


'''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()
__A	:	int     =     logging.get_logger(__name__)

__A	:	List[Any]     =     {
    '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',
}
__A	:	str     =     [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]





def      UpperCAmelCase      (		lowerCamelCase_  :List[str]				,      lowerCamelCase_  :Optional[int]				,      lowerCamelCase_  :Dict				,      lowerCamelCase_  :Dict				,      lowerCamelCase_  :str ):







		'''simple docstring'''
		for attribute in key.split(""".""" ):
				snake_case_		:							Union[str, Any]			       =						getattr(lowerCamelCase_				,      lowerCamelCase_ )

		if weight_type is not None:
				snake_case_		:							str			       =						getattr(lowerCamelCase_				,      lowerCamelCase_ ).shape
		else:
				snake_case_		:							Any			       =						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":
				snake_case_		:							Optional[int]			       =						value
		elif weight_type == "weight_g":
				snake_case_		:							Dict			       =						value
		elif weight_type == "weight_v":
				snake_case_		:							List[Any]			       =						value
		elif weight_type == "bias":
				snake_case_		:							List[str]			       =						value
		elif weight_type == "running_mean":
				snake_case_		:							Dict			       =						value
		elif weight_type == "running_var":
				snake_case_		:							int			       =						value
		elif weight_type == "num_batches_tracked":
				snake_case_		:							str			       =						value
		elif weight_type == "inv_freq":
				snake_case_		:							Optional[Any]			       =						value
		else:
				snake_case_		:							Dict			       =						value

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





def      UpperCAmelCase      (		lowerCamelCase_  :int				,      lowerCamelCase_  :Union[str, Any]				,      lowerCamelCase_  :Union[str, Any] ):







		'''simple docstring'''
		snake_case_		:							int			       =						[]
		snake_case_		:							int			       =						fairseq_model.state_dict()

		snake_case_		:							Optional[int]			       =						hf_model.wavaveca_conformer.feature_extractor

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

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





def      UpperCAmelCase      (		lowerCamelCase_  :str				,      lowerCamelCase_  :List[Any]				,      lowerCamelCase_  :List[str]				,      lowerCamelCase_  :Any				,      lowerCamelCase_  :str ):







		'''simple docstring'''
		snake_case_		:							Optional[int]			       =						full_name.split("""conv_layers.""" )[-1]
		snake_case_		:							List[str]			       =						name.split(""".""" )
		snake_case_		:							Optional[Any]			       =						int(items[0] )
		snake_case_		:							Union[str, Any]			       =						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.''' )
						snake_case_		:							List[Any]			       =						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.''' )
						snake_case_		:							Union[str, 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:
						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.''' )
						snake_case_		:							Union[str, Any]			       =						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.''' )
						snake_case_		:							Optional[int]			       =						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      UpperCAmelCase      (		lowerCamelCase_  :Union[str, Any]				,      lowerCamelCase_  :int				,      lowerCamelCase_  :Any=None				,      lowerCamelCase_  :int=None				,      lowerCamelCase_  :Any=True ):







		'''simple docstring'''
		if config_path is not None:
				snake_case_		:							Union[str, Any]			       =						WavaVecaConformerConfig.from_pretrained(lowerCamelCase_				,      hidden_act="""swish""" )
		else:
				snake_case_		:							int			       =						WavaVecaConformerConfig()

		if "rope" in checkpoint_path:
				snake_case_		:							Dict			       =						"""rotary"""

		if is_finetuned:
				if dict_path:
						snake_case_		:							Any			       =						Dictionary.load(lowerCamelCase_ )

						# important change bos & pad token id since CTC symbol is <pad> and
						# not <s> as in fairseq
						snake_case_		:							Tuple			       =						target_dict.pad_index
						snake_case_		:							Any			       =						target_dict.bos_index
						snake_case_		:							Union[str, Any]			       =						target_dict.eos_index
						snake_case_		:							Any			       =						len(target_dict.symbols )
						snake_case_		:							Optional[int]			       =						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_ )
						snake_case_		:							List[str]			       =						target_dict.indices

						# fairseq has the <pad> and <s> switched
						snake_case_		:							Optional[int]			       =						0
						snake_case_		:							Optional[Any]			       =						1
						with open(lowerCamelCase_				,      """w"""				,      encoding="""utf-8""" ) as vocab_handle:
								json.dump(lowerCamelCase_				,      lowerCamelCase_ )
						snake_case_		:							int			       =						WavaVecaCTCTokenizer(
						    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_				,      )
						snake_case_		:							Optional[Any]			       =						True if config.feat_extract_norm == """layer""" else False
						snake_case_		:							Optional[int]			       =						WavaVecaFeatureExtractor(
						    feature_size=1				,      sampling_rate=1_60_00				,      padding_value=0				,      do_normalize=lowerCamelCase_				,      return_attention_mask=lowerCamelCase_				,      )
						snake_case_		:							Optional[int]			       =						WavaVecaProcessor(feature_extractor=lowerCamelCase_				,      tokenizer=lowerCamelCase_ )
						processor.save_pretrained(lowerCamelCase_ )

