





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   lowerCAmelCase				(   __UpperCamelCase     ):
 def __init__( self    : int     ,	UpperCAmelCase    : Optional[int]     ,	UpperCAmelCase    : Any     ,	UpperCAmelCase    : Tuple=1024     ,	UpperCAmelCase    : Any=1024     ,	UpperCAmelCase    : List[str]=3.6      )     ->      Dict:
     lowerCamelCase__       : Optional[Any]   					=						tokenizer
     lowerCamelCase__       : List[Any]   					=						tokenizer.bos_token_id
     lowerCamelCase__       : int   					=						dataset
     lowerCamelCase__       : Any   					=						seq_length
     lowerCamelCase__       : str   					=						seq_length * chars_per_token * num_of_sequences







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


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase					)     ->	Tuple:
    lowerCamelCase__       : Optional[Any]   					=						{'streaming': True}
    lowerCamelCase__       : Tuple   					=						load_dataset(args.dataset_name		,      split='train'		,      **_UpperCAmelCase					)
    lowerCamelCase__       : List[Any]   					=						ConstantLengthDataset(_UpperCAmelCase		,      _UpperCAmelCase		,      seq_length=args.seq_length					)
    lowerCamelCase__       : Union[str, Any]   					=						DataLoader(_UpperCAmelCase		,      batch_size=args.batch_size					)
    return eval_dataloader


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase					)     ->	str:
    model.eval()
    lowerCamelCase__       : str   					=						[]
    for step, batch in enumerate(_UpperCAmelCase					):
        with torch.no_grad():
            lowerCamelCase__       : int   					=						model(_UpperCAmelCase		,      labels=_UpperCAmelCase					)
        lowerCamelCase__       : Tuple   					=						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__       : Union[str, Any]   					=						float('inf'					)
    return loss.item(), perplexity.item()


# Setup Accelerator
_UpperCAmelCase							:			Any        =							Accelerator()

# Parse configuration
_UpperCAmelCase							:			Tuple        =							HfArgumentParser(EvaluationArguments)
_UpperCAmelCase							:			Optional[int]        =							parser.parse_args()
set_seed(args.seed)

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

# Load dataset and dataloader
_UpperCAmelCase							:			Optional[int]        =							create_dataloader(args)

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

# Evaluate and save the last checkpoint
logger.info("""Evaluating and saving model after training""")
_UpperCAmelCase  ,_UpperCAmelCase							:			Dict        =							evaluate(args)


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












# Copyright 2023 The HuggingFace Inc. 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 ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool

class   lowerCAmelCase				(   __UpperCamelCase     ):
 UpperCAmelCase__						=						"""philschmid/bart-large-cnn-samsum"""
 UpperCAmelCase__						=						(
     """This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, """
     """and returns a summary of the text."""
 )
 UpperCAmelCase__						=						"""summarizer"""
 UpperCAmelCase__						=						AutoTokenizer
 UpperCAmelCase__						=						AutoModelForSeqaSeqLM

 UpperCAmelCase__						=						["""text"""]
 UpperCAmelCase__						=						["""text"""]
 def 	A_      ( self    : Tuple     ,	UpperCAmelCase    : List[Any]      )     ->      List[str]:
     return self.pre_processor(UpperCAmelCase     ,	return_tensors='pt'     ,	truncation=UpperCAmelCase      )
 def 	A_      ( self    : Optional[int]     ,	UpperCAmelCase    : Dict      )     ->      str:
     return self.model.generate(**UpperCAmelCase      )[0]







 def 	A_      ( self    : Tuple     ,	UpperCAmelCase    : List[Any]      )     ->      Union[str, Any]:
     return self.pre_processor.decode(UpperCAmelCase     ,	skip_special_tokens=UpperCAmelCase     ,	clean_up_tokenization_spaces=UpperCAmelCase      )





