

from typing import Mapping

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


__UpperCAmelCase     					=  logging.get_logger(__name__)

__UpperCAmelCase     					=  {
    'google/long-t5-local-base': 'https://huggingface.co/google/long-t5-local-base/blob/main/config.json',
    'google/long-t5-local-large': 'https://huggingface.co/google/long-t5-local-large/blob/main/config.json',
    'google/long-t5-tglobal-base': 'https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json',
    'google/long-t5-tglobal-large': 'https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json',
}





class A__     (			A			):



 """simple docstring"""







 _lowercase  :       Optional[Any]  			=    '''longt5'''
 _lowercase  :       List[str]  			=    ['''past_key_values''']
 _lowercase  :       Tuple  			=    {'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}

 def __init__(		self			:						Dict  ,    A_			:						int=3_2_1_2_8  ,    A_			:						Tuple=5_1_2  ,    A_			:						int=6_4  ,    A_			:						Optional[Any]=2_0_4_8  ,    A_			:						List[Any]=6  ,    A_			:						Tuple=None  ,    A_			:						Optional[int]=8  ,    A_			:						Any=1_2_7  ,    A_			:						Tuple=1_6  ,    A_			:						int=3_2  ,    A_			:						Dict=1_2_8  ,    A_			:						Tuple=0.1  ,    A_			:						str=1E-6  ,    A_			:						List[Any]=1.0  ,    A_			:						str="relu"  ,    A_			:						Tuple=True  ,    A_			:						Dict="local"  ,    A_			:						Tuple=True  ,    A_			:						Dict=0  ,    A_			:						Tuple=1  ,    **A_			:						List[str]  ,    ):






      '''simple docstring'''




      _lowerCAmelCase :       List[str]	   =     vocab_size
      _lowerCAmelCase :       int	   =     d_model
      _lowerCAmelCase :       Any	   =     d_kv
      _lowerCAmelCase :       int	   =     d_ff
      _lowerCAmelCase :       Optional[Any]	   =     num_layers
      # default = symmetry
      _lowerCAmelCase :       int	   =     num_decoder_layers if num_decoder_layers is not None else self.num_layers
      _lowerCAmelCase :       Union[str, Any]	   =     num_heads
      _lowerCAmelCase :       int	   =     local_radius
      _lowerCAmelCase :       Tuple	   =     global_block_size
      _lowerCAmelCase :       Union[str, Any]	   =     relative_attention_num_buckets
      _lowerCAmelCase :       int	   =     relative_attention_max_distance
      _lowerCAmelCase :       List[Any]	   =     dropout_rate
      _lowerCAmelCase :       List[Any]	   =     layer_norm_epsilon
      _lowerCAmelCase :       List[Any]	   =     initializer_factor
      _lowerCAmelCase :       Optional[Any]	   =     feed_forward_proj
      _lowerCAmelCase :       List[Any]	   =     encoder_attention_type
      _lowerCAmelCase :       Dict	   =     use_cache

      _lowerCAmelCase :       str	   =     self.feed_forward_proj.split("-"							)
      _lowerCAmelCase :       Dict	   =     act_info[-1]
      _lowerCAmelCase :       List[str]	   =     act_info[0] == "gated"

      if len(A_							) > 1 and act_info[0] != "gated" or len(A_							) > 2:
           raise ValueError(
               F'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
               "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
               "'gated-gelu' or 'relu'"							)

      # for backwards compatibility
      if feed_forward_proj == "gated-gelu":
           _lowerCAmelCase :       Dict	   =     "gelu_new"

      super().__init__(
          pad_token_id=A_  ,    eos_token_id=A_  ,    is_encoder_decoder=A_  ,    **A_  ,    )





class A__     (			A			):



 """simple docstring"""







 @property
 def  __magic_name__							(		self			:						Optional[int]							):






      '''simple docstring'''




      _lowerCAmelCase :       List[Any]	   =     {
          "input_ids": {0: "batch", 1: "encoder_sequence"},
          "attention_mask": {0: "batch", 1: "encoder_sequence"},
      }
      if self.use_past:
           _lowerCAmelCase :       Any	   =     "past_encoder_sequence + sequence"
           _lowerCAmelCase :       List[Any]	   =     {0: "batch"}
           _lowerCAmelCase :       Union[str, Any]	   =     {0: "batch", 1: "past_decoder_sequence + sequence"}
      else:
           _lowerCAmelCase :       Dict	   =     {0: "batch", 1: "decoder_sequence"}
           _lowerCAmelCase :       Any	   =     {0: "batch", 1: "decoder_sequence"}

      if self.use_past:
           self.fill_with_past_key_values_(A_  ,    direction="inputs"							)

      return common_inputs


 @property
 def  __magic_name__							(		self			:						str							):






      '''simple docstring'''




      return 1_3









import argparse
import itertools
import operator
import sys
from collections import OrderedDict

from run_eval import datetime_now, run_generate

from utils import ROUGE_KEYS


# A table of supported tasks and the list of scores in the order of importance to be sorted by.
# To add a new task, simply list the score names that `run_eval.run_generate()` returns
__UpperCAmelCase     					=  {
    'translation': ['bleu'],
    'summarization': ROUGE_KEYS,
}







def 						_snake_case		(     SCREAMING_SNAKE_CASE					)							->   Optional[Any]:

     """simple docstring"""







