
"""simple docstring"""
import functools
import operator

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


lowercase_							:    Union[str, Any]         =						logging.get_logger(__name__)

lowercase_							:    Any         =						{
    '''asapp/sew-d-tiny-100k''': '''https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json''',
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}
class       UpperCamelCase   (				_a	):
     A__			  =      """sew-d"""
     def __init__(  self	,  snake_case__=32	,  snake_case__=768	,  snake_case__=12	,  snake_case__=12	,  snake_case__=3072	,  snake_case__=2	,  snake_case__=512	,  snake_case__=256	,  snake_case__=True	,  snake_case__=True	,  snake_case__=("p2c", "c2p")	,  snake_case__="layer_norm"	,  snake_case__="gelu_python"	,  snake_case__=0.1	,  snake_case__=0.1	,  snake_case__=0.1	,  snake_case__=0.0	,  snake_case__=0.1	,  snake_case__=0.02	,  snake_case__=1E-7	,  snake_case__=1E-5	,  snake_case__="group"	,  snake_case__="gelu"	,  snake_case__=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)	,  snake_case__=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)	,  snake_case__=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)	,  snake_case__=False	,  snake_case__=128	,  snake_case__=16	,  snake_case__=True	,  snake_case__=0.05	,  snake_case__=10	,  snake_case__=2	,  snake_case__=0.0	,  snake_case__=10	,  snake_case__=0	,  snake_case__="mean"	,  snake_case__=False	,  snake_case__=False	,  snake_case__=256	,  snake_case__=0	,  snake_case__=1	,  snake_case__=2	,  **snake_case__	,  ):




       """simple docstring"""


       super().__init__(**__lowerCAmelCase	,  pad_token_id=__lowerCAmelCase	,  bos_token_id=__lowerCAmelCase	,  eos_token_id=__lowerCAmelCase					)
       _SCREAMING_SNAKE_CASE :					Any				     =			hidden_size
       _SCREAMING_SNAKE_CASE :					Optional[Any]				     =			feat_extract_norm
       _SCREAMING_SNAKE_CASE :					str				     =			feat_extract_activation
       _SCREAMING_SNAKE_CASE :					Optional[int]				     =			list(__lowerCAmelCase					)
       _SCREAMING_SNAKE_CASE :					Any				     =			list(__lowerCAmelCase					)
       _SCREAMING_SNAKE_CASE :					Optional[int]				     =			list(__lowerCAmelCase					)
       _SCREAMING_SNAKE_CASE :					List[Any]				     =			conv_bias
       _SCREAMING_SNAKE_CASE :					Optional[Any]				     =			num_conv_pos_embeddings
       _SCREAMING_SNAKE_CASE :					Any				     =			num_conv_pos_embedding_groups
       _SCREAMING_SNAKE_CASE :					Any				     =			len(self.conv_dim					)
       _SCREAMING_SNAKE_CASE :					Dict				     =			num_hidden_layers
       _SCREAMING_SNAKE_CASE :					Tuple				     =			intermediate_size
       _SCREAMING_SNAKE_CASE :					Any				     =			squeeze_factor
       _SCREAMING_SNAKE_CASE :					int				     =			max_position_embeddings
       _SCREAMING_SNAKE_CASE :					Optional[int]				     =			position_buckets
       _SCREAMING_SNAKE_CASE :					Optional[Any]				     =			share_att_key
       _SCREAMING_SNAKE_CASE :					Dict				     =			relative_attention
       _SCREAMING_SNAKE_CASE :					Union[str, Any]				     =			norm_rel_ebd
       _SCREAMING_SNAKE_CASE :					Any				     =			list(__lowerCAmelCase					)
       _SCREAMING_SNAKE_CASE :					str				     =			hidden_act
       _SCREAMING_SNAKE_CASE :					Optional[int]				     =			num_attention_heads
       _SCREAMING_SNAKE_CASE :					Optional[Any]				     =			hidden_dropout
       _SCREAMING_SNAKE_CASE :					Any				     =			attention_dropout
       _SCREAMING_SNAKE_CASE :					Union[str, Any]				     =			activation_dropout
       _SCREAMING_SNAKE_CASE :					Union[str, Any]				     =			feat_proj_dropout
       _SCREAMING_SNAKE_CASE :					List[str]				     =			final_dropout
       _SCREAMING_SNAKE_CASE :					List[str]				     =			layer_norm_eps
       _SCREAMING_SNAKE_CASE :					Any				     =			feature_layer_norm_eps
       _SCREAMING_SNAKE_CASE :					Tuple				     =			initializer_range
       _SCREAMING_SNAKE_CASE :					Optional[Any]				     =			vocab_size

