




'''simple docstring'''







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


lowercase      :   int      =       getLogger(__name__)


lowercase      :   List[Any]      =       """cuda""" if torch.cuda.is_available() else """cpu"""



def        lowerCamelCase__				(      __lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase = 8     ,   __lowercase = DEFAULT_DEVICE     ,   __lowercase=False     ,   __lowercase="summarization"     ,   __lowercase=None     ,   **__lowercase     ,   ):
     snake_case			:      Optional[Any]        =			Path(lowerCAmelCase__				).open("""w"""     ,   encoding="""utf-8"""				)
     snake_case			:      int        =			str(lowerCAmelCase__				)
     snake_case			:      Any        =			AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase__				).to(lowerCAmelCase__				)
     if fpaa:
          snake_case			:      Union[str, Any]        =			model.half()

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

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



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



def        lowerCamelCase__				(      __lowercase=True				):
     snake_case			:      str        =			argparse.ArgumentParser()
     parser.add_argument("""model_name"""     ,   type=lowerCAmelCase__     ,   help="""like facebook/bart-large-cnn,t5-base, etc."""				)
     parser.add_argument("""input_path"""     ,   type=lowerCAmelCase__     ,   help="""like cnn_dm/test.source"""				)
     parser.add_argument("""save_path"""     ,   type=lowerCAmelCase__     ,   help="""where to save summaries"""				)
     parser.add_argument("""--reference_path"""     ,   type=lowerCAmelCase__     ,   required=lowerCAmelCase__     ,   help="""like cnn_dm/test.target"""				)
     parser.add_argument("""--score_path"""     ,   type=lowerCAmelCase__     ,   required=lowerCAmelCase__     ,   default="""metrics.json"""     ,   help="""where to save metrics"""				)
     parser.add_argument("""--device"""     ,   type=lowerCAmelCase__     ,   required=lowerCAmelCase__     ,   default=lowerCAmelCase__     ,   help="""cuda, cuda:1, cpu etc."""				)
     parser.add_argument(
         """--prefix"""     ,   type=lowerCAmelCase__     ,   required=lowerCAmelCase__     ,   default=lowerCAmelCase__     ,   help="""will be added to the begininng of src examples"""				)
     parser.add_argument("""--task"""     ,   type=lowerCAmelCase__     ,   default="""summarization"""     ,   help="""used for task_specific_params + metrics"""				)
     parser.add_argument("""--bs"""     ,   type=lowerCAmelCase__     ,   default=8     ,   required=lowerCAmelCase__     ,   help="""batch size"""				)
     parser.add_argument(
         """--n_obs"""     ,   type=lowerCAmelCase__     ,   default=-1     ,   required=lowerCAmelCase__     ,   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=lowerCAmelCase__     ,   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
     snake_case ,		snake_case			:      List[Any]        =			parser.parse_known_args()
     snake_case			:      Union[str, Any]        =			parse_numeric_n_bool_cl_kwargs(lowerCAmelCase__				)
     if parsed_args and verbose:
          print(F'''parsed the following generate kwargs: {parsed_args}'''				)
     snake_case			:      Tuple        =			[""" """ + x.rstrip() if """t5""" in args.model_name else x.rstrip() for x in open(args.input_path				).readlines()]
     if args.n_obs > 0:
          snake_case			:      int        =			examples[: args.n_obs]
     Path(args.save_path				).parent.mkdir(exist_ok=lowerCAmelCase__				)

