


'''simple docstring'''






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    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.linear_k''': '''encoder.layers.*.self_attn.linear_k''',
    '''self_attn.linear_v''': '''encoder.layers.*.self_attn.linear_v''',
    '''self_attn.linear_q''': '''encoder.layers.*.self_attn.linear_q''',
    '''self_attn.pos_bias_u''': '''encoder.layers.*.self_attn.pos_bias_u''',
    '''self_attn.pos_bias_v''': '''encoder.layers.*.self_attn.pos_bias_v''',
    '''self_attn.linear_out''': '''encoder.layers.*.self_attn.linear_out''',
    '''self_attn.linear_pos''': '''encoder.layers.*.self_attn.linear_pos''',
    '''self_attn.rotary_emb''': '''encoder.embed_positions''',
    '''self_attn_layer_norm''': '''encoder.layers.*.self_attn_layer_norm''',
    '''conv_module.pointwise_conv1''': '''encoder.layers.*.conv_module.pointwise_conv1''',
    '''conv_module.pointwise_conv2''': '''encoder.layers.*.conv_module.pointwise_conv2''',
    '''conv_module.depthwise_conv''': '''encoder.layers.*.conv_module.depthwise_conv''',
    '''conv_module.batch_norm''': '''encoder.layers.*.conv_module.batch_norm''',
    '''conv_module.layer_norm''': '''encoder.layers.*.conv_module.layer_norm''',
    '''ffn1.w_1''': '''encoder.layers.*.ffn1.intermediate_dense''',
    '''ffn1.w_2''': '''encoder.layers.*.ffn1.output_dense''',
    '''ffn1.layer_norm''': '''encoder.layers.*.ffn1_layer_norm''',
    '''ffn2.w_1''': '''encoder.layers.*.ffn2.intermediate_dense''',
    '''ffn2.w_2''': '''encoder.layers.*.ffn2.output_dense''',
    '''ffn2.layer_norm''': '''encoder.layers.*.ffn2_layer_norm''',
    '''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    _snake_case  (	A					,       A					,       A					,       A					,       A			)		->       Optional[int]:
							for attribute in key.split('''.'''			):
														lowerCAmelCase__			  =      getattr(A					,       A			)

							if weight_type is not None:
														lowerCAmelCase__			  =      getattr(A					,       A			).shape
							else:
														lowerCAmelCase__			  =      hf_pointer.shape

							if hf_shape != value.shape:
														raise ValueError(
														    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":
														lowerCAmelCase__			  =      value
							elif weight_type == "weight_g":
														lowerCAmelCase__			  =      value
							elif weight_type == "weight_v":
														lowerCAmelCase__			  =      value
							elif weight_type == "bias":
														lowerCAmelCase__			  =      value
							elif weight_type == "running_mean":
														lowerCAmelCase__			  =      value
							elif weight_type == "running_var":
														lowerCAmelCase__			  =      value
							elif weight_type == "num_batches_tracked":
														lowerCAmelCase__			  =      value
							elif weight_type == "inv_freq":
														lowerCAmelCase__			  =      value
							else:
														lowerCAmelCase__			  =      value

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


def    _snake_case  (	A					,       A					,       A			)		->       int:
							lowerCAmelCase__			  =      []
							lowerCAmelCase__			  =      fairseq_model.state_dict()

