


import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig
def   snake_case_    (			__lowercase      ,							__lowercase      ,							__lowercase=0							):





   if name is None:
      UpperCAmelCase_				:   Tuple							= None
   else:
      UpperCAmelCase_				:   Union[str, Any]							= '''.''' * max(0      ,							spaces - 2							) + '''# {:''' + str(5_0 - spaces							) + '''s}'''
      UpperCAmelCase_				:   Union[str, Any]							= fmt.format(__snake_case							)

   # Print and recurse (if needed).
   if isinstance(__snake_case      ,							__snake_case							):
      if msg is not None:
         print(__snake_case							)
      for k in val.keys():
         recursive_print(__snake_case      ,							val[k]      ,							spaces + 2							)
   elif isinstance(__snake_case      ,							torch.Tensor							):
      print(__snake_case      ,							''':'''      ,							val.size()							)
   else:
      print(__snake_case      ,							''':'''      ,							__snake_case							)
def   snake_case_    (			__lowercase      ,							__lowercase      ,							__lowercase      ,							__lowercase							):
   UpperCAmelCase_				:   List[str]							= param.size()
   # other versions store [num_heads * num_splits * hidden_size, :]
   UpperCAmelCase_				:   Optional[int]							= (num_heads, num_splits, hidden_size) + input_shape[1:]
   UpperCAmelCase_				:   Union[str, Any]							= param.view(*__snake_case							)
   UpperCAmelCase_				:   str							= param.transpose(0      ,							1							).contiguous()
   UpperCAmelCase_				:   Tuple							= param.view(*__snake_case							)
   return param
def   snake_case_    (			__lowercase      ,							__lowercase							):
   UpperCAmelCase_				:   int							= config.n_positions
   UpperCAmelCase_				:   Dict							= config.n_layer
   UpperCAmelCase_				:   Optional[int]							= config.vocab_size
   UpperCAmelCase_				:   List[Any]							= config.n_head
   UpperCAmelCase_				:   Dict							= config.n_embd // config.n_head

   UpperCAmelCase_				:   Tuple							= sd_megatron['''model.language_model.embedding.word_embeddings.weight'''][:vocab_size, :]
   UpperCAmelCase_				:   Tuple							= {
       '''transformer.wte.weight''': word_embeddings,
       '''transformer.wpe.weight''': sd_megatron['''model.language_model.embedding.position_embeddings.weight'''],
       '''transformer.ln_f.weight''': sd_megatron['''model.language_model.encoder.final_layernorm.weight'''],
       '''transformer.ln_f.bias''': sd_megatron['''model.language_model.encoder.final_layernorm.bias'''],
   }

   UpperCAmelCase_				:   str							= '''model.language_model.encoder.layers.'''
   for i in range(__snake_case							):
      UpperCAmelCase_				:   Union[str, Any]							= torch.tril(torch.ones((n_positions, n_positions)      ,							dtype=torch.bool							)							)
      UpperCAmelCase_				:   str							= causal_mask.view(1      ,							1      ,							__snake_case      ,							__snake_case							)
      UpperCAmelCase_				:   Tuple							= causal_mask
      UpperCAmelCase_				:   str							= torch.tensor(-1e4      ,							dtype=torch.bfloataa							)

      UpperCAmelCase_				:   List[Any]							= sd_megatron[F'''{pf}{i}.input_layernorm.weight''']
      UpperCAmelCase_				:   Optional[Any]							= sd_megatron[F'''{pf}{i}.input_layernorm.bias''']

      UpperCAmelCase_				:   Optional[int]							= sd_megatron[F'''{pf}{i}.self_attention.query_key_value.weight''']
      UpperCAmelCase_				:   Optional[Any]							= fix_query_key_value_ordering(__snake_case      ,							3      ,							__snake_case      ,							__snake_case							)
      UpperCAmelCase_				:   List[str]							= vala.transpose(0      ,							1							).contiguous()

