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

_UpperCAmelCase :   Optional[int]			    =	{
    '''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''',
}
_UpperCAmelCase :   str			    =	[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''projector''',
    '''classifier''',
]







def     snake_case__						(   UpperCamelCase						)			->   Optional[int]:
		_UpperCamelCase							:			int     =   {}
		with open(__A      ,'''r'''						) as file:
				for line_number, line in enumerate(__A						):
						_UpperCamelCase							:			Union[str, Any]     =   line.strip()
						if line:
								_UpperCamelCase							:			List[str]     =   line.split()
								_UpperCamelCase							:			Optional[Any]     =   line_number
								_UpperCamelCase							:			Union[str, Any]     =   words[0]
								_UpperCamelCase							:			List[str]     =   value
		return result







def     snake_case__						(   UpperCamelCase      ,UpperCamelCase      ,UpperCamelCase      ,UpperCamelCase      ,UpperCamelCase						)			->   Dict:
		for attribute in key.split('''.'''						):
				_UpperCamelCase							:			Union[str, Any]     =   getattr(__A      ,__A						)

		_UpperCamelCase							:			str     =   None
		for param_key in PARAM_MAPPING.keys():
				if full_name.endswith(__A						):
						_UpperCamelCase							:			Optional[Any]     =   PARAM_MAPPING[full_name.split('''.'''						)[-1]]
						_UpperCamelCase							:			Any     =   '''param'''

		if weight_type is not None and weight_type != "param":
				_UpperCamelCase							:			List[str]     =   getattr(__A      ,__A						).shape
		elif weight_type is not None and weight_type == "param":
				_UpperCamelCase							:			str     =   hf_pointer
				for attribute in hf_param_name.split('''.'''						):
						_UpperCamelCase							:			Tuple     =   getattr(__A      ,__A						)
				_UpperCamelCase							:			str     =   shape_pointer.shape

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

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

		_UpperCamelCase							:			Dict     =   value if '''lm_head''' in full_key else value[0]


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







def     snake_case__						(   UpperCamelCase      ,UpperCamelCase      ,UpperCamelCase						)			->   str:
		_UpperCamelCase							:			Union[str, Any]     =   []
		_UpperCamelCase							:			Dict     =   fairseq_model.state_dict()

		_UpperCamelCase							:			str     =   hf_model.wavaveca.feature_extractor

		for name, value in fairseq_dict.items():
				_UpperCamelCase							:			List[Any]     =   False
				if "conv_layers" in name:
						load_conv_layer(
						    __A      ,__A      ,__A      ,__A      ,hf_model.config.feat_extract_norm == '''group'''      ,)
						_UpperCamelCase							:			List[Any]     =   True
				else:
						_UpperCamelCase							:			Any     =   load_wavaveca_layer(__A      ,__A      ,__A						)
				if not is_used:
						unused_weights.append(__A						)

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







def     snake_case__						(   UpperCamelCase      ,UpperCamelCase      ,UpperCamelCase      ,UpperCamelCase      ,UpperCamelCase						)			->   List[str]:
		_UpperCamelCase							:			Optional[int]     =   full_name.split('''conv_layers.'''						)[-1]
		_UpperCamelCase							:			Tuple     =   name.split('''.'''						)
		_UpperCamelCase							:			Optional[int]     =   int(items[0]						)
		_UpperCamelCase							:			int     =   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							:			Dict     =   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							:			Tuple     =   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							:			str     =   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							:			Optional[Any]     =   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__						(   UpperCamelCase      ,UpperCamelCase      ,UpperCamelCase=None      ,UpperCamelCase=None      ,UpperCamelCase=True      ,UpperCamelCase=False						)			->   Optional[Any]:
		if config_path is not None:
				_UpperCamelCase							:			Tuple     =   WavaVecaConfig.from_pretrained(__A						)
		else:
				_UpperCamelCase							:			Dict     =   WavaVecaConfig()

		if is_seq_class:
				_UpperCamelCase							:			str     =   read_txt_into_dict(__A						)
				_UpperCamelCase							:			List[str]     =   idalabel
				_UpperCamelCase							:			Optional[int]     =   WavaVecaForSequenceClassification(__A						)
				_UpperCamelCase							:			List[Any]     =   WavaVecaFeatureExtractor(
				    feature_size=1      ,sampling_rate=1_60_00      ,padding_value=0      ,do_normalize=__A      ,return_attention_mask=__A      ,)
				feature_extractor.save_pretrained(__A						)

		elif is_finetuned:
				if dict_path:
						_UpperCamelCase							:			Optional[Any]     =   Dictionary.load(__A						)

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

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

				_UpperCamelCase							:			str     =   WavaVecaForCTC(__A						)
		else:
				_UpperCamelCase							:			Optional[int]     =   WavaVecaForPreTraining(__A						)

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

				_UpperCamelCase,				_UpperCamelCase,				_UpperCamelCase							:			List[Any]     =   fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]      ,task=__A						)

		_UpperCamelCase							:			int     =   model[0].eval()

		recursively_load_weights(__A      ,__A      ,not is_finetuned						)

		hf_wavavec.save_pretrained(__A						)


if __name__ == "__main__":
	_UpperCAmelCase :   Dict			    =	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""",
	)
	_UpperCAmelCase :   List[Any]			    =	parser.parse_args()

	_UpperCAmelCase :   Dict			    =	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,
	)








from typing import TYPE_CHECKING

from ...utils import _LazyModule


_lowerCamelCase      :					Tuple					    =      {'''tokenization_byt5''': ['''ByT5Tokenizer''']}


if TYPE_CHECKING:
   from .tokenization_byta import ByTaTokenizer
else:
   import sys

   _lowerCamelCase      :					Optional[int]					    =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


