






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
__UpperCamelCase   :   int      =    logging.get_logger(__name__)

__UpperCamelCase   :   Optional[int]      =    {
    """post_extract_proj""": """feature_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.upsample.0""": """encoder.upsample.projection""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """layer_norm""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}





def 					snake_case	(  lowerCamelCase					,	lowerCamelCase					,	lowerCamelCase					,	lowerCamelCase					,	lowerCamelCase       ):




	'''simple docstring'''







	for attribute in key.split("""."""       ):
		__lowercase   							=	getattr(lowerCamelCase					,	lowerCamelCase       )

	if weight_type is not None:
		__lowercase   							=	getattr(lowerCamelCase					,	lowerCamelCase       ).shape
	else:
		__lowercase   							=	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":
		__lowercase   							=	value
	elif weight_type == "weight_g":
		__lowercase   							=	value
	elif weight_type == "weight_v":
		__lowercase   							=	value
	elif weight_type == "bias":
		__lowercase   							=	value
	else:
		__lowercase   							=	value

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





def 					snake_case	(  lowerCamelCase					,	lowerCamelCase					,	lowerCamelCase       ):




	'''simple docstring'''







	__lowercase   							=	[]
	__lowercase   							=	fairseq_model.state_dict()

	__lowercase   							=	hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

	for name, value in fairseq_dict.items():
		__lowercase   							=	False
		if "conv_layers" in name:
			load_conv_layer(
			    lowerCamelCase					,	lowerCamelCase					,	lowerCamelCase					,	lowerCamelCase					,	hf_model.config.feat_extract_norm == """group"""					,	)
			__lowercase   							=	True
		else:
			for key, mapped_key in MAPPING.items():
				__lowercase   							=	"""sew.""" + 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]:
					__lowercase   							=	True
					if "*" in mapped_key:
						__lowercase   							=	name.split(lowerCamelCase       )[0].split("""."""       )[-2]
						__lowercase   							=	mapped_key.replace("""*"""					,	lowerCamelCase       )
					if "weight_g" in name:
						__lowercase   							=	"""weight_g"""
					elif "weight_v" in name:
						__lowercase   							=	"""weight_v"""
					elif "weight" in name:
						__lowercase   							=	"""weight"""
					elif "bias" in name:
						__lowercase   							=	"""bias"""
					else:
						__lowercase   							=	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 					snake_case	(  lowerCamelCase					,	lowerCamelCase					,	lowerCamelCase					,	lowerCamelCase					,	lowerCamelCase       ):




	'''simple docstring'''







	__lowercase   							=	full_name.split("""conv_layers."""       )[-1]
	__lowercase   							=	name.split("""."""       )
	__lowercase   							=	int(items[0]       )
	__lowercase   							=	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.'
			)
			__lowercase   							=	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.'
			)
			__lowercase   							=	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."
			)
			__lowercase   							=	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.'
			)
			__lowercase   							=	value
			logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'       )
	else:
		unused_weights.append(lowerCamelCase       )





def 					snake_case	(  lowerCamelCase					,	lowerCamelCase       ):




	'''simple docstring'''







	__lowercase   							=	SEWConfig()
	if is_finetuned:
		__lowercase   							=	model.wav_encoder.wav_model.cfg
	else:
		__lowercase   							=	model.cfg

	__lowercase   							=	fs_config.conv_bias
	__lowercase   							=	eval(fs_config.conv_feature_layers       )
	__lowercase   							=	[x[0] for x in conv_layers]
	__lowercase   							=	[x[1] for x in conv_layers]
	__lowercase   							=	[x[2] for x in conv_layers]
	__lowercase   							=	"""gelu"""
	__lowercase   							=	"""layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
	__lowercase   							=	0.0
	__lowercase   							=	fs_config.activation_fn.name
	__lowercase   							=	fs_config.encoder_embed_dim
	__lowercase   							=	0.02
	__lowercase   							=	fs_config.encoder_ffn_embed_dim
	__lowercase   							=	1e-5
	__lowercase   							=	fs_config.encoder_layerdrop
	__lowercase   							=	fs_config.encoder_attention_heads
	__lowercase   							=	fs_config.conv_pos_groups
	__lowercase   							=	fs_config.conv_pos
	__lowercase   							=	len(lowerCamelCase       )
	__lowercase   							=	fs_config.encoder_layers
	__lowercase   							=	fs_config.squeeze_factor