							lowerCAmelCase__			  =      hf_model.wavaveca_conformer.feature_extractor

							for name, value in fairseq_dict.items():
														lowerCAmelCase__			  =      False
														if "conv_layers" in name:
																					load_conv_layer(
																					    A					,       A					,       A					,       A					,       hf_model.config.feat_extract_norm == '''group'''					,       )
																					lowerCAmelCase__			  =      True
														else:
																					for key, mapped_key in MAPPING.items():
																												lowerCAmelCase__			  =      '''wav2vec2_conformer.''' + 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]:
																																			lowerCAmelCase__			  =      True
																																			if "*" in mapped_key:
																																										lowerCAmelCase__			  =      name.split(A			)[0].split('''.'''			)[-2]
																																										lowerCAmelCase__			  =      mapped_key.replace('''*'''					,       A			)
																																			if "pos_bias_u" in name:
																																										lowerCAmelCase__			  =      None
																																			elif "pos_bias_v" in name:
																																										lowerCAmelCase__			  =      None
																																			elif "weight_g" in name:
																																										lowerCAmelCase__			  =      '''weight_g'''
																																			elif "weight_v" in name:
																																										lowerCAmelCase__			  =      '''weight_v'''
																																			elif "bias" in name:
																																										lowerCAmelCase__			  =      '''bias'''
																																			elif "weight" in name:
																																										# TODO: don't match quantizer.weight_proj
																																										lowerCAmelCase__			  =      '''weight'''
																																			elif "running_mean" in name:
																																										lowerCAmelCase__			  =      '''running_mean'''
																																			elif "inv_freq" in name:
																																										lowerCAmelCase__			  =      '''inv_freq'''
																																			elif "running_var" in name:
																																										lowerCAmelCase__			  =      '''running_var'''
																																			elif "num_batches_tracked" in name:
																																										lowerCAmelCase__			  =      '''num_batches_tracked'''
																																			else:
																																										lowerCAmelCase__			  =      None
																																			set_recursively(A					,       A					,       A					,       A					,       A			)
																												continue
														if not is_used:
																					unused_weights.append(A			)

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


def    _snake_case  (	A					,       A					,       A					,       A					,       A			)		->       Optional[Any]:
							lowerCAmelCase__			  =      full_name.split('''conv_layers.'''			)[-1]
							lowerCAmelCase__			  =      name.split('''.'''			)
							lowerCAmelCase__			  =      int(items[0]			)
							lowerCAmelCase__			  =      int(items[1]			)

							if type_id == 0:
														if "bias" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
																												raise ValueError(
																												    F"""{full_name} has size {value.shape}, but"""
																												    F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""			)
																					lowerCAmelCase__			  =      value
																					logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""			)
														elif "weight" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
																												raise ValueError(
																												    F"""{full_name} has size {value.shape}, but"""
																												    F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""			)
																					lowerCAmelCase__			  =      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:
																					if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
																												raise ValueError(
																												    F"""{full_name} has size {value.shape}, but"""
																												    F""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found."""			)
																					lowerCAmelCase__			  =      value
																					logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""			)
														elif "weight" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
																												raise ValueError(
																												    F"""{full_name} has size {value.shape}, but"""
																												    F""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found."""			)
																					lowerCAmelCase__			  =      value
																					logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""			)
							else:
														unused_weights.append(A			)


@torch.no_grad()
def    _snake_case  (	A					,       A					,       A=None					,       A=None					,       A=True			)		->       str:
							if config_path is not None:
														lowerCAmelCase__			  =      WavaVecaConformerConfig.from_pretrained(A					,       hidden_act='''swish'''			)
							else:
														lowerCAmelCase__			  =      WavaVecaConformerConfig()

							if "rope" in checkpoint_path:
														lowerCAmelCase__			  =      '''rotary'''

							if is_finetuned:
														if dict_path:
																					lowerCAmelCase__			  =      Dictionary.load(A			)

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

																					# fairseq has the <pad> and <s> switched
																					lowerCAmelCase__			  =      0
																					lowerCAmelCase__			  =      1
																					with open(A					,       '''w'''					,       encoding='''utf-8'''			) as vocab_handle:
																												json.dump(A					,       A			)
																					lowerCAmelCase__			  =      WavaVecaCTCTokenizer(
																					    A					,       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=A					,       )
																					lowerCAmelCase__			  =      True if config.feat_extract_norm == '''layer''' else False
																					lowerCAmelCase__			  =      WavaVecaFeatureExtractor(
																					    feature_size=1					,       sampling_rate=16000					,       padding_value=0					,       do_normalize=A					,       return_attention_mask=A					,       )
																					lowerCAmelCase__			  =      WavaVecaProcessor(feature_extractor=A					,       tokenizer=A			)
																					processor.save_pretrained(A			)

														lowerCAmelCase__			  =      WavaVecaConformerForCTC(A			)
							else:
														lowerCAmelCase__			  =      WavaVecaConformerForPreTraining(A			)

							if is_finetuned:
														lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__			  =      fairseq.checkpoint_utils.load_model_ensemble_and_task(
														    [checkpoint_path]					,       arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''			)[:-1]			)}			)
							else:
														lowerCAmelCase__			  =      argparse.Namespace(task='''audio_pretraining'''			)
														lowerCAmelCase__			  =      fairseq.tasks.setup_task(A			)

														lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__			  =      fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]					,       task=A			)

							lowerCAmelCase__			  =      model[0].eval()

							recursively_load_weights(A					,       A					,       not is_finetuned			)

							hf_wavavec.save_pretrained(A			)