				snake_case_		:							Any			       =						WavaVecaConformerForCTC(lowerCamelCase_ )
		else:
				snake_case_		:							Dict			       =						WavaVecaConformerForPreTraining(lowerCamelCase_ )

		if is_finetuned:
				snake_case_       ,      snake_case_       ,      snake_case_		:							Optional[Any]			       =						fairseq.checkpoint_utils.load_model_ensemble_and_task(
				    [checkpoint_path]				,      arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] )} )
		else:
				snake_case_		:							int			       =						argparse.Namespace(task="""audio_pretraining""" )
				snake_case_		:							int			       =						fairseq.tasks.setup_task(lowerCamelCase_ )

				snake_case_       ,      snake_case_       ,      snake_case_		:							List[str]			       =						fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]				,      task=lowerCamelCase_ )

		snake_case_		:							List[Any]			       =						model[0].eval()

		recursively_load_weights(lowerCamelCase_				,      lowerCamelCase_				,      not is_finetuned )

		hf_wavavec.save_pretrained(lowerCamelCase_ )


if __name__ == "__main__":
							__A	:	List[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_wavaveca_conformer_checkpoint(
							    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
							)



'''simple docstring'''






import torch

from transformers import AutoModel
class 						__UpperCamelCase	(						torch.nn.Module							):





							def __init__(  self		:Union[str, Any]  ,_UpperCamelCase		:Tuple="sayef/fsner-bert-base-uncased"			):
									super(_UpperCamelCase  ,self			).__init__()

									snake_case_		:							Dict			       =						AutoModel.from_pretrained(_UpperCamelCase  ,return_dict=_UpperCamelCase			)
									snake_case_		:							Dict			       =						torch.nn.CosineSimilarity(3  ,1E-0_8			)
									snake_case_		:							int			       =						torch.nn.Softmax(dim=1			)





							def 		a__   (  self		:Optional[Any]  ,**_UpperCamelCase		:Optional[Any]			):
									return self.bert(**_UpperCamelCase			).last_hidden_state





							def 		a__   (  self		:List[Any]  ,_UpperCamelCase		:Dict			):
									return token_embeddings.sum(2  ,keepdim=_UpperCamelCase			)





							def 		a__   (  self		:Optional[Any]  ,_UpperCamelCase		:List[str]  ,_UpperCamelCase		:List[str]  ,_UpperCamelCase		:List[str]=1			):
									return self.softmax(T * self.cos(_UpperCamelCase  ,_UpperCamelCase			)			)





							def 		a__   (  self		:Optional[Any]  ,_UpperCamelCase		:Optional[Any]  ,_UpperCamelCase		:int			):
									snake_case_		:							Union[str, Any]			       =						W_supports["""sizes"""].tolist()
									snake_case_		:							Dict			       =						W_supports["""start_token_id"""].item()
									snake_case_		:							int			       =						W_supports["""end_token_id"""].item()

									del W_supports["sizes"]
									del W_supports["start_token_id"]
									del W_supports["end_token_id"]

									snake_case_		:							int			       =						self.BERT(**_UpperCamelCase			)
									snake_case_		:							int			       =						self.BERT(**_UpperCamelCase			)

									snake_case_		:							Any			       =						None
									snake_case_		:							Any			       =						None

									snake_case_		:							Union[str, Any]			       =						W_supports["""input_ids"""] == start_token_id
									snake_case_		:							Tuple			       =						W_supports["""input_ids"""] == end_token_id

									for i, size in enumerate(_UpperCamelCase			):
											if i == 0:
													snake_case_		:							str			       =						0
											else:
													snake_case_		:							Optional[Any]			       =						support_sizes[i - 1]

											snake_case_		:							Dict			       =						S[s : s + size][start_token_masks[s : s + size]]
											snake_case_		:							Optional[Any]			       =						S[s : s + size][end_token_masks[s : s + size]]

											snake_case_		:							int			       =						torch.matmul(q[i]  ,s_start.T			).sum(1			).softmax(0			)
											snake_case_		:							List[str]			       =						torch.matmul(q[i]  ,s_end.T			).sum(1			).softmax(0			)

											if p_starts is not None:
													snake_case_		:							List[str]			       =						torch.vstack((p_starts, p_start)			)
													snake_case_		:							Tuple			       =						torch.vstack((p_ends, p_end)			)
											else:
													snake_case_		:							Any			       =						p_start
													snake_case_		:							List[str]			       =						p_end

									return p_starts, p_ends