code: 
  
  import argparse
  import os
  from os.path import isfile
  
  import torch
  
  from transformers import GPTaConfig
  
  
  def       __a       (   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE=0      )      ->     Any:
  
  
  
  
  
  
      '''simple docstring'''
      # Format the message.
      if name is None:
          __UpperCAmelCase									=			None
      else:
          __UpperCAmelCase									=			'''.''' * max(0       ,   spaces - 2      ) + '''# {:''' + str(5_0 - spaces      ) + '''s}'''
          __UpperCAmelCase									=			fmt.format(SCREAMING_SNAKE_CASE      )
  
      # Print and recurse (if needed).
      if isinstance(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      ):
          if msg is not None:
              print(SCREAMING_SNAKE_CASE      )
          for k in val.keys():
              recursive_print(SCREAMING_SNAKE_CASE       ,   val[k]       ,   spaces + 2      )
      elif isinstance(SCREAMING_SNAKE_CASE       ,   torch.Tensor      ):
          print(SCREAMING_SNAKE_CASE       ,   ''':'''       ,   val.size()      )
      else:
          print(SCREAMING_SNAKE_CASE       ,   ''':'''       ,   SCREAMING_SNAKE_CASE      )
  
  
  def       __a       (   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      )      ->     Optional[Any]:
  
  
  
  
  
  
      '''simple docstring'''
      __UpperCAmelCase									=			param.size()
      # other versions store [num_heads * num_splits * hidden_size, :]
      __UpperCAmelCase									=			(num_heads, num_splits, hidden_size) + input_shape[1:]
      __UpperCAmelCase									=			param.view(*SCREAMING_SNAKE_CASE      )
      __UpperCAmelCase									=			param.transpose(0       ,   1      ).contiguous()
      __UpperCAmelCase									=			param.view(*SCREAMING_SNAKE_CASE      )
      return param
  
  
  def       __a       (   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      )      ->     Optional[Any]:
  
  
  
  
  
  
      '''simple docstring'''
      __UpperCAmelCase									=			config.n_positions
      __UpperCAmelCase									=			config.n_layer
      __UpperCAmelCase									=			config.vocab_size
      __UpperCAmelCase									=			config.n_head
      __UpperCAmelCase									=			config.n_embd // config.n_head
  
      __UpperCAmelCase									=			sd_megatron['''model.language_model.embedding.word_embeddings.weight'''][:vocab_size, :]
      __UpperCAmelCase									=			{
          '''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									=			'''model.language_model.encoder.layers.'''
      for i in range(SCREAMING_SNAKE_CASE      ):
          __UpperCAmelCase									=			torch.tril(torch.ones((n_positions, n_positions)       ,   dtype=torch.bool      )      )
          __UpperCAmelCase									=			causal_mask.view(1       ,   1       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      )
          __UpperCAmelCase									=			causal_mask
          __UpperCAmelCase									=			torch.tensor(-1e4       ,   dtype=torch.bfloataa      )
  
          __UpperCAmelCase									=			sd_megatron[f'''{pf}{i}.input_layernorm.weight''']
          __UpperCAmelCase									=			sd_megatron[f'''{pf}{i}.input_layernorm.bias''']
  
          __UpperCAmelCase									=			sd_megatron[f'''{pf}{i}.self_attention.query_key_value.weight''']
          __UpperCAmelCase									=			fix_query_key_value_ordering(SCREAMING_SNAKE_CASE       ,   3       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      )
          __UpperCAmelCase									=			vala.transpose(0       ,   1      ).contiguous()
  
          __UpperCAmelCase									=			sd_megatron[f'''{pf}{i}.self_attention.query_key_value.bias''']
          __UpperCAmelCase									=			fix_query_key_value_ordering(SCREAMING_SNAKE_CASE       ,   3       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      )
          __UpperCAmelCase									=			vala
  
