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						(	__lowercase						:			Any						,       __lowercase						:			str						,       __lowercase						:			str						,       __lowercase						:			int = 8						,       __lowercase						:			int = 10_24						,       __lowercase						:			List[str]="val"						,       __lowercase						:			Optional[int]=None						,       __lowercase						:			Union[str, Any]=False						,       __lowercase						:			Dict="summarization"						,       __lowercase						:			Union[str, Any]=None						,       __lowercase						:			List[Any]=1						,       __lowercase						:			Dict = None						,       __lowercase						:			Dict=""						,       **__lowercase						:			Union[str, Any]						,       ):
		'''simple docstring'''




		A_		:    Dict         =				str(__lowercase     )
		assert local_rank is not None
		torch.distributed.init_process_group(backend='nccl'						,       rank=__lowercase     )

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

		A_		:    str         =				AutoTokenizer.from_pretrained(__lowercase     )
		logger.info(f'''Inferred tokenizer type: {tokenizer.__class__}'''     )  # if this is wrong, check config.model_type.

		if max_source_length is None:
				A_		:    Dict         =				tokenizer.model_max_length
		if prefix is None:
				A_		:    Optional[int]         =				prefix or getattr(model.config						,       'prefix'						,       ''     ) or ''
		A_		:    List[str]         =				SeqaSeqDataset(
		    __lowercase						,       __lowercase						,       __lowercase						,       max_target_length=10_24						,       type_path=__lowercase						,       n_obs=__lowercase						,       prefix=__lowercase						,       **__lowercase						,       )
		# 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_		:    Tuple         =				ds.make_sortish_sampler(__lowercase						,       distributed=__lowercase						,       add_extra_examples=__lowercase						,       shuffle=__lowercase     )
		A_		:    Union[str, Any]         =				DataLoader(__lowercase						,       sampler=__lowercase						,       batch_size=__lowercase						,       collate_fn=ds.collate_fn     )
		A_		:    int         =				[]
		for batch in tqdm(__lowercase     ):
				A_		:    Optional[Any]         =				model.generate(
				    input_ids=batch['input_ids'].to(model.device     )						,       attention_mask=batch['attention_mask'].to(model.device     )						,       num_return_sequences=__lowercase						,       num_beams=__lowercase						,       **__lowercase						,       )
				A_		:    Optional[Any]         =				tokenizer.batch_decode(__lowercase						,       skip_special_tokens=__lowercase						,       clean_up_tokenization_spaces=__lowercase     )
				A_		:    Union[str, Any]         =				batch['ids']
				if num_return_sequences > 1:
						A_		:    List[str]         =				chunks(__lowercase						,       __lowercase     )  # batch size chunks, each of size num_return_seq
				for i, pred in enumerate(__lowercase     ):
						results.append({'pred': pred, 'id': ids[i].item()}     )
		save_json(__lowercase						,       __lowercase     )
		return results, sampler.num_replicas




def     UpperCamelCase						(	):
		'''simple docstring'''




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

		parser.add_argument(
		    '--n_obs'						,       type=__lowercase						,       default=__lowercase						,       required=__lowercase						,       help='How many observations. Defaults to all.'     )
		parser.add_argument(
		    '--num_return_sequences'						,       type=__lowercase						,       default=1						,       required=__lowercase						,       help='How many sequences to return'     )
		parser.add_argument(
		    '--sync_timeout'						,       type=__lowercase						,       default=6_00						,       required=__lowercase						,       help='How long should master process wait for other processes to finish.'						,       )
		parser.add_argument('--src_lang'						,       type=__lowercase						,       default=__lowercase						,       required=__lowercase     )
		parser.add_argument('--tgt_lang'						,       type=__lowercase						,       default=__lowercase						,       required=__lowercase     )
		parser.add_argument(
		    '--prefix'						,       type=__lowercase						,       required=__lowercase						,       default=__lowercase						,       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_		:    Any         =				time.time()
		A_		:    List[str]         =				parser.parse_known_args()
		A_		:    Dict         =				parse_numeric_n_bool_cl_kwargs(__lowercase     )
		if generate_kwargs and args.local_rank <= 0:
				print(f'''parsed the following generate kwargs: {generate_kwargs}'''     )
		A_		:    str         =				Path(args.save_dir + '_tmp'     )
		Path(__lowercase     ).mkdir(exist_ok=__lowercase     )  # this handles locking.
		A_		:    Union[str, Any]         =				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_		:    Dict         =				{}
		if args.src_lang is not None:
				A_		:    List[Any]         =				args.src_lang
		if args.tgt_lang is not None:
				A_		:    Optional[Any]         =				args.tgt_lang

