

import os
from typing import List, Union

import tensorflow as tf
from tensorflow_text import BertTokenizer as BertTokenizerLayer
from tensorflow_text import FastBertTokenizer, ShrinkLongestTrimmer, case_fold_utfa, combine_segments, pad_model_inputs

from .tokenization_bert import BertTokenizer







class 						lowercase     (					tf.keras.layers.Layer				):


		"""simple docstring"""




		def __init__(					self						,							__snake_case						,							__snake_case						,							__snake_case = None						,							__snake_case = None						,							__snake_case = None						,							__snake_case = "longest"						,							__snake_case = True						,							__snake_case = 5_12						,							__snake_case = None						,							__snake_case = True						,							__snake_case = True						,							__snake_case = True						,							**__snake_case						,							):
			super().__init__()
			if use_fast_bert_tokenizer:
				_UpperCamelCase :		Any								=    FastBertTokenizer(
				    __snake_case						,							token_out_type=tf.intaa						,							lower_case_nfd_strip_accents=__snake_case						,							**__snake_case)
			else:
				_UpperCamelCase :		Dict								=    tf.lookup.StaticVocabularyTable(
				    tf.lookup.KeyValueTensorInitializer(
				        keys=__snake_case						,							key_dtype=tf.string						,							values=tf.range(tf.size(__snake_case						,							out_type=tf.intaa)						,							dtype=tf.intaa)						,							value_dtype=tf.intaa						,							)						,							num_oov_buckets=1						,							)
				_UpperCamelCase :		Any								=    BertTokenizerLayer(
				    __snake_case						,							token_out_type=tf.intaa						,							lower_case=__snake_case						,							**__snake_case)

			_UpperCamelCase :		Tuple								=    vocab_list
			_UpperCamelCase :		Optional[int]								=    do_lower_case
			_UpperCamelCase :		Union[str, Any]								=    cls_token_id or vocab_list.index('[CLS]')
			_UpperCamelCase :		Optional[int]								=    sep_token_id or vocab_list.index('[SEP]')
			_UpperCamelCase :		Any								=    pad_token_id or vocab_list.index('[PAD]')
			_UpperCamelCase :		List[Any]								=    ShrinkLongestTrimmer(max_length - 3						,							axis=1)  # Allow room for special tokens
			_UpperCamelCase :		Tuple								=    max_length
			_UpperCamelCase :		Dict								=    padding
			_UpperCamelCase :		int								=    truncation
			_UpperCamelCase :		Optional[int]								=    pad_to_multiple_of
			_UpperCamelCase :		List[str]								=    return_token_type_ids
			_UpperCamelCase :		int								=    return_attention_mask


		@classmethod
		def 		A__       (					cls						,							__snake_case						,							**__snake_case):  # noqa: F821
			_UpperCamelCase :		int								=    kwargs.pop('do_lower_case'						,							__snake_case)
			_UpperCamelCase :		List[str]								=    tokenizer.do_lower_case if do_lower_case is None else do_lower_case
			_UpperCamelCase :		Any								=    kwargs.pop('cls_token_id'						,							__snake_case)
			_UpperCamelCase :		Tuple								=    tokenizer.cls_token_id if cls_token_id is None else cls_token_id
			_UpperCamelCase :		str								=    kwargs.pop('sep_token_id'						,							__snake_case)
			_UpperCamelCase :		Optional[Any]								=    tokenizer.sep_token_id if sep_token_id is None else sep_token_id
			_UpperCamelCase :		List[str]								=    kwargs.pop('pad_token_id'						,							__snake_case)
			_UpperCamelCase :		Any								=    tokenizer.pad_token_id if pad_token_id is None else pad_token_id

			_UpperCamelCase :		List[str]								=    tokenizer.get_vocab()
			_UpperCamelCase :		Union[str, Any]								=    sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]						,							key=lambda __snake_case: x[1])
			_UpperCamelCase :		Optional[Any]								=    [entry[0] for entry in vocab]
			return cls(
			    vocab_list=__snake_case						,							do_lower_case=__snake_case						,							cls_token_id=__snake_case						,							sep_token_id=__snake_case						,							pad_token_id=__snake_case						,							**__snake_case						,							)