      UpperCAmelCase_				:   List[str]							= sd_megatron[F'''{pf}{i}.self_attention.query_key_value.bias''']
      UpperCAmelCase_				:   List[str]							= fix_query_key_value_ordering(__snake_case      ,							3      ,							__snake_case      ,							__snake_case							)
      UpperCAmelCase_				:   int							= vala

      UpperCAmelCase_				:   List[Any]							= sd_megatron[F'''{pf}{i}.self_attention.dense.weight'''].transpose(
          0      ,							1							)
      UpperCAmelCase_				:   int							= sd_megatron[F'''{pf}{i}.self_attention.dense.bias''']
      UpperCAmelCase_				:   Any							= sd_megatron[F'''{pf}{i}.post_attention_layernorm.weight''']
      UpperCAmelCase_				:   str							= sd_megatron[F'''{pf}{i}.post_attention_layernorm.bias''']
      UpperCAmelCase_				:   List[str]							= sd_megatron[F'''{pf}{i}.mlp.dense_h_to_4h.weight'''].transpose(0      ,							1							)
      UpperCAmelCase_				:   Optional[int]							= sd_megatron[F'''{pf}{i}.mlp.dense_h_to_4h.bias''']
      UpperCAmelCase_				:   Tuple							= sd_megatron[F'''{pf}{i}.mlp.dense_4h_to_h.weight'''].transpose(
          0      ,							1							)
      UpperCAmelCase_				:   Tuple							= sd_megatron[F'''{pf}{i}.mlp.dense_4h_to_h.bias''']

   # For LM head, transformers' wants the matrix to weight embeddings.
   UpperCAmelCase_				:   str							= word_embeddings

   return sd_hf
def   snake_case_    (			__lowercase      ,							__lowercase							):
   UpperCAmelCase_				:   Optional[Any]							= 6_4_0_0_0
   UpperCAmelCase_				:   List[Any]							= config_megatron['''encoder_seq_length''']
   UpperCAmelCase_				:   List[Any]							= config_megatron['''hidden_size''']
   UpperCAmelCase_				:   List[Any]							= config_megatron['''num_layers''']
   UpperCAmelCase_				:   Union[str, Any]							= config_megatron['''num_attention_heads''']
   UpperCAmelCase_				:   Dict							= config_megatron['''ffn_hidden_size''']
   UpperCAmelCase_				:   Optional[int]							= '''gelu'''
   UpperCAmelCase_				:   Union[str, Any]							= 0.1
   UpperCAmelCase_				:   Tuple							= 0.1
   UpperCAmelCase_				:   Union[str, Any]							= 0.1
   UpperCAmelCase_				:   Optional[int]							= config_megatron['''layernorm_epsilon''']  # 1e-5
   UpperCAmelCase_				:   int							= config_megatron['''init_method_std''']  # 0.02
   UpperCAmelCase_				:   str							= config_megatron['''apply_query_key_layer_scaling''']  # True
   UpperCAmelCase_				:   Tuple							= True
   UpperCAmelCase_				:   Optional[Any]							= True

   # This identifies the 6.7B (7B) model which uses a different tokenizer
   if config_megatron["hidden_size"] == 4_0_9_6:
      UpperCAmelCase_				:   Tuple							= 1  # <|endoftext|>
      UpperCAmelCase_				:   str							= 1  # <|endoftext|>
      UpperCAmelCase_				:   List[Any]							= 0  # <unk>
   else:
      UpperCAmelCase_				:   Tuple							= 2  # <s>
      UpperCAmelCase_				:   Optional[Any]							= 3  # <|endoftext|>
      UpperCAmelCase_				:   int							= 0  # <pad>

   return config_hf
def   snake_case_    (			__lowercase							):
   print(__snake_case							)

