


'''simple docstring'''



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       _UpperCamelCase (						UpperCamelCase__   ):
			UpperCAmelCase__					:   Any       				=       {}
			with open(UpperCamelCase__	, """r"""   ) as file:
						for line_number, line in enumerate(UpperCamelCase__   ):
									UpperCAmelCase__					:   Any       				=       line.strip()
									if line:
												UpperCAmelCase__					:   str       				=       line.split()
												UpperCAmelCase__					:   Any       				=       line_number
												UpperCAmelCase__					:   int       				=       words[0]
												UpperCAmelCase__					:   Dict       				=       value
			return result







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__   ):
			for attribute in key.split("""."""   ):
						UpperCAmelCase__					:   int       				=       getattr(UpperCamelCase__	, UpperCamelCase__   )

			UpperCAmelCase__					:   List[str]       				=       None
			for param_key in PARAM_MAPPING.keys():
						if full_name.endswith(UpperCamelCase__   ):
									UpperCAmelCase__					:   int       				=       PARAM_MAPPING[full_name.split("""."""   )[-1]]
									UpperCAmelCase__					:   List[str]       				=       """param"""

			if weight_type is not None and weight_type != "param":
						UpperCAmelCase__					:   Optional[int]       				=       getattr(UpperCamelCase__	, UpperCamelCase__   ).shape
			elif weight_type is not None and weight_type == "param":
						UpperCAmelCase__					:   Any       				=       hf_pointer
						for attribute in hf_param_name.split("""."""   ):
									UpperCAmelCase__					:   int       				=       getattr(UpperCamelCase__	, UpperCamelCase__   )
						UpperCAmelCase__					:   Union[str, Any]       				=       shape_pointer.shape

						# let's reduce dimension
						UpperCAmelCase__					:   Optional[int]       				=       value[0]
			else:
						UpperCAmelCase__					:   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":
						UpperCAmelCase__					:   Optional[int]       				=       value
			elif weight_type == "weight_g":
						UpperCAmelCase__					:   Optional[int]       				=       value
			elif weight_type == "weight_v":
						UpperCAmelCase__					:   Optional[Any]       				=       value
			elif weight_type == "bias":
						UpperCAmelCase__					:   int       				=       value
			elif weight_type == "param":
						for attribute in hf_param_name.split("""."""   ):
									UpperCAmelCase__					:   List[str]       				=       getattr(UpperCamelCase__	, UpperCamelCase__   )
						UpperCAmelCase__					:   List[str]       				=       value
			else:
						UpperCAmelCase__					:   List[Any]       				=       value

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







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__   ):
			UpperCAmelCase__					:   Dict       				=       None
			for param_key in PARAM_MAPPING.keys():
						if full_name.endswith(UpperCamelCase__   ):
									UpperCAmelCase__					:   Any       				=       PARAM_MAPPING[full_name.split("""."""   )[-1]]
									UpperCAmelCase__					:   Optional[int]       				=       """param"""

			if weight_type is not None and weight_type != "param":
						UpperCAmelCase__					:   Any       				=       """.""".join([key, weight_type]   )
			elif weight_type is not None and weight_type == "param":
						UpperCAmelCase__					:   int       				=       """.""".join([key, hf_param_name]   )
			else:
						UpperCAmelCase__					:   List[Any]       				=       key

			UpperCAmelCase__					:   List[str]       				=       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       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__=None	, UpperCamelCase__=None   ):
			UpperCAmelCase__					:   List[str]       				=       False
			for key, mapped_key in MAPPING.items():
						UpperCAmelCase__					:   List[str]       				=       """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]:
									UpperCAmelCase__					:   int       				=       True
									if "*" in mapped_key:
												UpperCAmelCase__					:   Union[str, Any]       				=       name.split(UpperCamelCase__   )[0].split("""."""   )[-2]
												UpperCAmelCase__					:   List[Any]       				=       mapped_key.replace("""*"""	, UpperCamelCase__   )
									if "weight_g" in name:
												UpperCAmelCase__					:   int       				=       """weight_g"""
									elif "weight_v" in name:
												UpperCAmelCase__					:   Optional[int]       				=       """weight_v"""
									elif "bias" in name:
												UpperCAmelCase__					:   Tuple       				=       """bias"""
									elif "weight" in name:
												# TODO: don't match quantizer.weight_proj
												UpperCAmelCase__					:   List[str]       				=       """weight"""
									else:
												UpperCAmelCase__					:   Tuple       				=       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       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__   ):
			UpperCAmelCase__					:   Any       				=       []
			UpperCAmelCase__					:   Union[str, Any]       				=       fairseq_model.state_dict()

