
"""simple docstring"""




import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A__      :	Dict          =    logging.get_logger(__name__)

A__      :	str          =    {
    """microsoft/git-base""": """https://huggingface.co/microsoft/git-base/resolve/main/config.json""",
}







class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							'git_vision_model'



     def __init__(      self ,			__UpperCamelCase=7_68 ,			__UpperCamelCase=30_72 ,			__UpperCamelCase=12 ,			__UpperCamelCase=12 ,			__UpperCamelCase=3 ,			__UpperCamelCase=2_24 ,			__UpperCamelCase=16 ,			__UpperCamelCase="quick_gelu" ,			__UpperCamelCase=1E-5 ,			__UpperCamelCase=0.0 ,			__UpperCamelCase=0.02 ,			**__UpperCamelCase ,			)->					Tuple:
          super().__init__(**__UpperCamelCase  )

          UpperCAmelCase__							: int 							=						hidden_size
          UpperCAmelCase__							: Tuple 							=						intermediate_size
          UpperCAmelCase__							: str 							=						num_hidden_layers
          UpperCAmelCase__							: List[str] 							=						num_attention_heads
          UpperCAmelCase__							: Dict 							=						num_channels
          UpperCAmelCase__							: Optional[int] 							=						patch_size
          UpperCAmelCase__							: Any 							=						image_size
          UpperCAmelCase__							: Tuple 							=						initializer_range
          UpperCAmelCase__							: int 							=						attention_dropout
          UpperCAmelCase__							: Dict 							=						layer_norm_eps
          UpperCAmelCase__							: str 							=						hidden_act



     @classmethod
     def  lowerCAmelCase__       (      cls ,			__UpperCamelCase ,			**__UpperCamelCase  )->					"PretrainedConfig":
          cls._set_token_in_kwargs(__UpperCamelCase  )

          UpperCAmelCase__							,	UpperCAmelCase__							: Tuple 							=						cls.get_config_dict(__UpperCamelCase ,			**__UpperCamelCase  )

          # get the vision config dict if we are loading from GITConfig
          if config_dict.get("model_type"  ) == "git":
               UpperCAmelCase__							: List[str] 							=						config_dict["vision_config"]

          if "model_type" in config_dict and hasattr(cls ,			"model_type"  ) and config_dict["model_type"] != cls.model_type:
               logger.warning(
                   F"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
                   F"{cls.model_type}. This is not supported for all configurations of models and can yield errors."  )

          return cls.from_dict(__UpperCamelCase ,			**__UpperCamelCase  )







class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							'git'



     def __init__(      self ,			__UpperCamelCase=None ,			__UpperCamelCase=3_05_22 ,			__UpperCamelCase=7_68 ,			__UpperCamelCase=6 ,			__UpperCamelCase=12 ,			__UpperCamelCase=30_72 ,			__UpperCamelCase="gelu" ,			__UpperCamelCase=0.1 ,			__UpperCamelCase=0.1 ,			__UpperCamelCase=10_24 ,			__UpperCamelCase=0.02 ,			__UpperCamelCase=1E-12 ,			__UpperCamelCase=0 ,			__UpperCamelCase="absolute" ,			__UpperCamelCase=True ,			__UpperCamelCase=False ,			__UpperCamelCase=1_01 ,			__UpperCamelCase=1_02 ,			__UpperCamelCase=None ,			**__UpperCamelCase ,			)->					List[str]:
          super().__init__(bos_token_id=__UpperCamelCase ,			eos_token_id=__UpperCamelCase ,			pad_token_id=__UpperCamelCase ,			**__UpperCamelCase  )

          if vision_config is None:
               UpperCAmelCase__							: List[str] 							=						{}
               logger.info("vision_config is None. initializing the GitVisionConfig with default values."  )

          UpperCAmelCase__							: Dict 							=						GitVisionConfig(**__UpperCamelCase  )
          UpperCAmelCase__							: Tuple 							=						vocab_size
          UpperCAmelCase__							: List[Any] 							=						hidden_size
          UpperCAmelCase__							: Any 							=						num_hidden_layers
          UpperCAmelCase__							: Optional[Any] 							=						num_attention_heads
          UpperCAmelCase__							: List[Any] 							=						hidden_act
          UpperCAmelCase__							: List[str] 							=						intermediate_size
          UpperCAmelCase__							: List[Any] 							=						hidden_dropout_prob
          UpperCAmelCase__							: Union[str, Any] 							=						attention_probs_dropout_prob
          UpperCAmelCase__							: List[str] 							=						max_position_embeddings
          UpperCAmelCase__							: List[Any] 							=						initializer_range
          UpperCAmelCase__							: Any 							=						layer_norm_eps
          UpperCAmelCase__							: Union[str, Any] 							=						position_embedding_type
          UpperCAmelCase__							: List[Any] 							=						use_cache
          UpperCAmelCase__							: str 							=						tie_word_embeddings
          UpperCAmelCase__							: str 							=						num_image_with_embedding

