

"""simple docstring"""
import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


_A						 =   {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}

class 	lowerCamelCase     (    nn.Module					):


  '''simple docstring'''







  def __init__(self				,    _lowerCamelCase     ):


        """simple docstring"""

        super().__init__()
        UpperCAmelCase__       :				Union[str, Any]						 =  torchvision.models.resnetaaa(pretrained=a_     )
        UpperCAmelCase__       :				Tuple						 =  list(model.children()     )[:-2]
        UpperCAmelCase__       :				Optional[int]						 =  nn.Sequential(*a_     )
        UpperCAmelCase__       :				int						 =  nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]     )




  def       _a	(self				,    _lowerCamelCase     ):


        """simple docstring"""

        UpperCAmelCase__       :				int						 =  self.pool(self.model(a_     )     )
        UpperCAmelCase__       :				Optional[Any]						 =  torch.flatten(a_				,    start_dim=2     )
        UpperCAmelCase__       :				Any						 =  out.transpose(1				,    2     ).contiguous()
        return out  # BxNx2048




class 	lowerCamelCase     (    __UpperCamelCase					):


  '''simple docstring'''







  def __init__(self				,    _lowerCamelCase				,    _lowerCamelCase				,    _lowerCamelCase				,    _lowerCamelCase				,    _lowerCamelCase     ):


        """simple docstring"""

        UpperCAmelCase__       :				List[str]						 =  [json.loads(a_     ) for l in open(a_     )]
        UpperCAmelCase__       :				Optional[int]						 =  os.path.dirname(a_     )
        UpperCAmelCase__       :				Optional[int]						 =  tokenizer
        UpperCAmelCase__       :				Optional[int]						 =  labels
        UpperCAmelCase__       :				int						 =  len(a_     )
        UpperCAmelCase__       :				Any						 =  max_seq_length

        UpperCAmelCase__       :				int						 =  transforms



  def __len__(self     ):


        """simple docstring"""

        return len(self.data     )



  def __getitem__(self				,    _lowerCamelCase     ):


        """simple docstring"""

        UpperCAmelCase__       :				List[Any]						 =  torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]				,    add_special_tokens=a_     )     )
        UpperCAmelCase__       :				Optional[Any]						 =  sentence[0], sentence[1:-1], sentence[-1]
        UpperCAmelCase__       :				List[Any]						 =  sentence[: self.max_seq_length]

        UpperCAmelCase__       :				Optional[int]						 =  torch.zeros(self.n_classes     )
        UpperCAmelCase__       :				List[str]						 =  1

        UpperCAmelCase__       :				Optional[int]						 =  Image.open(os.path.join(self.data_dir				,    self.data[index]["""img"""]     )     ).convert("""RGB"""     )
        UpperCAmelCase__       :				Optional[int]						 =  self.transforms(a_     )

        return {
            "image_start_token": start_token,
            "image_end_token": end_token,
            "sentence": sentence,
            "image": image,
            "label": label,
        }




  def       _a	(self     ):


        """simple docstring"""

        UpperCAmelCase__       :				List[str]						 =  Counter()
        for row in self.data:
              label_freqs.update(row["""label"""]     )
        return label_freqs




def  a__		(		lowerCAmelCase  )     ->      Tuple:
      UpperCAmelCase__       :				Optional[Any]						 =  [len(row["""sentence"""]  ) for row in batch]
      UpperCAmelCase__       :				Union[str, Any]						 =  len(_UpperCAmelCase  ), max(_UpperCAmelCase  )

      UpperCAmelCase__       :				Dict						 =  torch.zeros(_UpperCAmelCase       ,     _UpperCAmelCase       ,     dtype=torch.long  )
      UpperCAmelCase__       :				Union[str, Any]						 =  torch.zeros(_UpperCAmelCase       ,     _UpperCAmelCase       ,     dtype=torch.long  )

      for i_batch, (input_row, length) in enumerate(zip(_UpperCAmelCase       ,     _UpperCAmelCase  )  ):
            UpperCAmelCase__       :				Optional[int]						 =  input_row['''sentence''']
            UpperCAmelCase__       :				Optional[Any]						 =  1

