


"""simple docstring"""







import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig
def _A		(		lowercase ,					lowercase ,					lowercase=0	):







    """simple docstring"""
    # Format the message.
    if name is None:
        a					=None
    else:
        a					='''.''' * max(0 ,					spaces - 2	) + '''# {:''' + str(50 - spaces	) + '''s}'''
        a					=fmt.format(lowercase	)

    # Print and recurse (if needed).
    if isinstance(lowercase ,					lowercase	):
        if msg is not None:
            print(lowercase	)
        for k in val.keys():
            recursive_print(lowercase ,					val[k] ,					spaces + 2	)
    elif isinstance(lowercase ,					torch.Tensor	):
        print(lowercase ,					''':''' ,					val.size()	)
    else:
        print(lowercase ,					''':''' ,					lowercase	)
def _A		(		lowercase ,					lowercase ,					lowercase ,					lowercase	):







    """simple docstring"""
    # Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
    # for compatibility with later versions of NVIDIA Megatron-LM.
    # The inverse operation is performed inside Megatron-LM to read checkpoints:
    # https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
    # If param is the weight tensor of the self-attention block, the returned tensor
    # will have to be transposed one more time to be read by HuggingFace GPT2.
    a					=param.size()
    # other versions store [num_heads * num_splits * hidden_size, :]
    a					=(num_heads, num_splits, hidden_size) + input_shape[1:]
    a					=param.view(*lowercase	)
    a					=param.transpose(0 ,					1	).contiguous()
    a					=param.view(*lowercase	)
    return param
def _A		(		lowercase ,					lowercase	):







    """simple docstring"""
    a					=config.n_positions
    a					=config.n_layer
    a					=config.vocab_size
    a					=config.n_head
    a					=config.n_embd // config.n_head

    a					=sd_megatron['''model.language_model.embedding.word_embeddings.weight'''][:vocab_size, :]
    a					={
        '''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'''],
    }

    a					='''model.language_model.encoder.layers.'''
    for i in range(lowercase	):
        a					=torch.tril(torch.ones((n_positions, n_positions) ,					dtype=torch.bool	)	)
        a					=causal_mask.view(1 ,					1 ,					lowercase ,					lowercase	)
        a					=causal_mask
        a					=torch.tensor(-1E4 ,					dtype=torch.bfloataa	)

        a					=sd_megatron[f'''{pf}{i}.input_layernorm.weight''']
        a					=sd_megatron[f'''{pf}{i}.input_layernorm.bias''']

        a					=sd_megatron[f'''{pf}{i}.self_attention.query_key_value.weight''']
        a					=fix_query_key_value_ordering(lowercase ,					3 ,					lowercase ,					lowercase	)
        a					=vala.transpose(0 ,					1	).contiguous()

        a					=sd_megatron[f'''{pf}{i}.self_attention.query_key_value.bias''']
        a					=fix_query_key_value_ordering(lowercase ,					3 ,					lowercase ,					lowercase	)
        a					=vala

        a					=sd_megatron[f'''{pf}{i}.self_attention.dense.weight'''].transpose(
            0 ,					1	)
        a					=sd_megatron[f'''{pf}{i}.self_attention.dense.bias''']
        a					=sd_megatron[f'''{pf}{i}.post_attention_layernorm.weight''']
        a					=sd_megatron[f'''{pf}{i}.post_attention_layernorm.bias''']
        a					=sd_megatron[f'''{pf}{i}.mlp.dense_h_to_4h.weight'''].transpose(0 ,					1	)
        a					=sd_megatron[f'''{pf}{i}.mlp.dense_h_to_4h.bias''']
        a					=sd_megatron[f'''{pf}{i}.mlp.dense_4h_to_h.weight'''].transpose(
            0 ,					1	)
        a					=sd_megatron[f'''{pf}{i}.mlp.dense_4h_to_h.bias''']

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

    return sd_hf
def _A		(		lowercase ,					lowercase	):







    """simple docstring"""
    a					=6_40_00
    a					=config_megatron['''encoder_seq_length''']
    a					=config_megatron['''hidden_size''']
    a					=config_megatron['''num_layers''']
    a					=config_megatron['''num_attention_heads''']
    a					=config_megatron['''ffn_hidden_size''']
    a					='''gelu'''
    a					=0.1
    a					=0.1
    a					=0.1
    a					=config_megatron['''layernorm_epsilon''']  # 1e-5
    a					=config_megatron['''init_method_std''']  # 0.02
    a					=config_megatron['''apply_query_key_layer_scaling''']  # True
    a					=True
    a					=True

