




"""simple docstring"""






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__			(       __a  ):





      def __init__(							self      :	Optional[int]     ,a__      :	str     ,a__      :	List[Any]     ,a__      :	Optional[Any]=1024     ,a__      :	Union[str, Any]=1024     ,a__      :	Optional[int]=3.6)						->       Dict:




           """simple docstring"""







           _lowerCAmelCase:int									=  tokenizer
           _lowerCAmelCase:List[str]									=  tokenizer.bos_token_id
           _lowerCAmelCase:str									=  dataset
           _lowerCAmelCase:Union[str, Any]									=  seq_length
           _lowerCAmelCase:Any									=  seq_length * chars_per_token * num_of_sequences







      def __iter__(							self      :	Dict)						->       Optional[int]:




           """simple docstring"""







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





def 							UpperCAmelCase		(		snake_case			:					Optional[int]    ):
     _lowerCAmelCase:Optional[Any]									=  {'''streaming''': True}
     _lowerCAmelCase:str									=  load_dataset(args.dataset_name      ,       split='''train'''      ,       **snake_case    )
     _lowerCAmelCase:int									=  ConstantLengthDataset(snake_case      ,       snake_case      ,       seq_length=args.seq_length    )
     _lowerCAmelCase:Optional[int]									=  DataLoader(snake_case      ,       batch_size=args.batch_size    )
     return eval_dataloader





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

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


# Setup Accelerator
UpperCamelCase__						=			Accelerator()

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

# Logging
UpperCamelCase__						=			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__						=			AutoModelForCausalLM.from_pretrained(args.model_ckpt)
UpperCamelCase__						=			AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
UpperCamelCase__						=			create_dataloader(args)

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

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





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












"""simple docstring"""






from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
				from PIL import Image

				from ..image_utils import load_image

if is_torch_available():
				import torch

				from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

UpperCamelCase__						=			logging.get_logger(__name__)




@add_end_docstrings(UpperCamelCase_  )
class   a__			(       UpperCamelCase_  ):





						def __init__(							self      :	int     ,*a__      :	Optional[Any]     ,**a__      :	Union[str, Any])						->       Tuple:




											"""simple docstring"""







											super().__init__(*a__     ,**a__)
											requires_backends(self     ,'''vision''')
											self.check_model_type(a__)





						def __call__(							self      :	str     ,a__      :	Union[str, List[str], "Image.Image", List["Image.Image"]]     ,**a__      :	List[str])						->       Optional[int]:




											"""simple docstring"""







											return super().__call__(a__     ,**a__)





						def       __UpperCamelCase     (							self      :	Union[str, Any]     ,**a__      :	List[Any])						->       Any:




											"""simple docstring"""







											return {}, {}, {}





						def       __UpperCamelCase     (							self      :	Tuple     ,a__      :	Optional[int])						->       Optional[Any]:




											"""simple docstring"""







											_lowerCAmelCase:List[str]									=  load_image(a__)
											_lowerCAmelCase:int									=  image.size
											_lowerCAmelCase:int									=  self.image_processor(images=a__     ,return_tensors=self.framework)
											return model_inputs





						def       __UpperCamelCase     (							self      :	Dict     ,a__      :	List[str])						->       Union[str, Any]:




											"""simple docstring"""







											_lowerCAmelCase:Any									=  self.model(**a__)
											return model_outputs







						def       __UpperCamelCase     (							self      :	List[Any]     ,a__      :	Dict)						->       Any:




											"""simple docstring"""







											_lowerCAmelCase:Optional[int]									=  model_outputs.predicted_depth
											_lowerCAmelCase:Union[str, Any]									=  torch.nn.functional.interpolate(
											    predicted_depth.unsqueeze(1)     ,size=self.image_size[::-1]     ,mode='''bicubic'''     ,align_corners=a__)
											_lowerCAmelCase:List[str]									=  prediction.squeeze().cpu().numpy()
											_lowerCAmelCase:Any									=  (output * 255 / np.max(a__)).astype('''uint8''')
											_lowerCAmelCase:Dict									=  Image.fromarray(a__)
											_lowerCAmelCase:Tuple									=  {}
											_lowerCAmelCase:Optional[int]									=  predicted_depth
											_lowerCAmelCase:str									=  depth
											return output_dict






