'''simple docstring'''

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 (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

A_	   = {
    'post_extract_proj': 'feature_projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'attention.self.query_proj': 'encoder.encoder.layer.*.attention.self.query_proj',
    'attention.self.key_proj': 'encoder.encoder.layer.*.attention.self.key_proj',
    'attention.self.value_proj': 'encoder.encoder.layer.*.attention.self.value_proj',
    'attention.output.dense': 'encoder.encoder.layer.*.attention.output.dense',
    'attention.output.LayerNorm': 'encoder.encoder.layer.*.attention.output.LayerNorm',
    'intermediate.dense': 'encoder.encoder.layer.*.intermediate.dense',
    'output.dense': 'encoder.encoder.layer.*.output.dense',
    'output.LayerNorm': 'encoder.encoder.layer.*.output.LayerNorm',
    'encoder.encoder.rel_embeddings': 'encoder.encoder.rel_embeddings',
    'encoder.encoder.LayerNorm': 'encoder.encoder.LayerNorm',
    '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        UpperCAmelCase__   (				_lowerCAmelCase    :			str,      _lowerCAmelCase    :			Optional[Any],      _lowerCAmelCase    :			str,      _lowerCAmelCase    :			int,      _lowerCAmelCase    :			List[str])    ->		Any:



						"""simple docstring"""

						for attribute in key.split("."):
												snake_case :       Dict				=    getattr(_UpperCAmelCase,      _UpperCAmelCase)

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

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


def        UpperCAmelCase__   (				_lowerCAmelCase    :			Union[str, Any],      _lowerCAmelCase    :			List[Any],      _lowerCAmelCase    :			Tuple)    ->		Union[str, Any]:



						"""simple docstring"""

						snake_case :       Dict				=    []
						snake_case :       List[Any]				=    fairseq_model.state_dict()

						snake_case :       Tuple				=    hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

						for name, value in fairseq_dict.items():
												snake_case :       Dict				=    False
												if "conv_layers" in name:
																		load_conv_layer(
																		    _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      hf_model.config.feat_extract_norm == "group",      )
																		snake_case :       Dict				=    True
												else:
																		for key, mapped_key in MAPPING.items():
																								snake_case :       List[Any]				=    '''sew_d.''' + 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]:
																														snake_case :       Dict				=    True
																														if "*" in mapped_key:
																																				snake_case :       Union[str, Any]				=    name.split(_UpperCAmelCase)[0].split(".")[-2]
																																				if not layer_index.isnumeric():
																																										continue
																																				snake_case :       List[str]				=    mapped_key.replace("*",      _UpperCAmelCase)
																														if "weight_g" in name:
																																				snake_case :       str				=    '''weight_g'''
																														elif "weight_v" in name:
																																				snake_case :       Optional[Any]				=    '''weight_v'''
																														elif "weight" in name:
																																				snake_case :       int				=    '''weight'''
																														elif "bias" in name:
																																				snake_case :       Tuple				=    '''bias'''
																														else:
																																				snake_case :       Optional[Any]				=    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        UpperCAmelCase__   (				_lowerCAmelCase    :			Union[str, Any],      _lowerCAmelCase    :			Optional[Any],      _lowerCAmelCase    :			Any,      _lowerCAmelCase    :			List[Any],      _lowerCAmelCase    :			Tuple)    ->		Any:



						"""simple docstring"""

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


def        UpperCAmelCase__   (				_lowerCAmelCase    :			List[Any],      _lowerCAmelCase    :			int)    ->		Optional[Any]:



						"""simple docstring"""

						snake_case :       Union[str, Any]				=    SEWDConfig()
						if is_finetuned:
												snake_case :       str				=    model.wav_encoder.wav_model.cfg
						else:
												snake_case :       Optional[int]				=    model.cfg

