




import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

SCREAMING_SNAKE_CASE__ :   Optional[int]						=     {
    "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   A   (   _SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE					)			->				List[Any]:







							for attribute in key.split("."					):
														lowerCamelCase     :   Optional[int]    				=      getattr(_UpperCamelCase				,_UpperCamelCase					)

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

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

def   A   (   _SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE					)			->				Optional[Any]:
							lowerCamelCase     :   Union[str, Any]    				=      []
							lowerCamelCase     :   List[str]    				=      fairseq_model.state_dict()

							lowerCamelCase     :   str    				=      hf_model.feature_extractor

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

																												if key in name:
																																			lowerCamelCase     :   Tuple    				=      True
																																			if "*" in mapped_key:
																																										lowerCamelCase     :   Any    				=      name.split(_UpperCamelCase					)[0].split("."					)[-2]
																																										lowerCamelCase     :   Optional[int]    				=      mapped_key.replace("*"				,_UpperCamelCase					)
																																			if "weight_g" in name:
																																										lowerCamelCase     :   str    				=      "weight_g"
																																			elif "weight_v" in name:
																																										lowerCamelCase     :   Dict    				=      "weight_v"
																																			elif "weight" in name:
																																										lowerCamelCase     :   Any    				=      "weight"
																																			elif "bias" in name:
																																										lowerCamelCase     :   Optional[int]    				=      "bias"
																																			else:
																																										lowerCamelCase     :   Union[str, 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   A   (   _SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE					)			->				Any:
							lowerCamelCase     :   Any    				=      full_name.split("conv_layers."					)[-1]
							lowerCamelCase     :   Optional[int]    				=      name.split("."					)
							lowerCamelCase     :   int    				=      int(items[0]					)
							lowerCamelCase     :   List[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.'''
																					)
																					lowerCamelCase     :   Optional[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.'''
																					)
																					lowerCamelCase     :   Optional[int]    				=      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."
																					)
																					lowerCamelCase     :   Tuple    				=      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.'''
																					)
																					lowerCamelCase     :   Optional[int]    				=      value
																					logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''					)
							else:
														unused_weights.append(_UpperCamelCase					)

def   A   (   _SCREAMING_SNAKE_CASE					)			->				Any:
							lowerCamelCase     :   List[Any]    				=      HubertConfig()
							lowerCamelCase     :   int    				=      model.config

							lowerCamelCase     :   List[str]    				=      fs_config.activation_dropout
							lowerCamelCase     :   Dict    				=      False
							lowerCamelCase     :   str    				=      fs_config.attention_dropout
							lowerCamelCase     :   int    				=      False
							lowerCamelCase     :   int    				=      eval(fs_config.extractor_conv_feature_layers					)
							lowerCamelCase     :   Optional[int]    				=      [x[0] for x in conv_layers]
							lowerCamelCase     :   Union[str, Any]    				=      [x[1] for x in conv_layers]
							lowerCamelCase     :   str    				=      [x[2] for x in conv_layers]
							lowerCamelCase     :   Tuple    				=      "gelu"
							lowerCamelCase     :   str    				=      "layer" if fs_config.extractor_mode == "layer_norm" else "group"
							lowerCamelCase     :   Union[str, Any]    				=      False
							lowerCamelCase     :   List[str]    				=      0.0
							lowerCamelCase     :   List[Any]    				=      0.0
							lowerCamelCase     :   Dict    				=      fs_config.activation_fn
							lowerCamelCase     :   Optional[int]    				=      fs_config.dropout
							lowerCamelCase     :   Optional[Any]    				=      fs_config.encoder_embed_dim
							lowerCamelCase     :   List[str]    				=      0.02
							lowerCamelCase     :   List[str]    				=      fs_config.encoder_ffn_embed_dim
							lowerCamelCase     :   Tuple    				=      1e-5
							lowerCamelCase     :   int    				=      0.0
							lowerCamelCase     :   int    				=      fs_config.encoder_attention_heads
							lowerCamelCase     :   Optional[int]    				=      fs_config.conv_pos_groups
							lowerCamelCase     :   str    				=      fs_config.conv_pos
							lowerCamelCase     :   Tuple    				=      len(_UpperCamelCase					)
							lowerCamelCase     :   Tuple    				=      fs_config.encoder_layers

