



'''simple docstring'''





import argparse
import logging
import math
import time

import torch

from transformers import TransfoXLCorpus, TransfoXLLMHeadModel


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
A			:   str			=      logging.getLogger(__name__)
def 		_a   (  ):
   snake_case   :							str						  =argparse.ArgumentParser(description='''PyTorch Transformer Language Model'''							)
   parser.add_argument('''--model_name'''					,						type=lowerCamelCase_					,						default='''transfo-xl-wt103'''					,						help='''pretrained model name'''							)
   parser.add_argument(
       '''--split'''					,						type=lowerCamelCase_					,						default='''test'''					,						choices=['''all''', '''valid''', '''test''']					,						help='''which split to evaluate'''							)
   parser.add_argument('''--batch_size'''					,						type=lowerCamelCase_					,						default=10					,						help='''batch size'''							)
   parser.add_argument('''--tgt_len'''					,						type=lowerCamelCase_					,						default=1_28					,						help='''number of tokens to predict'''							)
   parser.add_argument('''--ext_len'''					,						type=lowerCamelCase_					,						default=0					,						help='''length of the extended context'''							)
   parser.add_argument('''--mem_len'''					,						type=lowerCamelCase_					,						default=16_00					,						help='''length of the retained previous heads'''							)
   parser.add_argument('''--clamp_len'''					,						type=lowerCamelCase_					,						default=10_00					,						help='''max positional embedding index'''							)
   parser.add_argument('''--no_cuda'''					,						action='''store_true'''					,						help='''Do not use CUDA even though CUA is available'''							)
   parser.add_argument('''--work_dir'''					,						type=lowerCamelCase_					,						required=lowerCamelCase_					,						help='''path to the work_dir'''							)
   parser.add_argument('''--no_log'''					,						action='''store_true'''					,						help='''do not log the eval result'''							)
   parser.add_argument('''--same_length'''					,						action='''store_true'''					,						help='''set same length attention with masking'''							)
   parser.add_argument('''--server_ip'''					,						type=lowerCamelCase_					,						default=''''''					,						help='''Can be used for distant debugging.'''							)
   parser.add_argument('''--server_port'''					,						type=lowerCamelCase_					,						default=''''''					,						help='''Can be used for distant debugging.'''							)
   snake_case   :							int						  =parser.parse_args()
   assert args.ext_len >= 0, "extended context length must be non-negative"

   if args.server_ip and args.server_port:
      # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
      import ptvsd

      print('''Waiting for debugger attach'''							)
      ptvsd.enable_attach(address=(args.server_ip, args.server_port)					,						redirect_output=lowerCamelCase_							)
      ptvsd.wait_for_attach()

   snake_case   :							Tuple						  =torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''							)
   logger.info('''device: {}'''.format(lowerCamelCase_							)							)

   # Load a pre-processed dataset
   # You can also build the corpus yourself using TransfoXLCorpus methods
   # The pre-processing involve computing word frequencies to prepare the Adaptive input and SoftMax
   # and tokenizing the dataset
   # The pre-processed corpus is a convertion (using the conversion script )
   snake_case   :							Optional[int]						  =TransfoXLCorpus.from_pretrained(args.model_name							)

   snake_case   :							List[str]						  =corpus.get_iterator('''valid'''					,						args.batch_size					,						args.tgt_len					,						device=lowerCamelCase_					,						ext_len=args.ext_len							)
   snake_case   :							Dict						  =corpus.get_iterator('''test'''					,						args.batch_size					,						args.tgt_len					,						device=lowerCamelCase_					,						ext_len=args.ext_len							)

   # Load a pre-trained model
   snake_case   :							int						  =TransfoXLLMHeadModel.from_pretrained(args.model_name							)
   model.to(lowerCamelCase_							)

   logger.info(
       '''Evaluating with bsz {} tgt_len {} ext_len {} mem_len {} clamp_len {}'''.format(
           args.batch_size					,						args.tgt_len					,						args.ext_len					,						args.mem_len					,						args.clamp_len							)							)

   model.reset_memory_length(args.mem_len							)
   if args.clamp_len > 0:
      snake_case   :							Dict						  =args.clamp_len
   if args.same_length:
      snake_case   :							Optional[int]						  =True

   ###############################################################################
   # Evaluation code
   ###############################################################################
   def evaluate(lowerCamelCase_							):
      # Turn on evaluation mode which disables dropout.
      model.eval()
      snake_case  ,    snake_case   :							Dict						  =0, 0.0
      snake_case   :							Optional[Any]						  =time.time()
      with torch.no_grad():
         snake_case   :							Union[str, Any]						  =None
         for idx, (data, target, seq_len) in enumerate(lowerCamelCase_							):
            snake_case   :							List[str]						  =model(lowerCamelCase_					,						lm_labels=lowerCamelCase_					,						mems=lowerCamelCase_							)
            snake_case  ,    snake_case  ,    snake_case   :							int						  =ret
            snake_case   :							Dict						  =loss.mean()
            total_loss += seq_len * loss.item()
            total_len += seq_len
         snake_case   :							Any						  =time.time() - start_time
      logger.info('''Time : {:.2f}s, {:.2f}ms/segment'''.format(lowerCamelCase_					,						10_00 * total_time / (idx + 1)							)							)
      return total_loss / total_len

