

'''simple docstring'''





from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase






def 				_lowerCAmelCase			(   __magic_name__						:     str						)				->  None:
						lowercase		,	lowercase :							Dict    		=analyze_text(__magic_name__						)
						lowercase :							int    		=list(''' ''' + ascii_lowercase						)
						# what is our total sum of probabilities.
						lowercase :							List[Any]    		=sum(single_char_strings.values()						)

						# one length string
						lowercase :							Tuple    		=0
						# for each alpha we go in our dict and if it is in it we calculate entropy
						for ch in my_alphas:
												if ch in single_char_strings:
																		lowercase :							str    		=single_char_strings[ch]
																		lowercase :							Dict    		=my_str / all_sum
																		my_fir_sum += prob * math.loga(__magic_name__						)  # entropy formula.

    # print entropy
						print(f'''{round(-1 * my_fir_sum						):.1f}'''						)

						# two len string
						lowercase :							Any    		=sum(two_char_strings.values()						)
						lowercase :							Union[str, Any]    		=0
						# for each alpha (two in size) calculate entropy.
						for cha in my_alphas:
												for cha in my_alphas:
																		lowercase :							Optional[Any]    		=cha + cha
																		if sequence in two_char_strings:
																								lowercase :							str    		=two_char_strings[sequence]
																								lowercase :							Optional[Any]    		=int(__magic_name__						) / all_sum
																								my_sec_sum += prob * math.loga(__magic_name__						)

    # print second entropy
						print(f'''{round(-1 * my_sec_sum						):.1f}'''						)

						# print the difference between them
						print(f'''{round((-1 * my_sec_sum) - (-1 * my_fir_sum)						):.1f}'''						)






def 				_lowerCAmelCase			(   __magic_name__						:     str						)				->  tuple[dict, dict]:
						lowercase :							Dict    		=Counter()  # type: ignore
						lowercase :							Tuple    		=Counter()  # type: ignore
						single_char_strings[text[-1]] += 1

						# first case when we have space at start.
						two_char_strings[" " + text[0]] += 1
						for i in range(0   , len(__magic_name__						) - 1						):
												single_char_strings[text[i]] += 1
												two_char_strings[text[i : i + 2]] += 1
						return single_char_strings, two_char_strings






def 				_lowerCAmelCase			(   )				->  Optional[int]:
						import doctest

						doctest.testmod()
						# text = (
						#     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
						#     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
						#     "jointure saw horrible. He private he on be imagine suppose. Fertile "
						#     "beloved evident through no service elderly is. Blind there if every no so "
						#     "at. Own neglected you preferred way sincerity delivered his attempted. To "
						#     "of message cottage windows do besides against uncivil.  Delightful "
						#     "unreserved impossible few estimating men favourable see entreaties. She "
						#     "propriety immediate was improving. He or entrance humoured likewise "
						#     "moderate. Much nor game son say feel. Fat make met can must form into "
						#     "gate. Me we offending prevailed discovery. "
						# )

						# calculate_prob(text)


if __name__ == "__main__":
							main()









import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
a			:			List[str]												=					logging.get_logger(__name__)


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			__lowercase			       = torch.load(_UpperCamelCase							,      map_location='''cpu'''	)
			if "model" in sd.keys():
						__lowercase			       = torch.load(_UpperCamelCase							,      map_location='''cpu'''	)['''model''']

			# pop unnecessary weights
			__lowercase			       = [
			    '''decoder.version''',
			    '''decoder.output_projection.weight''',
			]
			for key in keys_to_delete:
						if key in sd:
									sd.pop(_UpperCamelCase	)

			__lowercase			       = {
			    '''decoder.project_in_dim.weight''': '''decoder.project_in.weight''',
			    '''decoder.project_out_dim.weight''': '''decoder.project_out.weight''',
			    '''decoder.layer_norm.weight''': '''decoder.final_layer_norm.weight''',
			    '''decoder.layer_norm.bias''': '''decoder.final_layer_norm.bias''',
			}
			for old_key, new_key in keys_to_rename.items():
						if old_key in sd:
									__lowercase			       = sd.pop(_UpperCamelCase	)

			__lowercase			       = list(sd.keys()	)
			for key in keys:
						if ".qkv_proj." in key:
									__lowercase			       = sd[key]
									# We split QKV in separate Q,K,V

									__lowercase			       = key.replace('''.qkv_proj.'''							,      '''.q_proj.'''	)
									__lowercase			       = key.replace('''.qkv_proj.'''							,      '''.k_proj.'''	)
									__lowercase			       = key.replace('''.qkv_proj.'''							,      '''.v_proj.'''	)

									__lowercase			       = value.shape[0]
									assert depth % 3 == 0
									# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
									# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
									__lowercase       ,			__lowercase       ,			__lowercase			       = torch.split(_UpperCamelCase							,      depth // 3							,      dim=0	)

									__lowercase			       = q
									__lowercase			       = k
									__lowercase			       = v
									del sd[key]

			return sd


@torch.no_grad()
def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase=None	):


			'''simple docstring'''







			__lowercase			       = load_checkpoint(_UpperCamelCase	)

			if config is not None:
						__lowercase			       = OPTConfig.from_pretrained(_UpperCamelCase	)
			else:
						__lowercase			       = OPTConfig()

			__lowercase			       = OPTModel(_UpperCamelCase	).half().eval()
			model.load_state_dict(_UpperCamelCase	)

			# Check results
			Path(_UpperCamelCase	).mkdir(exist_ok=_UpperCamelCase	)
			model.save_pretrained(_UpperCamelCase	)


if __name__ == "__main__":
	a			:			List[Any]												=					argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--fairseq_path''',
	    type=str,
	    help=(
	        '''path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:'''
	        ''' https://huggingface.co/models?other=opt_metasq'''
	    ),
	)
	parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
	parser.add_argument('''--hf_config''', default=None, type=str, help='''Define HF config.''')
	a			:			Any												=					parser.parse_args()
	convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)