          __UpperCAmelCase									=			sd_megatron[f'''{pf}{i}.self_attention.dense.weight'''].transpose(
              0       ,   1      )
          __UpperCAmelCase									=			sd_megatron[f'''{pf}{i}.self_attention.dense.bias''']
          __UpperCAmelCase									=			sd_megatron[f'''{pf}{i}.post_attention_layernorm.weight''']
          __UpperCAmelCase									=			sd_megatron[f'''{pf}{i}.post_attention_layernorm.bias''']
          __UpperCAmelCase									=			sd_megatron[f'''{pf}{i}.mlp.dense_h_to_4h.weight'''].transpose(0       ,   1      )
          __UpperCAmelCase									=			sd_megatron[f'''{pf}{i}.mlp.dense_h_to_4h.bias''']
          __UpperCAmelCase									=			sd_megatron[f'''{pf}{i}.mlp.dense_4h_to_h.weight'''].transpose(
              0       ,   1      )
          __UpperCAmelCase									=			sd_megatron[f'''{pf}{i}.mlp.dense_4h_to_h.bias''']
  
      # For LM head, transformers' wants the matrix to weight embeddings.
      __UpperCAmelCase									=			word_embeddings
  
      return sd_hf
  
  
  def       __a       (   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      )      ->     List[Any]:
  
  
  
  
  
  
      '''simple docstring'''
      __UpperCAmelCase									=			6_4_0_0_0
      __UpperCAmelCase									=			config_megatron['''encoder_seq_length''']
      __UpperCAmelCase									=			config_megatron['''hidden_size''']
      __UpperCAmelCase									=			config_megatron['''num_layers''']
      __UpperCAmelCase									=			config_megatron['''num_attention_heads''']
      __UpperCAmelCase									=			config_megatron['''ffn_hidden_size''']
      __UpperCAmelCase									=			'''gelu'''
      __UpperCAmelCase									=			0.1
      __UpperCAmelCase									=			0.1
      __UpperCAmelCase									=			0.1
      __UpperCAmelCase									=			config_megatron['''layernorm_epsilon''']  # 1e-5
      __UpperCAmelCase									=			config_megatron['''init_method_std''']  # 0.02
      __UpperCAmelCase									=			config_megatron['''apply_query_key_layer_scaling''']  # True
      __UpperCAmelCase									=			True
      __UpperCAmelCase									=			True
  
      # This identifies the 6.7B (7B) model which uses a different tokenizer
      if config_megatron["hidden_size"] == 4_0_9_6:
          __UpperCAmelCase									=			1  # <|endoftext|>
          __UpperCAmelCase									=			1  # <|endoftext|>
          __UpperCAmelCase									=			0  # <unk>
      else:
          __UpperCAmelCase									=			2  # <s>
          __UpperCAmelCase									=			3  # <|endoftext|>
          __UpperCAmelCase									=			0  # <pad>
  
      return config_hf
  
  
  def       __a       (   SCREAMING_SNAKE_CASE      )      ->     Union[str, Any]:
  
  
  
  
  
  
      '''simple docstring'''
      print(SCREAMING_SNAKE_CASE      )
  
      __UpperCAmelCase									=			args.checkpoint_path
      __UpperCAmelCase									=			args.save_path
      if isfile(SCREAMING_SNAKE_CASE      ):
          raise FileNotFoundError(f'''ERROR! could not find file {checkpoint_path}'''      )
  
      # Load the model.
      __UpperCAmelCase									=			torch.load(SCREAMING_SNAKE_CASE       ,   map_location='''cpu'''      )
  
      # Load the config.
      __UpperCAmelCase									=			checkpoint['''hyper_parameters''']['''cfg''']
      __UpperCAmelCase									=			GPTaConfig()
      __UpperCAmelCase									=			copy_config(config_hf=SCREAMING_SNAKE_CASE       ,   config_megatron=SCREAMING_SNAKE_CASE      )
      __UpperCAmelCase									=			['''GPT2LMHeadModel''']
  
