


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

__snake_case      = {
    "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",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}






def 				_lowercase     (		SCREAMING_SNAKE_CASE_ :		str							,	SCREAMING_SNAKE_CASE_ :		Tuple							,	SCREAMING_SNAKE_CASE_ :		Optional[int]							,	SCREAMING_SNAKE_CASE_ :		str							,	SCREAMING_SNAKE_CASE_ :		Tuple						):





			"""simple docstring"""




			for attribute in key.split("""."""						):
						UpperCamelCase								=       getattr(_lowercase							,	_lowercase						)

			if weight_type is not None:
						UpperCamelCase								=       getattr(_lowercase							,	_lowercase						).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 				_lowercase     (		SCREAMING_SNAKE_CASE_ :		Union[str, Any]							,	SCREAMING_SNAKE_CASE_ :		Dict							,	SCREAMING_SNAKE_CASE_ :		Optional[int]						):





			"""simple docstring"""




			UpperCamelCase								=       []
			UpperCamelCase								=       fairseq_model.state_dict()

			UpperCamelCase								=       hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

			for name, value in fairseq_dict.items():
						UpperCamelCase								=       False
						if "conv_layers" in name:
									load_conv_layer(
									    _lowercase							,	_lowercase							,	_lowercase							,	_lowercase							,	hf_model.config.feat_extract_norm == """group"""							,	)
									UpperCamelCase								=       True
						else:
									for key, mapped_key in MAPPING.items():
												UpperCamelCase								=       """hubert.""" + 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] and not is_finetuned):
															UpperCamelCase								=       True
															if "*" in mapped_key:
																		UpperCamelCase								=       name.split(_lowercase						)[0].split("""."""						)[-2]
																		UpperCamelCase								=       mapped_key.replace("""*"""							,	_lowercase						)
															if "weight_g" in name:
																		UpperCamelCase								=       """weight_g"""
															elif "weight_v" in name:
																		UpperCamelCase								=       """weight_v"""
															elif "weight" in name:
																		UpperCamelCase								=       """weight"""
															elif "bias" in name:
																		UpperCamelCase								=       """bias"""
															else:
																		UpperCamelCase								=       None
															set_recursively(_lowercase							,	_lowercase							,	_lowercase							,	_lowercase							,	_lowercase						)
												continue
						if not is_used:
									unused_weights.append(_lowercase						)

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






def 				_lowercase     (		SCREAMING_SNAKE_CASE_ :		Optional[int]							,	SCREAMING_SNAKE_CASE_ :		Any							,	SCREAMING_SNAKE_CASE_ :		Union[str, Any]							,	SCREAMING_SNAKE_CASE_ :		Union[str, Any]							,	SCREAMING_SNAKE_CASE_ :		Dict						):





			"""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(_lowercase						)






@torch.no_grad()
def 				_lowercase     (		SCREAMING_SNAKE_CASE_ :		Dict							,	SCREAMING_SNAKE_CASE_ :		Tuple							,	SCREAMING_SNAKE_CASE_ :		Any=None							,	SCREAMING_SNAKE_CASE_ :		Dict=None							,	SCREAMING_SNAKE_CASE_ :		Optional[int]=True						):





			"""simple docstring"""




			if config_path is not None:
						UpperCamelCase								=       HubertConfig.from_pretrained(_lowercase						)
			else:
						UpperCamelCase								=       HubertConfig()

			if is_finetuned:
						if dict_path:
									UpperCamelCase								=       Dictionary.load(_lowercase						)

									# 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(_lowercase							,	"""vocab.json"""						)
									if not os.path.isdir(_lowercase						):
												logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(_lowercase						)						)
												return
									os.makedirs(_lowercase							,	exist_ok=_lowercase						)
									with open(_lowercase							,	"""w"""							,	encoding="""utf-8"""						) as vocab_handle:
												json.dump(target_dict.indices							,	_lowercase						)
									UpperCamelCase								=       WavaVecaCTCTokenizer(
									    _lowercase							,	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=_lowercase							,	)
									UpperCamelCase								=       True if config.feat_extract_norm == """layer""" else False
									UpperCamelCase								=       WavaVecaFeatureExtractor(
									    feature_size=1							,	sampling_rate=16_000							,	padding_value=0							,	do_normalize=_lowercase							,	return_attention_mask=_lowercase							,	)
									UpperCamelCase								=       WavaVecaProcessor(feature_extractor=_lowercase							,	tokenizer=_lowercase						)
									processor.save_pretrained(_lowercase						)