	# take care of any params that are overridden by the Wav2VecCtc model
	if is_finetuned:
		__lowercase   							=	model.cfg
		__lowercase   							=	fs_config.final_dropout
		__lowercase   							=	fs_config.layerdrop
	__lowercase   							=	fs_config.activation_dropout
	__lowercase   							=	fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
	__lowercase   							=	fs_config.attention_dropout
	__lowercase   							=	fs_config.dropout_input
	__lowercase   							=	fs_config.dropout
	__lowercase   							=	fs_config.mask_channel_length
	__lowercase   							=	fs_config.mask_channel_prob
	__lowercase   							=	fs_config.mask_length
	__lowercase   							=	fs_config.mask_prob

	__lowercase   							=	"""Wav2Vec2FeatureExtractor"""
	__lowercase   							=	"""Wav2Vec2CTCTokenizer"""

	return config





@torch.no_grad()
def 					snake_case	(  lowerCamelCase					,	lowerCamelCase					,	lowerCamelCase=None					,	lowerCamelCase=None					,	lowerCamelCase=True       ):




	'''simple docstring'''







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

	if config_path is not None:
		__lowercase   							=	SEWConfig.from_pretrained(lowerCamelCase       )
	else:
		__lowercase   							=	convert_config(model[0]					,	lowerCamelCase       )
	__lowercase   							=	model[0].eval()

	__lowercase   							=	True if config.feat_extract_norm == """layer""" else False
	__lowercase   							=	WavaVecaFeatureExtractor(
	    feature_size=1					,	sampling_rate=16_000					,	padding_value=0					,	do_normalize=lowerCamelCase					,	return_attention_mask=lowerCamelCase					,	)

	if is_finetuned:
		if dict_path:
			__lowercase   							=	Dictionary.load(lowerCamelCase       )

			# important change bos & pad token id since CTC symbol is <pad> and
			# not <s> as in fairseq
			__lowercase   							=	target_dict.pad_index
			__lowercase   							=	target_dict.bos_index
			__lowercase   							=	target_dict.pad_index
			__lowercase   							=	target_dict.bos_index
			__lowercase   							=	target_dict.eos_index
			__lowercase   							=	len(target_dict.symbols       )
			__lowercase   							=	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       )
			with open(lowerCamelCase					,	"""w"""					,	encoding="""utf-8"""       ) as vocab_handle:
				json.dump(target_dict.indices					,	lowerCamelCase       )
			__lowercase   							=	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					,	)
			__lowercase   							=	WavaVecaProcessor(feature_extractor=lowerCamelCase					,	tokenizer=lowerCamelCase       )
			processor.save_pretrained(lowerCamelCase       )

		__lowercase   							=	SEWForCTC(lowerCamelCase       )
	else:
		__lowercase   							=	SEWModel(lowerCamelCase       )
		feature_extractor.save_pretrained(lowerCamelCase       )

	recursively_load_weights(lowerCamelCase					,	lowerCamelCase					,	lowerCamelCase       )

	hf_model.save_pretrained(lowerCamelCase       )


if __name__ == "__main__":
						__UpperCamelCase   :   str      =    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(
						    """--is_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
						)
						__UpperCamelCase   :   Union[str, Any]      =    parser.parse_args()
						convert_sew_checkpoint(
						    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
						)









from __future__ import annotations

from collections.abc import Callable
from typing import Generic, TypeVar

lowercase    				=   TypeVar("T")
lowercase    				=   TypeVar("U")




class 					UpperCamelCase_ (     Generic[T, U]							):



	'''simple docstring'''




	def __init__(			self			,     a			,     a   )			->       List[Any]:
								snake_case_           =				key
								snake_case_           =				val
								snake_case_           =				None
								snake_case_           =				None






	def __repr__(			self   )			->       str:
								return (
								    F'''Node: key: {self.key}, val: {self.val}, '''
								    F'''has next: {bool(self.next   )}, has prev: {bool(self.prev   )}'''
								)






class 					UpperCamelCase_ (     Generic[T, U]							):



