






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()
SCREAMING_SNAKE_CASE__				     =			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__				     =			{
    '''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''',
}
SCREAMING_SNAKE_CASE__				     =			[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''projector''',
    '''classifier''',
]

def        UpperCAmelCase__		(						lowerCamelCase_       :       Tuple				):
			__a						:  str									=     {}
			with open(lowerCamelCase_   ,  'r'				) as file:
						for line_number, line in enumerate(lowerCamelCase_				):
									__a						:  Union[str, Any]									=     line.strip()
									if line:
												__a						:  List[Any]									=     line.split()
												__a						:  Union[str, Any]									=     line_number
												__a						:  Union[str, Any]									=     words[0]
												__a						:  Optional[int]									=     value
			return result

def        UpperCAmelCase__		(						lowerCamelCase_       :       Dict   ,  lowerCamelCase_       :       List[str]   ,  lowerCamelCase_       :       Tuple   ,  lowerCamelCase_       :       str   ,  lowerCamelCase_       :       Optional[int]				):
			for attribute in key.split('.'				):
						__a						:  Tuple									=     getattr(lowerCamelCase_   ,  lowerCamelCase_				)

			__a						:  Optional[int]									=     None
			for param_key in PARAM_MAPPING.keys():
						if full_name.endswith(lowerCamelCase_				):
									__a						:  str									=     PARAM_MAPPING[full_name.split('.'				)[-1]]
									__a						:  List[str]									=     'param'

			if weight_type is not None and weight_type != "param":
						__a						:  Optional[int]									=     getattr(lowerCamelCase_   ,  lowerCamelCase_				).shape
			elif weight_type is not None and weight_type == "param":
						__a						:  List[Any]									=     hf_pointer
						for attribute in hf_param_name.split('.'				):
									__a						:  Optional[Any]									=     getattr(lowerCamelCase_   ,  lowerCamelCase_				)
						__a						:  Tuple									=     shape_pointer.shape

						# let's reduce dimension
						__a						:  List[Any]									=     value[0]
			else:
						__a						:  Dict									=     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":
						__a						:  Union[str, Any]									=     value
			elif weight_type == "weight_g":
						__a						:  Optional[Any]									=     value
			elif weight_type == "weight_v":
						__a						:  Any									=     value
			elif weight_type == "bias":
						__a						:  List[Any]									=     value
			elif weight_type == "param":
						for attribute in hf_param_name.split('.'				):
									__a						:  Any									=     getattr(lowerCamelCase_   ,  lowerCamelCase_				)
						__a						:  Union[str, Any]									=     value
			else:
						__a						:  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_       :       int   ,  lowerCamelCase_       :       Dict   ,  lowerCamelCase_       :       Any   ,  lowerCamelCase_       :       Tuple				):
			__a						:  Optional[Any]									=     None
			for param_key in PARAM_MAPPING.keys():
						if full_name.endswith(lowerCamelCase_				):
									__a						:  Optional[int]									=     PARAM_MAPPING[full_name.split('.'				)[-1]]
									__a						:  Optional[int]									=     'param'

			if weight_type is not None and weight_type != "param":
						__a						:  Dict									=     '.'.join([key, weight_type]				)
			elif weight_type is not None and weight_type == "param":
						__a						:  List[str]									=     '.'.join([key, hf_param_name]				)
			else:
						__a						:  Optional[int]									=     key

			__a						:  Tuple									=     value if 'lm_head' in full_key else value[0]


SCREAMING_SNAKE_CASE__				     =			{
    '''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__		(						lowerCamelCase_       :       int   ,  lowerCamelCase_       :       List[str]   ,  lowerCamelCase_       :       Optional[int]=None   ,  lowerCamelCase_       :       str=None				):
			__a						:  str									=     False
			for key, mapped_key in MAPPING.items():
						__a						:  int									=     '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]:
									__a						:  str									=     True
									if "*" in mapped_key:
												__a						:  List[Any]									=     name.split(lowerCamelCase_				)[0].split('.'				)[-2]
												__a						:  List[Any]									=     mapped_key.replace('*'   ,  lowerCamelCase_				)
									if "weight_g" in name:
												__a						:  Union[str, Any]									=     'weight_g'
									elif "weight_v" in name:
												__a						:  str									=     'weight_v'
									elif "bias" in name:
												__a						:  Any									=     'bias'
									elif "weight" in name:
												# TODO: don't match quantizer.weight_proj
												__a						:  Optional[int]									=     'weight'
									else:
												__a						:  Tuple									=     None
									if hf_dict is not None:
												rename_dict(lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_				)
									else:
												set_recursively(lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_				)
									return is_used
			return is_used