		@classmethod
		def 		A__       (					cls						,							__snake_case						,							*__snake_case						,							**__snake_case):
			try:
				_UpperCamelCase :		Tuple								=    BertTokenizer.from_pretrained(__snake_case						,							*__snake_case						,							**__snake_case)
			except:  # noqa: E722
				from .tokenization_bert_fast import BertTokenizerFast

				_UpperCamelCase :		Optional[Any]								=    BertTokenizerFast.from_pretrained(__snake_case						,							*__snake_case						,							**__snake_case)
			return cls.from_tokenizer(__snake_case						,							**__snake_case)


		def 		A__       (					self						,							__snake_case):
			if self.do_lower_case:
				_UpperCamelCase :		Dict								=    case_fold_utfa(__snake_case)
			_UpperCamelCase :		Optional[Any]								=    self.tf_tokenizer.tokenize(__snake_case)
			return tokens.merge_dims(1						,							-1)


		def 		A__       (					self						,							__snake_case						,							__snake_case=None						,							__snake_case=None						,							__snake_case=None						,							__snake_case=None						,							__snake_case=None						,							__snake_case=None						,							__snake_case=None						,							):
			if padding is None:
				_UpperCamelCase :		Dict								=    self.padding
			if padding not in ("longest", "max_length"):
				raise ValueError('Padding must be either \'longest\' or \'max_length\'!')
			if max_length is not None and text_pair is not None:
				# Because we have to instantiate a Trimmer to do it properly
				raise ValueError('max_length cannot be overridden at call time when truncating paired texts!')
			if max_length is None:
				_UpperCamelCase :		Tuple								=    self.max_length
			if truncation is None:
				_UpperCamelCase :		Dict								=    self.truncation
			if pad_to_multiple_of is None:
				_UpperCamelCase :		Dict								=    self.pad_to_multiple_of
			if return_token_type_ids is None:
				_UpperCamelCase :		Optional[Any]								=    self.return_token_type_ids
			if return_attention_mask is None:
				_UpperCamelCase :		Dict								=    self.return_attention_mask
			if not isinstance(__snake_case						,							tf.Tensor):
				_UpperCamelCase :		List[str]								=    tf.convert_to_tensor(__snake_case)
			if text_pair is not None and not isinstance(__snake_case						,							tf.Tensor):
				_UpperCamelCase :		int								=    tf.convert_to_tensor(__snake_case)
			if text_pair is not None:
				if text.shape.rank > 1:
					raise ValueError('text argument should not be multidimensional when a text pair is supplied!')
				if text_pair.shape.rank > 1:
					raise ValueError('text_pair should not be multidimensional!')
			if text.shape.rank == 2:
				_UpperCamelCase						,		_UpperCamelCase :		Optional[Any]								=    text[:, 0], text[:, 1]
			_UpperCamelCase :		List[Any]								=    self.unpaired_tokenize(__snake_case)
			if text_pair is None:  # Unpaired text
				if truncation:
					_UpperCamelCase :		Any								=    text[:, : max_length - 2]  # Allow room for special tokens
				_UpperCamelCase						,		_UpperCamelCase :		int								=    combine_segments(
				    (text,)						,							start_of_sequence_id=self.cls_token_id						,							end_of_segment_id=self.sep_token_id)
			else:  # Paired text
				_UpperCamelCase :		Tuple								=    self.unpaired_tokenize(__snake_case)
				if truncation:
					_UpperCamelCase						,		_UpperCamelCase :		Dict								=    self.paired_trimmer.trim([text, text_pair])
				_UpperCamelCase						,		_UpperCamelCase :		Optional[Any]								=    combine_segments(
				    (text, text_pair)						,							start_of_sequence_id=self.cls_token_id						,							end_of_segment_id=self.sep_token_id)
			if padding == "longest":
				_UpperCamelCase :		str								=    input_ids.bounding_shape(axis=1)
				if pad_to_multiple_of is not None:
					# No ceiling division in tensorflow, so we negate floordiv instead
					_UpperCamelCase :		Optional[int]								=    pad_to_multiple_of * (-tf.math.floordiv(-pad_length						,							__snake_case))
			else:
				_UpperCamelCase :		List[Any]								=    max_length