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_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'''
		)
		__UpperCAmelCase       							=				parser.parse_args()
		convert_wavaveca_conformer_checkpoint(
		    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
		)







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

__a   :							Tuple						= "Hello world! cécé herlolip"



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      str      ,					__lowercase :      str      ,					__lowercase :      bool     )    ->   List[Any]:





    """simple docstring"""

    __A       =						FairseqRobertaModel.from_pretrained(__lowercase     )
    roberta.eval()  # disable dropout
    __A       =						roberta.model.encoder.sentence_encoder
    __A       =						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=5_1_4      ,					type_vocab_size=1      ,					layer_norm_eps=1E-5      ,					)
    if classification_head:
        __A       =						roberta.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

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

    __A       =						XLMRobertaXLForSequenceClassification(__lowercase     ) if classification_head else XLMRobertaXLForMaskedLM(__lowercase     )
    model.eval()

    # Now let's copy all the weights.
    # Embeddings
    __A       =						roberta_sent_encoder.embed_tokens.weight
    __A       =						roberta_sent_encoder.embed_positions.weight
    __A       =						torch.zeros_like(
        model.roberta.embeddings.token_type_embeddings.weight     )  # just zero them out b/c RoBERTa doesn't use them.

    __A       =						roberta_sent_encoder.layer_norm.weight
    __A       =						roberta_sent_encoder.layer_norm.bias

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

        __A       =						layer.attention
        __A       =						roberta_layer.self_attn_layer_norm.weight
        __A       =						roberta_layer.self_attn_layer_norm.bias

        # self attention
        __A       =						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)     )
        )

        __A       =						roberta_layer.self_attn.q_proj.weight
        __A       =						roberta_layer.self_attn.q_proj.bias
        __A       =						roberta_layer.self_attn.k_proj.weight
        __A       =						roberta_layer.self_attn.k_proj.bias
        __A       =						roberta_layer.self_attn.v_proj.weight
        __A       =						roberta_layer.self_attn.v_proj.bias

        # self-attention output
        __A       =						layer.attention.output
        assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
        __A       =						roberta_layer.self_attn.out_proj.weight
        __A       =						roberta_layer.self_attn.out_proj.bias

        # this one is final layer norm
        __A       =						roberta_layer.final_layer_norm.weight
        __A       =						roberta_layer.final_layer_norm.bias

        # intermediate
        __A       =						layer.intermediate
        assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
        __A       =						roberta_layer.fca.weight
        __A       =						roberta_layer.fca.bias

        # output
        __A       =						layer.output
        assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
        __A       =						roberta_layer.fca.weight
        __A       =						roberta_layer.fca.bias
        # end of layer

    if classification_head:
        __A       =						roberta.model.classification_heads["""mnli"""].dense.weight
        __A       =						roberta.model.classification_heads["""mnli"""].dense.bias
        __A       =						roberta.model.classification_heads["""mnli"""].out_proj.weight
        __A       =						roberta.model.classification_heads["""mnli"""].out_proj.bias
    else:
        # LM Head
        __A       =						roberta.model.encoder.lm_head.dense.weight
        __A       =						roberta.model.encoder.lm_head.dense.bias
        __A       =						roberta.model.encoder.lm_head.layer_norm.weight
        __A       =						roberta.model.encoder.lm_head.layer_norm.bias
        __A       =						roberta.model.encoder.lm_head.weight
        __A       =						roberta.model.encoder.lm_head.bias

    # Let's check that we get the same results.
    __A       =						roberta.encode(__lowercase     ).unsqueeze(0     )  # batch of size 1

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

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


if __name__ == "__main__":
  __a   :							List[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."
  )
  __a   :							Dict						= parser.parse_args()
  convert_xlm_roberta_xl_checkpoint_to_pytorch(
      args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
  )




