

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 			A_						(			SCREAMING_SNAKE_CASE							):
							def __init__(						self						:     Optional[Any]  ,SCREAMING_SNAKE_CASE__						:     str  ,SCREAMING_SNAKE_CASE__						:     Any  ,SCREAMING_SNAKE_CASE__						:     Union[str, Any]=1_0_2_4  ,SCREAMING_SNAKE_CASE__						:     Any=1_0_2_4  ,SCREAMING_SNAKE_CASE__						:     List[Any]=3.6):
								__lowerCamelCase							:							str   			=					tokenizer
								__lowerCamelCase							:							str   			=					tokenizer.bos_token_id
								__lowerCamelCase							:							Tuple   			=					dataset
								__lowerCamelCase							:							List[Any]   			=					seq_length
								__lowerCamelCase							:							Tuple   			=					seq_length * chars_per_token * num_of_sequences


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






def   SCREAMING_SNAKE_CASE__			(    lowerCamelCase__     )    ->	Optional[int]:
	__lowerCamelCase							:							Optional[int]   			=					{'streaming': True}
	__lowerCamelCase							:							Dict   			=					load_dataset(args.dataset_name      ,					split='train'      ,					**__UpperCamelCase     )
	__lowerCamelCase							:							Optional[Any]   			=					ConstantLengthDataset(__UpperCamelCase      ,					__UpperCamelCase      ,					seq_length=args.seq_length     )
	__lowerCamelCase							:							Optional[Any]   			=					DataLoader(__UpperCamelCase      ,					batch_size=args.batch_size     )
	return eval_dataloader






def   SCREAMING_SNAKE_CASE__			(    lowerCamelCase__     )    ->	Tuple:
	model.eval()
	__lowerCamelCase							:							Any   			=					[]
	for step, batch in enumerate(__UpperCamelCase     ):
		with torch.no_grad():
			__lowerCamelCase							:							List[Any]   			=					model(__UpperCamelCase      ,					labels=__UpperCamelCase     )
		__lowerCamelCase							:							List[str]   			=					outputs.loss.repeat(args.batch_size     )
		losses.append(accelerator.gather(__UpperCamelCase     )     )

		if args.max_eval_steps > 0 and step >= args.max_eval_steps:
			break
	__lowerCamelCase							:							str   			=					torch.mean(torch.cat(__UpperCamelCase     )     )
	try:
		__lowerCamelCase							:							Any   			=					torch.exp(__UpperCamelCase     )
	except OverflowError:
		__lowerCamelCase							:							List[str]   			=					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'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase_    	=				{
    "configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
    "tokenization_roformer": ["RoFormerTokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase_    	=				["RoFormerTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase_    	=				[
          "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "RoFormerForCausalLM",
          "RoFormerForMaskedLM",
          "RoFormerForMultipleChoice",
          "RoFormerForQuestionAnswering",
          "RoFormerForSequenceClassification",
          "RoFormerForTokenClassification",
          "RoFormerLayer",
          "RoFormerModel",
          "RoFormerPreTrainedModel",
          "load_tf_weights_in_roformer",
      ]


try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase_    	=				[
          "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFRoFormerForCausalLM",
          "TFRoFormerForMaskedLM",
          "TFRoFormerForMultipleChoice",
          "TFRoFormerForQuestionAnswering",
          "TFRoFormerForSequenceClassification",
          "TFRoFormerForTokenClassification",
          "TFRoFormerLayer",
          "TFRoFormerModel",
          "TFRoFormerPreTrainedModel",
      ]


try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase_    	=				[
          "FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "FlaxRoFormerForMaskedLM",
          "FlaxRoFormerForMultipleChoice",
          "FlaxRoFormerForQuestionAnswering",
          "FlaxRoFormerForSequenceClassification",
          "FlaxRoFormerForTokenClassification",
          "FlaxRoFormerModel",
          "FlaxRoFormerPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
      from .tokenization_roformer import RoFormerTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_roformer_fast import RoFormerTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roformer import (
                ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                RoFormerForCausalLM,
                RoFormerForMaskedLM,
                RoFormerForMultipleChoice,
                RoFormerForQuestionAnswering,
                RoFormerForSequenceClassification,
                RoFormerForTokenClassification,
                RoFormerLayer,
                RoFormerModel,
                RoFormerPreTrainedModel,
                load_tf_weights_in_roformer,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roformer import (
                TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRoFormerForCausalLM,
                TFRoFormerForMaskedLM,
                TFRoFormerForMultipleChoice,
                TFRoFormerForQuestionAnswering,
                TFRoFormerForSequenceClassification,
                TFRoFormerForTokenClassification,
                TFRoFormerLayer,
                TFRoFormerModel,
                TFRoFormerPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roformer import (
                FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                FlaxRoFormerForMaskedLM,
                FlaxRoFormerForMultipleChoice,
                FlaxRoFormerForQuestionAnswering,
                FlaxRoFormerForSequenceClassification,
                FlaxRoFormerForTokenClassification,
                FlaxRoFormerModel,
                FlaxRoFormerPreTrainedModel,
            )


else:
      import sys

      UpperCamelCase_    	=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


