
import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
lowercase__							:							List[str]									=			logging.get_logger(__name__)

lowercase__							:							Tuple									=			{
    "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",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "ctc_proj",
    "mask_emb": "masked_spec_embed",
}
lowercase__							:							Dict									=			[
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]




def 	A_ (							snake_case							:   Union[str, Any] ,      snake_case							:   Dict ,      snake_case							:   Union[str, Any] ,      snake_case							:   str ,      snake_case							:   Optional[Any] ,      snake_case							:   Union[str, Any] )      ->    List[str]:



						'''simple docstring'''

						for attribute in key.split('''.''' ):
												if is_finetuned:
																		if attribute in ["quantizer", "project_q", "project_hid"]:
																								# those layers are only relevant for pretraining and should be dropped
																								return

																		if attribute == "ctc_proj":
																								# we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
																								__UpperCamelCase       =			'''lm_head'''

												__UpperCamelCase       =			getattr(snake_case ,      snake_case )

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

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




def 	A_ (							snake_case							:   str ,      snake_case							:   str ,      snake_case							:   str )      ->    Optional[Any]:



						'''simple docstring'''

						__UpperCamelCase       =			[]
						__UpperCamelCase       =			fairseq_model.state_dict()

						__UpperCamelCase       =			hf_model.unispeech.feature_extractor

						for name, value in fairseq_dict.items():
												__UpperCamelCase       =			False
												if "conv_layers" in name:
																		load_conv_layer(
																		    snake_case ,      snake_case ,      snake_case ,      snake_case ,      hf_model.config.feat_extract_norm == '''group''' ,      )
																		__UpperCamelCase       =			True
												else:
																		for key, mapped_key in MAPPING.items():
																								__UpperCamelCase       =			'''unispeech.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
																								if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]:
																														__UpperCamelCase       =			True
																														if "*" in mapped_key:
																																				__UpperCamelCase       =			name.split(snake_case )[0].split('''.''' )[-2]
																																				__UpperCamelCase       =			mapped_key.replace('''*''' ,      snake_case )
																														if "weight_g" in name:
																																				__UpperCamelCase       =			'''weight_g'''
																														elif "weight_v" in name:
																																				__UpperCamelCase       =			'''weight_v'''
																														elif "bias" in name:
																																				__UpperCamelCase       =			'''bias'''
																														elif "weight" in name:
																																				# TODO: don't match quantizer.weight_proj
																																				__UpperCamelCase       =			'''weight'''
																														else:
																																				__UpperCamelCase       =			None
																														set_recursively(snake_case ,      snake_case ,      snake_case ,      snake_case ,      snake_case ,      snake_case )
																								continue
												if not is_used:
																		unused_weights.append(snake_case )

						logger.warning(f"Unused weights: {unused_weights}" )




def 	A_ (							snake_case							:   Dict ,      snake_case							:   Optional[Any] ,      snake_case							:   Optional[Any] ,      snake_case							:   Any ,      snake_case							:   str )      ->    int:



						'''simple docstring'''

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




@torch.no_grad()
def 	A_ (							snake_case							:   Tuple ,      snake_case							:   Optional[Any] ,      snake_case							:   List[Any]=None ,      snake_case							:   Dict=None ,      snake_case							:   int=True )      ->    int:



						'''simple docstring'''

						if config_path is not None:
												__UpperCamelCase       =			UniSpeechConfig.from_pretrained(snake_case )
						else:
												__UpperCamelCase       =			UniSpeechConfig()

						if is_finetuned:
												if dict_path:
																		__UpperCamelCase       =			Dictionary.load_from_json(snake_case )

																		# important change bos & pad token id since CTC symbol is <pad> and
																		# not <s> as in fairseq
																		__UpperCamelCase       =			target_dict.pad_index
																		__UpperCamelCase       =			target_dict.bos_index
																		__UpperCamelCase       =			target_dict.eos_index
																		__UpperCamelCase       =			len(target_dict.symbols )
																		__UpperCamelCase       =			os.path.join(snake_case ,      '''vocab.json''' )
																		if not os.path.isdir(snake_case ):
																								logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(snake_case ) )
																								return
																		os.makedirs(snake_case ,      exist_ok=snake_case )
																		__UpperCamelCase       =			target_dict.indices

																		# fairseq has the <pad> and <s> switched
																		__UpperCamelCase       =			42
																		__UpperCamelCase       =			43
																		with open(snake_case ,      '''w''' ,      encoding='''utf-8''' ) as vocab_handle:
																								json.dump(snake_case ,      snake_case )
																		__UpperCamelCase       =			WavaVecaPhonemeCTCTokenizer(
																		    snake_case ,      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=snake_case ,      )
																		__UpperCamelCase       =			True if config.feat_extract_norm == '''layer''' else False
																		__UpperCamelCase       =			WavaVecaFeatureExtractor(
																		    feature_size=1 ,      sampling_rate=16000 ,      padding_value=0 ,      do_normalize=snake_case ,      return_attention_mask=snake_case ,      )
																		__UpperCamelCase       =			WavaVecaProcessor(feature_extractor=snake_case ,      tokenizer=snake_case )
																		processor.save_pretrained(snake_case )

												__UpperCamelCase       =			UniSpeechForCTC(snake_case )
						else:
												__UpperCamelCase       =			UniSpeechForPreTraining(snake_case )

						if is_finetuned:
												__UpperCamelCase  ,							__UpperCamelCase  ,							__UpperCamelCase       =			fairseq.checkpoint_utils.load_model_ensemble_and_task(
												    [checkpoint_path] ,      arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] ), '''w2v_path''': checkpoint_path} )
						else:
												__UpperCamelCase  ,							__UpperCamelCase  ,							__UpperCamelCase       =			fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )

						__UpperCamelCase       =			model[0].eval()

						recursively_load_weights(snake_case ,      snake_case ,      snake_case )

						hf_unispeech.save_pretrained(snake_case )


if __name__ == "__main__":
				lowercase__							:							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"
				)
				lowercase__							:							Dict									=			parser.parse_args()
				convert_unispeech_checkpoint(
				    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
				)






from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
    MaskedBertForMultipleChoice,
    MaskedBertForQuestionAnswering,
    MaskedBertForSequenceClassification,
    MaskedBertForTokenClassification,
    MaskedBertModel,
)
from .modules import *




