
import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE							:str										=    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							:List[Any]										=    {
    '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',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}


def 						UpperCAmelCase      (     a_		,       a_		,       a_		,       a_		,       a_						)		->     Optional[Any]:







	"""simple docstring"""



	for attribute in key.split("."						):
		__A             =			getattr(a_		,       a_						)

	if weight_type is not None:
		__A             =			getattr(a_		,       a_						).shape
	else:
		__A             =			hf_pointer.shape

	assert hf_shape == value.shape, (
	    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             =			value
	elif weight_type == "weight_g":
		__A             =			value
	elif weight_type == "weight_v":
		__A             =			value
	elif weight_type == "bias":
		__A             =			value
	else:
		__A             =			value

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


def 						UpperCAmelCase      (     a_		,       a_		,       a_						)		->     List[Any]:







	"""simple docstring"""



	__A             =			[]
	__A             =			fairseq_model.state_dict()

	__A             =			hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

	for name, value in fairseq_dict.items():
		__A             =			False
		if "conv_layers" in name:
			load_conv_layer(
			    a_		,       a_		,       a_		,       a_		,       hf_model.config.feat_extract_norm == "group"		,       )
			__A             =			True
		else:
			for key, mapped_key in MAPPING.items():
				__A             =			"hubert." + mapped_key if (is_finetuned and mapped_key != "lm_head") else mapped_key

				if key in name or (key.split("w2v_model."						)[-1] == name.split("."						)[0] and not is_finetuned):
					__A             =			True
					if "*" in mapped_key:
						__A             =			name.split(a_						)[0].split("."						)[-2]
						__A             =			mapped_key.replace("*"		,       a_						)
					if "weight_g" in name:
						__A             =			"weight_g"
					elif "weight_v" in name:
						__A             =			"weight_v"
					elif "weight" in name:
						__A             =			"weight"
					elif "bias" in name:
						__A             =			"bias"
					else:
						__A             =			None
					set_recursively(a_		,       a_		,       a_		,       a_		,       a_						)
				continue
		if not is_used:
			unused_weights.append(a_						)

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


def 						UpperCAmelCase      (     a_		,       a_		,       a_		,       a_		,       a_						)		->     int:







	"""simple docstring"""



	__A             =			full_name.split("conv_layers."						)[-1]
	__A             =			name.split("."						)
	__A             =			int(items[0]						)
	__A             =			int(items[1]						)

	if type_id == 0:
		if "bias" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
			    F'''{full_name} has size {value.shape}, but'''
			    F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
			)
			__A             =			value
			logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''						)
		elif "weight" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
			    F'''{full_name} has size {value.shape}, but'''
			    F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
			)
			__A             =			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:
			assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
			    F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
			    " found."
			)
			__A             =			value
			logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''						)
		elif "weight" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
			    F'''{full_name} has size {value.shape}, but'''
			    F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
			)
			__A             =			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 						UpperCAmelCase      (     a_		,       a_		,       a_=None		,       a_=None		,       a_=True						)		->     Union[str, Any]:







	"""simple docstring"""



	if config_path is not None:
		__A             =			HubertConfig.from_pretrained(a_						)
	else:
		__A             =			HubertConfig()

	if is_finetuned:
		if dict_path:
			__A             =			Dictionary.load(a_						)

			# important change bos & pad token id since CTC symbol is <pad> and
			# not <s> as in fairseq
			__A             =			target_dict.pad_index
			__A             =			target_dict.bos_index
			__A             =			target_dict.eos_index
			__A             =			len(target_dict.symbols						)
			__A             =			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_						)
			with open(a_		,       "w"		,       encoding="utf-8"						) as vocab_handle:
				json.dump(target_dict.indices		,       a_						)
			__A             =			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_		,       )
			__A             =			True if config.feat_extract_norm == "layer" else False
			__A             =			WavaVecaFeatureExtractor(
			    feature_size=1		,       sampling_rate=1_6_0_0_0		,       padding_value=0		,       do_normalize=a_		,       return_attention_mask=a_		,       )
			__A             =			WavaVecaProcessor(feature_extractor=a_		,       tokenizer=a_						)
			processor.save_pretrained(a_						)

		__A             =			HubertForCTC(a_						)
	else:
		__A             =			HubertModel(a_						)

	if is_finetuned:
		__A      ,							__A      ,							__A             =			fairseq.checkpoint_utils.load_model_ensemble_and_task(
		    [checkpoint_path]		,       arg_overrides={"data": "/".join(dict_path.split("/"						)[:-1]						)}						)
	else:
		__A      ,							__A      ,							__A             =			fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]						)

