLengthDataset(_lowerCAmelCase,      _lowerCAmelCase,      seq_length=args.seq_length				)
			SCREAMING_SNAKE_CASE_		:     int					=					DataLoader(_lowerCAmelCase,      batch_size=args.batch_size				)
			return eval_dataloader






def 						a__			(				A__				):
			model.eval()
			SCREAMING_SNAKE_CASE_		:     List[str]					=					[]
			for step, batch in enumerate(_lowerCAmelCase				):
						with torch.no_grad():
									SCREAMING_SNAKE_CASE_		:     List[Any]					=					model(_lowerCAmelCase,      labels=_lowerCAmelCase				)
						SCREAMING_SNAKE_CASE_		:     Tuple					=					outputs.loss.repeat(args.batch_size				)
						losses.append(accelerator.gather(_lowerCAmelCase				)				)

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


# Setup Accelerator
lowerCAmelCase__			:      int								=Accelerator()

# Parse configuration
lowerCAmelCase__			:      Tuple								=HfArgumentParser(EvaluationArguments)
lowerCAmelCase__			:      Any								=parser.parse_args()
set_seed(args.seed)

# Logging
lowerCAmelCase__			:      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
lowerCAmelCase__			:      str								=AutoModelForCausalLM.from_pretrained(args.model_ckpt)
lowerCAmelCase__			:      Optional[int]								=AutoTokenizer.from_pretrained(args.model_ckpt)

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

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

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



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







"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE_					     =	{
    '''configuration_rag''': ['''RagConfig'''],
    '''retrieval_rag''': ['''RagRetriever'''],
    '''tokenization_rag''': ['''RagTokenizer'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE_					     =	[
			    '''RagModel''',
			    '''RagPreTrainedModel''',
			    '''RagSequenceForGeneration''',
			    '''RagTokenForGeneration''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE_					     =	[
			    '''TFRagModel''',
			    '''TFRagPreTrainedModel''',
			    '''TFRagSequenceForGeneration''',
			    '''TFRagTokenForGeneration''',
			]


if TYPE_CHECKING:
			from .configuration_rag import RagConfig
			from .retrieval_rag import RagRetriever
			from .tokenization_rag import RagTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_rag import (
						    TFRagModel,
						    TFRagPreTrainedModel,
						    TFRagSequenceForGeneration,
						    TFRagTokenForGeneration,
						)

else:
			import sys

			SCREAMING_SNAKE_CASE_					     =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
