code: 
  
  
  
  
  
  
  """simple docstring"""
  
  
  
  
  
  
  import argparse
  import os
  import re
  import zipfile
  
  import torch
  
  from transformers import MegatronBertConfig
  
  
  
  
  def 						_UpperCamelCase			(  A    ,       A    ,       A=0			):
  
     '''simple docstring'''
  
  
  
  
  
     if name is None:
        UpperCamelCase_     =None
     else:
        UpperCamelCase_     ="." * max(0    ,       spaces - 2			) + "# {:" + str(50 - spaces			) + "s}"
        UpperCamelCase_     =fmt.format(A			)
  
     # Print and recurse (if needed).
     if isinstance(A    ,       A			):
        if msg is not None:
           print(A			)
        for k in val.keys():
           recursive_print(A    ,       val[k]    ,       spaces + 2			)
     elif isinstance(A    ,       torch.Tensor			):
        print(A    ,       ":"    ,       val.size()			)
     else:
        print(A    ,       ":"    ,       A			)
  
  
  
  
  def 						_UpperCamelCase			(  A    ,       A    ,       A    ,       A    ,       A			):
  
     '''simple docstring'''
  
  
  
  
  
     UpperCamelCase_     =param.size()
     if checkpoint_version == 1.0:
        # version 1.0 stores [num_heads * hidden_size * num_splits, :]
        UpperCamelCase_     =(num_heads, hidden_size, num_splits) + input_shape[1:]
        UpperCamelCase_     =param.view(*A			)
        UpperCamelCase_     =param.transpose(0    ,       2			)
        UpperCamelCase_     =param.transpose(1    ,       2			).contiguous()
     elif checkpoint_version >= 2.0:
        # other versions store [num_heads * num_splits * hidden_size, :]
        UpperCamelCase_     =(num_heads, num_splits, hidden_size) + input_shape[1:]
        UpperCamelCase_     =param.view(*A			)
        UpperCamelCase_     =param.transpose(0    ,       1			).contiguous()
     UpperCamelCase_     =param.view(*A			)
     return param
  
  
  
  
  def 						_UpperCamelCase			(  A    ,       A    ,       A			):
  
     '''simple docstring'''
  
  
  
  
  
     UpperCamelCase_     ={}
  
     # old versions did not store training args
     UpperCamelCase_     =input_state_dict.get("args"    ,       A			)
     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))
  
        UpperCamelCase_     =ds_args.tokenizer_type
        UpperCamelCase_     =ds_args.padded_vocab_size
        UpperCamelCase_     =ds_args.max_position_embeddings
        UpperCamelCase_     =ds_args.hidden_size
        UpperCamelCase_     =ds_args.num_layers
        UpperCamelCase_     =ds_args.num_attention_heads
        UpperCamelCase_     =ds_args.ffn_hidden_size if "ffn_hidden_size" in ds_args else 4 * ds_args.hidden_size
        # pprint(config)
  
     # The number of heads.
     UpperCamelCase_     =config.num_attention_heads
     # The hidden_size per head.
     UpperCamelCase_     =config.hidden_size // heads
     # Megatron-LM checkpoint version
     if "checkpoint_version" in input_state_dict.keys():
        UpperCamelCase_     =input_state_dict["checkpoint_version"]
     else:
        UpperCamelCase_     =0.0
  
     # The model.
     UpperCamelCase_     =input_state_dict["model"]
     # The language model.
     UpperCamelCase_     =model["language_model"]
     # The embeddings.
     UpperCamelCase_     =lm["embedding"]
  
     # The word embeddings.
     UpperCamelCase_     =embeddings["word_embeddings"]["weight"]
     # Truncate the embedding table to vocab_size rows.
     UpperCamelCase_     =word_embeddings[: config.vocab_size, :]
     # Store the word embeddings.
     UpperCamelCase_     =word_embeddings
  
     # The position embeddings.
     UpperCamelCase_     =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.
     UpperCamelCase_     =pos_embeddings
  
     # The token-type embeddings.
     UpperCamelCase_     =embeddings["tokentype_embeddings"]["weight"]
     # Store the position embeddings.
     UpperCamelCase_     =tokentype_embeddings
  
     # The transformer.
     UpperCamelCase_     =lm["transformer"] if "transformer" in lm.keys() else lm["encoder"]
  
     # The regex to extract layer names.
     UpperCamelCase_     =re.compile(R"layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)"			)
  
