





"""simple docstring"""




import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
a						: Optional[Any]     	=			logging.get_logger(__name__)

a						: int     	=			{
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    int    ,       _lowercase						:    int    ,       _lowercase						:    List[str]    ,       _lowercase						:    List[str]    ,       _lowercase						:    Dict					) ->Optional[Any]:






	'''simple docstring'''






	for attribute in key.split("."					):
		a      :		Dict	=				getattr(_lowercase    ,       _lowercase					)

	if weight_type is not None:
		a      :		Optional[Any]	=				getattr(_lowercase    ,       _lowercase					).shape
	else:
		a      :		Tuple	=				hf_pointer.shape

	assert hf_shape == value.shape, (
	    F"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
	    F""" {value.shape} for {full_name}"""
	)

	if weight_type == "weight":
		a      :		List[str]	=				value
	elif weight_type == "weight_g":
		a      :		int	=				value
	elif weight_type == "weight_v":
		a      :		int	=				value
	elif weight_type == "bias":
		a      :		Tuple	=				value
	else:
		a      :		Union[str, Any]	=				value

	logger.info(F"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."""					)






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Any    ,       _lowercase						:    List[str]    ,       _lowercase						:    int					) ->Any:






	'''simple docstring'''






	a      :		Dict	=				[]
	a      :		int	=				fairseq_model.state_dict()

	a      :		Tuple	=				hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

	for name, value in fairseq_dict.items():
		a      :		int	=				False
		if "conv_layers" in name:
			load_conv_layer(
			    _lowercase    ,       _lowercase    ,       _lowercase    ,       _lowercase    ,       hf_model.config.feat_extract_norm == "group"    ,       )
			a      :		Optional[Any]	=				True
		else:
			for key, mapped_key in MAPPING.items():
				a      :		Optional[Any]	=				"hubert." + mapped_key if (is_finetuned and mapped_key != "lm_head") else mapped_key

				if key in name or (key.split("w2v_model."					)[-1] == name.split("."					)[0] and not is_finetuned):
					a      :		List[str]	=				True
					if "*" in mapped_key:
						a      :		Union[str, Any]	=				name.split(_lowercase					)[0].split("."					)[-2]
						a      :		str	=				mapped_key.replace("*"    ,       _lowercase					)
					if "weight_g" in name:
						a      :		Optional[int]	=				"weight_g"
					elif "weight_v" in name:
						a      :		Optional[Any]	=				"weight_v"
					elif "weight" in name:
						a      :		Tuple	=				"weight"
					elif "bias" in name:
						a      :		Tuple	=				"bias"
					else:
						a      :		Union[str, Any]	=				None
					set_recursively(_lowercase    ,       _lowercase    ,       _lowercase    ,       _lowercase    ,       _lowercase					)
				continue
		if not is_used:
			unused_weights.append(_lowercase					)

	logger.warning(F"""Unused weights: {unused_weights}"""					)






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Optional[int]    ,       _lowercase						:    Optional[Any]    ,       _lowercase						:    Optional[Any]    ,       _lowercase						:    str    ,       _lowercase						:    Optional[Any]					) ->List[Any]:






	'''simple docstring'''






	a      :		List[Any]	=				full_name.split("conv_layers."					)[-1]
	a      :		Any	=				name.split("."					)
	a      :		List[str]	=				int(items[0]					)
	a      :		Any	=				int(items[1]					)

	if type_id == 0:
		if "bias" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
			    F"""{full_name} has size {value.shape}, but"""
			    F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
			)
			a      :		Tuple	=				value
			logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""					)
		elif "weight" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
			    F"""{full_name} has size {value.shape}, but"""
			    F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
			)
			a      :		str	=				value
			logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""					)
	elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
		if "bias" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
			    F"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
			    " found."
			)
			a      :		str	=				value
			logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""					)
		elif "weight" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
			    F"""{full_name} has size {value.shape}, but"""
			    F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
			)
			a      :		str	=				value
			logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""					)
	else:
		unused_weights.append(_lowercase					)






@torch.no_grad()
def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Any    ,       _lowercase						:    Tuple    ,       _lowercase						:    List[str]=None    ,       _lowercase						:    Dict=None    ,       _lowercase						:    int=True					) ->List[Any]:






	'''simple docstring'''






	if config_path is not None:
		a      :		Tuple	=				HubertConfig.from_pretrained(_lowercase					)
	else:
		a      :		Any	=				HubertConfig()

	if is_finetuned:
		if dict_path:
			a      :		str	=				Dictionary.load(_lowercase					)

			# important change bos & pad token id since CTC symbol is <pad> and
			# not <s> as in fairseq
			a      :		int	=				target_dict.pad_index
			a      :		Optional[int]	=				target_dict.bos_index
			a      :		Dict	=				target_dict.eos_index
			a      :		Optional[int]	=				len(target_dict.symbols					)
			a      :		List[str]	=				os.path.join(_lowercase    ,       "vocab.json"					)
			if not os.path.isdir(_lowercase					):
				logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(_lowercase					)					)
				return
			os.makedirs(_lowercase    ,       exist_ok=_lowercase					)
			with open(_lowercase    ,       "w"    ,       encoding="utf-8"					) as vocab_handle:
				json.dump(target_dict.indices    ,       _lowercase					)
			a      :		Optional[int]	=				WavaVecaCTCTokenizer(
			    _lowercase    ,       unk_token=target_dict.unk_word    ,       pad_token=target_dict.pad_word    ,       bos_token=target_dict.bos_word    ,       eos_token=target_dict.eos_word    ,       word_delimiter_token="|"    ,       do_lower_case=_lowercase    ,       )
			a      :		int	=				True if config.feat_extract_norm == "layer" else False
			a      :		Any	=				WavaVecaFeatureExtractor(
			    feature_size=1    ,       sampling_rate=1_6000    ,       padding_value=0    ,       do_normalize=_lowercase    ,       return_attention_mask=_lowercase    ,       )
			a      :		str	=				WavaVecaProcessor(feature_extractor=_lowercase    ,       tokenizer=_lowercase					)
			processor.save_pretrained(_lowercase					)

