

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				( UpperCAmelCase_	):




			"""simple docstring"""







			def __init__( self  :     str   ,						lowerCAmelCase__  :     Optional[Any]   ,						lowerCAmelCase__  :     Optional[Any]   ,						lowerCAmelCase__  :     List[Any]=1024   ,						lowerCAmelCase__  :     int=1024   ,						lowerCAmelCase__  :     Optional[Any]=3.6):
									SCREAMING_SNAKE_CASE_: List[Any]  		=       tokenizer
									SCREAMING_SNAKE_CASE_: str  		=       tokenizer.bos_token_id
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       dataset
									SCREAMING_SNAKE_CASE_: Tuple  		=       seq_length
									SCREAMING_SNAKE_CASE_: str  		=       seq_length * chars_per_token * num_of_sequences




			def __iter__( self  :     Union[str, Any]):
									SCREAMING_SNAKE_CASE_: Dict  		=       iter(self.dataset)
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       True
									while more_examples:
															SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Optional[int]  		=       [], 0
															while True:
																					if buffer_len >= self.input_characters:
																											break
																					try:
																											buffer.append(next(lowerCAmelCase__)["content"])
																											buffer_len += len(buffer[-1])
																					except StopIteration:
																											SCREAMING_SNAKE_CASE_: str  		=       False
																											break
															SCREAMING_SNAKE_CASE_: str  		=       tokenizer(lowerCAmelCase__   ,						truncation=lowerCAmelCase__)["input_ids"]
															SCREAMING_SNAKE_CASE_: Optional[Any]  		=       []
															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):
																					SCREAMING_SNAKE_CASE_: Tuple  		=       all_token_ids[i : i + self.seq_length]
																					if len(lowerCAmelCase__) == self.seq_length:
																											yield torch.tensor(lowerCAmelCase__)







def 			A_      (    _UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: Any  		=       {"streaming": True}
						SCREAMING_SNAKE_CASE_: Any  		=       load_dataset(args.dataset_name				,	split="train"				,	**_UpperCAmelCase    )
						SCREAMING_SNAKE_CASE_: int  		=       ConstantLengthDataset(_UpperCAmelCase				,	_UpperCAmelCase				,	seq_length=args.seq_length    )
						SCREAMING_SNAKE_CASE_: Tuple  		=       DataLoader(_UpperCAmelCase				,	batch_size=args.batch_size    )
						return eval_dataloader







def 			A_      (    _UpperCAmelCase    ):
						model.eval()
						SCREAMING_SNAKE_CASE_: Optional[Any]  		=       []
						for step, batch in enumerate(_UpperCAmelCase    ):
												with torch.no_grad():
																		SCREAMING_SNAKE_CASE_: Optional[int]  		=       model(_UpperCAmelCase				,	labels=_UpperCAmelCase    )
												SCREAMING_SNAKE_CASE_: int  		=       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_: Optional[int]  		=       torch.mean(torch.cat(_UpperCAmelCase    )    )
						try:
												SCREAMING_SNAKE_CASE_: Dict  		=       torch.exp(_UpperCAmelCase    )
						except OverflowError:
												SCREAMING_SNAKE_CASE_: Any  		=       float("inf"    )
						return loss.item(), perplexity.item()


# Setup Accelerator
lowerCAmelCase							:  Optional[Any]   	=						Accelerator()

# Parse configuration
lowerCAmelCase							:  List[str]   	=						HfArgumentParser(EvaluationArguments)
lowerCAmelCase							:  List[str]   	=						parser.parse_args()
set_seed(args.seed)

# Logging
lowerCAmelCase							:  List[str]   	=						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							:  Optional[Any]   	=						AutoModelForCausalLM.from_pretrained(args.model_ckpt)
lowerCAmelCase							:  Any   	=						AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
lowerCAmelCase							:  Optional[Any]   	=						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							:  List[str]   	=						evaluate(args)



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









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase							:  Optional[int]   	=						{"""configuration_wavlm""": ["""WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """WavLMConfig"""]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase							:  Any   	=						[
						    """WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """WavLMForAudioFrameClassification""",
						    """WavLMForCTC""",
						    """WavLMForSequenceClassification""",
						    """WavLMForXVector""",
						    """WavLMModel""",
						    """WavLMPreTrainedModel""",
						]

if TYPE_CHECKING:
						from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_wavlm import (
												    WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    WavLMForAudioFrameClassification,
												    WavLMForCTC,
												    WavLMForSequenceClassification,
												    WavLMForXVector,
												    WavLMModel,
												    WavLMPreTrainedModel,
												)

else:
						import sys

						lowerCAmelCase							:  Union[str, Any]   	=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






