



'''simple docstring'''

import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


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

__lowerCAmelCase									=   {
    '''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''': '''ctc_proj''',
    '''mask_emb''': '''masked_spec_embed''',
}
__lowerCAmelCase									=   [
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      int:
		for attribute in key.split('.'  ):
				if is_finetuned:
						if attribute in ["quantizer", "project_q", "project_hid"]:
								# those layers are only relevant for pretraining and should be dropped
								return

						if attribute == "ctc_proj":
								# we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
								_a     :      List[Any]        =							'lm_head'

				_a     :      Tuple        =							getattr(lowerCAmelCase_						,			lowerCAmelCase_  )

		if weight_type is not None:
				_a     :      int        =							getattr(lowerCAmelCase_						,			lowerCAmelCase_  ).shape
		else:
				_a     :      List[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     :      Optional[Any]        =							value
		elif weight_type == "weight_g":
				_a     :      str        =							value
		elif weight_type == "weight_v":
				_a     :      Optional[int]        =							value
		elif weight_type == "bias":
				_a     :      Any        =							value
		else:
				_a     :      Tuple        =							value

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



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      Any:
		_a     :      List[Any]        =							[]
		_a     :      Dict        =							fairseq_model.state_dict()

		_a     :      Union[str, Any]        =							hf_model.unispeech.feature_extractor

		for name, value in fairseq_dict.items():
				_a     :      Tuple        =							False
				if "conv_layers" in name:
						load_conv_layer(
						    lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			hf_model.config.feat_extract_norm == 'group'						,			)
						_a     :      Union[str, Any]        =							True
				else:
						for key, mapped_key in MAPPING.items():
								_a     :      List[str]        =							'unispeech.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
								if key in name or key.split('w2v_model.'  )[-1] == name.split('.'  )[0]:
										_a     :      int        =							True
										if "*" in mapped_key:
												_a     :      Dict        =							name.split(lowerCAmelCase_  )[0].split('.'  )[-2]
												_a     :      int        =							mapped_key.replace('*'						,			lowerCAmelCase_  )
										if "weight_g" in name:
												_a     :      Any        =							'weight_g'
										elif "weight_v" in name:
												_a     :      List[Any]        =							'weight_v'
										elif "bias" in name:
												_a     :      Union[str, Any]        =							'bias'
										elif "weight" in name:
												# TODO: don't match quantizer.weight_proj
												_a     :      int        =							'weight'
										else:
												_a     :      List[str]        =							None
										set_recursively(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )
								continue
				if not is_used:
						unused_weights.append(lowerCAmelCase_  )

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



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



@torch.no_grad()
def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_=None						,			lowerCAmelCase_=None						,			lowerCAmelCase_=True  )			->      Dict:
		if config_path is not None:
				_a     :      List[str]        =							UniSpeechConfig.from_pretrained(lowerCAmelCase_  )
		else:
				_a     :      Tuple        =							UniSpeechConfig()

		if is_finetuned:
				if dict_path:
						_a     :      Optional[Any]        =							Dictionary.load_from_json(lowerCAmelCase_  )

						# important change bos & pad token id since CTC symbol is <pad> and
						# not <s> as in fairseq
						_a     :      Any        =							target_dict.pad_index
						_a     :      Optional[Any]        =							target_dict.bos_index
						_a     :      Optional[int]        =							target_dict.eos_index
						_a     :      List[Any]        =							len(target_dict.symbols  )
						_a     :      Tuple        =							os.path.join(lowerCAmelCase_						,			'vocab.json'  )
						if not os.path.isdir(lowerCAmelCase_  ):
								logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(lowerCAmelCase_  )  )
								return
						os.makedirs(lowerCAmelCase_						,			exist_ok=lowerCAmelCase_  )
						_a     :      List[str]        =							target_dict.indices