		a      :		int	=				HubertForCTC(_lowercase					)
	else:
		a      :		str	=				HubertModel(_lowercase					)

	if is_finetuned:
		a,       a,       a      :		List[str]	=				fairseq.checkpoint_utils.load_model_ensemble_and_task(
		    [checkpoint_path]    ,       arg_overrides={"data": "/".join(dict_path.split("/"					)[:-1]					)}					)
	else:
		a,       a,       a      :		int	=				fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]					)

	a      :		Optional[int]	=				model[0].eval()

	recursively_load_weights(_lowercase    ,       _lowercase    ,       _lowercase					)

	hf_wavavec.save_pretrained(_lowercase					)


if __name__ == "__main__":
		a						: Tuple     	=			argparse.ArgumentParser()
		parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
		parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
		parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
		parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
		parser.add_argument(
		    '''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
		)
		a						: Optional[Any]     	=			parser.parse_args()
		convert_hubert_checkpoint(
		    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
		)








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,
)


_snake_case										=       getLogger(__name__)







def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_,snake_case_ = 8,snake_case_ = 1024,snake_case_="val",snake_case_=None,snake_case_=False,snake_case_="summarization",snake_case_=None,snake_case_=1,snake_case_ = None,snake_case_="",**snake_case_,):
      _A							:     Dict			=						str(snake_case_     )
      assert local_rank is not None
      torch.distributed.init_process_group(backend="""nccl""",rank=snake_case_     )

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

      _A							:     Optional[Any]			=						AutoTokenizer.from_pretrained(snake_case_     )
      logger.info(f'''Inferred tokenizer type: {tokenizer.__class__}'''     )  # if this is wrong, check config.model_type.

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







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

      parser.add_argument(
          """--n_obs""",type=snake_case_,default=snake_case_,required=snake_case_,help="""How many observations. Defaults to all."""     )
      parser.add_argument(
          """--num_return_sequences""",type=snake_case_,default=1,required=snake_case_,help="""How many sequences to return"""     )
      parser.add_argument(
          """--sync_timeout""",type=snake_case_,default=600,required=snake_case_,help="""How long should master process wait for other processes to finish.""",)
      parser.add_argument("""--src_lang""",type=snake_case_,default=snake_case_,required=snake_case_     )
      parser.add_argument("""--tgt_lang""",type=snake_case_,default=snake_case_,required=snake_case_     )
      parser.add_argument(
          """--prefix""",type=snake_case_,required=snake_case_,default=snake_case_,help="""will be added to the begininng of src examples"""     )
      parser.add_argument("""--fp16""",action="""store_true"""     )
      parser.add_argument("""--debug""",action="""store_true"""     )
      _A							:     Union[str, Any]			=						time.time()
      _A			,   _A							:     List[str]			=						parser.parse_known_args()
      _A							:     List[str]			=						parse_numeric_n_bool_cl_kwargs(snake_case_     )
      if generate_kwargs and args.local_rank <= 0:
            print(f'''parsed the following generate kwargs: {generate_kwargs}'''     )
      _A							:     Dict			=						Path(args.save_dir + """_tmp"""     )
      Path(snake_case_     ).mkdir(exist_ok=snake_case_     )  # this handles locking.
      _A							:     int			=						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.
      _A							:     Any			=						{}
      if args.src_lang is not None:
            _A							:     int			=						args.src_lang
      if args.tgt_lang is not None:
            _A							:     Dict			=						args.tgt_lang

      Path(args.save_dir     ).mkdir(exist_ok=snake_case_     )
      _A			,   _A							:     str			=						eval_data_dir(
          args.data_dir,snake_case_,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=snake_case_,**snake_case_,)

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

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







def 				lowerCAmelCase_	(     snake_case_     ):
      _A							:     Dict			=						[]
      for partial_result in partial_results:
            records.extend(snake_case_     )
      _A							:     Optional[Any]			=						sorted(snake_case_,key=lambda snake_case_     : x["id"]     )
      _A							:     List[str]			=						[x["""pred"""] for x in records]
      return preds







def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_     ):
      # WAIT FOR lots of .json files
      _A							:     Optional[Any]			=						time.time()
      logger.info("""waiting for all nodes to finish"""     )
      _A							:     List[str]			=						None
      while (time.time() - start_wait) < timeout:
            _A							:     str			=						list(save_dir.glob("""rank_*.json"""     )     )
            if len(snake_case_     ) < num_replicas:
                  continue
            try:
                  # make sure all json files are fully saved
                  _A							:     List[str]			=						lmap(snake_case_,snake_case_     )
                  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()