							return config

@torch.no_grad()
def   A   (   _SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE=None					)			->				Optional[int]:
							lowerCamelCase     :   Any    				=      distilhubert().model.model

							if config_path is not None:
														lowerCamelCase     :   int    				=      HubertConfig.from_pretrained(_UpperCamelCase					)
							else:
														lowerCamelCase     :   Union[str, Any]    				=      convert_config(_UpperCamelCase					)
							lowerCamelCase     :   List[str]    				=      model.eval()

							lowerCamelCase     :   Tuple    				=      WavaVecaFeatureExtractor(
							    feature_size=1				,sampling_rate=1_6000				,padding_value=0				,do_normalize=_UpperCamelCase				,return_attention_mask=_UpperCamelCase				,)
							lowerCamelCase     :   Dict    				=      HubertModel(_UpperCamelCase					)

							recursively_load_weights(_UpperCamelCase				,_UpperCamelCase					)

							feature_extractor.save_pretrained(_UpperCamelCase					)
							hf_model.save_pretrained(_UpperCamelCase					)


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__ :   Optional[int]						=     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')
							SCREAMING_SNAKE_CASE__ :   Dict						=     parser.parse_args()
							convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)












import os
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
       import torch

       from transformers import GPTaLMHeadModel




@require_torch
@require_sentencepiece
@require_tokenizers
class 	UpperCamelCase__			(unittest.TestCase  ):

    '''simple docstring'''



    @slow
    @unittest.skip("Model is not available."  )
    def 					_lowercase  (					self  )  ->   List[Any]:
           lowerCamelCase     :   List[Any]    				=      "nvidia/megatron-gpt2-345m/"
           if "MYDIR" in os.environ:
                  lowerCamelCase     :   Union[str, Any]    				=      os.path.join(os.environ["MYDIR"]  ,						UpperCamelCase__  )
           lowerCamelCase     :   Any    				=      GPTaLMHeadModel.from_pretrained(UpperCamelCase__  )
           model.to(UpperCamelCase__  )
           model.half()

           lowerCamelCase     :   Dict    				=      torch.tensor(
               [[101, 7110, 1005, 1056, 2023, 1_1333, 1_7413, 1029, 102]]  ,						device=UpperCamelCase__  ,						dtype=torch.long  ,						)

           with torch.no_grad():
                  lowerCamelCase     :   Any    				=      model(UpperCamelCase__  ).logits

           lowerCamelCase     :   Union[str, Any]    				=      torch.Size((1, 9, 5_0257)  )
           self.assertEqual(output.shape  ,						UpperCamelCase__  )

           lowerCamelCase     :   List[str]    				=      torch.tensor(
               [
                   4.9414,
                   -0.2920,
                   -1.2148,
                   -4.0273,
                   -0.5161,
                   -5.2109,
                   -1.2412,
                   -1.8301,
                   -1.7734,
                   -4.7148,
                   -0.2317,
                   -1.0811,
                   -2.1777,
                   0.4141,
                   -3.7969,
                   -4.0586,
                   -2.5332,
                   -3.3809,
                   4.3867,
               ]  ,						device=UpperCamelCase__  ,						dtype=torch.half  ,						)

           for i in range(19  ):
                  lowerCamelCase				,   lowerCamelCase     :   Optional[int]    				=      8 * i // 17, 2792 * i  # along the diagonal
                  lowerCamelCase				,   lowerCamelCase     :   Tuple    				=      output[0, r, c], expected_diag[i]
                  lowerCamelCase     :   List[str]    				=      F'''row={r} col={c} computed={computed} expected={expected}'''
                  self.assertAlmostEqual(UpperCamelCase__  ,						UpperCamelCase__  ,						delta=1e-4  ,						msg=UpperCamelCase__  )






