


def 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      generate_pascal_triangle(SCREAMING_SNAKE_CASE     )
		for row_idx in range(SCREAMING_SNAKE_CASE     ):
				# Print left spaces
				for _ in range(num_rows - row_idx - 1     ):
						print(end=''' '''     )
				# Print row values
				for col_idx in range(row_idx + 1     ):
						if col_idx != row_idx:
								print(triangle[row_idx][col_idx]						,				end=''' '''     )
						else:
								print(triangle[row_idx][col_idx]						,				end=''''''     )
				print()
def 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		if not isinstance(SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ):
				raise TypeError('''The input value of \'num_rows\' should be \'int\''''     )

		if num_rows == 0:
				return []
		elif num_rows < 0:
				raise ValueError(
				    '''The input value of \'num_rows\' should be greater than or equal to 0'''     )

		lowercase__            =      []
		for current_row_idx in range(SCREAMING_SNAKE_CASE     ):
				lowercase__            =      populate_current_row(SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     )
				triangle.append(SCREAMING_SNAKE_CASE     )
		return triangle
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      [-1] * (current_row_idx + 1)
		# first and last elements of current row are equal to 1
		lowercase__     ,		lowercase__            =      1, 1
		for current_col_idx in range(1						,				SCREAMING_SNAKE_CASE     ):
				calculate_current_element(
				    SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     )
		return current_row
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				):


		"""simple docstring"""




		lowercase__            =      triangle[current_row_idx - 1][current_col_idx - 1]
		lowercase__            =      triangle[current_row_idx - 1][current_col_idx]
		lowercase__            =      above_to_left_elt + above_to_right_elt
def 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		if not isinstance(SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ):
				raise TypeError('''The input value of \'num_rows\' should be \'int\''''     )

		if num_rows == 0:
				return []
		elif num_rows < 0:
				raise ValueError(
				    '''The input value of \'num_rows\' should be greater than or equal to 0'''     )

		lowercase__            =      [[1]]

		for row_index in range(1						,				SCREAMING_SNAKE_CASE     ):
				lowercase__            =      [0] + result[-1] + [0]
				lowercase__            =      row_index + 1
				# Calculate the number of distinct elements in a row
				lowercase__            =      sum(divmod(SCREAMING_SNAKE_CASE						,				2     )     )
				lowercase__            =      [
				    temp_row[i - 1] + temp_row[i] for i in range(1						,				distinct_elements + 1     )
				]
				lowercase__            =      row_first_half[: (row_index + 1) // 2]
				row_second_half.reverse()
				lowercase__            =      row_first_half + row_second_half
				result.append(SCREAMING_SNAKE_CASE     )

		return result
def 			_a (      ):


		"""simple docstring"""




		from collections.abc import Callable
		from timeit import timeit

		def benchmark_a_function(SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ) -> None:
				lowercase__            =      f'{func.__name__}({value})'
				lowercase__            =      timeit(f'__main__.{call}'						,				setup='''import __main__'''     )
				# print(f"{call:38} = {func(value)} -- {timing:.4f} seconds")
				print(f'{call:38} -- {timing:.4f} seconds'     )

		for value in range(15     ):  # (1, 7, 14):
				for func in (generate_pascal_triangle, generate_pascal_triangle_optimized):
						benchmark_a_function(SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     )
				print()


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			benchmark()





"""simple docstring"""





import argparse
import json
import os
import re
import shutil

import torch

from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

_a       =							2




class 					_lowerCAmelCase  :







 """simple docstring"""







 def __init__(			self							:			Dict,					*,  # begin keyword-only arguments
     UpperCAmelCase__							:			str="<s>",					UpperCAmelCase__							:			Tuple="<pad>",					UpperCAmelCase__							:			str="</s>",					UpperCAmelCase__							:			Optional[Any]="<unk>",					UpperCAmelCase__							:			List[Any]=None,					):
    __lowercase		,__lowercase		,__lowercase		,__lowercase								=						bos, unk, pad, eos
    __lowercase								=						[]
    __lowercase								=						[]
    __lowercase								=						{}
    __lowercase								=						self.add_symbol(UpperCAmelCase__	)
    __lowercase								=						self.add_symbol(UpperCAmelCase__	)
    __lowercase								=						self.add_symbol(UpperCAmelCase__	)
    __lowercase								=						self.add_symbol(UpperCAmelCase__	)
    if extra_special_symbols:
       for s in extra_special_symbols:
          self.add_symbol(UpperCAmelCase__	)
    __lowercase								=						len(self.symbols	)


 def __eq__(			self							:			List[str],					UpperCAmelCase__							:			Dict	):
    return self.indices == other.indices


 def __getitem__(			self							:			Optional[int],					UpperCAmelCase__							:			List[str]	):
    if idx < len(self.symbols	):
       return self.symbols[idx]
    return self.unk_word


 def __len__(			self							:			str	):
    return len(self.symbols	)


 def __contains__(			self							:			Any,					UpperCAmelCase__							:			Optional[Any]	):
    return sym in self.indices