   # Run on test data.
   if args.split == "all":
      snake_case   :							Any						  =evaluate(lowerCamelCase_							)
      snake_case   :							str						  =evaluate(lowerCamelCase_							)
   elif args.split == "valid":
      snake_case   :							Tuple						  =evaluate(lowerCamelCase_							)
      snake_case   :							List[Any]						  =None
   elif args.split == "test":
      snake_case   :							Dict						  =evaluate(lowerCamelCase_							)
      snake_case   :							Any						  =None

   def format_log(lowerCamelCase_					,						lowerCamelCase_							):
      snake_case   :							Dict						  ='''| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '''.format(lowerCamelCase_					,						lowerCamelCase_					,						math.exp(lowerCamelCase_							)							)
      return log_str

   snake_case   :							Dict						  =''''''
   if valid_loss is not None:
      log_str += format_log(lowerCamelCase_					,						'''valid'''							)
   if test_loss is not None:
      log_str += format_log(lowerCamelCase_					,						'''test'''							)

   logger.info('''=''' * 1_00							)
   logger.info(lowerCamelCase_							)
   logger.info('''=''' * 1_00							)


if __name__ == "__main__":
      main()










'''simple docstring'''





import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
A			:   Dict			=      logging.get_logger(__name__)

A			:   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""",
    """mask_emb""": """masked_spec_embed""",
}
def 		_a   (  lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_							):
   for attribute in key.split('''.'''							):
      snake_case   :							Optional[Any]						  =getattr(lowerCamelCase_					,						lowerCamelCase_							)

   if weight_type is not None:
      snake_case   :							int						  =getattr(lowerCamelCase_					,						lowerCamelCase_							).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   :							Dict						  =value
   elif weight_type == "weight_g":
      snake_case   :							Any						  =value
   elif weight_type == "weight_v":
      snake_case   :							int						  =value
   elif weight_type == "bias":
      snake_case   :							int						  =value
   else:
      snake_case   :							str						  =value

   logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''							)
def 		_a   (  lowerCamelCase_					,						lowerCamelCase_							):
   snake_case   :							str						  =[]
   snake_case   :							Tuple						  =fairseq_model.state_dict()

   snake_case   :							Union[str, Any]						  =hf_model.feature_extractor

   for name, value in fairseq_dict.items():
      snake_case   :							Optional[int]						  =False
      if "conv_layers" in name:
         load_conv_layer(
             lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_					,						hf_model.config.feat_extract_norm == '''group'''					,						)
         snake_case   :							int						  =True
      else:
         for key, mapped_key in MAPPING.items():
            snake_case   :							Dict						  =mapped_key

            if key in name:
               snake_case   :							str						  =True
               if "*" in mapped_key:
                  snake_case   :							Tuple						  =name.split(lowerCamelCase_							)[0].split('''.'''							)[-2]
                  snake_case   :							Optional[int]						  =mapped_key.replace('''*'''					,						lowerCamelCase_							)
               if "weight_g" in name:
                  snake_case   :							int						  ='''weight_g'''
               elif "weight_v" in name:
                  snake_case   :							List[str]						  ='''weight_v'''
               elif "weight" in name:
                  snake_case   :							Any						  ='''weight'''
               elif "bias" in name:
                  snake_case   :							List[str]						  ='''bias'''
               else:
                  snake_case   :							Tuple						  =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_							):
   snake_case   :							List[str]						  =full_name.split('''conv_layers.'''							)[-1]
   snake_case   :							Any						  =name.split('''.'''							)
   snake_case   :							Tuple						  =int(items[0]							)
   snake_case   :							int						  =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   :							Optional[int]						  =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   :							str						  =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   :							Optional[int]						  =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   :							Union[str, Any]						  =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_							):
   snake_case   :							Any						  =HubertConfig()
   snake_case   :							List[str]						  =model.config

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

   return config
@torch.no_grad()
def 		_a   (  lowerCamelCase_					,						lowerCamelCase_=None							):
   snake_case   :							Union[str, Any]						  =distilhubert().model.model

   if config_path is not None:
      snake_case   :							int						  =HubertConfig.from_pretrained(lowerCamelCase_							)
   else:
      snake_case   :							Optional[Any]						  =convert_config(lowerCamelCase_							)
   snake_case   :							Tuple						  =model.eval()

   snake_case   :							Optional[Any]						  =WavaVecaFeatureExtractor(
       feature_size=1					,						sampling_rate=1_60_00					,						padding_value=0					,						do_normalize=lowerCamelCase_					,						return_attention_mask=lowerCamelCase_					,						)
   snake_case   :							Any						  =HubertModel(lowerCamelCase_							)

   recursively_load_weights(lowerCamelCase_					,						lowerCamelCase_							)

   feature_extractor.save_pretrained(lowerCamelCase_							)
   hf_model.save_pretrained(lowerCamelCase_							)


if __name__ == "__main__":
      A			:   Tuple			=      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""")
      A			:   int			=      parser.parse_args()
      convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)