      __UpperCAmelCase									=			checkpoint['''state_dict''']
  
      # Convert.
      print('''Converting'''      )
      __UpperCAmelCase									=			convert_megatron_checkpoint(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      )
  
      # Print the structure of converted state dict.
      if args.print_checkpoint_structure:
          recursive_print(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      )
  
      __UpperCAmelCase									=			'''GPTSw3Tokenizer'''
  
      # Store the config to file.
      print('''Saving config'''      )
      config_hf.save_pretrained(SCREAMING_SNAKE_CASE      )
  
      # Store the state_dict to file.
      __UpperCAmelCase									=			os.path.join(SCREAMING_SNAKE_CASE       ,   '''pytorch_model.bin'''      )
      print(f'''Saving checkpoint to "{output_checkpoint_file}"'''      )
      torch.save(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      )
  
  
  if __name__ == "__main__":
         A_		:			str										=   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')
         A_		:			List[str]										=   parser.parse_args()
         main(_args)
  
  
  
  
  
  
style_context: 
  
  from __future__ import annotations
  
  import unittest
  
  from transformers import MPNetConfig, is_tf_available
  from transformers.testing_utils import require_tf, slow
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_tf_available():
         import tensorflow as tf
  
         from transformers.models.mpnet.modeling_tf_mpnet import (
             TFMPNetForMaskedLM,
             TFMPNetForMultipleChoice,
             TFMPNetForQuestionAnswering,
             TFMPNetForSequenceClassification,
             TFMPNetForTokenClassification,
             TFMPNetModel,
         )
  class 					A_   :
  
  
      '''simple docstring'''
  
  
  
  
  
  
      def __init__(self     ,      lowercase__     ,      lowercase__=13     ,      lowercase__=7     ,      lowercase__=True     ,      lowercase__=True     ,      lowercase__=False     ,      lowercase__=True     ,      lowercase__=99     ,      lowercase__=64     ,      lowercase__=2     ,      lowercase__=4     ,      lowercase__=64     ,      lowercase__="gelu"     ,      lowercase__=0.1     ,      lowercase__=0.1     ,      lowercase__=512     ,      lowercase__=16     ,      lowercase__=2     ,      lowercase__=0.02     ,      lowercase__=3     ,      lowercase__=4     ,      lowercase__=None     ,      )     ->      Tuple:
          __UpperCAmelCase									=			parent
          __UpperCAmelCase									=			batch_size
          __UpperCAmelCase									=			seq_length
          __UpperCAmelCase									=			is_training
          __UpperCAmelCase									=			use_input_mask
          __UpperCAmelCase									=			use_token_type_ids
          __UpperCAmelCase									=			use_labels
          __UpperCAmelCase									=			vocab_size
          __UpperCAmelCase									=			hidden_size
          __UpperCAmelCase									=			num_hidden_layers
          __UpperCAmelCase									=			num_attention_heads
          __UpperCAmelCase									=			intermediate_size
          __UpperCAmelCase									=			hidden_act
          __UpperCAmelCase									=			hidden_dropout_prob
          __UpperCAmelCase									=			attention_probs_dropout_prob
          __UpperCAmelCase									=			max_position_embeddings
          __UpperCAmelCase									=			type_vocab_size
          __UpperCAmelCase									=			type_sequence_label_size
          __UpperCAmelCase									=			initializer_range
          __UpperCAmelCase									=			num_labels
          __UpperCAmelCase									=			num_choices
          __UpperCAmelCase									=			scope
  
  
      def      lowerCAmelCase_						(self  )     ->      str:
          __UpperCAmelCase									=			ids_tensor([self.batch_size, self.seq_length]     ,      self.vocab_size  )
  
          __UpperCAmelCase									=			None
          if self.use_input_mask:
              __UpperCAmelCase									=			random_attention_mask([self.batch_size, self.seq_length]  )
  