						snake_case :       List[Any]				=    fs_config.conv_bias
						snake_case :       Union[str, Any]				=    eval(fs_config.conv_feature_layers)
						snake_case :       List[Any]				=    [x[0] for x in conv_layers]
						snake_case :       Dict				=    [x[1] for x in conv_layers]
						snake_case :       Dict				=    [x[2] for x in conv_layers]
						snake_case :       Optional[Any]				=    '''gelu'''
						snake_case :       Any				=    '''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
						snake_case :       int				=    0.0
						snake_case :       str				=    fs_config.activation_fn.name
						snake_case :       Optional[Any]				=    fs_config.encoder_embed_dim
						snake_case :       int				=    0.02
						snake_case :       List[Any]				=    fs_config.encoder_ffn_embed_dim
						snake_case :       List[str]				=    1E-5
						snake_case :       Union[str, Any]				=    fs_config.encoder_layerdrop
						snake_case :       Union[str, Any]				=    fs_config.encoder_attention_heads
						snake_case :       List[str]				=    fs_config.conv_pos_groups
						snake_case :       Optional[int]				=    fs_config.conv_pos
						snake_case :       List[Any]				=    len(_UpperCAmelCase)
						snake_case :       Optional[int]				=    fs_config.encoder_layers
						snake_case :       Tuple				=    fs_config.squeeze_factor
						# DeBERTa-specific parameters:
						snake_case :       Dict				=    fs_config.max_position_embeddings
						snake_case :       Optional[Any]				=    fs_config.position_buckets
						snake_case :       Union[str, Any]				=    fs_config.share_att_key
						snake_case :       Any				=    fs_config.relative_attention
						snake_case :       Union[str, Any]				=    fs_config.position_biased_input
						snake_case :       Optional[int]				=    tuple(fs_config.pos_att_type.split("|"))
						snake_case :       List[str]				=    fs_config.norm_rel_ebd

						# take care of any params that are overridden by the Wav2VecCtc model
						if is_finetuned:
												snake_case :       int				=    model.cfg
												snake_case :       Tuple				=    fs_config.final_dropout
												snake_case :       Tuple				=    fs_config.layerdrop
						snake_case :       int				=    fs_config.activation_dropout
						snake_case :       List[str]				=    fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
						snake_case :       Tuple				=    fs_config.attention_dropout
						snake_case :       Dict				=    fs_config.dropout_input
						snake_case :       Dict				=    fs_config.dropout
						snake_case :       Union[str, Any]				=    fs_config.mask_channel_length
						snake_case :       Any				=    fs_config.mask_channel_prob
						snake_case :       Tuple				=    fs_config.mask_length
						snake_case :       Union[str, Any]				=    fs_config.mask_prob

						snake_case :       Union[str, Any]				=    '''Wav2Vec2FeatureExtractor'''
						snake_case :       Optional[Any]				=    '''Wav2Vec2CTCTokenizer'''

						return config


@torch.no_grad()
def        UpperCAmelCase__   (				_lowerCAmelCase    :			Dict,      _lowerCAmelCase    :			List[Any],      _lowerCAmelCase    :			Optional[int]=None,      _lowerCAmelCase    :			Union[str, Any]=None,      _lowerCAmelCase    :			Dict=True)    ->		List[str]:



						"""simple docstring"""

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

						if config_path is not None:
												snake_case :       str				=    SEWDConfig.from_pretrained(_UpperCAmelCase)
						else:
												snake_case :       List[str]				=    convert_config(model[0],      _UpperCAmelCase)
						snake_case :       Optional[int]				=    model[0].eval()

						snake_case :       Optional[Any]				=    True if config.feat_extract_norm == '''layer''' else False
						snake_case :       Any				=    WavaVecaFeatureExtractor(
						    feature_size=1,      sampling_rate=1_6000,      padding_value=0,      do_normalize=_UpperCAmelCase,      return_attention_mask=_UpperCAmelCase,      )

						if is_finetuned:
												if dict_path:
																		snake_case :       Union[str, Any]				=    Dictionary.load(_UpperCAmelCase)

																		# important change bos & pad token id since CTC symbol is <pad> and
																		# not <s> as in fairseq
																		snake_case :       List[str]				=    target_dict.pad_index
																		snake_case :       Optional[Any]				=    target_dict.bos_index
																		snake_case :       List[str]				=    target_dict.pad_index
																		snake_case :       List[Any]				=    target_dict.bos_index
																		snake_case :       str				=    target_dict.eos_index
																		snake_case :       List[str]				=    len(target_dict.symbols)
																		snake_case :       Optional[int]				=    os.path.join(_UpperCAmelCase,      "vocab.json")
																		if not os.path.isdir(_UpperCAmelCase):
																								logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(_UpperCAmelCase))
																								return
																		os.makedirs(_UpperCAmelCase,      exist_ok=_UpperCAmelCase)
																		with open(_UpperCAmelCase,      "w",      encoding="utf-8") as vocab_handle:
																								json.dump(target_dict.indices,      _UpperCAmelCase)
																		snake_case :       Optional[Any]				=    WavaVecaCTCTokenizer(
																		    _UpperCAmelCase,      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=_UpperCAmelCase,      )
																		snake_case :       List[str]				=    WavaVecaProcessor(feature_extractor=_UpperCAmelCase,      tokenizer=_UpperCAmelCase)
																		processor.save_pretrained(_UpperCAmelCase)

												snake_case :       int				=    SEWDForCTC(_UpperCAmelCase)
						else:
												snake_case :       Any				=    SEWDModel(_UpperCAmelCase)
												feature_extractor.save_pretrained(_UpperCAmelCase)

						recursively_load_weights(_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase)

						hf_model.save_pretrained(_UpperCAmelCase)


if __name__ == "__main__":
						A_	   = 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"
						)
						A_	   = parser.parse_args()
						convert_sew_checkpoint(
						    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
						)