def        UpperCAmelCase__		(						lowerCamelCase_       :       Dict   ,  lowerCamelCase_       :       Any   ,  lowerCamelCase_       :       str				):
			__a						:  Tuple									=     []
			__a						:  Dict									=     fairseq_model.state_dict()

			__a						:  List[str]									=     hf_model.wavaveca.feature_extractor

			for name, value in fairseq_dict.items():
						__a						:  Optional[int]									=     False
						if "conv_layers" in name:
									load_conv_layer(
									    lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_   ,  hf_model.config.feat_extract_norm == 'group'   ,  )
									__a						:  int									=     True
						else:
									__a						:  Tuple									=     load_wavaveca_layer(lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_				)
						if not is_used:
									unused_weights.append(lowerCamelCase_				)

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

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

			if is_seq_class:
						__a						:  int									=     read_txt_into_dict(lowerCamelCase_				)
						__a						:  Any									=     idalabel
						__a						:  Optional[Any]									=     WavaVecaForSequenceClassification(lowerCamelCase_				)
						__a						:  Union[str, Any]									=     WavaVecaFeatureExtractor(
						    feature_size=1   ,  sampling_rate=1_6_0_0_0   ,  padding_value=0   ,  do_normalize=lowerCamelCase_   ,  return_attention_mask=lowerCamelCase_   ,  )
						feature_extractor.save_pretrained(lowerCamelCase_				)

			elif is_finetuned:
						if dict_path:
									__a						:  List[str]									=     Dictionary.load(lowerCamelCase_				)

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

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

						__a						:  List[Any]									=     WavaVecaForCTC(lowerCamelCase_				)
			else:
						__a						:  List[str]									=     WavaVecaForPreTraining(lowerCamelCase_				)

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

						__a						,					__a						,					__a						:  Optional[int]									=     fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]   ,  task=lowerCamelCase_				)

			__a						:  str									=     model[0].eval()

			recursively_load_weights(lowerCamelCase_   ,  lowerCamelCase_   ,  not is_finetuned				)

			hf_wavavec.save_pretrained(lowerCamelCase_				)


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__				     =			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''',
							)
							SCREAMING_SNAKE_CASE__				     =			parser.parse_args()

							SCREAMING_SNAKE_CASE__				     =			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 typing import List, Optional

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


__lowerCamelCase												=						logging.get_logger(__name__)

__lowerCamelCase												=						{
    '''huggingface/autoformer-tourism-monthly''': '''https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json''',
}




class 				UpperCAmelCase			(			_snake_case						):
 UpperCAmelCase				=					"autoformer"
 UpperCAmelCase				=					{
     "hidden_size": "d_model",
     "num_attention_heads": "encoder_attention_heads",
     "num_hidden_layers": "encoder_layers",
 }