   UpperCAmelCase_				:   Dict							= args.checkpoint_path
   UpperCAmelCase_				:   Any							= args.save_path
   if isfile(__snake_case							):
      raise FileNotFoundError(F'''ERROR! could not find file {checkpoint_path}'''							)

   # Load the model.
   UpperCAmelCase_				:   Union[str, Any]							= torch.load(__snake_case      ,							map_location='''cpu'''							)

   # Load the config.
   UpperCAmelCase_				:   Union[str, Any]							= checkpoint['''hyper_parameters''']['''cfg''']
   UpperCAmelCase_				:   List[str]							= GPTaConfig()
   UpperCAmelCase_				:   Optional[int]							= copy_config(config_hf=__snake_case      ,							config_megatron=__snake_case							)
   UpperCAmelCase_				:   Optional[int]							= ['''GPT2LMHeadModel''']

   UpperCAmelCase_				:   List[str]							= checkpoint['''state_dict''']

   # Convert.
   print('''Converting'''							)
   UpperCAmelCase_				:   str							= convert_megatron_checkpoint(__snake_case      ,							__snake_case							)

   # Print the structure of converted state dict.
   if args.print_checkpoint_structure:
      recursive_print(__snake_case      ,							__snake_case							)

   UpperCAmelCase_				:   List[Any]							= '''GPTSw3Tokenizer'''

   # Store the config to file.
   print('''Saving config'''							)
   config_hf.save_pretrained(__snake_case							)

   # Store the state_dict to file.
   UpperCAmelCase_				:   Any							= os.path.join(__snake_case      ,							'''pytorch_model.bin'''							)
   print(F'''Saving checkpoint to "{output_checkpoint_file}"'''							)
   torch.save(__snake_case      ,							__snake_case							)


if __name__ == "__main__":
       __UpperCamelCase					:	List[Any]          =				argparse.ArgumentParser()
       parser.add_argument(
           '--checkpoint_path',
           type=str,
           required=True,
           help='e.g. megatron_gpt--val_loss=2.42-step=38000-consumed_samples=54720000',
       )
       parser.add_argument('--save_path', type=str, required=True, help='e.g. /home/user/gpt-sw3/hf')
       parser.add_argument('--print-checkpoint-structure', action='store_true')
       __UpperCamelCase					:	Optional[Any]          =				parser.parse_args()
       main(_args)







"""simple docstring"""



import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list

class 			a	(  unittest.TestCase      ):







    def      UpperCamelCase_ (     self			):
      lowercase				       =			'<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K \' X J Q Z'.split(' '			)
      lowercase				       =			dict(zip(_lowerCamelCase				,						range(len(_lowerCamelCase			)			)			)			)

      lowercase				       =			{
          'pad_token': '<pad>',
          'unk_token': '<unk>',
          'bos_token': '<s>',
          'eos_token': '</s>',
      }
      lowercase				       =			{
          'feature_size': 1,
          'padding_value': 0.0,
          'sampling_rate': 1_6_0_0_0,
          'return_attention_mask': False,
          'do_normalize': True,
      }

      lowercase				       =			tempfile.mkdtemp()
      lowercase				       =			os.path.join(self.tmpdirname				,						VOCAB_FILES_NAMES['vocab_file']			)
      lowercase				       =			os.path.join(self.tmpdirname				,						_lowerCamelCase			)
      with open(self.vocab_file				,						'w'				,						encoding='utf-8'			) as fp:
        fp.write(json.dumps(_lowerCamelCase			) + '\n'			)

      with open(self.feature_extraction_file				,						'w'				,						encoding='utf-8'			) as fp:
        fp.write(json.dumps(_lowerCamelCase			) + '\n'			)







    def      UpperCamelCase_ (     self				,						**_lowerCamelCase			):
      lowercase				       =			self.add_kwargs_tokens_map.copy()
      kwargs.update(_lowerCamelCase			)
      return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname				,						**_lowerCamelCase			)