		Path(args.save_dir     ).mkdir(exist_ok=__lowercase     )
		A_		:    Union[str, Any]         =				eval_data_dir(
		    args.data_dir						,       __lowercase						,       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=__lowercase						,       **__lowercase						,       )

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

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




def     UpperCamelCase						(	__lowercase						:			List[str]     ):
		'''simple docstring'''




		A_		:    List[str]         =				[]
		for partial_result in partial_results:
				records.extend(__lowercase     )
		A_		:    Union[str, Any]         =				sorted(__lowercase						,       key=lambda __lowercase     : x["id"]     )
		A_		:    Optional[Any]         =				[x['pred'] for x in records]
		return preds




def     UpperCamelCase						(	__lowercase						:			Dict						,       __lowercase						:			int						,       __lowercase						:			Tuple     ):
		'''simple docstring'''




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







import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
_UpperCAmelCase            =		logging.get_logger(__name__)

_UpperCAmelCase            =		{
    """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""",
    """quantizer.weight_proj""": """quantizer.weight_proj""",
    """quantizer.vars""": """quantizer.codevectors""",
    """project_q""": """project_q""",
    """final_proj""": """project_hid""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}
_UpperCAmelCase            =		[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]




def     UpperCamelCase						(	__lowercase						:			Optional[Any]						,__lowercase						:			Tuple						,__lowercase						:			str						,__lowercase						:			List[Any]						,__lowercase						:			Optional[int]     ):
		'''simple docstring'''




		for attribute in key.split('.'     ):
				A_		:    List[Any]         =				getattr(__lowercase						,__lowercase     )

		if weight_type is not None:
				A_		:    Dict         =				getattr(__lowercase						,__lowercase     ).shape
		else:
				A_		:    Union[str, Any]         =				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_		:    Dict         =				value
		elif weight_type == "weight_g":
				A_		:    str         =				value
		elif weight_type == "weight_v":
				A_		:    int         =				value
		elif weight_type == "bias":
				A_		:    int         =				value
		else:
				A_		:    List[Any]         =				value

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




def     UpperCamelCase						(	__lowercase						:			Optional[Any]						,__lowercase						:			Union[str, Any]     ):
		'''simple docstring'''




		A_		:    List[Any]         =				[]
		A_		:    int         =				fairseq_model.state_dict()

		A_		:    Optional[Any]         =				hf_model.feature_extractor
		A_		:    List[str]         =				hf_model.adapter

		for name, value in fairseq_dict.items():
				A_		:    Tuple         =				False
				if "conv_layers" in name:
						load_conv_layer(
						    __lowercase						,__lowercase						,__lowercase						,__lowercase						,hf_model.config.feat_extract_norm == 'group'						,)
						A_		:    Optional[Any]         =				True
				elif any(x in name for x in ['adaptor', 'w2v_encoder.proj.', 'w2v_proj_ln.']     ):
						load_adapter(__lowercase						,__lowercase						,__lowercase						,__lowercase     )
						A_		:    Union[str, Any]         =				True
				else:
						for key, mapped_key in MAPPING.items():
								if key in name or key.split('w2v_model.'     )[-1] == name.split('.'     )[0]:
										A_		:    Tuple         =				True
										if "*" in mapped_key:
												A_		:    Optional[Any]         =				name.split(__lowercase     )[0].split('.'     )[-2]
												A_		:    List[Any]         =				mapped_key.replace('*'						,__lowercase     )
										if "weight_g" in name:
												A_		:    Optional[int]         =				'weight_g'
										elif "weight_v" in name:
												A_		:    Union[str, Any]         =				'weight_v'
										elif "bias" in name:
												A_		:    Any         =				'bias'
										elif "weight" in name:
												A_		:    str         =				'weight'
										else:
												A_		:    Optional[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     UpperCamelCase						(	__lowercase						:			Dict						,__lowercase						:			List[Any]						,__lowercase						:			Tuple						,__lowercase						:			Dict						,__lowercase						:			Any     ):
		'''simple docstring'''