          UpperCAmelCase__							: str 							=						bos_token_id
          UpperCAmelCase__							: Any 							=						eos_token_id



     def  lowerCAmelCase__       (      self  )->					Optional[int]:
          UpperCAmelCase__							: int 							=						copy.deepcopy(self.__dict__  )
          UpperCAmelCase__							: int 							=						self.vision_config.to_dict()
          UpperCAmelCase__							: Dict 							=						self.__class__.model_type
          return output





"""simple docstring"""




import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
    SeqaSeqDataset,
    calculate_bleu,
    calculate_rouge,
    chunks,
    lmap,
    load_json,
    parse_numeric_n_bool_cl_kwargs,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


A__      :	int          =    getLogger(__name__)



def 							a__ (				lowerCAmelCase	:       List[Any]       ,    lowerCAmelCase	:       str       ,    lowerCAmelCase	:       str       ,    lowerCAmelCase	:       int = 8       ,    lowerCAmelCase	:       int = 1024       ,    lowerCAmelCase	:       List[Any]="val"       ,    lowerCAmelCase	:       str=None       ,    lowerCAmelCase	:       int=False       ,    lowerCAmelCase	:       Dict="summarization"       ,    lowerCAmelCase	:       int=None       ,    lowerCAmelCase	:       List[str]=1       ,    lowerCAmelCase	:       Dict = None       ,    lowerCAmelCase	:       List[str]=""       ,    **lowerCAmelCase	:       int       ,    ):






     '''simple docstring'''
     UpperCAmelCase__							: Dict 							=						str(lowerCAmelCase		)
     assert local_rank is not None
     torch.distributed.init_process_group(backend="nccl"       ,    rank=lowerCAmelCase		)

     UpperCAmelCase__							: List[str] 							=						Path(lowerCAmelCase		)
     UpperCAmelCase__							: str 							=						save_dir.joinpath(F"rank_{local_rank}_output.json"		)
     torch.cuda.set_device(lowerCAmelCase		)
     UpperCAmelCase__							: Optional[Any] 							=						AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase		).cuda()
     if fpaa:
          UpperCAmelCase__							: List[Any] 							=						model.half()
     # determine if we need to increase num_beams
     use_task_specific_params(lowerCAmelCase       ,    lowerCAmelCase		)  # update config with task specific params
     UpperCAmelCase__							: List[Any] 							=						generate_kwargs.pop("num_beams"       ,    model.config.num_beams		)  # AttributeError risk?
     if num_return_sequences > num_beams:
          UpperCAmelCase__							: Any 							=						num_return_sequences

     UpperCAmelCase__							: List[Any] 							=						AutoTokenizer.from_pretrained(lowerCAmelCase		)
     logger.info(F"Inferred tokenizer type: {tokenizer.__class__}"		)  # if this is wrong, check config.model_type.

     if max_source_length is None:
          UpperCAmelCase__							: int 							=						tokenizer.model_max_length
     if prefix is None:
          UpperCAmelCase__							: Union[str, Any] 							=						prefix or getattr(model.config       ,    "prefix"       ,    ""		) or ""
     UpperCAmelCase__							: str 							=						SeqaSeqDataset(
         lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase       ,    max_target_length=1024       ,    type_path=lowerCAmelCase       ,    n_obs=lowerCAmelCase       ,    prefix=lowerCAmelCase       ,    **lowerCAmelCase       ,    )
     # I set shuffle=True for a more accurate progress bar.
     # If all the longest samples are first, the prog bar estimate is too high at the beginning.
     UpperCAmelCase__							: Union[str, Any] 							=						ds.make_sortish_sampler(lowerCAmelCase       ,    distributed=lowerCAmelCase       ,    add_extra_examples=lowerCAmelCase       ,    shuffle=lowerCAmelCase		)
     UpperCAmelCase__							: Optional[int] 							=						DataLoader(lowerCAmelCase       ,    sampler=lowerCAmelCase       ,    batch_size=lowerCAmelCase       ,    collate_fn=ds.collate_fn		)
     UpperCAmelCase__							: str 							=						[]
     for batch in tqdm(lowerCAmelCase		):
          UpperCAmelCase__							: Dict 							=						model.generate(
              input_ids=batch["input_ids"].to(model.device		)       ,    attention_mask=batch["attention_mask"].to(model.device		)       ,    num_return_sequences=lowerCAmelCase       ,    num_beams=lowerCAmelCase       ,    **lowerCAmelCase       ,    )
          UpperCAmelCase__							: int 							=						tokenizer.batch_decode(lowerCAmelCase       ,    skip_special_tokens=lowerCAmelCase       ,    clean_up_tokenization_spaces=lowerCAmelCase		)
          UpperCAmelCase__							: int 							=						batch["ids"]
          if num_return_sequences > 1:
               UpperCAmelCase__							: str 							=						chunks(lowerCAmelCase       ,    lowerCAmelCase		)  # batch size chunks, each of size num_return_seq
          for i, pred in enumerate(lowerCAmelCase		):
               results.append({"pred": pred, "id": ids[i].item()}		)
     save_json(lowerCAmelCase       ,    lowerCAmelCase		)
     return results, sampler.num_replicas



