code: 
  
  # Copyright (c) 2021-, NVIDIA CORPORATION.  All rights reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  ####################################################################################################
  
  #
  # Note: If when running this conversion script you're getting an exception:
  #     ModuleNotFoundError: No module named 'megatron.model.enums'
  # you need to tell python where to find the clone of Megatron-LM, e.g.:
  #
  # cd /tmp
  # git clone https://github.com/NVIDIA/Megatron-LM
  # PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_bert/convert_megatron_bert_checkpoint.py ...
  #
  # if you already have it cloned elsewhere, simply adjust the path to the existing path
  #
  # If the training was done using a Megatron-LM fork, e.g.,
  # https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
  # in your path, i.e., /path/to/Megatron-DeepSpeed/
  #
  
  import argparse
  import os
  import re
  import zipfile
  
  import torch
  
  from transformers import MegatronBertConfig
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Dict			,  SCREAMING_SNAKE_CASE_	:					Any			,  SCREAMING_SNAKE_CASE_	:					Any=0    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       if name is None:
            lowercase__					:     Tuple   = None
       else:
            lowercase__					:     Union[str, Any]   = '.' * max(0			,  spaces - 2    ) + '# {:' + str(50 - spaces    ) + 's}'
            lowercase__					:     str   = 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 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Tuple			,  SCREAMING_SNAKE_CASE_	:					Any			,  SCREAMING_SNAKE_CASE_	:					Optional[int]			,  SCREAMING_SNAKE_CASE_	:					Optional[int]			,  SCREAMING_SNAKE_CASE_	:					Any    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     Optional[int]   = param.size()
       if checkpoint_version == 1.0:
            # version 1.0 stores [num_heads * hidden_size * num_splits, :]
            lowercase__					:     Tuple   = (num_heads, hidden_size, num_splits) + input_shape[1:]
            lowercase__					:     Any   = param.view(*SCREAMING_SNAKE_CASE_    )
            lowercase__					:     str   = param.transpose(0			,  2    )
            lowercase__					:     str   = param.transpose(1			,  2    ).contiguous()
       elif checkpoint_version >= 2.0:
            # other versions store [num_heads * num_splits * hidden_size, :]
            lowercase__					:     Union[str, Any]   = (num_heads, num_splits, hidden_size) + input_shape[1:]
            lowercase__					:     List[Any]   = param.view(*SCREAMING_SNAKE_CASE_    )
            lowercase__					:     int   = param.transpose(0			,  1    ).contiguous()
       lowercase__					:     str   = param.view(*SCREAMING_SNAKE_CASE_    )
       return param
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Tuple			,  SCREAMING_SNAKE_CASE_	:					Optional[Any]			,  SCREAMING_SNAKE_CASE_	:					List[str]    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     str   = {}
  
       # old versions did not store training args
       lowercase__					:     Optional[Any]   = input_state_dict.get('args'			,  SCREAMING_SNAKE_CASE_    )
       if ds_args is not None:
            # do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
            # from pprint import pprint
            # pprint(vars(ds_args))
  
            lowercase__					:     Dict   = ds_args.tokenizer_type
            lowercase__					:     Optional[Any]   = ds_args.padded_vocab_size
            lowercase__					:     Any   = ds_args.max_position_embeddings
            lowercase__					:     Dict   = ds_args.hidden_size
            lowercase__					:     Optional[Any]   = ds_args.num_layers
            lowercase__					:     Union[str, Any]   = ds_args.num_attention_heads
            lowercase__					:     Union[str, Any]   = ds_args.ffn_hidden_size if 'ffn_hidden_size' in ds_args else 4 * ds_args.hidden_size
            # pprint(config)
  
       # The number of heads.
       lowercase__					:     Optional[int]   = config.num_attention_heads
       # The hidden_size per head.
       lowercase__					:     Union[str, Any]   = config.hidden_size // heads
       # Megatron-LM checkpoint version
       if "checkpoint_version" in input_state_dict.keys():
            lowercase__					:     Dict   = input_state_dict['checkpoint_version']
       else:
            lowercase__					:     Tuple   = 0.0
  
       # The model.
       lowercase__					:     Optional[int]   = input_state_dict['model']
       # The language model.
       lowercase__					:     Any   = model['language_model']
       # The embeddings.
       lowercase__					:     Union[str, Any]   = lm['embedding']
  
       # The word embeddings.
       lowercase__					:     List[str]   = embeddings['word_embeddings']['weight']
       # Truncate the embedding table to vocab_size rows.
       lowercase__					:     int   = word_embeddings[: config.vocab_size, :]
       # Store the word embeddings.
       lowercase__					:     List[str]   = word_embeddings
  
