




import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


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

__A        =    {
    "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",
    "adapter_layer": "encoder.layers.*.adapter_layer",
    "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",
    "pooling_layer.linear": "projector",
    "pooling_layer.projection": "classifier",
}
__A        =    [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "projector",
    "classifier",
]







def 			lowerCamelCase_		(       UpperCamelCase__				:      List[str]      )     ->       int:





				"""simple docstring"""


				__lowerCamelCase		       =    {}
				with open(UpperCamelCase__ ,     'r'      ) as file:
								for line_number, line in enumerate(UpperCamelCase__      ):
												__lowerCamelCase		       =    line.strip()
												if line:
																__lowerCamelCase		       =    line.split()
																__lowerCamelCase		       =    line_number
																__lowerCamelCase		       =    words[0]
																__lowerCamelCase		       =    value
				return result







def 			lowerCamelCase_		(       UpperCamelCase__				:      Dict ,     UpperCamelCase__				:      int ,     UpperCamelCase__				:      Tuple ,     UpperCamelCase__				:      str ,     UpperCamelCase__				:      Union[str, Any]      )     ->       Tuple:





				"""simple docstring"""


				for attribute in key.split('.'      ):
								__lowerCamelCase		       =    getattr(UpperCamelCase__ ,     UpperCamelCase__      )

				__lowerCamelCase		       =    None
				for param_key in PARAM_MAPPING.keys():
								if full_name.endswith(UpperCamelCase__      ):
												__lowerCamelCase		       =    PARAM_MAPPING[full_name.split('.'      )[-1]]
												__lowerCamelCase		       =    'param'

				if weight_type is not None and weight_type != "param":
								__lowerCamelCase		       =    getattr(UpperCamelCase__ ,     UpperCamelCase__      ).shape
				elif weight_type is not None and weight_type == "param":
								__lowerCamelCase		       =    hf_pointer
								for attribute in hf_param_name.split('.'      ):
												__lowerCamelCase		       =    getattr(UpperCamelCase__ ,     UpperCamelCase__      )
								__lowerCamelCase		       =    shape_pointer.shape

								# let's reduce dimension
								__lowerCamelCase		       =    value[0]
				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 == "param":
								for attribute in hf_param_name.split('.'      ):
												__lowerCamelCase		       =    getattr(UpperCamelCase__ ,     UpperCamelCase__      )
								__lowerCamelCase		       =    value
				else:
								__lowerCamelCase		       =    value

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







def 			lowerCamelCase_		(       UpperCamelCase__				:      List[Any] ,     UpperCamelCase__				:      List[Any] ,     UpperCamelCase__				:      List[str] ,     UpperCamelCase__				:      List[Any] ,     UpperCamelCase__				:      Any      )     ->       Union[str, Any]:





				"""simple docstring"""


				__lowerCamelCase		       =    None
				for param_key in PARAM_MAPPING.keys():
								if full_name.endswith(UpperCamelCase__      ):
												__lowerCamelCase		       =    PARAM_MAPPING[full_name.split('.'      )[-1]]
												__lowerCamelCase		       =    'param'

				if weight_type is not None and weight_type != "param":
								__lowerCamelCase		       =    '.'.join([key, weight_type]      )
				elif weight_type is not None and weight_type == "param":
								__lowerCamelCase		       =    '.'.join([key, hf_param_name]      )
				else:
								__lowerCamelCase		       =    key

				__lowerCamelCase		       =    value if 'lm_head' in full_key else value[0]


__A        =    {
    "W_a": "linear_1.weight",
    "W_b": "linear_2.weight",
    "b_a": "linear_1.bias",
    "b_b": "linear_2.bias",
    "ln_W": "norm.weight",
    "ln_b": "norm.bias",
}







def 			lowerCamelCase_		(       UpperCamelCase__				:      List[Any] ,     UpperCamelCase__				:      str ,     UpperCamelCase__				:      Union[str, Any]=None ,     UpperCamelCase__				:      Optional[int]=None      )     ->       Union[str, Any]:





				"""simple docstring"""