						# fairseq has the <pad> and <s> switched
						_a     :      List[Any]        =							42
						_a     :      Any        =							43
						with open(lowerCAmelCase_						,			'w'						,			encoding='utf-8'  ) as vocab_handle:
								json.dump(lowerCAmelCase_						,			lowerCAmelCase_  )
						_a     :      Optional[Any]        =							WavaVecaPhonemeCTCTokenizer(
						    lowerCAmelCase_						,			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=lowerCAmelCase_						,			)
						_a     :      List[Any]        =							True if config.feat_extract_norm == 'layer' else False
						_a     :      List[str]        =							WavaVecaFeatureExtractor(
						    feature_size=1						,			sampling_rate=16000						,			padding_value=0						,			do_normalize=lowerCAmelCase_						,			return_attention_mask=lowerCAmelCase_						,			)
						_a     :      Dict        =							WavaVecaProcessor(feature_extractor=lowerCAmelCase_						,			tokenizer=lowerCAmelCase_  )
						processor.save_pretrained(lowerCAmelCase_  )

				_a     :      List[str]        =							UniSpeechForCTC(lowerCAmelCase_  )
		else:
				_a     :      int        =							UniSpeechForPreTraining(lowerCAmelCase_  )

		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]  ), 'w2v_path': checkpoint_path}  )
		else:
				_a       ,					_a       ,					_a     :      int        =							fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]  )

		_a     :      Optional[Any]        =							model[0].eval()

		recursively_load_weights(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )

		hf_unispeech.save_pretrained(lowerCAmelCase_  )


if __name__ == "__main__":
							__lowerCAmelCase									=   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'''
							)
							__lowerCAmelCase									=   parser.parse_args()
							convert_unispeech_checkpoint(
							    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
							)








'''simple docstring'''




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


__snake_case    						=logging.get_logger(__name__)

__snake_case    						={
    """facebook/s2t-wav2vec2-large-en-de""": (
        """https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"""
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}

class 		UpperCAmelCase_      (  __lowercase		):
     lowerCamelCase				:	Union[str, Any]		=     '''speech_to_text_2'''
     lowerCamelCase				:	Any		=     ['''past_key_values''']
     lowerCamelCase				:	Optional[Any]		=     {'''num_attention_heads''': '''decoder_attention_heads''', '''hidden_size''': '''d_model'''}





     def __init__( self       :      Optional[int]    ,      UpperCAmelCase__       :      Optional[Any]=1_0_0_0_0    ,      UpperCAmelCase__       :      int=6    ,      UpperCAmelCase__       :      Optional[Any]=2_0_4_8    ,      UpperCAmelCase__       :      Union[str, Any]=4    ,      UpperCAmelCase__       :      List[Any]=0.0    ,      UpperCAmelCase__       :      str=True    ,      UpperCAmelCase__       :      str="relu"    ,      UpperCAmelCase__       :      Any=2_5_6    ,      UpperCAmelCase__       :      Optional[Any]=0.1    ,      UpperCAmelCase__       :      Any=0.0    ,      UpperCAmelCase__       :      Tuple=0.0    ,      UpperCAmelCase__       :      List[Any]=0.02    ,      UpperCAmelCase__       :      List[Any]=2    ,      UpperCAmelCase__       :      str=True    ,      UpperCAmelCase__       :      List[str]=1    ,      UpperCAmelCase__       :      Any=0    ,      UpperCAmelCase__       :      Dict=2    ,      UpperCAmelCase__       :      int=1_0_2_4    ,      **UpperCAmelCase__       :      Optional[Any]    ,      )				->  Dict:
        lowerCAmelCase     =			vocab_size
        lowerCAmelCase     =			d_model
        lowerCAmelCase     =			decoder_ffn_dim
        lowerCAmelCase     =			decoder_layers
        lowerCAmelCase     =			decoder_attention_heads
        lowerCAmelCase     =			dropout
        lowerCAmelCase     =			attention_dropout
        lowerCAmelCase     =			activation_dropout
        lowerCAmelCase     =			activation_function
        lowerCAmelCase     =			init_std
        lowerCAmelCase     =			decoder_layerdrop
        lowerCAmelCase     =			use_cache
        lowerCAmelCase     =			decoder_layers
        lowerCAmelCase     =			scale_embedding  # scale factor will be sqrt(d_model) if True
        lowerCAmelCase     =			max_target_positions

        super().__init__(
            pad_token_id=UpperCAmelCase__    ,      bos_token_id=UpperCAmelCase__    ,      eos_token_id=UpperCAmelCase__    ,      decoder_start_token_id=UpperCAmelCase__    ,      **UpperCAmelCase__    ,      )