       # The position embeddings.
       lowercase__					:     Optional[Any]   = embeddings['position_embeddings']['weight']
       assert pos_embeddings.size(0    ) == config.max_position_embeddings and pos_embeddings.size(1    ) == config.hidden_size
       # Store the position embeddings.
       lowercase__					:     Dict   = pos_embeddings
  
       # The token-type embeddings.
       lowercase__					:     Union[str, Any]   = embeddings['tokentype_embeddings']['weight']
       # Store the position embeddings.
       lowercase__					:     Optional[Any]   = tokentype_embeddings
  
       # The transformer.
       lowercase__					:     Dict   = lm['transformer'] if 'transformer' in lm.keys() else lm['encoder']
  
       # The regex to extract layer names.
       lowercase__					:     Optional[int]   = re.compile(R'layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)'    )
  
       # The simple map of names for "automated" rules.
       lowercase__					:     str   = {
           'attention.dense': '.attention.output.dense.',
           'self_attention.dense': '.attention.output.dense.',
           'mlp.dense_h_to_4h': '.intermediate.dense.',
           'mlp.dense_4h_to_h': '.output.dense.',
       }
  
       # Keep track of the attention/query/value tensor.
       lowercase__					:     int   = None
  
       # Extract the layers.
       for key, val in transformer.items():
            # Match the name.
            lowercase__					:     str   = layer_re.match(SCREAMING_SNAKE_CASE_    )
  
            # Stop if that's not a layer
            if m is None:
                 break
  
            # The index of the layer.
            lowercase__					:     str   = int(m.group(1    )    )
            # The name of the operation.
            lowercase__					:     Any   = m.group(2    )
            # Is it a weight or a bias?
            lowercase__					:     Optional[int]   = m.group(3    )
  
            # The name of the layer.
            lowercase__					:     Dict   = f"""bert.encoder.layer.{layer_idx}"""
  
            # For layernorm(s), simply store the layer norm.
            if op_name.endswith('layernorm'    ):
                 lowercase__					:     List[str]   = 'attention.ln' if op_name.startswith('input'    ) else 'ln'
                 lowercase__					:     Dict   = val
  
            # Transpose the QKV matrix.
            elif (
                op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
            ) and weight_or_bias == "weight":
                 # Make sure the QKV pointer is nil.
                 assert attention_qkv_weight is None, ""
  
                 lowercase__					:     int   = fix_query_key_value_ordering(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  3			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    )
                 # Store the tensor as we need the bias as well to interleave QKV and biases.
                 lowercase__					:     Optional[int]   = out_val
  
            # Transpose the bias.
            elif (
                op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
            ) and weight_or_bias == "bias":
                 # Make sure we read the weight tensor.
                 assert attention_qkv_weight is not None, ""
  
                 # Split the QKV matrix into Q, K and V. Megatron stores Q,K,V interleaved.
                 lowercase__					:     Optional[Any]   = attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
                 lowercase__					:     Any   = attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
                 lowercase__					:     Optional[Any]   = attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]
  
                 lowercase__					:     Optional[Any]   = fix_query_key_value_ordering(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  3			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    )
                 # Split the bias.
                 lowercase__					:     List[str]   = out_val[0 * config.hidden_size : 1 * config.hidden_size]
                 lowercase__					:     Union[str, Any]   = out_val[1 * config.hidden_size : 2 * config.hidden_size]
                 lowercase__					:     Tuple   = out_val[2 * config.hidden_size : 3 * config.hidden_size]
  
                 # Store.
                 lowercase__					:     List[Any]   = q
                 lowercase__					:     int   = q_bias
                 lowercase__					:     Dict   = k
                 lowercase__					:     Dict   = k_bias
                 lowercase__					:     int   = v
                 lowercase__					:     Tuple   = v_bias
  
                 # Clear the stored tensor.
                 lowercase__					:     Union[str, Any]   = None
  
            # Copy weights and biases as is.
            elif weight_or_bias in ["weight", "bias"]:
                 lowercase__					:     Optional[int]   = megatron_to_transformers[op_name]
                 lowercase__					:     Optional[Any]   = val
  
      # The final layernorm.
       lowercase__					:     Optional[int]   = transformer['final_layernorm.weight']
       lowercase__					:     int   = transformer['final_layernorm.bias']
  
       # The pooler.
       lowercase__					:     str   = lm['pooler']
  
       # Store the matrix and the bias.
       lowercase__					:     Union[str, Any]   = pooler['dense.weight']
       lowercase__					:     Tuple   = pooler['dense.bias']
  
       # The LM head from Megatron (for RACE).
       lowercase__					:     Dict   = model['lm_head']
  
       # The transform matrix.
       lowercase__					:     int   = lm_head['dense.weight']
       lowercase__					:     Dict   = lm_head['dense.bias']
  