	'''simple docstring'''




	def __init__(			self   )			->       None:
								snake_case_           =				DoubleLinkedListNode(a			,     a   )
								snake_case_           =				DoubleLinkedListNode(a			,     a   )
								snake_case_     ,						snake_case_           =				self.rear, self.head




	def __repr__(			self   )			->       str:
								snake_case_           =				['DoubleLinkedList']
								snake_case_           =				self.head
								while node.next is not None:
															rep.append(str(a   )   )
															snake_case_           =				node.next
								rep.append(str(self.rear   )   )
								return ",\n    ".join(a   )




	def 				_UpperCamelCase     (			self			,     a   )			->       None:
								snake_case_           =				self.rear.prev

								# All nodes other than self.head are guaranteed to have non-None previous
								assert previous is not None

								snake_case_           =				node
								snake_case_           =				previous
								snake_case_           =				node
								snake_case_           =				self.rear






	def 				_UpperCamelCase     (			self			,     a   )			->       DoubleLinkedListNode[T, U] | None:

								if node.prev is None or node.next is None:
															return None

								snake_case_           =				node.next
								snake_case_           =				node.prev
								snake_case_           =				None
								snake_case_           =				None
								return node






class 					UpperCamelCase_ (     Generic[T, U]							):



	'''simple docstring'''




	lowerCAmelCase						=					{}




	def __init__(			self			,     a   )			->       str:
								snake_case_           =				DoubleLinkedList()
								snake_case_           =				capacity
								snake_case_           =				0
								snake_case_           =				0
								snake_case_           =				0
								snake_case_           =				{}




	def __repr__(			self   )			->       str:
								return (
								    F'''CacheInfo(hits={self.hits}, misses={self.miss}, '''
								    F'''capacity={self.capacity}, current size={self.num_keys})'''
								)




	def __contains__(			self			,     a   )			->       bool:
								return key in self.cache




	def 				_UpperCamelCase     (			self			,     a   )			->       U | None:
								# Note: pythonic interface would throw KeyError rather than return None

								if key in self.cache:
															self.hits += 1
															snake_case_           =				self.cache[key]
															snake_case_           =				self.list.remove(self.cache[key]   )
															assert node == value_node

															# node is guaranteed not None because it is in self.cache
															assert node is not None
															self.list.add(a   )
															return node.val
								self.miss += 1
								return None




	def 				_UpperCamelCase     (			self			,     a			,     a   )			->       None:

								if key not in self.cache:
															if self.num_keys >= self.capacity:
																						# delete first node (oldest) when over capacity
																						snake_case_           =				self.list.head.next

																						# guaranteed to have a non-None first node when num_keys > 0
																						# explain to type checker via assertions
																						assert first_node is not None
																						assert first_node.key is not None
																						assert (
																						    self.list.remove(a   ) is not None
																						)  # node guaranteed to be in list assert node.key is not None

																						del self.cache[first_node.key]
																						self.num_keys -= 1
															snake_case_           =				DoubleLinkedListNode(a			,     a   )
															self.list.add(self.cache[key]   )
															self.num_keys += 1

								else:
															# bump node to the end of the list, update value
															snake_case_           =				self.list.remove(self.cache[key]   )
															assert node is not None  # node guaranteed to be in list
															snake_case_           =				value
															self.list.add(a   )






	@classmethod
	def 				_UpperCamelCase     (			cls			,     a = 1_28   )			->       Callable[[Callable[[T], U]], Callable[..., U]]:

								def cache_decorator_inner(a   ) -> Callable[..., U]:
															def cache_decorator_wrapper(*a   ) -> U:
																						if func not in cls.decorator_function_to_instance_map:
																													snake_case_           =				LRUCache(a   )

																						snake_case_           =				cls.decorator_function_to_instance_map[func].get(args[0]   )
																						if result is None:
																													snake_case_           =				func(*a   )
																													cls.decorator_function_to_instance_map[func].put(args[0]			,     a   )
																						return result

															def cache_info() -> LRUCache[T, U]:
																						return cls.decorator_function_to_instance_map[func]

															setattr(a			,     'cache_info'			,     a   )  # noqa: B010

															return cache_decorator_wrapper

								return cache_decorator_inner


if __name__ == "__main__":
	import doctest

	doctest.testmod()





