


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()
A__			:    int         =				logging.get_logger(__name__)

A__			:    Dict         =				{
    "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 						a    (		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 						a    (		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 						a    (		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 						a    (		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 						a    (		lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_=None	,     lowerCamelCase_=None	,     lowerCamelCase_=True       ):






				'''simple docstring'''

				if is_finetuned:
								lowercase__     				=    fairseq.checkpoint_utils.load_model_ensemble_and_task(
								    [checkpoint_path]	,     arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''       )[:-1]       )}       )
				else:
								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=1_6000	,     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__":
							A__			:    List[Any]         =				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__			:    Dict         =				parser.parse_args()
							convert_sew_checkpoint(
							    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
							)












from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				lowercase__       							= DistilBertTokenizer
				lowercase__       							= DistilBertTokenizerFast
				lowercase__       							= True




				@slow
				def        lowercase__     (  self		:					str						):

								'''simple docstring'''
								lowercase__     				=    DistilBertTokenizer.from_pretrained('''distilbert-base-uncased'''						)

								lowercase__     				=    tokenizer.encode('''sequence builders''',						add_special_tokens=lowerCamelCase						)
								lowercase__     				=    tokenizer.encode('''multi-sequence build''',						add_special_tokens=lowerCamelCase						)

								lowercase__     				=    tokenizer.build_inputs_with_special_tokens(lowerCamelCase						)
								lowercase__     				=    tokenizer.build_inputs_with_special_tokens(lowerCamelCase,						lowerCamelCase						)

								assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
								assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
								    tokenizer.sep_token_id
								]