       if (
           (len(self.conv_stride					) != self.num_feat_extract_layers)
           or (len(self.conv_kernel					) != self.num_feat_extract_layers)
           or (len(self.conv_dim					) != self.num_feat_extract_layers)
       ):
         raise ValueError(
             "Configuration for convolutional layers is incorrect."
             "It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"
             F'''but is `len(config.conv_dim) = {len(self.conv_dim					)}`, `len(config.conv_stride)'''
             F'''= {len(self.conv_stride					)}`, `len(config.conv_kernel) = {len(self.conv_kernel					)}`.'''					)

       # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
       _SCREAMING_SNAKE_CASE :					Union[str, Any]				     =			apply_spec_augment
       _SCREAMING_SNAKE_CASE :					Optional[Any]				     =			mask_time_prob
       _SCREAMING_SNAKE_CASE :					Optional[Any]				     =			mask_time_length
       _SCREAMING_SNAKE_CASE :					int				     =			mask_time_min_masks
       _SCREAMING_SNAKE_CASE :					Any				     =			mask_feature_prob
       _SCREAMING_SNAKE_CASE :					Any				     =			mask_feature_length
       _SCREAMING_SNAKE_CASE :					List[Any]				     =			mask_feature_min_masks

       # ctc loss
       _SCREAMING_SNAKE_CASE :					int				     =			ctc_loss_reduction
       _SCREAMING_SNAKE_CASE :					str				     =			ctc_zero_infinity

       # sequence classification
       _SCREAMING_SNAKE_CASE :					Dict				     =			use_weighted_layer_sum
       _SCREAMING_SNAKE_CASE :					List[str]				     =			classifier_proj_size




     @property
     def 					__SCREAMING_SNAKE_CASE				(  self					):




       """simple docstring"""


       return functools.reduce(operator.mul	,  self.conv_stride	,  1					)










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,
)


UpperCamelCase__           =			getLogger(__name__)





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






							"""simple docstring"""





							UpperCamelCase__					  =      str(a__							)
							assert local_rank is not None
							torch.distributed.init_process_group(backend="""nccl""" ,      rank=a__							)

							UpperCamelCase__					  =      Path(a__							)
							UpperCamelCase__					  =      save_dir.joinpath(f"""rank_{local_rank}_output.json"""							)
							torch.cuda.set_device(a__							)
							UpperCamelCase__					  =      AutoModelForSeqaSeqLM.from_pretrained(a__							).cuda()
							if fpaa:
														UpperCamelCase__					  =      model.half()
							# determine if we need to increase num_beams
							use_task_specific_params(a__ ,      a__							)  # update config with task specific params
							UpperCamelCase__					  =      generate_kwargs.pop("""num_beams""" ,      model.config.num_beams							)  # AttributeError risk?
							if num_return_sequences > num_beams:
														UpperCamelCase__					  =      num_return_sequences

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

							if max_source_length is None:
														UpperCamelCase__					  =      tokenizer.model_max_length
							if prefix is None:
														UpperCamelCase__					  =      prefix or getattr(model.config ,      """prefix""" ,      """"""							) or """"""
							UpperCamelCase__					  =      SeqaSeqDataset(
							    a__ ,      a__ ,      a__ ,      max_target_length=1024 ,      type_path=a__ ,      n_obs=a__ ,      prefix=a__ ,      **a__ ,      )
							# 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__					  =      ds.make_sortish_sampler(a__ ,      distributed=a__ ,      add_extra_examples=a__ ,      shuffle=a__							)
							UpperCamelCase__					  =      DataLoader(a__ ,      sampler=a__ ,      batch_size=a__ ,      collate_fn=ds.collate_fn							)
							UpperCamelCase__					  =      []
							for batch in tqdm(a__							):
														UpperCamelCase__					  =      model.generate(
														    input_ids=batch["""input_ids"""].to(model.device							) ,      attention_mask=batch["""attention_mask"""].to(model.device							) ,      num_return_sequences=a__ ,      num_beams=a__ ,      **a__ ,      )
														UpperCamelCase__					  =      tokenizer.batch_decode(a__ ,      skip_special_tokens=a__ ,      clean_up_tokenization_spaces=a__							)
														UpperCamelCase__					  =      batch["""ids"""]
														if num_return_sequences > 1:
																					UpperCamelCase__					  =      chunks(a__ ,      a__							)  # batch size chunks, each of size num_return_seq
														for i, pred in enumerate(a__							):
																					results.append({"""pred""": pred, """id""": ids[i].item()}							)
							save_json(a__ ,      a__							)
							return results, sampler.num_replicas





def      _UpperCamelCase  ():