          __UpperCAmelCase									=			None
          __UpperCAmelCase									=			None
          __UpperCAmelCase									=			None
          if self.use_labels:
              __UpperCAmelCase									=			ids_tensor([self.batch_size]     ,      self.type_sequence_label_size  )
              __UpperCAmelCase									=			ids_tensor([self.batch_size, self.seq_length]     ,      self.num_labels  )
              __UpperCAmelCase									=			ids_tensor([self.batch_size]     ,      self.num_choices  )
  
          __UpperCAmelCase									=			MPNetConfig(
              vocab_size=self.vocab_size     ,      hidden_size=self.hidden_size     ,      num_hidden_layers=self.num_hidden_layers     ,      num_attention_heads=self.num_attention_heads     ,      intermediate_size=self.intermediate_size     ,      hidden_act=self.hidden_act     ,      hidden_dropout_prob=self.hidden_dropout_prob     ,      attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,      max_position_embeddings=self.max_position_embeddings     ,      initializer_range=self.initializer_range     ,      )
          return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__  )     ->      List[Any]:
          __UpperCAmelCase									=			TFMPNetModel(config=lowercase__  )
          __UpperCAmelCase									=			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
          __UpperCAmelCase									=			model(lowercase__  )
          __UpperCAmelCase									=			[input_ids, input_mask]
          __UpperCAmelCase									=			model(lowercase__  )
          self.parent.assertEqual(result.last_hidden_state.shape     ,      (self.batch_size, self.seq_length, self.hidden_size)  )
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__  )     ->      Dict:
          __UpperCAmelCase									=			TFMPNetForMaskedLM(config=lowercase__  )
          __UpperCAmelCase									=			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
          __UpperCAmelCase									=			model(lowercase__  )
          self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.seq_length, self.vocab_size)  )
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__  )     ->      List[Any]:
          __UpperCAmelCase									=			TFMPNetForQuestionAnswering(config=lowercase__  )
          __UpperCAmelCase									=			{
              '''input_ids''': input_ids,
              '''attention_mask''': input_mask,
          }
          __UpperCAmelCase									=			model(lowercase__  )
          self.parent.assertEqual(result.start_logits.shape     ,      (self.batch_size, self.seq_length)  )
          self.parent.assertEqual(result.end_logits.shape     ,      (self.batch_size, self.seq_length)  )
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__  )     ->      str:
          __UpperCAmelCase									=			self.num_labels
          __UpperCAmelCase									=			TFMPNetForSequenceClassification(lowercase__  )
          __UpperCAmelCase									=			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
          __UpperCAmelCase									=			model(lowercase__  )
          self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.num_labels)  )
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__  )     ->      str:
          __UpperCAmelCase									=			self.num_choices
          __UpperCAmelCase									=			TFMPNetForMultipleChoice(lowercase__  )
          __UpperCAmelCase									=			tf.tile(tf.expand_dims(lowercase__     ,      1  )     ,      (1, self.num_choices, 1)  )
          __UpperCAmelCase									=			tf.tile(tf.expand_dims(lowercase__     ,      1  )     ,      (1, self.num_choices, 1)  )
          __UpperCAmelCase									=			{
              '''input_ids''': multiple_choice_inputs_ids,
              '''attention_mask''': multiple_choice_input_mask,
          }
          __UpperCAmelCase									=			model(lowercase__  )
          self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.num_choices)  )
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__  )     ->      List[str]:
          __UpperCAmelCase									=			self.num_labels
          __UpperCAmelCase									=			TFMPNetForTokenClassification(lowercase__  )
          __UpperCAmelCase									=			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
          __UpperCAmelCase									=			model(lowercase__  )
          self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.seq_length, self.num_labels)  )
  
  
  