    # This identifies the 6.7B (7B) model which uses a different tokenizer
    if config_megatron["hidden_size"] == 40_96:
        a					=1  # <|endoftext|>
        a					=1  # <|endoftext|>
        a					=0  # <unk>
    else:
        a					=2  # <s>
        a					=3  # <|endoftext|>
        a					=0  # <pad>

    return config_hf
def _A		(		lowercase	):







    """simple docstring"""
    print(lowercase	)

    a					=args.checkpoint_path
    a					=args.save_path
    if isfile(lowercase	):
        raise FileNotFoundError(f'''ERROR! could not find file {checkpoint_path}'''	)

    # Load the model.
    a					=torch.load(lowercase ,					map_location='''cpu'''	)

    # Load the config.
    a					=checkpoint['''hyper_parameters''']['''cfg''']
    a					=GPTaConfig()
    a					=copy_config(config_hf=lowercase ,					config_megatron=lowercase	)
    a					=['''GPT2LMHeadModel''']

    a					=checkpoint['''state_dict''']

    # Convert.
    print('''Converting'''	)
    a					=convert_megatron_checkpoint(lowercase ,					lowercase	)

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

    a					='''GPTSw3Tokenizer'''

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

    # Store the state_dict to file.
    a					=os.path.join(lowercase ,					'''pytorch_model.bin'''	)
    print(f'''Saving checkpoint to "{output_checkpoint_file}"'''	)
    torch.save(lowercase ,					lowercase	)


if __name__ == "__main__":
       lowerCamelCase_     :     Dict    		=						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""")
       lowerCamelCase_     :     int    		=						parser.parse_args()
       main(_args)



"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase_     :     Any    		=						logging.get_logger(__name__)

lowerCamelCase_     :     Tuple    		=						{
    """apple/mobilevitv2-1.0""": """https://huggingface.co/apple/mobilevitv2-1.0/resolve/main/config.json""",
}






class    __A       ( _SCREAMING_SNAKE_CASE    ):






 """simple docstring"""




 __lowerCAmelCase				      =						"mobilevitv2"


 def __init__(	self				,					__A=3				,					__A=256				,					__A=2				,					__A=2.0				,					__A="swish"				,					__A=3				,					__A=32				,					__A=0.1				,					__A=0.02				,					__A=1E-5				,					__A=512				,					__A=[6, 12, 18]				,					__A=0.1				,					__A=255				,					__A=[2, 4, 3]				,					__A=[128, 192, 256]				,					__A=1.0				,					__A=2				,					__A=0.0				,					__A=0.0				,					**__A				,					)      ->  int:
     super().__init__(**__A		)

     a					=num_channels
     a					=image_size
     a					=patch_size
     a					=expand_ratio
     a					=hidden_act
     a					=conv_kernel_size
     a					=output_stride
     a					=initializer_range
     a					=layer_norm_eps
     a					=n_attn_blocks
     a					=base_attn_unit_dims
     a					=width_multiplier
     a					=ffn_multiplier
     a					=ffn_dropout
     a					=attn_dropout
     a					=classifier_dropout_prob

     # decode head attributes for semantic segmentation
     a					=aspp_out_channels
     a					=atrous_rates
     a					=aspp_dropout_prob
     a					=semantic_loss_ignore_index






class    __A       ( _SCREAMING_SNAKE_CASE    ):






 """simple docstring"""




 __lowerCAmelCase				      =						version.parse("1.11"    )


 @property
 def    SCREAMING_SNAKE_CASE							(	self		)      ->  Mapping[str, Mapping[int, str]]:
     return OrderedDict([('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''})]		)


 @property
 def    SCREAMING_SNAKE_CASE							(	self		)      ->  Mapping[str, Mapping[int, str]]:
     if self.task == "image-classification":
         return OrderedDict([('''logits''', {0: '''batch'''})]		)
     else:
         return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})]		)





 @property
 def    SCREAMING_SNAKE_CASE							(	self		)      ->  float:
     return 1E-4