						UpperCamelCase								=       HubertForCTC(_lowercase						)
			else:
						UpperCamelCase								=       HubertModel(_lowercase						)

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

			UpperCamelCase								=       model[0].eval()

			recursively_load_weights(_lowercase							,	_lowercase							,	_lowercase						)

			hf_wavavec.save_pretrained(_lowercase						)


if __name__ == "__main__":
			__snake_case      = 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"
			)
			__snake_case      = parser.parse_args()
			convert_hubert_checkpoint(
			    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
			)






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_snake_case      = logging.get_logger(__name__)

_snake_case      = {
    '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/config.json''',
    '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/config.json''',
    '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/config.json''',
    '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/config.json''',
    '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json''',
    '''roberta-large-openai-detector''': '''https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json''',
}
class       _lowerCAmelCase  (		__magic_name__							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					Dict       						="roberta"

 def __init__(						self			:			Union[str, Any]					, SCREAMING_SNAKE_CASE__			:			Tuple=5_02_65					, SCREAMING_SNAKE_CASE__			:			List[str]=7_68					, SCREAMING_SNAKE_CASE__			:			List[str]=12					, SCREAMING_SNAKE_CASE__			:			Any=12					, SCREAMING_SNAKE_CASE__			:			Tuple=30_72					, SCREAMING_SNAKE_CASE__			:			str="gelu"					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]=0.1					, SCREAMING_SNAKE_CASE__			:			int=0.1					, SCREAMING_SNAKE_CASE__			:			int=5_12					, SCREAMING_SNAKE_CASE__			:			Optional[Any]=2					, SCREAMING_SNAKE_CASE__			:			Dict=0.02					, SCREAMING_SNAKE_CASE__			:			List[str]=1e-12					, SCREAMING_SNAKE_CASE__			:			Any=1					, SCREAMING_SNAKE_CASE__			:			Optional[Any]=0					, SCREAMING_SNAKE_CASE__			:			str=2					, SCREAMING_SNAKE_CASE__			:			Any="absolute"					, SCREAMING_SNAKE_CASE__			:			Any=True					, SCREAMING_SNAKE_CASE__			:			List[Any]=None					, **SCREAMING_SNAKE_CASE__			:			Optional[int]					, ):






      """simple docstring"""



      super().__init__(pad_token_id=SCREAMING_SNAKE_CASE__					, bos_token_id=SCREAMING_SNAKE_CASE__					, eos_token_id=SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__   )

      UpperCamelCase						    =					vocab_size
      UpperCamelCase						    =					hidden_size
      UpperCamelCase						    =					num_hidden_layers
      UpperCamelCase						    =					num_attention_heads
      UpperCamelCase						    =					hidden_act
      UpperCamelCase						    =					intermediate_size
      UpperCamelCase						    =					hidden_dropout_prob
      UpperCamelCase						    =					attention_probs_dropout_prob
      UpperCamelCase						    =					max_position_embeddings
      UpperCamelCase						    =					type_vocab_size
      UpperCamelCase						    =					initializer_range
      UpperCamelCase						    =					layer_norm_eps
      UpperCamelCase						    =					position_embedding_type
      UpperCamelCase						    =					use_cache
      UpperCamelCase						    =					classifier_dropout




class       _lowerCAmelCase  (		__magic_name__							):

 """simple docstring"""

 @property
 def      __lowerCAmelCase  (						self			:			Optional[int]   ):






      """simple docstring"""



      if self.task == "multiple-choice":
           UpperCamelCase						    =					{0: 'batch', 1: 'choice', 2: 'sequence'}
      else:
           UpperCamelCase						    =					{0: 'batch', 1: 'sequence'}
      return OrderedDict(
          [
              ('input_ids', dynamic_axis),
              ('attention_mask', dynamic_axis),
          ]   )