				__lowerCamelCase		       =    False
				for key, mapped_key in MAPPING.items():
								__lowerCamelCase		       =    'wav2vec2.' + 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(UpperCamelCase__      )[0].split('.'      )[-2]
																__lowerCamelCase		       =    mapped_key.replace('*' ,     UpperCamelCase__      )
												if "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'
												else:
																__lowerCamelCase		       =    None
												if hf_dict is not None:
																rename_dict(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__      )
												else:
																set_recursively(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__      )
												return is_used
				return is_used







def 			lowerCamelCase_		(       UpperCamelCase__				:      List[str] ,     UpperCamelCase__				:      List[Any] ,     UpperCamelCase__				:      Union[str, Any]      )     ->       Optional[Any]:





				"""simple docstring"""


				__lowerCamelCase		       =    []
				__lowerCamelCase		       =    fairseq_model.state_dict()

				__lowerCamelCase		       =    hf_model.wavaveca.feature_extractor

				for name, value in fairseq_dict.items():
								__lowerCamelCase		       =    False
								if "conv_layers" in name:
												load_conv_layer(
												    UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__ ,     hf_model.config.feat_extract_norm == 'group' ,     )
												__lowerCamelCase		       =    True
								else:
												__lowerCamelCase		       =    load_wavaveca_layer(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__      )
								if not is_used:
												unused_weights.append(UpperCamelCase__      )

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







def 			lowerCamelCase_		(       UpperCamelCase__				:      Dict ,     UpperCamelCase__				:      Optional[Any] ,     UpperCamelCase__				:      Dict ,     UpperCamelCase__				:      str ,     UpperCamelCase__				:      int      )     ->       Any:





				"""simple docstring"""


				__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(UpperCamelCase__      )







@torch.no_grad()
def 			lowerCamelCase_		(       UpperCamelCase__				:      Any ,     UpperCamelCase__				:      int ,     UpperCamelCase__				:      Any=None ,     UpperCamelCase__				:      str=None ,     UpperCamelCase__				:      int=True ,     UpperCamelCase__				:      str=False      )     ->       str:





				"""simple docstring"""


				if config_path is not None:
								__lowerCamelCase		       =    WavaVecaConfig.from_pretrained(UpperCamelCase__      )
				else:
								__lowerCamelCase		       =    WavaVecaConfig()

				if is_seq_class:
								__lowerCamelCase		       =    read_txt_into_dict(UpperCamelCase__      )
								__lowerCamelCase		       =    idalabel
								__lowerCamelCase		       =    WavaVecaForSequenceClassification(UpperCamelCase__      )
								__lowerCamelCase		       =    WavaVecaFeatureExtractor(
								    feature_size=1 ,     sampling_rate=1_6000 ,     padding_value=0 ,     do_normalize=UpperCamelCase__ ,     return_attention_mask=UpperCamelCase__ ,     )
								feature_extractor.save_pretrained(UpperCamelCase__      )

				elif is_finetuned:
								if dict_path:
												__lowerCamelCase		       =    Dictionary.load(UpperCamelCase__      )

												# 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(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__      )
												__lowerCamelCase		       =    target_dict.indices

												# fairseq has the <pad> and <s> switched
												__lowerCamelCase		       =    0
												__lowerCamelCase		       =    1
												with open(UpperCamelCase__ ,     'w' ,     encoding='utf-8'      ) as vocab_handle:
																json.dump(UpperCamelCase__ ,     UpperCamelCase__      )
												__lowerCamelCase		       =    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__ ,     )
												__lowerCamelCase		       =    True if config.feat_extract_norm == 'layer' else False
												__lowerCamelCase		       =    WavaVecaFeatureExtractor(
												    feature_size=1 ,     sampling_rate=1_6000 ,     padding_value=0 ,     do_normalize=UpperCamelCase__ ,     return_attention_mask=UpperCamelCase__ ,     )
												__lowerCamelCase		       =    WavaVecaProcessor(feature_extractor=UpperCamelCase__ ,     tokenizer=UpperCamelCase__      )
												processor.save_pretrained(UpperCamelCase__      )

								__lowerCamelCase		       =    WavaVecaForCTC(UpperCamelCase__      )
				else:
								__lowerCamelCase		       =    WavaVecaForPreTraining(UpperCamelCase__      )

				if is_finetuned or is_seq_class:
								__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(UpperCamelCase__      )

								__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase		       =    fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] ,     task=UpperCamelCase__      )