def 							a__ (				):






     '''simple docstring'''
     UpperCAmelCase__							: str 							=						argparse.ArgumentParser(
         epilog="Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate"		)
     parser.add_argument("--data_dir"       ,    type=lowerCAmelCase       ,    help="like cnn_dm/test.source"		)
     parser.add_argument(
         "--model_name"       ,    type=lowerCAmelCase       ,    help="like facebook/bart-large-cnn,t5-base, etc."       ,    default="sshleifer/distilbart-xsum-12-3"       ,    )
     parser.add_argument("--save_dir"       ,    type=lowerCAmelCase       ,    help="where to save"       ,    default="tmp_gen"		)
     parser.add_argument("--max_source_length"       ,    type=lowerCAmelCase       ,    default=lowerCAmelCase		)
     parser.add_argument(
         "--type_path"       ,    type=lowerCAmelCase       ,    default="test"       ,    help="which subset to evaluate typically train/val/test"		)
     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(
         "--local_rank"       ,    type=lowerCAmelCase       ,    default=-1       ,    required=lowerCAmelCase       ,    help="should be passed by distributed.launch"		)

     parser.add_argument(
         "--n_obs"       ,    type=lowerCAmelCase       ,    default=lowerCAmelCase       ,    required=lowerCAmelCase       ,    help="How many observations. Defaults to all."		)
     parser.add_argument(
         "--num_return_sequences"       ,    type=lowerCAmelCase       ,    default=1       ,    required=lowerCAmelCase       ,    help="How many sequences to return"		)
     parser.add_argument(
         "--sync_timeout"       ,    type=lowerCAmelCase       ,    default=600       ,    required=lowerCAmelCase       ,    help="How long should master process wait for other processes to finish."       ,    )
     parser.add_argument("--src_lang"       ,    type=lowerCAmelCase       ,    default=lowerCAmelCase       ,    required=lowerCAmelCase		)
     parser.add_argument("--tgt_lang"       ,    type=lowerCAmelCase       ,    default=lowerCAmelCase       ,    required=lowerCAmelCase		)
     parser.add_argument(
         "--prefix"       ,    type=lowerCAmelCase       ,    required=lowerCAmelCase       ,    default=lowerCAmelCase       ,    help="will be added to the begininng of src examples"		)
     parser.add_argument("--fp16"       ,    action="store_true"		)
     parser.add_argument("--debug"       ,    action="store_true"		)
     UpperCAmelCase__							: Optional[int] 							=						time.time()
     UpperCAmelCase__							,	UpperCAmelCase__							: List[Any] 							=						parser.parse_known_args()
     UpperCAmelCase__							: int 							=						parse_numeric_n_bool_cl_kwargs(lowerCAmelCase		)
     if generate_kwargs and args.local_rank <= 0:
          print(F"parsed the following generate kwargs: {generate_kwargs}"		)
     UpperCAmelCase__							: Dict 							=						Path(args.save_dir + "_tmp"		)
     Path(lowerCAmelCase		).mkdir(exist_ok=lowerCAmelCase		)  # this handles locking.
     UpperCAmelCase__							: List[str] 							=						list(json_save_dir.glob("rank_*.json"		)		)
     if intermediate_files:
          raise ValueError(F"Found files at {json_save_dir} please move or remove them."		)
          # In theory, a node could finish and save before another node hits this. If this happens, we can address later.
     UpperCAmelCase__							: List[str] 							=						{}
     if args.src_lang is not None:
          UpperCAmelCase__							: str 							=						args.src_lang
     if args.tgt_lang is not None:
          UpperCAmelCase__							: List[str] 							=						args.tgt_lang