      UpperCAmelCase__       :				Any						 =  torch.stack([row["""image"""] for row in batch]  )
      UpperCAmelCase__       :				int						 =  torch.stack([row["""label"""] for row in batch]  )
      UpperCAmelCase__       :				List[str]						 =  torch.stack([row["""image_start_token"""] for row in batch]  )
      UpperCAmelCase__       :				Union[str, Any]						 =  torch.stack([row["""image_end_token"""] for row in batch]  )

      return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor




def  a__		(		)     ->      int:
      return [
          "Crime",
          "Drama",
          "Thriller",
          "Action",
          "Comedy",
          "Romance",
          "Documentary",
          "Short",
          "Mystery",
          "History",
          "Family",
          "Adventure",
          "Fantasy",
          "Sci-Fi",
          "Western",
          "Horror",
          "Sport",
          "War",
          "Music",
          "Musical",
          "Animation",
          "Biography",
          "Film-Noir",
      ]





def  a__		(		)     ->      int:
      return transforms.Compose(
          [
              transforms.Resize(2_56  ),
              transforms.CenterCrop(2_24  ),
              transforms.ToTensor(),
              transforms.Normalize(
                  mean=[0.4677_7044, 0.4453_1429, 0.4066_1017]       ,     std=[0.1222_1994, 0.1214_5835, 0.1438_0469]       ,     ),
          ]  )






import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


__A   			=getLogger(__name__)


__A   			="cuda" if torch.cuda.is_available() else "cpu"

def        a						(      _UpperCAmelCase							:					List[str] ,   _UpperCAmelCase							:					str ,   _UpperCAmelCase							:					str ,   _UpperCAmelCase							:					int = 8 ,   _UpperCAmelCase							:					str = DEFAULT_DEVICE ,   _UpperCAmelCase							:					Optional[Any]=False ,   _UpperCAmelCase							:					Optional[int]="summarization" ,   _UpperCAmelCase							:					Dict=None ,   **_UpperCAmelCase							:					List[str] ,   ):







		'''simple docstring'''





		__UpperCAmelCase    :     Dict													=	Path(_UpperCAmelCase						).open('''w''' ,   encoding='''utf-8'''						)
		__UpperCAmelCase    :     Optional[int]													=	str(_UpperCAmelCase						)
		__UpperCAmelCase    :     List[Any]													=	AutoModelForSeqaSeqLM.from_pretrained(_UpperCAmelCase						).to(_UpperCAmelCase						)
		if fpaa:
				__UpperCAmelCase    :     Dict													=	model.half()

		__UpperCAmelCase    :     Tuple													=	AutoTokenizer.from_pretrained(_UpperCAmelCase						)
		logger.info(f'Inferred tokenizer type: {tokenizer.__class__}'						)  # if this is wrong, check config.model_type.

		__UpperCAmelCase    :     Dict													=	time.time()
		# update config with task specific params
		use_task_specific_params(_UpperCAmelCase ,   _UpperCAmelCase						)
		if prefix is None:
				__UpperCAmelCase    :     List[Any]													=	prefix or getattr(model.config ,   '''prefix''' ,   ''''''						) or ''''''
		for examples_chunk in tqdm(list(chunks(_UpperCAmelCase ,   _UpperCAmelCase						)						)						):
				__UpperCAmelCase    :     Any													=	[prefix + text for text in examples_chunk]
				__UpperCAmelCase    :     Union[str, Any]													=	tokenizer(_UpperCAmelCase ,   return_tensors='''pt''' ,   truncation=_UpperCAmelCase ,   padding='''longest'''						).to(_UpperCAmelCase						)
				__UpperCAmelCase    :     Dict													=	model.generate(
				    input_ids=batch.input_ids ,   attention_mask=batch.attention_mask ,   **_UpperCAmelCase ,   )
				__UpperCAmelCase    :     List[Any]													=	tokenizer.batch_decode(_UpperCAmelCase ,   skip_special_tokens=_UpperCAmelCase ,   clean_up_tokenization_spaces=_UpperCAmelCase						)
				for hypothesis in dec:
						fout.write(hypothesis + '''\n'''						)
						fout.flush()
		fout.close()
		__UpperCAmelCase    :     Dict													=	int(time.time() - start_time						)  # seconds
		__UpperCAmelCase    :     Union[str, Any]													=	len(_UpperCAmelCase						)
		return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs ,   4						)}

def        a						(      ):







		'''simple docstring'''





		return datetime.datetime.now().strftime('''%Y-%m-%d %H:%M:%S'''						)

def        a						(      _UpperCAmelCase							:					Any=True						):







		'''simple docstring'''