				__lowerCamelCase		       =    model[0].eval()

				recursively_load_weights(UpperCamelCase__ ,     UpperCamelCase__ ,     not is_finetuned      )

				hf_wavavec.save_pretrained(UpperCamelCase__      )


if __name__ == "__main__":
					__A        =    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"
					)
					parser.add_argument(
					    "--is_seq_class",
					    action="store_true",
					    help="Whether the model to convert is a fine-tuned sequence classification model or not",
					)
					__A        =    parser.parse_args()

					__A        =    not args.not_finetuned and not args.is_seq_class
					convert_wavaveca_checkpoint(
					    args.checkpoint_path,
					    args.pytorch_dump_folder_path,
					    args.config_path,
					    args.dict_path,
					    is_finetuned,
					    args.is_seq_class,
					)






'''simple docstring'''

import warnings

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


lowerCamelCase							:   str  						=	logging.get_logger(__name__)

lowerCamelCase							:   int  						=	{
    'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}
class   __lowerCAmelCase						(lowercase_     ):





      '''simple docstring'''






      lowerCAmelCase__					:     Union[str, Any]  					=							"""mvp"""
      lowerCAmelCase__					:     Optional[Any]  					=							["""past_key_values"""]
      lowerCAmelCase__					:     List[str]  					=							{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}



      def __init__(self    :    Any   ,						UpperCamelCase    :    Optional[int]=50267   ,						UpperCamelCase    :    Tuple=1024   ,						UpperCamelCase    :    int=12   ,						UpperCamelCase    :    Tuple=4096   ,						UpperCamelCase    :    Dict=16   ,						UpperCamelCase    :    int=12   ,						UpperCamelCase    :    Optional[int]=4096   ,						UpperCamelCase    :    Optional[int]=16   ,						UpperCamelCase    :    Tuple=0.0   ,						UpperCamelCase    :    Tuple=0.0   ,						UpperCamelCase    :    List[Any]="gelu"   ,						UpperCamelCase    :    Union[str, Any]=1024   ,						UpperCamelCase    :    Optional[Any]=0.1   ,						UpperCamelCase    :    str=0.0   ,						UpperCamelCase    :    str=0.0   ,						UpperCamelCase    :    Optional[Any]=0.02   ,						UpperCamelCase    :    List[str]=0.0   ,						UpperCamelCase    :    List[str]=False   ,						UpperCamelCase    :    Optional[int]=True   ,						UpperCamelCase    :    Any=1   ,						UpperCamelCase    :    int=0   ,						UpperCamelCase    :    int=2   ,						UpperCamelCase    :    Any=True   ,						UpperCamelCase    :    Optional[Any]=2   ,						UpperCamelCase    :    Optional[Any]=2   ,						UpperCamelCase    :    Tuple=False   ,						UpperCamelCase    :    int=100   ,						UpperCamelCase    :    Optional[Any]=800   ,						**UpperCamelCase    :    str   ,						):

             '''simple docstring'''







             lowercase__      			= vocab_size
             lowercase__      			= max_position_embeddings
             lowercase__      			= d_model
             lowercase__      			= encoder_ffn_dim
             lowercase__      			= encoder_layers
             lowercase__      			= encoder_attention_heads
             lowercase__      			= decoder_ffn_dim
             lowercase__      			= decoder_layers
             lowercase__      			= decoder_attention_heads
             lowercase__      			= dropout
             lowercase__      			= attention_dropout
             lowercase__      			= activation_dropout
             lowercase__      			= activation_function
             lowercase__      			= init_std
             lowercase__      			= encoder_layerdrop
             lowercase__      			= decoder_layerdrop
             lowercase__      			= classifier_dropout
             lowercase__      			= use_cache
             lowercase__      			= encoder_layers
             lowercase__      			= scale_embedding  # scale factor will be sqrt(d_model) if True
             lowercase__      			= use_prompt
             lowercase__      			= prompt_length
             lowercase__      			= prompt_mid_dim

             super().__init__(
                 pad_token_id=UpperCamelCase   ,						bos_token_id=UpperCamelCase   ,						eos_token_id=UpperCamelCase   ,						is_encoder_decoder=UpperCamelCase   ,						decoder_start_token_id=UpperCamelCase   ,						forced_eos_token_id=UpperCamelCase   ,						**UpperCamelCase   ,						)

             if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated'''   ,						UpperCamelCase ):
                    lowercase__      			= self.bos_token_id
                    warnings.warn(
                        f"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. "
                        '''The config can simply be saved and uploaded again to be fixed.''' )