       # The transform LN.
       lowercase__					:     List[Any]   = lm_head['layernorm.weight']
       lowercase__					:     Optional[int]   = lm_head['layernorm.bias']
  
       # For the decoder, we replicate the weights.
       lowercase__					:     Union[str, Any]   = word_embeddings
       lowercase__					:     List[str]   = lm_head['bias']
  
       # The classifier from Megatron (for MLNI).
       lowercase__					:     Any   = model['binary_head']
  
       # Store the classifier.
       lowercase__					:     List[Any]   = binary_head['weight']
       lowercase__					:     int   = binary_head['bias']
  
       # It should be done!
       return output_state_dict
  
  def 			snake_case__    (					):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     List[Any]   = argparse.ArgumentParser()
       parser.add_argument('--print-checkpoint-structure'			,  action='store_true'    )
       parser.add_argument('path_to_checkpoint'			,  type=SCREAMING_SNAKE_CASE_			,  help='Path to the ZIP file containing the checkpoint'    )
       parser.add_argument(
           '--config_file'			,  default=''			,  type=SCREAMING_SNAKE_CASE_			,  help='An optional config json file describing the pre-trained model.'			,  )
       lowercase__					:     Optional[int]   = parser.parse_args()
  
       # Extract the basename.
       lowercase__					:     Dict   = os.path.dirname(args.path_to_checkpoint    )
  
       # Load the model.
       # the .zip is very optional, let's keep it for backward compatibility
       print(f"""Extracting PyTorch state dictionary from \"{args.path_to_checkpoint}\""""    )
       if args.path_to_checkpoint.endswith('.zip'    ):
            with zipfile.ZipFile(args.path_to_checkpoint			,  'r'    ) as checkpoint:
                 with checkpoint.open('release/mp_rank_00/model_optim_rng.pt'    ) as pytorch_dict:
                      lowercase__					:     List[str]   = torch.load(SCREAMING_SNAKE_CASE_			,  map_location='cpu'    )
       else:
            lowercase__					:     Dict   = torch.load(args.path_to_checkpoint			,  map_location='cpu'    )
  
       if args.config_file == "":
            # Default config of megatron-bert 345m
            lowercase__					:     Tuple   = MegatronBertConfig()
  
            # different megatron-bert-*-345m models have different vocab sizes, so override the default
            # config (which is for megatron-bert-cased-345m) with the actual vocab dimension
            lowercase__					:     Optional[int]   = input_state_dict['model']['lm_head']['bias'].numel()
       else:
            lowercase__					:     int   = MegatronBertConfig.from_json_file(args.config_file    )
  
       # Convert.
       print('Converting'    )
       lowercase__					:     Tuple   = convert_megatron_checkpoint(SCREAMING_SNAKE_CASE_			,  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_    )
  
       # Store the config to file.
       print('Saving config'    )
       config.save_pretrained(SCREAMING_SNAKE_CASE_    )
  
       # Store the state_dict to file.
       lowercase__					:     Optional[int]   = 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__":
         main()
  
  ####################################################################################################
  
  
style_context: 
  
  from transformers.models.cpm.tokenization_cpm import CpmTokenizer
  from transformers.testing_utils import custom_tokenizers
  
  from ..xlnet.test_modeling_xlnet import XLNetModelTest
  
  
  @custom_tokenizers
  class    SCREAMING_SNAKE_CASE__      (__snake_case  ):
  
  
  
  
  
      def    snake_case_       (   self		,     a		,     a		,     a		,     a		,     a):
           return True
  
  
  
  
  
      def    snake_case_       (   self):
           lowercase__					:     Tuple   = CpmTokenizer.from_pretrained('TsinghuaAI/CPM-Generate')
           lowercase__					:     str   = 'Hugging Face大法好，谁用谁知道。'
           lowercase__					:     Optional[int]   = 'Hugging Face大法好,谁用谁知道。<unk>'
           lowercase__					:     Any   = '▁Hu gg ing ▁ ▂ ▁F ace ▁大法 ▁好 ▁ , ▁谁 ▁用 ▁谁 ▁知 道 ▁ 。'.split()
  
           lowercase__					:     Any   = tokenizer.tokenize(a)
           self.assertListEqual(a		,     a)
  
           lowercase__					:     List[Any]   = tokens + [tokenizer.unk_token]
  
           lowercase__					:     Any   = [1_3789, 1_3283, 1421, 8, 10, 1164, 1_3608, 1_6528, 63, 8, 9, 440, 108, 440, 121, 90, 8, 12, 0]
           self.assertListEqual(tokenizer.convert_tokens_to_ids(a)		,     a)
  
           lowercase__					:     Optional[Any]   = tokenizer.decode(a)
           self.assertEqual(a		,     a)
  
  