	__A             =			model[0].eval()

	recursively_load_weights(a_		,       a_		,       a_						)

	hf_wavavec.save_pretrained(a_						)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Optional[int]										=    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'
					)
					SCREAMING_SNAKE_CASE							:str										=    parser.parse_args()
					convert_hubert_checkpoint(
					    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
					)


import copy
import re





class 							UpperCAmelCase				:




	'''simple docstring'''





	snake_case_    =					"hp"
	snake_case_    =					{}
	snake_case_    =					None






	@classmethod
	def     UpperCamelCase_  (	cls :			Dict							,A :			Dict							,A :			Any			):
					__A             =			prefix
					__A             =			defaults
					cls.build_naming_info()






	@staticmethod
	def     UpperCamelCase_  (	A :			Dict							,A :			int			):
					if len(A			) == 0:
									return ""
					__A             =			None
					if any(char.isdigit() for char in word			):
									raise Exception(f'''Parameters should not contain numbers: \'{word}\' contains a number'''			)
					if word in info["short_word"]:
									return info["short_word"][word]
					for prefix_len in range(1							,len(A			) + 1			):
									__A             =			word[:prefix_len]
									if prefix in info["reverse_short_word"]:
													continue
									else:
													__A             =			prefix
													break

					if short_word is None:
									# Paranoid fallback
									def int_to_alphabetic(A :			str			):
													__A             =			""
													while integer != 0:
																	__A             =			chr(ord("A"			) + integer % 10			) + s
																	integer //= 10
													return s

									__A             =			0
									while True:
													__A             =			word + "#" + int_to_alphabetic(A			)
													if sword in info["reverse_short_word"]:
																	continue
													else:
																	__A             =			sword
																	break

					__A             =			short_word
					__A             =			word
					return short_word






	@staticmethod
	def     UpperCamelCase_  (	A :			int							,A :			Tuple			):
					__A             =			param_name.split("_"			)

					__A             =			[TrialShortNamer.shortname_for_word(A							,A			) for word in words]

					# We try to create a separatorless short name, but if there is a collision we have to fallback
					# to a separated short name
					__A             =			["", "_"]

					for separator in separators:
									__A             =			separator.join(A			)
									if shortname not in info["reverse_short_param"]:
													__A             =			shortname
													__A             =			param_name
													return shortname

					return param_name






	@staticmethod
	def     UpperCamelCase_  (	A :			Optional[Any]							,A :			Tuple			):
					__A             =			TrialShortNamer.shortname_for_key(A							,A			)
					__A             =			short_name
					__A             =			param_name






	@classmethod
	def     UpperCamelCase_  (	cls :			Dict			):
					if cls.NAMING_INFO is not None:
									return

					__A             =			{
					    "short_word": {},
					    "reverse_short_word": {},
					    "short_param": {},
					    "reverse_short_param": {},
					}

					__A             =			list(cls.DEFAULTS.keys()			)

					for k in field_keys:
									cls.add_new_param_name(A							,A			)

					__A             =			info






	@classmethod
	def     UpperCamelCase_  (	cls :			Dict							,A :			List[str]			):
					cls.build_naming_info()
					assert cls.PREFIX is not None
					__A             =			[copy.copy(cls.PREFIX			)]

					for k, v in params.items():
									if k not in cls.DEFAULTS:
													raise Exception(f'''You should provide a default value for the param name {k} with value {v}'''			)
									if v == cls.DEFAULTS[k]:
													# The default value is not added to the name
													continue

									__A             =			cls.NAMING_INFO["short_param"][k]

									if isinstance(A							,A			):
													__A             =			1 if v else 0

									__A             =			"" if isinstance(A							,(int, float)			) else "-"
									__A             =			f'''{key}{sep}{v}'''
									name.append(A			)

					return "_".join(A			)







	@classmethod
	def     UpperCamelCase_  (	cls :			Tuple							,A :			Tuple			):
					__A             =			repr[len(cls.PREFIX			) + 1 :]
					if repr == "":
									__A             =			[]
					else:
									__A             =			repr.split("_"			)

					__A             =			{}

					for value in values:
									if "-" in value:
													__A      ,							__A             =			value.split("-"			)
									else:
													__A             =			re.sub("[0-9.]"							,""							,A			)
													__A             =			float(re.sub("[^0-9.]"							,""							,A			)			)

									__A             =			cls.NAMING_INFO["reverse_short_param"][p_k]

									__A             =			p_v

					for k in cls.DEFAULTS:
									if k not in parameters:
													__A             =			cls.DEFAULTS[k]

					return parameters