     # The simple map of names for "automated" rules.
     UpperCamelCase_     ={
         "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.
     UpperCamelCase_     =None
  
     # Extract the layers.
     for key, val in transformer.items():
        # Match the name.
        UpperCamelCase_     =layer_re.match(A			)
  
        # Stop if that's not a layer
        if m is None:
           break
  
        # The index of the layer.
        UpperCamelCase_     =int(m.group(1			)			)
        # The name of the operation.
        UpperCamelCase_     =m.group(2			)
        # Is it a weight or a bias?
        UpperCamelCase_     =m.group(3			)
  
        # The name of the layer.
        UpperCamelCase_     =f"""bert.encoder.layer.{layer_idx}"""
  
        # For layernorm(s), simply store the layer norm.
        if op_name.endswith("layernorm"			):
           UpperCamelCase_     ="attention.ln" if op_name.startswith("input"			) else "ln"
           UpperCamelCase_     =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, ""
  
           UpperCamelCase_     =fix_query_key_value_ordering(A    ,       A    ,       3    ,       A    ,       A			)
           # Store the tensor as we need the bias as well to interleave QKV and biases.
           UpperCamelCase_     =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.
           UpperCamelCase_     =attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
           UpperCamelCase_     =attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
           UpperCamelCase_     =attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]
  
           UpperCamelCase_     =fix_query_key_value_ordering(A    ,       A    ,       3    ,       A    ,       A			)
           # Split the bias.
           UpperCamelCase_     =out_val[0 * config.hidden_size : 1 * config.hidden_size]
           UpperCamelCase_     =out_val[1 * config.hidden_size : 2 * config.hidden_size]
           UpperCamelCase_     =out_val[2 * config.hidden_size : 3 * config.hidden_size]
  
           # Store.
           UpperCamelCase_     =q
           UpperCamelCase_     =q_bias
           UpperCamelCase_     =k
           UpperCamelCase_     =k_bias
           UpperCamelCase_     =v
           UpperCamelCase_     =v_bias
  
           # Clear the stored tensor.
           UpperCamelCase_     =None
  
        # Copy weights and biases as is.
        elif weight_or_bias in ["weight", "bias"]:
           UpperCamelCase_     =megatron_to_transformers[op_name]
           UpperCamelCase_     =val
  
      # The final layernorm.
     UpperCamelCase_     =transformer["final_layernorm.weight"]
     UpperCamelCase_     =transformer["final_layernorm.bias"]
  
     # The pooler.
     UpperCamelCase_     =lm["pooler"]
  
     # Store the matrix and the bias.
     UpperCamelCase_     =pooler["dense.weight"]
     UpperCamelCase_     =pooler["dense.bias"]
  
     # The LM head from Megatron (for RACE).
     UpperCamelCase_     =model["lm_head"]
  
     # The transform matrix.
     UpperCamelCase_     =lm_head["dense.weight"]
     UpperCamelCase_     =lm_head["dense.bias"]
  
     # The transform LN.
     UpperCamelCase_     =lm_head["layernorm.weight"]
     UpperCamelCase_     =lm_head["layernorm.bias"]
  
     # For the decoder, we replicate the weights.
     UpperCamelCase_     =word_embeddings
     UpperCamelCase_     =lm_head["bias"]
  
     # The classifier from Megatron (for MLNI).
     UpperCamelCase_     =model["binary_head"]
  
     # Store the classifier.
     UpperCamelCase_     =binary_head["weight"]
     UpperCamelCase_     =binary_head["bias"]
  
     # It should be done!
     return output_state_dict
  
  
  
  
  def 						_UpperCamelCase			(  ):
  
     '''simple docstring'''
  
  
  
  
  
     UpperCamelCase_     =argparse.ArgumentParser()
     parser.add_argument("--print-checkpoint-structure"    ,       action="store_true"			)
     parser.add_argument("path_to_checkpoint"    ,       type=A    ,       help="Path to the ZIP file containing the checkpoint"			)
     parser.add_argument(
         "--config_file"    ,       default=""    ,       type=A    ,       help="An optional config json file describing the pre-trained model."    ,       )
     UpperCamelCase_     =parser.parse_args()
  