			_UpperCamelCase						,		_UpperCamelCase :		List[Any]								=    pad_model_inputs(__snake_case						,							max_seq_length=__snake_case						,							pad_value=self.pad_token_id)
			_UpperCamelCase :		Dict								=    {'input_ids': input_ids}
			if return_attention_mask:
				_UpperCamelCase :		str								=    attention_mask
			if return_token_type_ids:
				_UpperCamelCase						,		_UpperCamelCase :		Any								=    pad_model_inputs(
				    __snake_case						,							max_seq_length=__snake_case						,							pad_value=self.pad_token_id)
				_UpperCamelCase :		str								=    token_type_ids
			return output






		def 		A__       (					self):
			return {
			    "vocab_list": self.vocab_list,
			    "do_lower_case": self.do_lower_case,
			    "cls_token_id": self.cls_token_id,
			    "sep_token_id": self.sep_token_id,
			    "pad_token_id": self.pad_token_id,
			}







import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
lowerCAmelCase__             =		logging.get_logger(__name__)

lowerCAmelCase__             =		{
    """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""",
    """mask_emb""": """masked_spec_embed""",
}






def        lowerCamelCase_  (				UpperCAmelCase_				:  Dict				,							UpperCAmelCase_				:  List[Any]				,							UpperCAmelCase_				:  Optional[int]				,							UpperCAmelCase_				:  str				,							UpperCAmelCase_				:  List[Any]						)    ->					Any:




	'''simple docstring'''




	for attribute in key.split('.'						):
		_UpperCamelCase :		Any								=    getattr(UpperCAmelCase_				,							UpperCAmelCase_						)

	if weight_type is not None:
		_UpperCamelCase :		List[str]								=    getattr(UpperCAmelCase_				,							UpperCAmelCase_						).shape
	else:
		_UpperCamelCase :		Optional[int]								=    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":
		_UpperCamelCase :		Tuple								=    value
	elif weight_type == "weight_g":
		_UpperCamelCase :		Optional[Any]								=    value
	elif weight_type == "weight_v":
		_UpperCamelCase :		Tuple								=    value
	elif weight_type == "bias":
		_UpperCamelCase :		Any								=    value
	else:
		_UpperCamelCase :		Tuple								=    value

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






def        lowerCamelCase_  (				UpperCAmelCase_				:  int				,							UpperCAmelCase_				:  Union[str, Any]						)    ->					int:




	'''simple docstring'''




	_UpperCamelCase :		Union[str, Any]								=    []
	_UpperCamelCase :		List[Any]								=    fairseq_model.state_dict()

	_UpperCamelCase :		int								=    hf_model.feature_extractor

	for name, value in fairseq_dict.items():
		_UpperCamelCase :		Dict								=    False
		if "conv_layers" in name:
			load_conv_layer(
			    UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_				,							hf_model.config.feat_extract_norm == 'group'				,							)
			_UpperCamelCase :		Union[str, Any]								=    True
		else:
			for key, mapped_key in MAPPING.items():
				_UpperCamelCase :		Tuple								=    mapped_key

				if key in name:
					_UpperCamelCase :		List[Any]								=    True
					if "*" in mapped_key:
						_UpperCamelCase :		List[Any]								=    name.split(UpperCAmelCase_						)[0].split('.'						)[-2]
						_UpperCamelCase :		Optional[Any]								=    mapped_key.replace('*'				,							UpperCAmelCase_						)
					if "weight_g" in name:
						_UpperCamelCase :		List[Any]								=    'weight_g'
					elif "weight_v" in name:
						_UpperCamelCase :		Tuple								=    'weight_v'
					elif "weight" in name:
						_UpperCamelCase :		Optional[Any]								=    'weight'
					elif "bias" in name:
						_UpperCamelCase :		List[Any]								=    'bias'
					else:
						_UpperCamelCase :		str								=    None
					set_recursively(UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_						)
				continue
		if not is_used:
			unused_weights.append(UpperCAmelCase_						)