     _lowerCAmelCase :       Dict	   =     search.split()
     _lowerCAmelCase :       int	   =     dict((g.split("="					) for g in groups)					)
     _lowerCAmelCase :       Dict	   =     list(entries.keys()					)
     _lowerCAmelCase :       Tuple	   =     [[f'''--{k} {v}''' for v in vs.split(":"					)] for k, vs in entries.items()]
     _lowerCAmelCase :       Dict	   =     [list(SCREAMING_SNAKE_CASE					) for x in itertools.product(*SCREAMING_SNAKE_CASE					)]
     return matrix, entry_names







def 						_snake_case		(     )							->   Tuple:

     """simple docstring"""







     _lowerCAmelCase :       List[str]	   =     sys.argv[0]

     _lowerCAmelCase :       Any	   =     argparse.ArgumentParser(
         usage=(
             "\n\nImportant: this script accepts all arguments `run_eval.py` accepts and then a few extra, therefore"
             " refer to `run_eval.py -h` for the complete list."
         )					)
     parser.add_argument(
         "--search"		,				type=SCREAMING_SNAKE_CASE		,				required=SCREAMING_SNAKE_CASE		,				help="param space to search, e.g. \"num_beams=5:10 length_penalty=0.8:1.0:1.2\""		,				)
     parser.add_argument(
         "--bs"		,				type=SCREAMING_SNAKE_CASE		,				default=8		,				required=SCREAMING_SNAKE_CASE		,				help="initial batch size (may get reduced if it's too big)"					)
     parser.add_argument("--task"		,				type=SCREAMING_SNAKE_CASE		,				help="used for task_specific_params + metrics"					)
     parser.add_argument(
         "--info"		,				nargs="?"		,				type=SCREAMING_SNAKE_CASE		,				const=datetime_now()		,				help=(
             "add custom notes to be printed before the results table. If no value is passed, the current datetime"
             " string will be used."
         )		,				)
     _lowerCAmelCase   ,      _lowerCAmelCase :       List[str]	   =     parser.parse_known_args()
     # we share some of the args
     args_main.extend(["--task", args.task]					)
     _lowerCAmelCase :       str	   =     [prog] + args_main

     # to support variations like translation_en_to_de"
     _lowerCAmelCase :       Any	   =     "translation" if "translation" in args.task else "summarization"

     _lowerCAmelCase   ,      _lowerCAmelCase :       int	   =     parse_search_arg(args.search					)
     _lowerCAmelCase :       Optional[Any]	   =     task_score_names[task]  # score cols first
     _lowerCAmelCase :       int	   =     {col: len(str(SCREAMING_SNAKE_CASE					)					) for col in col_names}
     _lowerCAmelCase :       Optional[int]	   =     []
     for r in matrix:
          _lowerCAmelCase :       List[str]	   =     dict((x.replace("--"		,				""					).split() for x in r)					)
          _lowerCAmelCase :       Any	   =     " ".join(SCREAMING_SNAKE_CASE					).split()
          args_exp.extend(["--bs", str(args.bs					)]					)  # in case we need to reduce its size due to CUDA OOM
          _lowerCAmelCase :       List[str]	   =     args_normal + args_exp

          # XXX: need to trap CUDA OOM and lower args.bs if that happens and retry

          _lowerCAmelCase :       Any	   =     run_generate(verbose=SCREAMING_SNAKE_CASE					)
          # make sure scores are first in the table
          _lowerCAmelCase :       Any	   =     OrderedDict()
          for score in task_score_names[task]:
               _lowerCAmelCase :       Dict	   =     scores[score]
          result.update(SCREAMING_SNAKE_CASE					)
          results.append(SCREAMING_SNAKE_CASE					)

          # find widest entries
          for k, v in result.items():
               _lowerCAmelCase :       Any	   =     len(str(SCREAMING_SNAKE_CASE					)					)
               if l > col_widths[k]:
                    _lowerCAmelCase :       str	   =     l

     _lowerCAmelCase :       Optional[Any]	   =     sorted(SCREAMING_SNAKE_CASE		,				key=operator.itemgetter(*task_score_names[task]					)		,				reverse=SCREAMING_SNAKE_CASE					)
     print(" | ".join([f'''{col:{col_widths[col]}}''' for col in col_names]					)					)
     print(" | ".join([f'''{'-'*col_widths[col]}''' for col in col_names]					)					)
     for row in results_sorted:
          print(" | ".join([f'''{row[col]:{col_widths[col]}}''' for col in col_names]					)					)

     _lowerCAmelCase :       Optional[int]	   =     results_sorted[0]
     for score in task_score_names[task]:
          del best[score]
     _lowerCAmelCase :       Tuple	   =     [f'''--{k} {v}''' for k, v in best.items()]
     _lowerCAmelCase :       Union[str, Any]	   =     ["--bs", str(args.bs					)]
     if args.info:
          print(f'''\nInfo: {args.info}'''					)
     print("\nBest score args:"					)
     print(" ".join(args_main + best_args + dyn_args					)					)

     return results_sorted


if __name__ == "__main__":
  # Usage:
  # [normal-run_eval_search.py cmd plus] \
  # --search="num_beams=1:5:10 length_penalty=0.8:1:1.2 early_stopping=true:false"
  #
  # Example:
  # PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval_search.py $MODEL_NAME \
  # $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target \
  # --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation \
  # --search="num_beams=1:5:10 length_penalty=0.8:1:1.2 early_stopping=true:false"
  run_search()