			UpperCAmelCase__					:   Union[str, Any]       				=       hf_model.wavaveca.feature_extractor

			for name, value in fairseq_dict.items():
						UpperCAmelCase__					:   Union[str, Any]       				=       False
						if "conv_layers" in name:
									load_conv_layer(
									    UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, hf_model.config.feat_extract_norm == """group"""	, )
									UpperCAmelCase__					:   Dict       				=       True
						else:
									UpperCAmelCase__					:   Tuple       				=       load_wavaveca_layer(UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__   )
						if not is_used:
									unused_weights.append(UpperCamelCase__   )

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







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__   ):
			UpperCAmelCase__					:   List[str]       				=       full_name.split("""conv_layers."""   )[-1]
			UpperCAmelCase__					:   Optional[int]       				=       name.split("""."""   )
			UpperCAmelCase__					:   str       				=       int(items[0]   )
			UpperCAmelCase__					:   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.'''   )
									UpperCAmelCase__					:   Tuple       				=       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.'''   )
									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:
									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.'''   )
									UpperCAmelCase__					:   Optional[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.'''   )
									UpperCAmelCase__					:   Any       				=       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       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__=None	, UpperCamelCase__=None	, UpperCamelCase__=True	, UpperCamelCase__=False   ):
			if config_path is not None:
						UpperCAmelCase__					:   int       				=       WavaVecaConfig.from_pretrained(UpperCamelCase__   )
			else:
						UpperCAmelCase__					:   List[str]       				=       WavaVecaConfig()

			if is_seq_class:
						UpperCAmelCase__					:   str       				=       read_txt_into_dict(UpperCamelCase__   )
						UpperCAmelCase__					:   Dict       				=       idalabel
						UpperCAmelCase__					:   List[str]       				=       WavaVecaForSequenceClassification(UpperCamelCase__   )
						UpperCAmelCase__					:   Dict       				=       WavaVecaFeatureExtractor(
						    feature_size=1	, sampling_rate=1_6_0_0_0	, padding_value=0	, do_normalize=UpperCamelCase__	, return_attention_mask=UpperCamelCase__	, )
						feature_extractor.save_pretrained(UpperCamelCase__   )

			elif is_finetuned:
						if dict_path:
									UpperCAmelCase__					:   List[Any]       				=       Dictionary.load(UpperCamelCase__   )

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

									# fairseq has the <pad> and <s> switched
									UpperCAmelCase__					:   Optional[int]       				=       0
									UpperCAmelCase__					:   List[str]       				=       1
									with open(UpperCamelCase__	, """w"""	, encoding="""utf-8"""   ) as vocab_handle:
												json.dump(UpperCamelCase__	, UpperCamelCase__   )
									UpperCAmelCase__					:   Union[str, Any]       				=       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__	, )
									UpperCAmelCase__					:   List[str]       				=       True if config.feat_extract_norm == """layer""" else False
									UpperCAmelCase__					:   Any       				=       WavaVecaFeatureExtractor(
									    feature_size=1	, sampling_rate=1_6_0_0_0	, padding_value=0	, do_normalize=UpperCamelCase__	, return_attention_mask=UpperCamelCase__	, )
									UpperCAmelCase__					:   List[str]       				=       WavaVecaProcessor(feature_extractor=UpperCamelCase__	, tokenizer=UpperCamelCase__   )
									processor.save_pretrained(UpperCamelCase__   )

						UpperCAmelCase__					:   int       				=       WavaVecaForCTC(UpperCamelCase__   )
			else:
						UpperCAmelCase__					:   Any       				=       WavaVecaForPreTraining(UpperCamelCase__   )

			if is_finetuned or is_seq_class:
						UpperCAmelCase__					:   List[Any]       				=       fairseq.checkpoint_utils.load_model_ensemble_and_task(
						    [checkpoint_path]	, arg_overrides={"""data""": """/""".join(dict_path.split("""/"""   )[:-1]   )}   )
			else:
						UpperCAmelCase__					:   List[str]       				=       argparse.Namespace(task="""audio_pretraining"""   )
						UpperCAmelCase__					:   int       				=       fairseq.tasks.setup_task(UpperCamelCase__   )