		__UpperCAmelCase    :     Dict													=	argparse.ArgumentParser()
		parser.add_argument('''model_name''' ,   type=_UpperCAmelCase ,   help='''like facebook/bart-large-cnn,t5-base, etc.'''						)
		parser.add_argument('''input_path''' ,   type=_UpperCAmelCase ,   help='''like cnn_dm/test.source'''						)
		parser.add_argument('''save_path''' ,   type=_UpperCAmelCase ,   help='''where to save summaries'''						)
		parser.add_argument('''--reference_path''' ,   type=_UpperCAmelCase ,   required=_UpperCAmelCase ,   help='''like cnn_dm/test.target'''						)
		parser.add_argument('''--score_path''' ,   type=_UpperCAmelCase ,   required=_UpperCAmelCase ,   default='''metrics.json''' ,   help='''where to save metrics'''						)
		parser.add_argument('''--device''' ,   type=_UpperCAmelCase ,   required=_UpperCAmelCase ,   default=_UpperCAmelCase ,   help='''cuda, cuda:1, cpu etc.'''						)
		parser.add_argument(
		    '''--prefix''' ,   type=_UpperCAmelCase ,   required=_UpperCAmelCase ,   default=_UpperCAmelCase ,   help='''will be added to the begininng of src examples'''						)
		parser.add_argument('''--task''' ,   type=_UpperCAmelCase ,   default='''summarization''' ,   help='''used for task_specific_params + metrics'''						)
		parser.add_argument('''--bs''' ,   type=_UpperCAmelCase ,   default=8 ,   required=_UpperCAmelCase ,   help='''batch size'''						)
		parser.add_argument(
		    '''--n_obs''' ,   type=_UpperCAmelCase ,   default=-1 ,   required=_UpperCAmelCase ,   help='''How many observations. Defaults to all.'''						)
		parser.add_argument('''--fp16''' ,   action='''store_true'''						)
		parser.add_argument('''--dump-args''' ,   action='''store_true''' ,   help='''print the custom hparams with the results'''						)
		parser.add_argument(
		    '''--info''' ,   nargs='''?''' ,   type=_UpperCAmelCase ,   const=datetime_now() ,   help=(
		        '''use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'''
		        ''' lang=en-ru. If no value is passed, the current datetime string will be used.'''
		    ) ,   )
		# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
		__UpperCAmelCase							,					__UpperCAmelCase    :     Dict													=	parser.parse_known_args()
		__UpperCAmelCase    :     Optional[Any]													=	parse_numeric_n_bool_cl_kwargs(_UpperCAmelCase						)
		if parsed_args and verbose:
				print(f'parsed the following generate kwargs: {parsed_args}'						)
		__UpperCAmelCase    :     Optional[int]													=	[''' ''' + x.rstrip() if '''t5''' in args.model_name else x.rstrip() for x in open(args.input_path						).readlines()]
		if args.n_obs > 0:
				__UpperCAmelCase    :     Optional[int]													=	examples[: args.n_obs]
		Path(args.save_path						).parent.mkdir(exist_ok=_UpperCAmelCase						)

		if args.reference_path is None and Path(args.score_path						).exists():
				warnings.warn(f'score_path {args.score_path} will be overwritten unless you type ctrl-c.'						)

		if args.device == "cpu" and args.fpaa:
				# this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
				raise ValueError('''Can\'t mix --fp16 and --device cpu'''						)

		__UpperCAmelCase    :     int													=	generate_summaries_or_translations(
		    _UpperCAmelCase ,   args.save_path ,   args.model_name ,   batch_size=args.bs ,   device=args.device ,   fpaa=args.fpaa ,   task=args.task ,   prefix=args.prefix ,   **_UpperCAmelCase ,   )

		if args.reference_path is None:
				return {}

		# Compute scores
		__UpperCAmelCase    :     str													=	calculate_bleu if '''translation''' in args.task else calculate_rouge
		__UpperCAmelCase    :     Tuple													=	[x.rstrip() for x in open(args.save_path						).readlines()]
		__UpperCAmelCase    :     int													=	[x.rstrip() for x in open(args.reference_path						).readlines()][: len(_UpperCAmelCase						)]
		__UpperCAmelCase    :     dict													=	score_fn(_UpperCAmelCase ,   _UpperCAmelCase						)
		scores.update(_UpperCAmelCase						)

		if args.dump_args:
				scores.update(_UpperCAmelCase						)
		if args.info:
				__UpperCAmelCase    :     Dict													=	args.info

		if verbose:
				print(_UpperCAmelCase						)

		if args.score_path is not None:
				json.dump(_UpperCAmelCase ,   open(args.score_path ,   '''w'''						)						)

		return scores


if __name__ == "__main__":
				# Usage for MT:
				# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
				run_generate(verbose=True)





