






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


snake_case     					=		logging.get_logger(__name__)

snake_case     					=		{
    """xlm-mlm-en-2048""": """https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json""",
    """xlm-mlm-ende-1024""": """https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json""",
    """xlm-mlm-enfr-1024""": """https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json""",
    """xlm-mlm-enro-1024""": """https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json""",
    """xlm-mlm-tlm-xnli15-1024""": """https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json""",
    """xlm-mlm-xnli15-1024""": """https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json""",
    """xlm-clm-enfr-1024""": """https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json""",
    """xlm-clm-ende-1024""": """https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json""",
    """xlm-mlm-17-1280""": """https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json""",
    """xlm-mlm-100-1280""": """https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json""",
}


class 			SCREAMING_SNAKE_CASE      (      _lowerCAmelCase       ):
     '''simple docstring'''


     UpperCamelCase_				:		Optional[Any]								=						'''xlm'''
     UpperCamelCase_				:		int								=						{
         '''hidden_size''': '''emb_dim''',
         '''num_attention_heads''': '''n_heads''',
         '''num_hidden_layers''': '''n_layers''',
         '''n_words''': '''vocab_size''',  # For backward compatibility
     }


     def __init__(					self							:						List[Any]       ,			UpperCAmelCase_							:						Optional[Any]=3_0145       ,			UpperCAmelCase_							:						Any=2048       ,			UpperCAmelCase_							:						int=12       ,			UpperCAmelCase_							:						int=16       ,			UpperCAmelCase_							:						List[Any]=0.1       ,			UpperCAmelCase_							:						Tuple=0.1       ,			UpperCAmelCase_							:						Union[str, Any]=True       ,			UpperCAmelCase_							:						int=False       ,			UpperCAmelCase_							:						Any=False       ,			UpperCAmelCase_							:						Dict=False       ,			UpperCAmelCase_							:						int=1       ,			UpperCAmelCase_							:						int=True       ,			UpperCAmelCase_							:						List[Any]=512       ,			UpperCAmelCase_							:						Dict=2048**-0.5       ,			UpperCAmelCase_							:						Union[str, Any]=1E-12       ,			UpperCAmelCase_							:						List[Any]=0.02       ,			UpperCAmelCase_							:						List[Any]=0       ,			UpperCAmelCase_							:						List[Any]=1       ,			UpperCAmelCase_							:						Tuple=2       ,			UpperCAmelCase_							:						str=3       ,			UpperCAmelCase_							:						str=5       ,			UpperCAmelCase_							:						Tuple=True       ,			UpperCAmelCase_							:						Dict="first"       ,			UpperCAmelCase_							:						List[Any]=True       ,			UpperCAmelCase_							:						Any=None       ,			UpperCAmelCase_							:						Optional[Any]=True       ,			UpperCAmelCase_							:						Dict=0.1       ,			UpperCAmelCase_							:						Tuple=5       ,			UpperCAmelCase_							:						Optional[Any]=5       ,			UpperCAmelCase_							:						Tuple=0       ,			UpperCAmelCase_							:						List[str]=0       ,			UpperCAmelCase_							:						Dict=2       ,			UpperCAmelCase_							:						str=0       ,			**UpperCAmelCase_							:						Union[str, Any]       ,			):
       SCREAMING_SNAKE_CASE							:   str						    =     vocab_size
       SCREAMING_SNAKE_CASE							:   int						    =     emb_dim
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     n_layers
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     n_heads
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     dropout
       SCREAMING_SNAKE_CASE							:   Any						    =     attention_dropout
       SCREAMING_SNAKE_CASE							:   str						    =     gelu_activation
       SCREAMING_SNAKE_CASE							:   int						    =     sinusoidal_embeddings
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     causal
       SCREAMING_SNAKE_CASE							:   List[str]						    =     asm
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     n_langs
       SCREAMING_SNAKE_CASE							:   Dict						    =     use_lang_emb
       SCREAMING_SNAKE_CASE							:   Dict						    =     layer_norm_eps
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     bos_index
       SCREAMING_SNAKE_CASE							:   str						    =     eos_index
       SCREAMING_SNAKE_CASE							:   int						    =     pad_index
       SCREAMING_SNAKE_CASE							:   Dict						    =     unk_index
       SCREAMING_SNAKE_CASE							:   Dict						    =     mask_index
       SCREAMING_SNAKE_CASE							:   Tuple						    =     is_encoder
       SCREAMING_SNAKE_CASE							:   Dict						    =     max_position_embeddings
       SCREAMING_SNAKE_CASE							:   List[str]						    =     embed_init_std
       SCREAMING_SNAKE_CASE							:   Dict						    =     init_std
       SCREAMING_SNAKE_CASE							:   Any						    =     summary_type
       SCREAMING_SNAKE_CASE							:   List[str]						    =     summary_use_proj
       SCREAMING_SNAKE_CASE							:   int						    =     summary_activation
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     summary_proj_to_labels
       SCREAMING_SNAKE_CASE							:   str						    =     summary_first_dropout
       SCREAMING_SNAKE_CASE							:   str						    =     start_n_top
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     end_n_top
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     mask_token_id
       SCREAMING_SNAKE_CASE							:   int						    =     lang_id