		A_		:    List[Any]         =				full_name.split('conv_layers.'     )[-1]
		A_		:    Optional[int]         =				name.split('.'     )
		A_		:    Tuple         =				int(items[0]     )
		A_		:    int         =				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_		:    Union[str, Any]         =				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_		:    List[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_		:    List[Any]         =				value
						logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''     )
		else:
				unused_weights.append(__lowercase     )




def     UpperCamelCase						(	__lowercase						:			Any						,__lowercase						:			Tuple						,__lowercase						:			Optional[Any]						,__lowercase						:			Union[str, Any]     ):
		'''simple docstring'''




		A_		:    Union[str, Any]         =				full_name.split('adaptor.'     )[-1]
		A_		:    List[Any]         =				name.split('.'     )

		if items[1].isdigit():
				A_		:    Union[str, Any]         =				int(items[1]     )
		else:
				A_		:    Tuple         =				None

		if "adaptor" not in full_name:
				if "proj_ln" in full_name:
						# has to be layer norm
						if "bias" in name:
								assert (
								    value.shape == adapter.proj_layer_norm.bias.data.shape
								), f'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found.'''
								A_		:    Dict         =				value
								logger.info(f'''Adapter proj layer norm bias was initialized from {full_name}.'''     )
						if "weight" in name:
								assert (
								    value.shape == adapter.proj_layer_norm.weight.data.shape
								), f'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found.'''
								A_		:    int         =				value
				else:
						# has to be projection layer
						if "bias" in name:
								assert (
								    value.shape == adapter.proj.bias.data.shape
								), f'''{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found.'''
								A_		:    Dict         =				value
								logger.info(f'''Adapter proj layer bias was initialized from {full_name}.'''     )
						if "weight" in name:
								assert (
								    value.shape == adapter.proj.weight.data.shape
								), f'''{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found.'''
								A_		:    Tuple         =				value
								logger.info(f'''Adapter proj layer weight was initialized from {full_name}.'''     )
		elif isinstance(__lowercase						,__lowercase     ):
				if "bias" in name:
						assert (
						    value.shape == adapter.layers[layer_id].conv.bias.data.shape
						), f'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found.'''
						A_		:    Tuple         =				value
						logger.info(f'''Adapter layer {layer_id} bias was initialized from {full_name}.'''     )
				elif "weight" in name:
						assert (
						    value.shape == adapter.layers[layer_id].conv.weight.data.shape
						), f'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found.'''
						A_		:    str         =				value
						logger.info(f'''Adapter layer {layer_id} bias was initialized from {full_name}.'''     )
		else:
				unused_weights.append(__lowercase     )




def     UpperCamelCase						(	__lowercase						:			List[Any]     ):
		'''simple docstring'''




		A_       ,      A_		:    Any         =				emb.weight.shape
		A_		:    Tuple         =				nn.Linear(__lowercase						,__lowercase						,bias=__lowercase     )
		A_		:    Optional[Any]         =				emb.weight.data
		return lin_layer




@torch.no_grad()
def     UpperCamelCase						(	__lowercase						:			Any						,__lowercase						:			Optional[int]						,__lowercase						:			Any						,__lowercase						:			str						,__lowercase						:			Dict						,__lowercase						:			Dict						,__lowercase						:			Tuple						,__lowercase						:			Optional[int]						,__lowercase						:			List[str]						,__lowercase						:			List[Any]						,__lowercase						:			str						,):
		'''simple docstring'''




		A_		:    Optional[int]         =				WavaVecaConfig.from_pretrained(
		    __lowercase						,add_adapter=__lowercase						,adapter_stride=__lowercase						,adapter_kernel_size=__lowercase						,use_auth_token=__lowercase						,output_hidden_size=__lowercase						,)
		A_		:    Any         =				MBartConfig.from_pretrained(__lowercase     )