  
  
  
      def      lowerCAmelCase_						(self  )     ->      Optional[Any]:
          __UpperCAmelCase									=			self.prepare_config_and_inputs()
          ((__UpperCAmelCase)					,     (__UpperCAmelCase)					,     (__UpperCAmelCase)					,     (__UpperCAmelCase)					,     (__UpperCAmelCase)					,     (__UpperCAmelCase))									=			config_and_inputs
          __UpperCAmelCase									=			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
          return config, inputs_dict
  
  
  
  
  
  
  @require_tf
  class 					A_   (      _a ,     _a ,     unittest.TestCase						):
  
  
      '''simple docstring'''
  
  
  
  
  
  
      a__     							=       (
          (
              TFMPNetForMaskedLM,
              TFMPNetForMultipleChoice,
              TFMPNetForQuestionAnswering,
              TFMPNetForSequenceClassification,
              TFMPNetForTokenClassification,
              TFMPNetModel,
          )
          if is_tf_available()
          else ()
      )
      a__     							=       (
          {
              "feature-extraction": TFMPNetModel,
              "fill-mask": TFMPNetForMaskedLM,
              "question-answering": TFMPNetForQuestionAnswering,
              "text-classification": TFMPNetForSequenceClassification,
              "token-classification": TFMPNetForTokenClassification,
              "zero-shot": TFMPNetForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )
      a__     							=       False
      a__     							=       False
  
  
      def      lowerCAmelCase_						(self  )     ->      int:
          __UpperCAmelCase									=			TFMPNetModelTester(self  )
          __UpperCAmelCase									=			ConfigTester(self     ,      config_class=lowercase__     ,      hidden_size=37  )
  
  
      def      lowerCAmelCase_						(self  )     ->      Optional[int]:
          self.config_tester.run_common_tests()
  
  
      def      lowerCAmelCase_						(self  )     ->      Dict:
          __UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mpnet_model(*lowercase__  )
  
  
      def      lowerCAmelCase_						(self  )     ->      Any:
          __UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mpnet_for_masked_lm(*lowercase__  )
  
  
      def      lowerCAmelCase_						(self  )     ->      int:
          __UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mpnet_for_question_answering(*lowercase__  )
  
  
      def      lowerCAmelCase_						(self  )     ->      Optional[int]:
          __UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mpnet_for_sequence_classification(*lowercase__  )
  
  
      def      lowerCAmelCase_						(self  )     ->      str:
          __UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mpnet_for_multiple_choice(*lowercase__  )
  
  
      def      lowerCAmelCase_						(self  )     ->      str:
          __UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mpnet_for_token_classification(*lowercase__  )
  
  
  
  
  
  
      @slow
      def      lowerCAmelCase_						(self  )     ->      List[str]:
          for model_name in ["microsoft/mpnet-base"]:
              __UpperCAmelCase									=			TFMPNetModel.from_pretrained(lowercase__  )
              self.assertIsNotNone(lowercase__  )
  
  
  
  
  
  
  @require_tf
  class 					A_   (      unittest.TestCase						):
  
  
      '''simple docstring'''
  
  
  
  
  
  
      @slow
      def      lowerCAmelCase_						(self  )     ->      Any:
          __UpperCAmelCase									=			TFMPNetModel.from_pretrained('''microsoft/mpnet-base'''  )
          __UpperCAmelCase									=			tf.constant([[0, 1, 2, 3, 4, 5]]  )
          __UpperCAmelCase									=			model(lowercase__  )[0]
  
          __UpperCAmelCase									=			[1, 6, 768]
          self.assertEqual(output.shape     ,      lowercase__  )
  
          __UpperCAmelCase									=			tf.constant(
              [
                  [
                      [-0.1067172, 0.08216473, 0.0024543],
                      [-0.03465879, 0.8354118, -0.03252288],
                      [-0.06569476, -0.12424111, -0.0494436],
                  ]
              ]  )
          tf.debugging.assert_near(output[:, :3, :3]     ,      lowercase__     ,      atol=1E-4  )
  
  
  
  
  