
"""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 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE  ):
   def __init__(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__=1024  ,     snake_case__=1024  ,     snake_case__=3.6				):




     """simple docstring"""


     _SCREAMING_SNAKE_CASE   :				int							    = tokenizer
     _SCREAMING_SNAKE_CASE   :				List[str]							    = tokenizer.bos_token_id
     _SCREAMING_SNAKE_CASE   :				Optional[int]							    = dataset
     _SCREAMING_SNAKE_CASE   :				str							    = seq_length
     _SCREAMING_SNAKE_CASE   :				int							    = seq_length * chars_per_token * num_of_sequences




   def __iter__(     self				):




     """simple docstring"""


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





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





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

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


# Setup Accelerator
lowercase_       :						int     					=	Accelerator()

# Parse configuration
lowercase_       :						str     					=	HfArgumentParser(EvaluationArguments)
lowercase_       :						Dict     					=	parser.parse_args()
set_seed(args.seed)

# Logging
lowercase_       :						Any     					=	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
lowercase_       :						Any     					=	AutoModelForCausalLM.from_pretrained(args.model_ckpt)
lowercase_       :						Any     					=	AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
lowercase_       :						Dict     					=	create_dataloader(args)

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

# Evaluate and save the last checkpoint
logger.info('''Evaluating and saving model after training''')
lowercase_       :						Union[str, Any]     					=	evaluate(args)





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





"""simple docstring"""
# 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.
import re

from ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool


if is_vision_available():
      from PIL import Image
class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE  ):
     A__      =			"""naver-clova-ix/donut-base-finetuned-docvqa"""
     A__      =			(
         """This is a tool that answers a question about an document (pdf). It takes an input named `document` which """
         """should be the document containing the information, as well as a `question` that is the question about the """
         """document. It returns a text that contains the answer to the question."""
     )
     A__      =			"""document_qa"""
     A__      =			AutoProcessor
     A__      =			VisionEncoderDecoderModel

     A__      =			["""image""", """text"""]
     A__      =			["""text"""]
     def __init__(     self  ,     *snake_case__  ,     **snake_case__				):




       """simple docstring"""


       if not is_vision_available():
         raise ValueError("Pillow must be installed to use the DocumentQuestionAnsweringTool."				)

       super().__init__(*snake_case__  ,     **snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[str]							    = "<s_docvqa><s_question>{user_input}</s_question><s_answer>"
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = task_prompt.replace("{user_input}"  ,     snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Tuple							    = self.pre_processor.tokenizer(
           snake_case__  ,     add_special_tokens=snake_case__  ,     return_tensors="pt"				).input_ids
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = self.pre_processor(snake_case__  ,     return_tensors="pt"				).pixel_values

       return {"decoder_input_ids": decoder_input_ids, "pixel_values": pixel_values}
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       return self.model.generate(
           inputs["pixel_values"].to(self.device				)  ,     decoder_input_ids=inputs["decoder_input_ids"].to(self.device				)  ,     max_length=self.model.decoder.config.max_position_embeddings  ,     early_stopping=snake_case__  ,     pad_token_id=self.pre_processor.tokenizer.pad_token_id  ,     eos_token_id=self.pre_processor.tokenizer.eos_token_id  ,     use_cache=snake_case__  ,     num_beams=1  ,     bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]]  ,     return_dict_in_generate=snake_case__  ,     ).sequences




     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				int							    = self.pre_processor.batch_decode(snake_case__				)[0]
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = sequence.replace(self.pre_processor.tokenizer.eos_token  ,     ""				)
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = sequence.replace(self.pre_processor.tokenizer.pad_token  ,     ""				)
       _SCREAMING_SNAKE_CASE   :				Dict							    = re.sub(r"<.*?>"  ,     ""  ,     snake_case__  ,     count=1				).strip()  # remove first task start token
       _SCREAMING_SNAKE_CASE   :				Dict							    = self.pre_processor.tokenajson(snake_case__				)

       return sequence["answer"]