		# load model
		A_       ,      A_       ,      A_		:    Union[str, Any]         =				fairseq.checkpoint_utils.load_model_ensemble_and_task(
		    [checkpoint_path]						,arg_overrides={
		        'config_yaml': config_yaml_path,
		        'data': '/'.join(dict_path.split('/'     )[:-1]     ),
		        'w2v_path': checkpoint_path,
		        'load_pretrained_decoder_from': None,
		    }						,)
		A_		:    Union[str, Any]         =				model[0].eval()

		# load feature extractor
		A_		:    Any         =				WavaVecaFeatureExtractor.from_pretrained(__lowercase						,use_auth_token=__lowercase     )

		# set weights for wav2vec2 encoder
		A_		:    Optional[Any]         =				WavaVecaModel(__lowercase     )

		recursively_load_weights_wavaveca(model.encoder						,__lowercase     )

		# load decoder weights
		A_		:    Dict         =				MBartForCausalLM(__lowercase     )
		A_       ,      A_		:    Union[str, Any]         =				hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()						,strict=__lowercase     )
		logger.warning(f'''The following keys are missing when loading the decoder weights: {missing_keys}'''     )
		logger.warning(f'''The following keys are unexpected when loading the decoder weights: {unexpected_keys}'''     )

		A_		:    Optional[int]         =				SpeechEncoderDecoderModel(encoder=__lowercase						,decoder=__lowercase     )
		A_		:    Any         =				False

		A_		:    List[Any]         =				MBartaaTokenizer(__lowercase     )
		tokenizer.save_pretrained(__lowercase     )

		A_		:    Dict         =				hf_wavavec.config.to_dict()
		A_		:    Any         =				tokenizer.pad_token_id
		A_		:    Optional[Any]         =				tokenizer.bos_token_id
		A_		:    Union[str, Any]         =				tokenizer.eos_token_id
		A_		:    Dict         =				'mbart50'
		A_		:    str         =				'wav2vec2'

		A_		:    int         =				tokenizer.eos_token_id
		A_		:    List[str]         =				25_00_04
		A_		:    int         =				tokenizer.eos_token_id

		A_		:    Optional[Any]         =				SpeechEncoderDecoderConfig.from_dict(__lowercase     )

		hf_wavavec.save_pretrained(__lowercase     )
		feature_extractor.save_pretrained(__lowercase     )


if __name__ == "__main__":
	_UpperCAmelCase            =		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_yaml_path""", default=None, type=str, help="""Path to yaml file of fine-tuned model""")
	parser.add_argument(
	    """--encoder_config_path""",
	    default="""facebook/wav2vec2-xls-r-1b""",
	    type=str,
	    help="""Path to hf encoder wav2vec2 checkpoint config""",
	)
	parser.add_argument(
	    """--decoder_config_path""",
	    default="""facebook/mbart-large-50-one-to-many-mmt""",
	    type=str,
	    help="""Path to hf decoder checkpoint config""",
	)
	parser.add_argument("""--add_adapter""", default=True, type=bool, help="""whethere to add model adapter layers""")
	parser.add_argument("""--adapter_stride""", default=2, type=int, help="""stride of adapter layers""")
	parser.add_argument("""--adapter_kernel_size""", default=3, type=int, help="""kernel size of adapter layers""")
	parser.add_argument("""--encoder_output_dim""", default=1024, type=int, help="""encoder output dim""")
	parser.add_argument("""--start_token_id""", default=250004, type=int, help="""`decoder_start_token_id` of model config""")

	_UpperCAmelCase            =		parser.parse_args()
	convert_wavaveca_checkpoint(
	    args.checkpoint_path,
	    args.pytorch_dump_folder_path,
	    args.dict_path,
	    args.config_yaml_path,
	    encoder_config_path=args.encoder_config_path,
	    decoder_config_path=args.decoder_config_path,
	    add_adapter=args.add_adapter,
	    adapter_kernel_size=args.adapter_kernel_size,
	    adapter_stride=args.adapter_stride,
	    decoder_start_token_id=args.start_token_id,
	    encoder_output_dim=args.encoder_output_dim,
	)