    def      UpperCamelCase_ (     self				,						**_lowerCamelCase			):
      return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname				,						**_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      shutil.rmtree(self.tmpdirname			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_tokenizer()
      lowercase				       =			self.get_feature_extractor()

      lowercase				       =			WavaVecaProcessor(tokenizer=_lowerCamelCase				,						feature_extractor=_lowerCamelCase			)

      processor.save_pretrained(self.tmpdirname			)
      lowercase				       =			WavaVecaProcessor.from_pretrained(self.tmpdirname			)

      self.assertEqual(processor.tokenizer.get_vocab()				,						tokenizer.get_vocab()			)
      self.assertIsInstance(processor.tokenizer				,						_lowerCamelCase			)

      self.assertEqual(processor.feature_extractor.to_json_string()				,						feature_extractor.to_json_string()			)
      self.assertIsInstance(processor.feature_extractor				,						_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			WavaVecaProcessor(tokenizer=self.get_tokenizer()				,						feature_extractor=self.get_feature_extractor()			)
      processor.save_pretrained(self.tmpdirname			)

      lowercase				       =			self.get_tokenizer(bos_token='(BOS)'				,						eos_token='(EOS)'			)
      lowercase				       =			self.get_feature_extractor(do_normalize=_lowerCamelCase				,						padding_value=1.0			)

      lowercase				       =			WavaVecaProcessor.from_pretrained(
          self.tmpdirname				,						bos_token='(BOS)'				,						eos_token='(EOS)'				,						do_normalize=_lowerCamelCase				,						padding_value=1.0			)

      self.assertEqual(processor.tokenizer.get_vocab()				,						tokenizer_add_kwargs.get_vocab()			)
      self.assertIsInstance(processor.tokenizer				,						_lowerCamelCase			)

      self.assertEqual(processor.feature_extractor.to_json_string()				,						feature_extractor_add_kwargs.to_json_string()			)
      self.assertIsInstance(processor.feature_extractor				,						_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_feature_extractor()
      lowercase				       =			self.get_tokenizer()

      lowercase				       =			WavaVecaProcessor(tokenizer=_lowerCamelCase				,						feature_extractor=_lowerCamelCase			)

      lowercase				       =			floats_list((3, 1_0_0_0)			)

      lowercase				       =			feature_extractor(_lowerCamelCase				,						return_tensors='np'			)
      lowercase				       =			processor(_lowerCamelCase				,						return_tensors='np'			)

      for key in input_feat_extract.keys():
        self.assertAlmostEqual(input_feat_extract[key].sum()				,						input_processor[key].sum()				,						delta=1e-2			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_feature_extractor()
      lowercase				       =			self.get_tokenizer()

      lowercase				       =			WavaVecaProcessor(tokenizer=_lowerCamelCase				,						feature_extractor=_lowerCamelCase			)

      lowercase				       =			'This is a test string'

      lowercase				       =			processor(text=_lowerCamelCase			)

      lowercase				       =			tokenizer(_lowerCamelCase			)

      for key in encoded_tok.keys():
        self.assertListEqual(encoded_tok[key]				,						encoded_processor[key]			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_feature_extractor()
      lowercase				       =			self.get_tokenizer()

      lowercase				       =			WavaVecaProcessor(tokenizer=_lowerCamelCase				,						feature_extractor=_lowerCamelCase			)

      lowercase				       =			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

      lowercase				       =			processor.batch_decode(_lowerCamelCase			)
      lowercase				       =			tokenizer.batch_decode(_lowerCamelCase			)

      self.assertListEqual(_lowerCamelCase				,						_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_feature_extractor()
      lowercase				       =			self.get_tokenizer()

      lowercase				       =			WavaVecaProcessor(tokenizer=_lowerCamelCase				,						feature_extractor=_lowerCamelCase			)

      self.assertListEqual(
          processor.model_input_names				,						feature_extractor.model_input_names				,						msg='`processor` and `feature_extractor` model input names do not match'				,						)


