


import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig






def   lowerCamelCase_					( _a			,     _a			,     _a=0 ):




		"""simple docstring"""






		if name is None:
				lowerCAmelCase__     :    Union[str, Any]					  =      None
		else:
				lowerCAmelCase__     :    Union[str, Any]					  =      '''.''' * max(0			,     spaces - 2 ) + '''# {:''' + str(50 - spaces ) + '''s}'''
				lowerCAmelCase__     :    Optional[int]					  =      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   lowerCamelCase_					( _a			,     _a			,     _a			,     _a ):




		"""simple docstring"""






		lowerCAmelCase__     :    Tuple					  =      param.size()
		# other versions store [num_heads * num_splits * hidden_size, :]
		lowerCAmelCase__     :    int					  =      (num_heads, num_splits, hidden_size) + input_shape[1:]
		lowerCAmelCase__     :    Optional[Any]					  =      param.view(*_a )
		lowerCAmelCase__     :    str					  =      param.transpose(0			,     1 ).contiguous()
		lowerCAmelCase__     :    Any					  =      param.view(*_a )
		return param






def   lowerCamelCase_					( _a			,     _a ):




		"""simple docstring"""






		lowerCAmelCase__     :    Tuple					  =      config.n_positions
		lowerCAmelCase__     :    Union[str, Any]					  =      config.n_layer
		lowerCAmelCase__     :    str					  =      config.vocab_size
		lowerCAmelCase__     :    Union[str, Any]					  =      config.n_head
		lowerCAmelCase__     :    Dict					  =      config.n_embd // config.n_head

		lowerCAmelCase__     :    Optional[int]					  =      sd_megatron['''model.language_model.embedding.word_embeddings.weight'''][:vocab_size, :]
		lowerCAmelCase__     :    str					  =      {
		    '''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'''],
		}

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

				lowerCAmelCase__     :    Any					  =      sd_megatron[f'{pf}{i}.input_layernorm.weight']
				lowerCAmelCase__     :    int					  =      sd_megatron[f'{pf}{i}.input_layernorm.bias']

				lowerCAmelCase__     :    int					  =      sd_megatron[f'{pf}{i}.self_attention.query_key_value.weight']
				lowerCAmelCase__     :    Any					  =      fix_query_key_value_ordering(_a			,     3			,     _a			,     _a )
				lowerCAmelCase__     :    int					  =      vala.transpose(0			,     1 ).contiguous()

				lowerCAmelCase__     :    Tuple					  =      sd_megatron[f'{pf}{i}.self_attention.query_key_value.bias']
				lowerCAmelCase__     :    Dict					  =      fix_query_key_value_ordering(_a			,     3			,     _a			,     _a )
				lowerCAmelCase__     :    Tuple					  =      vala

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

		# For LM head, transformers' wants the matrix to weight embeddings.
		lowerCAmelCase__     :    List[Any]					  =      word_embeddings

		return sd_hf






def   lowerCamelCase_					( _a			,     _a ):




		"""simple docstring"""






		lowerCAmelCase__     :    str					  =      64_000
		lowerCAmelCase__     :    int					  =      config_megatron['''encoder_seq_length''']
		lowerCAmelCase__     :    Any					  =      config_megatron['''hidden_size''']
		lowerCAmelCase__     :    Optional[int]					  =      config_megatron['''num_layers''']
		lowerCAmelCase__     :    List[str]					  =      config_megatron['''num_attention_heads''']
		lowerCAmelCase__     :    Union[str, Any]					  =      config_megatron['''ffn_hidden_size''']
		lowerCAmelCase__     :    List[str]					  =      '''gelu'''
		lowerCAmelCase__     :    str					  =      0.1
		lowerCAmelCase__     :    List[str]					  =      0.1
		lowerCAmelCase__     :    Dict					  =      0.1
		lowerCAmelCase__     :    Optional[Any]					  =      config_megatron['''layernorm_epsilon''']  # 1e-5
		lowerCAmelCase__     :    Dict					  =      config_megatron['''init_method_std''']  # 0.02
		lowerCAmelCase__     :    Tuple					  =      config_megatron['''apply_query_key_layer_scaling''']  # True
		lowerCAmelCase__     :    Optional[Any]					  =      True
		lowerCAmelCase__     :    Dict					  =      True

		# This identifies the 6.7B (7B) model which uses a different tokenizer
		if config_megatron["hidden_size"] == 4_096:
				lowerCAmelCase__     :    List[Any]					  =      1  # <|endoftext|>
				lowerCAmelCase__     :    List[str]					  =      1  # <|endoftext|>
				lowerCAmelCase__     :    List[str]					  =      0  # <unk>
		else:
				lowerCAmelCase__     :    Tuple					  =      2  # <s>
				lowerCAmelCase__     :    Dict					  =      3  # <|endoftext|>
				lowerCAmelCase__     :    List[str]					  =      0  # <pad>

		return config_hf






def   lowerCamelCase_					( _a ):




		"""simple docstring"""






		print(_a )

		lowerCAmelCase__     :    Union[str, Any]					  =      args.checkpoint_path
		lowerCAmelCase__     :    Union[str, Any]					  =      args.save_path
		if isfile(_a ):
				raise FileNotFoundError(f'ERROR! could not find file {checkpoint_path}' )

		# Load the model.
		lowerCAmelCase__     :    Dict					  =      torch.load(_a			,     map_location='''cpu''' )

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

		lowerCAmelCase__     :    Optional[Any]					  =      checkpoint['''state_dict''']

		# Convert.
		print('''Converting''' )
		lowerCAmelCase__     :    Union[str, Any]					  =      convert_megatron_checkpoint(_a			,     _a )

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

		lowerCAmelCase__     :    int					  =      '''GPTSw3Tokenizer'''

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

		# Store the state_dict to file.
		lowerCAmelCase__     :    List[str]					  =      os.path.join(_a			,     '''pytorch_model.bin''' )
		print(f'Saving checkpoint to "{output_checkpoint_file}"' )
		torch.save(_a			,     _a )


if __name__ == "__main__":
		lowerCamelCase		=	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		=	parser.parse_args()
		main(_args)








from itertools import count
from math import asin, pi, sqrt






def   lowerCamelCase_					( _a ):




		"""simple docstring"""






		return (
		    (1 - 2 * point) * sqrt(point - point**2 ) + 2 * point + asin(sqrt(1 - point ) )
		) / 4






def   lowerCamelCase_					( _a ):




		"""simple docstring"""






		lowerCAmelCase__     :    Union[str, Any]					  =      (circles_number + 1 - sqrt(2 * circles_number )) / (
		    2 * (circles_number**2 + 1)
		)
		lowerCAmelCase__     :    Tuple					  =      circles_number * intersection_y

		lowerCAmelCase__     :    Any					  =      intersection_x * intersection_y / 2
		lowerCAmelCase__     :    Optional[Any]					  =      circle_bottom_arc_integral(
		    1 / 2 ) - circle_bottom_arc_integral(_a )

		return triangle_area + concave_region_area






def   lowerCamelCase_					( _a = 1 / 1_000 ):




		"""simple docstring"""






		lowerCAmelCase__     :    Any					  =      (1 - pi / 4) / 4

		for n in count(1 ):
				if concave_triangle_area(_a ) / l_section_area < fraction:
						return n

		return -1


if __name__ == "__main__":
		print(f'''{solution() = }''')