     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"""				)

     snake_case			:      List[Any]        =			generate_summaries_or_translations(
         lowerCAmelCase__     ,   args.save_path     ,   args.model_name     ,   batch_size=args.bs     ,   device=args.device     ,   fpaa=args.fpaa     ,   task=args.task     ,   prefix=args.prefix     ,   **lowerCAmelCase__     ,   )

     if args.reference_path is None:
          return {}

     # Compute scores
     snake_case			:      Union[str, Any]        =			calculate_bleu if """translation""" in args.task else calculate_rouge
     snake_case			:      str        =			[x.rstrip() for x in open(args.save_path				).readlines()]
     snake_case			:      Union[str, Any]        =			[x.rstrip() for x in open(args.reference_path				).readlines()][: len(lowerCAmelCase__				)]
     snake_case			:      Optional[int]        =			score_fn(lowerCAmelCase__     ,   lowerCAmelCase__				)
     scores.update(lowerCAmelCase__				)

     if args.dump_args:
          scores.update(lowerCAmelCase__				)
     if args.info:
          snake_case			:      str        =			args.info

     if verbose:
          print(lowerCAmelCase__				)

     if args.score_path is not None:
          json.dump(lowerCAmelCase__     ,   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)







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


SCREAMING_SNAKE_CASE							    =							{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     __UpperCAmelCase							(       nn.Module							):


     """simple docstring"""


     def __init__(	self  ,       __A    ):
        super().__init__()
        __a					=  torchvision.models.resnetaaa(pretrained=__A    )
        __a					=  list(model.children()    )[:-2]
        __a					=  nn.Sequential(*__A    )
        __a					=  nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]    )


     def 	snake_case_  (	self  ,       __A    ):
        # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
        __a					=  self.pool(self.model(__A    )    )
        __a					=  torch.flatten(__A  ,       start_dim=2    )
        __a					=  out.transpose(1  ,       2    ).contiguous()
        return out  # BxNx2048







class     __UpperCAmelCase							(       __A							):


     """simple docstring"""


     def __init__(	self  ,       __A  ,       __A  ,       __A  ,       __A  ,       __A    ):
        __a					=  [json.loads(__A    ) for l in open(__A    )]
        __a					=  os.path.dirname(__A    )
        __a					=  tokenizer
        __a					=  labels
        __a					=  len(__A    )
        __a					=  max_seq_length

        __a					=  transforms


     def __len__(	self    ):
        return len(self.data    )


     def __getitem__(	self  ,       __A    ):
        __a					=  torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]  ,       add_special_tokens=__A    )    )
        __a	,    __a	,    __a					=  sentence[0], sentence[1:-1], sentence[-1]
        __a					=  sentence[: self.max_seq_length]

        __a					=  torch.zeros(self.n_classes    )
        __a					=  1

        __a					=  Image.open(os.path.join(self.data_dir  ,       self.data[index]["""img"""]    )    ).convert("""RGB"""    )
        __a					=  self.transforms(__A    )

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


     def 	snake_case_  (	self    ):
        __a					=  Counter()
        for row in self.data:
           label_freqs.update(row["""label"""]    )
        return label_freqs







def  a   (lowerCAmelCase__   ):
   __a					=  [len(row["""sentence"""]   ) for row in batch]
   __a	,    __a					=  len(lowerCAmelCase__   ), max(lowerCAmelCase__   )

   __a					=  torch.zeros(lowerCAmelCase__		,				lowerCAmelCase__		,				dtype=torch.long   )
   __a					=  torch.zeros(lowerCAmelCase__		,				lowerCAmelCase__		,				dtype=torch.long   )

   for i_batch, (input_row, length) in enumerate(zip(lowerCAmelCase__		,				lowerCAmelCase__   )   ):
      __a					=  input_row["""sentence"""]
      __a					=  1

   __a					=  torch.stack([row["""image"""] for row in batch]   )
   __a					=  torch.stack([row["""label"""] for row in batch]   )
   __a					=  torch.stack([row["""image_start_token"""] for row in batch]   )
   __a					=  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   ():
   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   ():
   return transforms.Compose(
       [
           transforms.Resize(256   ),
           transforms.CenterCrop(224   ),
           transforms.ToTensor(),
           transforms.Normalize(
               mean=[0.4_6_7_7_7_0_4_4, 0.4_4_5_3_1_4_2_9, 0.4_0_6_6_1_0_1_7]		,				std=[0.1_2_2_2_1_9_9_4, 0.1_2_1_4_5_8_3_5, 0.1_4_3_8_0_4_6_9]		,				),
       ]   )