							"""simple docstring"""





							UpperCamelCase__					  =      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=a__ ,      help="""like cnn_dm/test.source"""							)
							parser.add_argument(
							    """--model_name""" ,      type=a__ ,      help="""like facebook/bart-large-cnn,t5-base, etc.""" ,      default="""sshleifer/distilbart-xsum-12-3""" ,      )
							parser.add_argument("""--save_dir""" ,      type=a__ ,      help="""where to save""" ,      default="""tmp_gen"""							)
							parser.add_argument("""--max_source_length""" ,      type=a__ ,      default=a__							)
							parser.add_argument(
							    """--type_path""" ,      type=a__ ,      default="""test""" ,      help="""which subset to evaluate typically train/val/test"""							)
							parser.add_argument("""--task""" ,      type=a__ ,      default="""summarization""" ,      help="""used for task_specific_params + metrics"""							)
							parser.add_argument("""--bs""" ,      type=a__ ,      default=8 ,      required=a__ ,      help="""batch size"""							)
							parser.add_argument(
							    """--local_rank""" ,      type=a__ ,      default=-1 ,      required=a__ ,      help="""should be passed by distributed.launch"""							)

							parser.add_argument(
							    """--n_obs""" ,      type=a__ ,      default=a__ ,      required=a__ ,      help="""How many observations. Defaults to all."""							)
							parser.add_argument(
							    """--num_return_sequences""" ,      type=a__ ,      default=1 ,      required=a__ ,      help="""How many sequences to return"""							)
							parser.add_argument(
							    """--sync_timeout""" ,      type=a__ ,      default=600 ,      required=a__ ,      help="""How long should master process wait for other processes to finish.""" ,      )
							parser.add_argument("""--src_lang""" ,      type=a__ ,      default=a__ ,      required=a__							)
							parser.add_argument("""--tgt_lang""" ,      type=a__ ,      default=a__ ,      required=a__							)
							parser.add_argument(
							    """--prefix""" ,      type=a__ ,      required=a__ ,      default=a__ ,      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__					  =      time.time()
							UpperCamelCase__  ,  UpperCamelCase__					  =      parser.parse_known_args()
							UpperCamelCase__					  =      parse_numeric_n_bool_cl_kwargs(a__							)
							if generate_kwargs and args.local_rank <= 0:
														print(f"""parsed the following generate kwargs: {generate_kwargs}"""							)
							UpperCamelCase__					  =      Path(args.save_dir + """_tmp"""							)
							Path(a__							).mkdir(exist_ok=a__							)  # this handles locking.
							UpperCamelCase__					  =      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__					  =      {}
							if args.src_lang is not None:
														UpperCamelCase__					  =      args.src_lang
							if args.tgt_lang is not None:
														UpperCamelCase__					  =      args.tgt_lang

							Path(args.save_dir							).mkdir(exist_ok=a__							)
							UpperCamelCase__  ,  UpperCamelCase__					  =      eval_data_dir(
							    args.data_dir ,      a__ ,      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=a__ ,      **a__ ,      )

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

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





def      _UpperCamelCase  (a__ :Dict							):






							"""simple docstring"""





							UpperCamelCase__					  =      []
							for partial_result in partial_results:
														records.extend(a__							)
							UpperCamelCase__					  =      sorted(a__ ,      key=lambda a__							: x["id"]							)
							UpperCamelCase__					  =      [x["""pred"""] for x in records]
							return preds





def      _UpperCamelCase  (a__ :List[str] ,      a__ :str ,      a__ :List[Any]							):






							"""simple docstring"""





							UpperCamelCase__					  =      time.time()
							logger.info("""waiting for all nodes to finish"""							)
							UpperCamelCase__					  =      None
							while (time.time() - start_wait) < timeout:
														UpperCamelCase__					  =      list(save_dir.glob("""rank_*.json"""							)							)
														if len(a__							) < num_replicas:
																					continue
														try:
																					# make sure all json files are fully saved
																					UpperCamelCase__					  =      lmap(a__ ,      a__							)
																					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()




