





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__: List[Any]							   =						logging.get_logger(__name__)

a__: Any							   =						{
    '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       UpperCamelCase__(			UpperCamelCase__     :			Optional[int]					,	UpperCamelCase__     :			Union[str, Any]					,	UpperCamelCase__     :			int					,	UpperCamelCase__     :			str					,	UpperCamelCase__     :			Optional[int]    )->Tuple:
       for attribute in key.split('''.'''    ):
              A__		   =		getattr(UpperCamelCase__					,	UpperCamelCase__    )

       if weight_type is not None:
              A__		   =		getattr(UpperCamelCase__					,	UpperCamelCase__    ).shape
       else:
              A__		   =		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__		   =		value
       elif weight_type == "weight_g":
              A__		   =		value
       elif weight_type == "weight_v":
              A__		   =		value
       elif weight_type == "bias":
              A__		   =		value
       else:
              A__		   =		value

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


def       UpperCamelCase__(			UpperCamelCase__     :			Dict					,	UpperCamelCase__     :			List[str]					,	UpperCamelCase__     :			List[Any]    )->str:
       A__		   =		[]
       A__		   =		fairseq_model.state_dict()

       A__		   =		hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

       for name, value in fairseq_dict.items():
              A__		   =		False
              if "conv_layers" in name:
                     load_conv_layer(
                         UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	hf_model.config.feat_extract_norm == '''group'''					,	)
                     A__		   =		True
              else:
                     for key, mapped_key in MAPPING.items():
                            A__		   =		'''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__		   =		True
                                   if "*" in mapped_key:
                                          A__		   =		name.split(UpperCamelCase__    )[0].split('''.'''    )[-2]
                                          A__		   =		mapped_key.replace('''*'''					,	UpperCamelCase__    )
                                   if "weight_g" in name:
                                          A__		   =		'''weight_g'''
                                   elif "weight_v" in name:
                                          A__		   =		'''weight_v'''
                                   elif "weight" in name:
                                          A__		   =		'''weight'''
                                   elif "bias" in name:
                                          A__		   =		'''bias'''
                                   else:
                                          A__		   =		None
                                   set_recursively(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__    )
                            continue
              if not is_used:
                     unused_weights.append(UpperCamelCase__    )

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


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


@torch.no_grad()
def       UpperCamelCase__(			UpperCamelCase__     :			List[Any]					,	UpperCamelCase__     :			Dict					,	UpperCamelCase__     :			List[Any]=None					,	UpperCamelCase__     :			Any=None					,	UpperCamelCase__     :			Union[str, Any]=True    )->Any:
       if config_path is not None:
              A__		   =		HubertConfig.from_pretrained(UpperCamelCase__    )
       else:
              A__		   =		HubertConfig()

       if is_finetuned:
              if dict_path:
                     A__		   =		Dictionary.load(UpperCamelCase__    )

                     # important change bos & pad token id since CTC symbol is <pad> and
                     # not <s> as in fairseq
                     A__		   =		target_dict.pad_index
                     A__		   =		target_dict.bos_index
                     A__		   =		target_dict.eos_index
                     A__		   =		len(target_dict.symbols    )
                     A__		   =		os.path.join(UpperCamelCase__					,	'''vocab.json'''    )
                     if not os.path.isdir(UpperCamelCase__    ):
                            logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(UpperCamelCase__    )    )
                            return
                     os.makedirs(UpperCamelCase__					,	exist_ok=UpperCamelCase__    )
                     with open(UpperCamelCase__					,	'''w'''					,	encoding='''utf-8'''    ) as vocab_handle:
                            json.dump(target_dict.indices					,	UpperCamelCase__    )
                     A__		   =		WavaVecaCTCTokenizer(
                         UpperCamelCase__					,	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=UpperCamelCase__					,	)
                     A__		   =		True if config.feat_extract_norm == '''layer''' else False
                     A__		   =		WavaVecaFeatureExtractor(
                         feature_size=1					,	sampling_rate=1_60_00					,	padding_value=0					,	do_normalize=UpperCamelCase__					,	return_attention_mask=UpperCamelCase__					,	)
                     A__		   =		WavaVecaProcessor(feature_extractor=UpperCamelCase__					,	tokenizer=UpperCamelCase__    )
                     processor.save_pretrained(UpperCamelCase__    )

              A__		   =		HubertForCTC(UpperCamelCase__    )
       else:
              A__		   =		HubertModel(UpperCamelCase__    )

       if is_finetuned:
              A__   ,      A__   ,      A__		   =		fairseq.checkpoint_utils.load_model_ensemble_and_task(
                  [checkpoint_path]					,	arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''    )[:-1]    )}    )
       else:
              A__   ,      A__   ,      A__		   =		fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]    )

       A__		   =		model[0].eval()

       recursively_load_weights(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__    )

       hf_wavavec.save_pretrained(UpperCamelCase__    )


if __name__ == "__main__":
       a__: Optional[Any]							   =						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[int]							   =						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 unittest
from dataclasses import dataclass

import pytest

from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict




@dataclass
class        SCREAMING_SNAKE_CASE__					(		UpperCamelCase__ ):
 __SCREAMING_SNAKE_CASE							=      ComputeEnvironment.AMAZON_SAGEMAKER
 __SCREAMING_SNAKE_CASE							=      True
 __SCREAMING_SNAKE_CASE							=      '''ml.p3.2xlarge'''
 __SCREAMING_SNAKE_CASE							=      '''accelerate_sagemaker_execution_role'''
 __SCREAMING_SNAKE_CASE							=      '''hf-sm'''
 __SCREAMING_SNAKE_CASE							=      '''us-east-1'''
 __SCREAMING_SNAKE_CASE							=      1
 __SCREAMING_SNAKE_CASE							=      '''accelerate-sagemaker-1'''
 __SCREAMING_SNAKE_CASE							=      '''1.6'''
 __SCREAMING_SNAKE_CASE							=      '''4.4'''
 __SCREAMING_SNAKE_CASE							=      '''train.py'''
 __SCREAMING_SNAKE_CASE							=      [
     '''--model_name_or_path''',
     '''bert''',
     '''--do_train''',
     '''False''',
     '''--epochs''',
     '''3''',
     '''--learning_rate''',
     '''5e-5''',
     '''--max_steps''',
     '''50.5''',
 ]
 __SCREAMING_SNAKE_CASE							=      [
     '''--model_name_or_path''',
     '''bert''',
     '''--do_train''',
     '''--do_test''',
     '''False''',
     '''--do_predict''',
     '''--epochs''',
     '''3''',
     '''--learning_rate''',
     '''5e-5''',
     '''--max_steps''',
     '''50.5''',
 ]




class        SCREAMING_SNAKE_CASE__					(		unittest.TestCase ):



 def 							UpperCamelCase     (				self					):
        # If no defaults are changed, `to_kwargs` returns an empty dict.
        A__		   =		_convert_nargs_to_dict(MockLaunchConfig.success_training_script_args					)
        assert isinstance(converted_args['''model_name_or_path'''],__lowerCamelCase					)
        assert isinstance(converted_args['''do_train'''],__lowerCamelCase					)
        assert isinstance(converted_args['''epochs'''],__lowerCamelCase					)
        assert isinstance(converted_args['''learning_rate'''],__lowerCamelCase					)
        assert isinstance(converted_args['''max_steps'''],__lowerCamelCase					)

        with pytest.raises(__lowerCamelCase					):
               _convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args					)





