





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   UpperCamelCase_       (     UpperCamelCase__    ):


							def __init__(							self    :Optional[int]							,				__A    :List[str]							,				__A    :Tuple							,				__A    :int=1024							,				__A    :int=1024							,				__A    :str=3.6		)   ->   Tuple:



									"""simple docstring"""



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


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



									"""simple docstring"""



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






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Dict    ):
		SCREAMING_SNAKE_CASE__			=      {"""streaming""": True}
		SCREAMING_SNAKE_CASE__			=      load_dataset(args.dataset_name					,   split="""train"""					,   **UpperCamelCase__    )
		SCREAMING_SNAKE_CASE__			=      ConstantLengthDataset(UpperCamelCase__					,   UpperCamelCase__					,   seq_length=args.seq_length    )
		SCREAMING_SNAKE_CASE__			=      DataLoader(UpperCamelCase__					,   batch_size=args.batch_size    )
		return eval_dataloader






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

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


# Setup Accelerator
_lowerCamelCase   				=			Accelerator()

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

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

# Load dataset and dataloader
_lowerCamelCase   				=			create_dataloader(args)

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

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

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






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCamelCase   				=			{
    'configuration_xmod': [
        'XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'XmodConfig',
        'XmodOnnxConfig',
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase   				=			[
	    'XMOD_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'XmodForCausalLM',
	    'XmodForMaskedLM',
	    'XmodForMultipleChoice',
	    'XmodForQuestionAnswering',
	    'XmodForSequenceClassification',
	    'XmodForTokenClassification',
	    'XmodModel',
	    'XmodPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_xmod import (
		    XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
		    XmodForCausalLM,
		    XmodForMaskedLM,
		    XmodForMultipleChoice,
		    XmodForQuestionAnswering,
		    XmodForSequenceClassification,
		    XmodForTokenClassification,
		    XmodModel,
		    XmodPreTrainedModel,
		)

else:
	import sys

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