
"""simple docstring"""




import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('1.0.0a'):
					raise Exception('requires fairseq >= 1.0.0a')

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

UpperCAmelCase__      :    List[str]    							= 'Hello world! cécé herlolip'

def lowercase_  (   _snake_case   ,_snake_case   ,_snake_case						):
							SCREAMING_SNAKE_CASE__						:       int           =   FairseqRobertaModel.from_pretrained(_snake_case						)
							roberta.eval()  # disable dropout
							SCREAMING_SNAKE_CASE__						:       Any           =   roberta.model.encoder.sentence_encoder
							SCREAMING_SNAKE_CASE__						:       Any           =   XLMRobertaConfig(
							    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings   ,hidden_size=roberta.cfg.model.encoder_embed_dim   ,num_hidden_layers=roberta.cfg.model.encoder_layers   ,num_attention_heads=roberta.cfg.model.encoder_attention_heads   ,intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim   ,max_position_embeddings=514   ,type_vocab_size=1   ,layer_norm_eps=1E-5   ,)
							if classification_head:
														SCREAMING_SNAKE_CASE__						:       Dict           =   roberta.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

							print("""Our RoBERTa config:"""   ,_snake_case						)

							SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   XLMRobertaXLForSequenceClassification(_snake_case						) if classification_head else XLMRobertaXLForMaskedLM(_snake_case						)
							model.eval()

							# Now let's copy all the weights.
							# Embeddings
							SCREAMING_SNAKE_CASE__						:       Optional[int]           =   roberta_sent_encoder.embed_tokens.weight
							SCREAMING_SNAKE_CASE__						:       int           =   roberta_sent_encoder.embed_positions.weight
							SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   torch.zeros_like(
							    model.roberta.embeddings.token_type_embeddings.weight						)  # just zero them out b/c RoBERTa doesn't use them.

							SCREAMING_SNAKE_CASE__						:       Optional[int]           =   roberta_sent_encoder.layer_norm.weight
							SCREAMING_SNAKE_CASE__						:       Dict           =   roberta_sent_encoder.layer_norm.bias

							for i in range(config.num_hidden_layers						):
														# Encoder: start of layer
														SCREAMING_SNAKE_CASE__						:       BertLayer           =   model.roberta.encoder.layer[i]
														SCREAMING_SNAKE_CASE__						:       TransformerSentenceEncoderLayer           =   roberta_sent_encoder.layers[i]

														SCREAMING_SNAKE_CASE__						:       RobertaAttention           =   layer.attention
														SCREAMING_SNAKE_CASE__						:       List[str]           =   roberta_layer.self_attn_layer_norm.weight
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   roberta_layer.self_attn_layer_norm.bias

														# self attention
														SCREAMING_SNAKE_CASE__						:       BertSelfAttention           =   layer.attention.self
														assert (
														    roberta_layer.self_attn.k_proj.weight.data.shape
														    == roberta_layer.self_attn.q_proj.weight.data.shape
														    == roberta_layer.self_attn.v_proj.weight.data.shape
														    == torch.Size((config.hidden_size, config.hidden_size)						)
														)

														SCREAMING_SNAKE_CASE__						:       List[str]           =   roberta_layer.self_attn.q_proj.weight
														SCREAMING_SNAKE_CASE__						:       Tuple           =   roberta_layer.self_attn.q_proj.bias
														SCREAMING_SNAKE_CASE__						:       Tuple           =   roberta_layer.self_attn.k_proj.weight
														SCREAMING_SNAKE_CASE__						:       int           =   roberta_layer.self_attn.k_proj.bias
														SCREAMING_SNAKE_CASE__						:       List[str]           =   roberta_layer.self_attn.v_proj.weight
														SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   roberta_layer.self_attn.v_proj.bias

														# self-attention output
														SCREAMING_SNAKE_CASE__						:       BertSelfOutput           =   layer.attention.output
														assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
														SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   roberta_layer.self_attn.out_proj.weight
														SCREAMING_SNAKE_CASE__						:       List[str]           =   roberta_layer.self_attn.out_proj.bias

														# this one is final layer norm
														SCREAMING_SNAKE_CASE__						:       Tuple           =   roberta_layer.final_layer_norm.weight
														SCREAMING_SNAKE_CASE__						:       Optional[int]           =   roberta_layer.final_layer_norm.bias

														# intermediate
														SCREAMING_SNAKE_CASE__						:       BertIntermediate           =   layer.intermediate
														assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   roberta_layer.fca.weight
														SCREAMING_SNAKE_CASE__						:       Tuple           =   roberta_layer.fca.bias

														# output
														SCREAMING_SNAKE_CASE__						:       BertOutput           =   layer.output
														assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
														SCREAMING_SNAKE_CASE__						:       Tuple           =   roberta_layer.fca.weight
														SCREAMING_SNAKE_CASE__						:       Optional[int]           =   roberta_layer.fca.bias
														# end of layer

							if classification_head:
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   roberta.model.classification_heads["""mnli"""].dense.weight
														SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   roberta.model.classification_heads["""mnli"""].dense.bias
														SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   roberta.model.classification_heads["""mnli"""].out_proj.weight
														SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   roberta.model.classification_heads["""mnli"""].out_proj.bias
							else:
														# LM Head
														SCREAMING_SNAKE_CASE__						:       str           =   roberta.model.encoder.lm_head.dense.weight
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   roberta.model.encoder.lm_head.dense.bias
														SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   roberta.model.encoder.lm_head.layer_norm.weight
														SCREAMING_SNAKE_CASE__						:       Dict           =   roberta.model.encoder.lm_head.layer_norm.bias
														SCREAMING_SNAKE_CASE__						:       Optional[int]           =   roberta.model.encoder.lm_head.weight
														SCREAMING_SNAKE_CASE__						:       List[str]           =   roberta.model.encoder.lm_head.bias