       if "n_words" in kwargs:
         SCREAMING_SNAKE_CASE							:   List[Any]						    =     kwargs["n_words"]

       super().__init__(pad_token_id=UpperCAmelCase_       ,			bos_token_id=UpperCAmelCase_       ,			**UpperCAmelCase_   )





class 			SCREAMING_SNAKE_CASE      (      _lowerCAmelCase       ):
     '''simple docstring'''


     @property
     def 					_A							(					self							:						List[str]   ):
       if self.task == "multiple-choice":
         SCREAMING_SNAKE_CASE							:   Dict						    =     {0: "batch", 1: "choice", 2: "sequence"}
       else:
         SCREAMING_SNAKE_CASE							:   int						    =     {0: "batch", 1: "sequence"}
       return OrderedDict(
           [
               ("input_ids", dynamic_axis),
               ("attention_mask", dynamic_axis),
               ("token_type_ids", dynamic_axis),
           ]   )














'''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


__UpperCamelCase   :							int         =   getLogger(__name__)


__UpperCamelCase   :							int         =   """cuda""" if torch.cuda.is_available() else """cpu"""



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						List[str],       SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						int = 8,       SCREAMING_SNAKE_CASE__:						str = DEFAULT_DEVICE,       SCREAMING_SNAKE_CASE__:						Any=False,       SCREAMING_SNAKE_CASE__:						Tuple="summarization",       SCREAMING_SNAKE_CASE__:						List[Any]=None,       **SCREAMING_SNAKE_CASE__:						int,       )	->      Dict:

   """simple docstring"""
   __a					  =     Path(SCREAMING_SNAKE_CASE__     ).open('w',       encoding='utf-8'     )
   __a					  =     str(SCREAMING_SNAKE_CASE__     )
   __a					  =     AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE__     ).to(SCREAMING_SNAKE_CASE__     )
   if fpaa:
      __a					  =     model.half()

   __a					  =     AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__     )
   logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}"""     )  # if this is wrong, check config.model_type.

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



def  __UpperCAmelCase						(					)	->      List[str]:

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



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Optional[int]=True     )	->      List[Any]:

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

   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'     )

   __a					  =     generate_summaries_or_translations(
       SCREAMING_SNAKE_CASE__,       args.save_path,       args.model_name,       batch_size=args.bs,       device=args.device,       fpaa=args.fpaa,       task=args.task,       prefix=args.prefix,       **SCREAMING_SNAKE_CASE__,       )

   if args.reference_path is None:
      return {}

   # Compute scores
   __a					  =     calculate_bleu if 'translation' in args.task else calculate_rouge
   __a					  =     [x.rstrip() for x in open(args.save_path     ).readlines()]
   __a					  =     [x.rstrip() for x in open(args.reference_path     ).readlines()][: len(SCREAMING_SNAKE_CASE__     )]
   __a					  =     score_fn(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )
   scores.update(SCREAMING_SNAKE_CASE__     )

   if args.dump_args:
      scores.update(SCREAMING_SNAKE_CASE__     )
   if args.info:
      __a					  =     args.info

   if verbose:
      print(SCREAMING_SNAKE_CASE__     )

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