 def __init__(      self							:	List[str]			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	str = "student_t"			,       __lowerCamelCase							:	str = "nll"			,       __lowerCamelCase							:	int = 1			,       __lowerCamelCase							:	List[int] = [1, 2, 3, 4, 5, 6, 7]			,       __lowerCamelCase							:	bool = True			,       __lowerCamelCase							:	int = 0			,       __lowerCamelCase							:	int = 0			,       __lowerCamelCase							:	int = 0			,       __lowerCamelCase							:	int = 0			,       __lowerCamelCase							:	Optional[List[int]] = None			,       __lowerCamelCase							:	Optional[List[int]] = None			,       __lowerCamelCase							:	int = 6_4			,       __lowerCamelCase							:	int = 2			,       __lowerCamelCase							:	int = 2			,       __lowerCamelCase							:	int = 2			,       __lowerCamelCase							:	int = 2			,       __lowerCamelCase							:	int = 3_2			,       __lowerCamelCase							:	int = 3_2			,       __lowerCamelCase							:	str = "gelu"			,       __lowerCamelCase							:	float = 0.1			,       __lowerCamelCase							:	float = 0.1			,       __lowerCamelCase							:	float = 0.1			,       __lowerCamelCase							:	float = 0.1			,       __lowerCamelCase							:	float = 0.1			,       __lowerCamelCase							:	int = 1_0_0			,       __lowerCamelCase							:	float = 0.02			,       __lowerCamelCase							:	bool = True			,       __lowerCamelCase							:	Optional[int]=True			,       __lowerCamelCase							:	int = 1_0			,       __lowerCamelCase							:	int = 2_5			,       __lowerCamelCase							:	int = 3			,       **__lowerCamelCase							:	int			,       ):
    # time series specific configuration
    UpperCAmelCase__	:Any      =      prediction_length
    UpperCAmelCase__	:Optional[int]      =      context_length if context_length is not None else prediction_length
    UpperCAmelCase__	:int      =      distribution_output
    UpperCAmelCase__	:str      =      loss
    UpperCAmelCase__	:Tuple      =      input_size
    UpperCAmelCase__	:Optional[int]      =      num_time_features
    UpperCAmelCase__	:List[Any]      =      lags_sequence
    UpperCAmelCase__	:Any      =      scaling
    UpperCAmelCase__	:Union[str, Any]      =      num_dynamic_real_features
    UpperCAmelCase__	:Dict      =      num_static_real_features
    UpperCAmelCase__	:Optional[int]      =      num_static_categorical_features
    if cardinality is not None and num_static_categorical_features > 0:
       if len(__lowerCamelCase ) != num_static_categorical_features:
          raise ValueError(
              '''The cardinality should be a list of the same length as `num_static_categorical_features`''' )
       UpperCAmelCase__	:Any      =      cardinality
    else:
       UpperCAmelCase__	:Dict      =      [0]
    if embedding_dimension is not None and num_static_categorical_features > 0:
       if len(__lowerCamelCase ) != num_static_categorical_features:
          raise ValueError(
              '''The embedding dimension should be a list of the same length as `num_static_categorical_features`''' )
       UpperCAmelCase__	:List[str]      =      embedding_dimension
    else:
       UpperCAmelCase__	:int      =      [min(5_0			,       (cat + 1) // 2 ) for cat in self.cardinality]
    UpperCAmelCase__	:Optional[Any]      =      num_parallel_samples

    # Transformer architecture configuration
    UpperCAmelCase__	:Optional[Any]      =      input_size * len(self.lags_sequence ) + self._number_of_features
    UpperCAmelCase__	:Optional[Any]      =      d_model
    UpperCAmelCase__	:Any      =      encoder_attention_heads
    UpperCAmelCase__	:Any      =      decoder_attention_heads
    UpperCAmelCase__	:int      =      encoder_ffn_dim
    UpperCAmelCase__	:Union[str, Any]      =      decoder_ffn_dim
    UpperCAmelCase__	:Optional[Any]      =      encoder_layers
    UpperCAmelCase__	:str      =      decoder_layers

    UpperCAmelCase__	:Optional[Any]      =      dropout
    UpperCAmelCase__	:str      =      attention_dropout
    UpperCAmelCase__	:int      =      activation_dropout
    UpperCAmelCase__	:Optional[Any]      =      encoder_layerdrop
    UpperCAmelCase__	:Dict      =      decoder_layerdrop

    UpperCAmelCase__	:Union[str, Any]      =      activation_function
    UpperCAmelCase__	:List[Any]      =      init_std

    UpperCAmelCase__	:List[str]      =      use_cache

    # Autoformer
    UpperCAmelCase__	:List[str]      =      label_length
    UpperCAmelCase__	:Dict      =      moving_average
    UpperCAmelCase__	:str      =      autocorrelation_factor

    super().__init__(is_encoder_decoder=__lowerCamelCase			,       **__lowerCamelCase )





 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	int ):
    return (
        sum(self.embedding_dimension )
        + self.num_dynamic_real_features
        + self.num_time_features
        + self.num_static_real_features
        + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
    )