							# Let's check that we get the same results.
							SCREAMING_SNAKE_CASE__						:       torch.Tensor           =   roberta.encode(_snake_case						).unsqueeze(0						)  # batch of size 1

							SCREAMING_SNAKE_CASE__						:       Tuple           =   model(_snake_case						)[0]
							if classification_head:
														SCREAMING_SNAKE_CASE__						:       Dict           =   roberta.model.classification_heads["""mnli"""](roberta.extract_features(_snake_case						)						)
							else:
														SCREAMING_SNAKE_CASE__						:       Tuple           =   roberta.model(_snake_case						)[0]
							print(our_output.shape   ,their_output.shape						)
							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   torch.max(torch.abs(our_output - their_output						)						).item()
							print(f'''max_absolute_diff = {max_absolute_diff}'''						)  # ~ 1e-7
							SCREAMING_SNAKE_CASE__						:       Tuple           =   torch.allclose(_snake_case   ,_snake_case   ,atol=1E-3						)
							print("""Do both models output the same tensors?"""   ,"""🔥""" if success else """💩"""						)
							if not success:
														raise Exception("""Something went wRoNg"""						)

							pathlib.Path(_snake_case						).mkdir(parents=_snake_case   ,exist_ok=_snake_case						)
							print(f'''Saving model to {pytorch_dump_folder_path}'''						)
							model.save_pretrained(_snake_case						)


if __name__ == "__main__":
					UpperCAmelCase__      :    Union[str, Any]    							= argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--roberta_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
					)
					parser.add_argument(
					    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
					)
					parser.add_argument(
					    '--classification_head', action='store_true', help='Whether to convert a final classification head.'
					)
					UpperCAmelCase__      :    Any    							= parser.parse_args()
					convert_xlm_roberta_xl_checkpoint_to_pytorch(
					    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
					)





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()
__lowercase							 =					logging.get_logger(__name__)

__lowercase							 =					{
    '''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 lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    for attribute in key.split('''.'''	):
        __UpperCamelCase     :str			  =		getattr(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)

    if weight_type is not None:
        __UpperCamelCase     :Any			  =		getattr(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	).shape
    else:
        __UpperCamelCase     :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":
        __UpperCamelCase     :str			  =		value
    elif weight_type == "weight_g":
        __UpperCamelCase     :List[str]			  =		value
    elif weight_type == "weight_v":
        __UpperCamelCase     :str			  =		value
    elif weight_type == "bias":
        __UpperCamelCase     :Union[str, Any]			  =		value
    else:
        __UpperCamelCase     :str			  =		value

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



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







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

    __UpperCamelCase     :List[Any]			  =		hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

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

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



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







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



@torch.no_grad()
def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE=None  ,			SCREAMING_SNAKE_CASE=None  ,			SCREAMING_SNAKE_CASE=True	):
    '''simple docstring'''







    if config_path is not None:
        __UpperCamelCase     :Tuple			  =		HubertConfig.from_pretrained(SCREAMING_SNAKE_CASE	)
    else:
        __UpperCamelCase     :Optional[int]			  =		HubertConfig()

    if is_finetuned:
        if dict_path:
            __UpperCamelCase     :Optional[int]			  =		Dictionary.load(SCREAMING_SNAKE_CASE	)

            # important change bos & pad token id since CTC symbol is <pad> and
            # not <s> as in fairseq
            __UpperCamelCase     :Optional[int]			  =		target_dict.pad_index
            __UpperCamelCase     :Dict			  =		target_dict.bos_index
            __UpperCamelCase     :str			  =		target_dict.eos_index
            __UpperCamelCase     :Dict			  =		len(target_dict.symbols	)
            __UpperCamelCase     :List[Any]			  =		os.path.join(SCREAMING_SNAKE_CASE  ,			'''vocab.json'''	)
            if not os.path.isdir(SCREAMING_SNAKE_CASE	):
                logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(SCREAMING_SNAKE_CASE	)	)
                return
            os.makedirs(SCREAMING_SNAKE_CASE  ,			exist_ok=SCREAMING_SNAKE_CASE	)
            with open(SCREAMING_SNAKE_CASE  ,			'''w'''  ,			encoding='''utf-8'''	) as vocab_handle:
                json.dump(target_dict.indices  ,			SCREAMING_SNAKE_CASE	)
            __UpperCamelCase     :Optional[int]			  =		WavaVecaCTCTokenizer(
                SCREAMING_SNAKE_CASE  ,			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=SCREAMING_SNAKE_CASE  ,			)
            __UpperCamelCase     :Union[str, Any]			  =		True if config.feat_extract_norm == '''layer''' else False
            __UpperCamelCase     :Any			  =		WavaVecaFeatureExtractor(
                feature_size=1  ,			sampling_rate=16_000  ,			padding_value=0  ,			do_normalize=SCREAMING_SNAKE_CASE  ,			return_attention_mask=SCREAMING_SNAKE_CASE  ,			)
            __UpperCamelCase     :Any			  =		WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE  ,			tokenizer=SCREAMING_SNAKE_CASE	)
            processor.save_pretrained(SCREAMING_SNAKE_CASE	)

        __UpperCamelCase     :List[str]			  =		HubertForCTC(SCREAMING_SNAKE_CASE	)
    else:
        __UpperCamelCase     :str			  =		HubertModel(SCREAMING_SNAKE_CASE	)

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

    __UpperCamelCase     :Dict			  =		model[0].eval()

    recursively_load_weights(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)

    hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE	)


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