     Path(args.save_dir		).mkdir(exist_ok=lowerCAmelCase		)
     UpperCAmelCase__							,	UpperCAmelCase__							: Optional[int] 							=						eval_data_dir(
         args.data_dir       ,    lowerCAmelCase       ,    args.model_name       ,    type_path=args.type_path       ,    bs=args.bs       ,    fpaa=args.fpaa       ,    task=args.task       ,    local_rank=args.local_rank       ,    n_obs=args.n_obs       ,    max_source_length=args.max_source_length       ,    num_return_sequences=args.num_return_sequences       ,    prefix=args.prefix       ,    dataset_kwargs=lowerCAmelCase       ,    **lowerCAmelCase       ,    )

     if args.local_rank <= 0:
          UpperCAmelCase__							: str 							=						Path(args.save_dir		)
          save_dir.mkdir(exist_ok=lowerCAmelCase		)
          UpperCAmelCase__							: Tuple 							=						gather_results_from_each_node(lowerCAmelCase       ,    lowerCAmelCase       ,    args.sync_timeout		)
          UpperCAmelCase__							: Union[str, Any] 							=						combine_partial_results(lowerCAmelCase		)
          if args.num_return_sequences > 1:
               UpperCAmelCase__							: int 							=						save_dir.joinpath("pseudolabel_results.json"		)
               print(F"Saving aggregated results at {save_path}, intermediate in {json_save_dir}/"		)
               save_json(lowerCAmelCase       ,    lowerCAmelCase		)
               return
          UpperCAmelCase__							: Optional[Any] 							=						Path(args.data_dir		).joinpath(args.type_path + ".target"		)
          with open(lowerCAmelCase		) as f:
               UpperCAmelCase__							: Optional[int] 							=						[x.rstrip() for x in f.readlines()][: len(lowerCAmelCase		)]

          # Calculate metrics, save metrics,  and save _generations.txt
          UpperCAmelCase__							: List[Any] 							=						"translation" in args.task
          UpperCAmelCase__							: Optional[Any] 							=						calculate_bleu if calc_bleu else calculate_rouge
          UpperCAmelCase__							: Optional[Any] 							=						"bleu" if calc_bleu else "rouge"
          UpperCAmelCase__							: Dict 							=						score_fn(lowerCAmelCase       ,    lowerCAmelCase		)
          UpperCAmelCase__							: List[Any] 							=						len(lowerCAmelCase		)
          UpperCAmelCase__							: Union[str, Any] 							=						time.time() - start_time
          UpperCAmelCase__							: Optional[int] 							=						round(runtime / metrics["n_obs"]       ,    4		)
          UpperCAmelCase__							: Tuple 							=						num_replicas
          # TODO(@stas00): add whatever metadata to metrics
          UpperCAmelCase__							: Any 							=						save_dir.joinpath(F"{args.type_path}_{metric_name}.json"		)
          save_json(lowerCAmelCase       ,    lowerCAmelCase       ,    indent=lowerCAmelCase		)
          print(lowerCAmelCase		)
          write_txt_file(lowerCAmelCase       ,    save_dir.joinpath(F"{args.type_path}_generations.txt"		)		)
          if args.debug:
               write_txt_file(lowerCAmelCase       ,    save_dir.joinpath(F"{args.type_path}.target"		)		)
          else:
               shutil.rmtree(lowerCAmelCase		)



def 							a__ (				lowerCAmelCase	:       Tuple		):






     '''simple docstring'''
     UpperCAmelCase__							: str 							=						[]
     for partial_result in partial_results:
          records.extend(lowerCAmelCase		)
     UpperCAmelCase__							: Dict 							=						sorted(lowerCAmelCase       ,    key=lambda lowerCAmelCase		: x["id"]		)
     UpperCAmelCase__							: List[str] 							=						[x["pred"] for x in records]
     return preds



def 							a__ (				lowerCAmelCase	:       List[Any]       ,    lowerCAmelCase	:       int       ,    lowerCAmelCase	:       Optional[int]		):






     '''simple docstring'''
     # WAIT FOR lots of .json files
     UpperCAmelCase__							: int 							=						time.time()
     logger.info("waiting for all nodes to finish"		)
     UpperCAmelCase__							: Dict 							=						None
     while (time.time() - start_wait) < timeout:
          UpperCAmelCase__							: str 							=						list(save_dir.glob("rank_*.json"		)		)
          if len(lowerCAmelCase		) < num_replicas:
               continue
          try:
               # make sure all json files are fully saved
               UpperCAmelCase__							: Union[str, Any] 							=						lmap(lowerCAmelCase       ,    lowerCAmelCase		)
               return json_data
          except JSONDecodeError:
               continue
     else:
          raise TimeoutError("Rank 0 gave up on waiting for other processes"		)
     # Unreachable


if __name__ == "__main__":
    # Usage for MT:
    run_generate()