"""simple docstring"""

import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_xglm import XGLMTokenizer
else:
		_lowerCamelCase	:      Union[str, Any]   =							None


_lowerCamelCase	:      Optional[Any]   =							logging.get_logger(__name__)

_lowerCamelCase	:      Tuple   =							{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

_lowerCamelCase	:      Union[str, Any]   =							{
    'vocab_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model',
    },
    'tokenizer_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json',
    },
}

_lowerCamelCase	:      Dict   =							{
    'facebook/xglm-564M': 2048,
}

class 							lowercase   (     __UpperCAmelCase):
				__lowerCAmelCase     :     Optional[int]								= VOCAB_FILES_NAMES
				__lowerCAmelCase     :     Tuple								= PRETRAINED_VOCAB_FILES_MAP
				__lowerCAmelCase     :     List[Any]								= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__lowerCAmelCase     :     Union[str, Any]								= ["""input_ids""", """attention_mask"""]
				__lowerCAmelCase     :     List[str]								= XGLMTokenizer







				def __init__(				self :       str   ,  _lowerCamelCase :       int=None   ,  _lowerCamelCase :       Any=None   ,  _lowerCamelCase :       List[str]="<s>"   ,  _lowerCamelCase :       int="</s>"   ,  _lowerCamelCase :       Any="</s>"   ,  _lowerCamelCase :       Optional[int]="<s>"   ,  _lowerCamelCase :       List[Any]="<unk>"   ,  _lowerCamelCase :       Optional[int]="<pad>"   ,  **_lowerCamelCase :       List[Any]   ,  ):



					"""simple docstring"""

					A_	:					Tuple						 =		7
					A_	:					Dict						 =		[F"""<madeupword{i}>""" for i in range(self.num_madeup_words							)]

					A_	:					str						 =		kwargs.get('''additional_special_tokens'''   ,  []							)
					kwargs["additional_special_tokens"] += [
					    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
					]

					super().__init__(
					    _lowerCamelCase   ,  tokenizer_file=_lowerCamelCase   ,  bos_token=_lowerCamelCase   ,  eos_token=_lowerCamelCase   ,  sep_token=_lowerCamelCase   ,  cls_token=_lowerCamelCase   ,  unk_token=_lowerCamelCase   ,  pad_token=_lowerCamelCase   ,  **_lowerCamelCase   ,  )

					A_	:					Tuple						 =		vocab_file
					A_	:					str						 =		False if not self.vocab_file else True







				def        a_	(				self :       List[Any]   ,  _lowerCamelCase :       List[int]   ,  _lowerCamelCase :       Optional[List[int]] = None							):



					"""simple docstring"""

					if token_ids_a is None:
						return [self.sep_token_id] + token_ids_a
					A_	:					List[Any]						 =		[self.sep_token_id]
					return sep + token_ids_a + sep + sep + token_ids_a







				def        a_	(				self :       List[str]   ,  _lowerCamelCase :       List[int]   ,  _lowerCamelCase :       Optional[List[int]] = None							):



					"""simple docstring"""

					A_	:					Dict						 =		[self.sep_token_id]

					if token_ids_a is None:
						return len(sep + token_ids_a							) * [0]
					return len(sep + token_ids_a + sep + sep + token_ids_a							) * [0]







				def        a_	(				self :       List[str]   ,  _lowerCamelCase :       str   ,  _lowerCamelCase :       Optional[str] = None							):



					"""simple docstring"""

					if not self.can_save_slow_tokenizer:
						raise ValueError(
						    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
						    '''tokenizer.'''							)

					if not os.path.isdir(_lowerCamelCase							):
						logger.error(F"""Vocabulary path ({save_directory}) should be a directory."""							)
						return
					A_	:					Tuple						 =		os.path.join(
					    _lowerCamelCase   ,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)

					if os.path.abspath(self.vocab_file							) != os.path.abspath(_lowerCamelCase							):
						copyfile(self.vocab_file   ,  _lowerCamelCase							)

					return (out_vocab_file,)