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






def        lowerCamelCase_  (				UpperCAmelCase_				:  Union[str, Any]				,							UpperCAmelCase_				:  List[Any]				,							UpperCAmelCase_				:  Optional[Any]				,							UpperCAmelCase_				:  int				,							UpperCAmelCase_				:  int						)    ->					Dict:




	'''simple docstring'''




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






def        lowerCamelCase_  (				UpperCAmelCase_				:  Tuple						)    ->					List[str]:




	'''simple docstring'''




	_UpperCamelCase :		Union[str, Any]								=    HubertConfig()
	_UpperCamelCase :		List[str]								=    model.config

	_UpperCamelCase :		Any								=    fs_config.activation_dropout
	_UpperCamelCase :		Tuple								=    False
	_UpperCamelCase :		Tuple								=    fs_config.attention_dropout
	_UpperCamelCase :		Dict								=    False
	_UpperCamelCase :		Optional[Any]								=    eval(fs_config.extractor_conv_feature_layers						)
	_UpperCamelCase :		Dict								=    [x[0] for x in conv_layers]
	_UpperCamelCase :		Any								=    [x[1] for x in conv_layers]
	_UpperCamelCase :		Any								=    [x[2] for x in conv_layers]
	_UpperCamelCase :		Dict								=    'gelu'
	_UpperCamelCase :		List[Any]								=    'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
	_UpperCamelCase :		List[Any]								=    False
	_UpperCamelCase :		List[Any]								=    0.0
	_UpperCamelCase :		Optional[int]								=    0.0
	_UpperCamelCase :		Optional[int]								=    fs_config.activation_fn
	_UpperCamelCase :		Optional[int]								=    fs_config.dropout
	_UpperCamelCase :		Union[str, Any]								=    fs_config.encoder_embed_dim
	_UpperCamelCase :		Tuple								=    0.0_2
	_UpperCamelCase :		int								=    fs_config.encoder_ffn_embed_dim
	_UpperCamelCase :		int								=    1e-5
	_UpperCamelCase :		Optional[Any]								=    0.0
	_UpperCamelCase :		List[str]								=    fs_config.encoder_attention_heads
	_UpperCamelCase :		List[str]								=    fs_config.conv_pos_groups
	_UpperCamelCase :		Union[str, Any]								=    fs_config.conv_pos
	_UpperCamelCase :		int								=    len(UpperCAmelCase_						)
	_UpperCamelCase :		Tuple								=    fs_config.encoder_layers

	return config






@torch.no_grad()
def        lowerCamelCase_  (				UpperCAmelCase_				:  str				,							UpperCAmelCase_				:  Dict=None						)    ->					int:




	'''simple docstring'''




	_UpperCamelCase :		Tuple								=    distilhubert().model.model

	if config_path is not None:
		_UpperCamelCase :		Optional[Any]								=    HubertConfig.from_pretrained(UpperCAmelCase_						)
	else:
		_UpperCamelCase :		Optional[int]								=    convert_config(UpperCAmelCase_						)
	_UpperCamelCase :		List[Any]								=    model.eval()

	_UpperCamelCase :		List[Any]								=    WavaVecaFeatureExtractor(
	    feature_size=1				,							sampling_rate=1_6_0_0_0				,							padding_value=0				,							do_normalize=UpperCAmelCase_				,							return_attention_mask=UpperCAmelCase_				,							)
	_UpperCamelCase :		Optional[Any]								=    HubertModel(UpperCAmelCase_						)

	recursively_load_weights(UpperCAmelCase_				,							UpperCAmelCase_						)

	feature_extractor.save_pretrained(UpperCAmelCase_						)
	hf_model.save_pretrained(UpperCAmelCase_						)


if __name__ == "__main__":
							lowerCAmelCase__             =		argparse.ArgumentParser()
							parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
							parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
							lowerCAmelCase__             =		parser.parse_args()
							convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)