     # Extract the basename.
     UpperCamelCase_     =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:
              UpperCamelCase_     =torch.load(A    ,       map_location="cpu"			)
     else:
        UpperCamelCase_     =torch.load(args.path_to_checkpoint    ,       map_location="cpu"			)
  
     if args.config_file == "":
        # Default config of megatron-bert 345m
        UpperCamelCase_     =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
        UpperCamelCase_     =input_state_dict["model"]["lm_head"]["bias"].numel()
     else:
        UpperCamelCase_     =MegatronBertConfig.from_json_file(args.config_file			)
  
     # Convert.
     print("Converting"			)
     UpperCamelCase_     =convert_megatron_checkpoint(A    ,       A    ,       A			)
  
     # Print the structure of converted state dict.
     if args.print_checkpoint_structure:
        recursive_print(A    ,       A			)
  
     # Store the config to file.
     print("Saving config"			)
     config.save_pretrained(A			)
  
     # Store the state_dict to file.
     UpperCamelCase_     =os.path.join(A    ,       "pytorch_model.bin"			)
     print(f"""Saving checkpoint to \"{output_checkpoint_file}\""""			)
     torch.save(A    ,       A			)
  
  
  ####################################################################################################
  
  if __name__ == "__main__":
      main()
  
  ####################################################################################################
  
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  
  
  
  
  
  
  import warnings
  
  from ...processing_utils import ProcessorMixin
  from ...tokenization_utils_base import BatchEncoding
  class 						__lowerCAmelCase   (			UpperCAmelCase       ):
  
  
  
  
  
  
  
    '''simple docstring'''
  
  
    __lowerCamelCase       :    Optional[Any]     	=			["image_processor", "tokenizer"]
    __lowerCamelCase       :    List[str]     	=			"VideoMAEImageProcessor"
    __lowerCamelCase       :    Dict     	=			("CLIPTokenizer", "CLIPTokenizerFast")
    def __init__(      self:		str				,			UpperCamelCase_:		Dict=None				,			UpperCamelCase_:		str=None				,			**UpperCamelCase_:		str					):
       UpperCamelCase_     =None
       if "feature_extractor" in kwargs:
          warnings.warn(
              "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
              " instead."				,			UpperCamelCase_				,			)
          UpperCamelCase_     =kwargs.pop("feature_extractor"					)
  
       UpperCamelCase_     =image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
          raise ValueError("You need to specify an `image_processor`."					)
       if tokenizer is None:
          raise ValueError("You need to specify a `tokenizer`."					)
  
       super().__init__(UpperCamelCase_				,			UpperCamelCase_					)
       UpperCamelCase_     =self.image_processor
    def __call__(      self:		Tuple				,			UpperCamelCase_:		str=None				,			UpperCamelCase_:		str=None				,			UpperCamelCase_:		Optional[Any]=None				,			**UpperCamelCase_:		int					):
  
       if text is None and videos is None:
          raise ValueError("You have to specify either text or videos. Both cannot be none."					)
  
       if text is not None:
          UpperCamelCase_     =self.tokenizer(UpperCamelCase_				,			return_tensors=UpperCamelCase_				,			**UpperCamelCase_					)
  
       if videos is not None:
          UpperCamelCase_     =self.image_processor(UpperCamelCase_				,			return_tensors=UpperCamelCase_				,			**UpperCamelCase_					)
  
       if text is not None and videos is not None:
          UpperCamelCase_     =image_features.pixel_values
          return encoding
       elif text is not None:
          return encoding
       else:
          return BatchEncoding(data=dict(**UpperCamelCase_					)				,			tensor_type=UpperCamelCase_					)
    def 					UpperCamelCase__   (      self:		Any				,			*UpperCamelCase_:		List[str]				,			**UpperCamelCase_:		Optional[int]					):
       return self.tokenizer.batch_decode(*UpperCamelCase_				,			**UpperCamelCase_					)
    def 					UpperCamelCase__   (      self:		Optional[Any]				,			*UpperCamelCase_:		List[str]				,			**UpperCamelCase_:		Tuple					):
       return self.tokenizer.decode(*UpperCamelCase_				,			**UpperCamelCase_					)
    @property
    def 					UpperCamelCase__   (      self:		List[Any]					):
       return ["input_ids", "attention_mask", "position_ids", "pixel_values"]
    @property
    def 					UpperCamelCase__   (      self:		str					):
       warnings.warn(
           "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."				,			UpperCamelCase_				,			)
       return self.image_processor_class
  
  
  
  
  
    @property
    def 					UpperCamelCase__   (      self:		Optional[Any]					):
       warnings.warn(
           "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."				,			UpperCamelCase_				,			)
       return self.image_processor
  