						UpperCAmelCase__					:   int       				=       fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]	, task=UpperCamelCase__   )

			UpperCAmelCase__					:   List[Any]       				=       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'''



from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer


@dataclass
class 						_snake_case   (       a__		):
					lowerCAmelCase :torch.FloatTensor


class 						_snake_case   (       a__    ,				a__		):




					@register_to_config
					def __init__( self							,							_lowerCamelCase = 3							,							_lowerCamelCase = 3							,							_lowerCamelCase = ("DownEncoderBlock2D",)							,							_lowerCamelCase = ("UpDecoderBlock2D",)							,							_lowerCamelCase = (64,)							,							_lowerCamelCase = 1							,							_lowerCamelCase = "silu"							,							_lowerCamelCase = 3							,							_lowerCamelCase = 32							,							_lowerCamelCase = 256							,							_lowerCamelCase = 32							,							_lowerCamelCase = None							,							_lowerCamelCase = 0.18215							,							_lowerCamelCase = "group"							,							):
								super().__init__()

								# pass init params to Encoder
								UpperCAmelCase__					:   str       				=       Encoder(
								    in_channels=_lowerCamelCase							,							out_channels=_lowerCamelCase							,							down_block_types=_lowerCamelCase							,							block_out_channels=_lowerCamelCase							,							layers_per_block=_lowerCamelCase							,							act_fn=_lowerCamelCase							,							norm_num_groups=_lowerCamelCase							,							double_z=_lowerCamelCase							,							)

								UpperCAmelCase__					:   Optional[Any]       				=       vq_embed_dim if vq_embed_dim is not None else latent_channels

								UpperCAmelCase__					:   Any       				=       nn.Convad(_lowerCamelCase							,							_lowerCamelCase							,							1)
								UpperCAmelCase__					:   Optional[int]       				=       VectorQuantizer(_lowerCamelCase							,							_lowerCamelCase							,							beta=0.25							,							remap=_lowerCamelCase							,							sane_index_shape=_lowerCamelCase)
								UpperCAmelCase__					:   Optional[int]       				=       nn.Convad(_lowerCamelCase							,							_lowerCamelCase							,							1)

								# pass init params to Decoder
								UpperCAmelCase__					:   str       				=       Decoder(
								    in_channels=_lowerCamelCase							,							out_channels=_lowerCamelCase							,							up_block_types=_lowerCamelCase							,							block_out_channels=_lowerCamelCase							,							layers_per_block=_lowerCamelCase							,							act_fn=_lowerCamelCase							,							norm_num_groups=_lowerCamelCase							,							norm_type=_lowerCamelCase							,							)




					@apply_forward_hook
					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase = True):
								UpperCAmelCase__					:   Union[str, Any]       				=       self.encoder(_lowerCamelCase)
								UpperCAmelCase__					:   str       				=       self.quant_conv(_lowerCamelCase)

								if not return_dict:
											return (h,)

								return VQEncoderOutput(latents=_lowerCamelCase)




					@apply_forward_hook
					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase = False							,							_lowerCamelCase = True):
								# also go through quantization layer
								if not force_not_quantize:
											UpperCAmelCase__				,    UpperCAmelCase__				,    UpperCAmelCase__					:   Optional[Any]       				=       self.quantize(_lowerCamelCase)
								else:
											UpperCAmelCase__					:   Union[str, Any]       				=       h
								UpperCAmelCase__					:   Any       				=       self.post_quant_conv(_lowerCamelCase)
								UpperCAmelCase__					:   Any       				=       self.decoder(_lowerCamelCase							,							quant if self.config.norm_type == """spatial""" else None)

								if not return_dict:
											return (dec,)

								return DecoderOutput(sample=_lowerCamelCase)




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase = True):
								UpperCAmelCase__					:   Dict       				=       sample
								UpperCAmelCase__					:   Dict       				=       self.encode(_lowerCamelCase).latents
								UpperCAmelCase__					:   List[str]       				=       self.decode(_lowerCamelCase).sample

								if not return_dict:
											return (dec,)

								return DecoderOutput(sample=_lowerCamelCase)