 @classmethod
 def 					_lowercase (			cls							:			List[Any],					UpperCAmelCase__							:			Optional[Any]	):
    __lowercase								=						cls()
    d.add_from_file(UpperCAmelCase__	)
    return d


 def 					_lowercase (			self							:			Dict,					UpperCAmelCase__							:			Optional[Any],					UpperCAmelCase__							:			List[Any]=1,					UpperCAmelCase__							:			str=False	):
    if word in self.indices and not overwrite:
       __lowercase								=						self.indices[word]
       __lowercase								=						self.count[idx] + n
       return idx
    else:
       __lowercase								=						len(self.symbols	)
       __lowercase								=						idx
       self.symbols.append(UpperCAmelCase__	)
       self.count.append(UpperCAmelCase__	)
       return idx


 def 					_lowercase (			self							:			Any,					UpperCAmelCase__							:			str	):
    return 0



 def 					_lowercase (			self							:			Tuple,					UpperCAmelCase__							:			List[Any]	):
    if isinstance(UpperCAmelCase__,					UpperCAmelCase__	):
       try:
          with open(UpperCAmelCase__,					"r",					encoding="utf-8"	) as fd:
             self.add_from_file(UpperCAmelCase__	)
       except FileNotFoundError as fnfe:
          raise fnfe
       except UnicodeError:
          raise Exception("Incorrect encoding detected in {}, please rebuild the dataset".format(UpperCAmelCase__	)	)
       return

    __lowercase								=						f.readlines()
    __lowercase								=						self._load_meta(UpperCAmelCase__	)

    for line in lines[indices_start_line:]:
       try:
          __lowercase		,__lowercase								=						line.rstrip().rsplit(" ",					1	)
          if field == "#fairseq:overwrite":
             __lowercase								=						True
             __lowercase		,__lowercase								=						line.rsplit(" ",					1	)
          else:
             __lowercase								=						False
          __lowercase								=						int(UpperCAmelCase__	)
          __lowercase								=						line
          if word in self and not overwrite:
             raise RuntimeError(
                 "Duplicate word found when loading Dictionary: '{}'. "
                 "Duplicate words can overwrite earlier ones by adding the "
                 "#fairseq:overwrite flag at the end of the corresponding row "
                 "in the dictionary file. If using the Camembert model, please "
                 "download an updated copy of the model file.".format(UpperCAmelCase__	)	)
          self.add_symbol(UpperCAmelCase__,					n=UpperCAmelCase__,					overwrite=UpperCAmelCase__	)
       except ValueError:
          raise ValueError("Incorrect dictionary format, expected '<token> <cnt> [flags]'"	)







def   _A  (  UpperCamelCase_     :       int)	->						str:
   '''simple docstring'''


   __lowercase								=						dict((re.sub(r"@@$",			"",			UpperCamelCase_), v) if k.endswith("@@") else (re.sub(r"$",			"</w>",			UpperCamelCase_), v) for k, v in d.items())
   __lowercase								=						"<s> <pad> </s> <unk>".split()
   # restore the special tokens
   for k in keep_keys:
      del da[F"""{k}</w>"""]
      __lowercase								=						d[k]  # restore
   return da




def   _A  (  UpperCamelCase_     :       str,			UpperCamelCase_     :       str)	->						List[Any]:
   '''simple docstring'''


   if not os.path.exists(UpperCamelCase_):
      raise ValueError(F"""path {biogpt_checkpoint_path} does not exist!""")
   os.makedirs(UpperCamelCase_,			exist_ok=UpperCamelCase_)
   print(F"""Writing results to {pytorch_dump_folder_path}""")

   # handle various types of models

   __lowercase								=						os.path.join(UpperCamelCase_,			"checkpoint.pt")
   if not os.path.isfile(UpperCamelCase_):
      raise ValueError(F"""path to the file {checkpoint_file} does not exist!""")
   __lowercase								=						torch.load(UpperCamelCase_,			map_location="cpu")

   __lowercase								=						chkpt["cfg"]["model"]

   # dicts
   __lowercase								=						os.path.join(UpperCamelCase_,			"dict.txt")
   if not os.path.isfile(UpperCamelCase_):
      raise ValueError(F"""path to the file {dict_file} does not exist!""")
   __lowercase								=						Dictionary.load(UpperCamelCase_)
   __lowercase								=						rewrite_dict_keys(src_dict.indices)
   __lowercase								=						len(UpperCamelCase_)
   __lowercase								=						os.path.join(UpperCamelCase_,			VOCAB_FILES_NAMES["vocab_file"])
   print(F"""Generating {src_vocab_file} of {src_vocab_size} records""")
   with open(UpperCamelCase_,			"w",			encoding="utf-8") as f:
      f.write(json.dumps(UpperCamelCase_,			ensure_ascii=UpperCamelCase_,			indent=UpperCamelCase_))

   # merges_file (bpecodes)
   __lowercase								=						os.path.join(UpperCamelCase_,			"bpecodes")
   if not os.path.isfile(UpperCamelCase_):
      raise ValueError(F"""path to the file {bpecodes_file} does not exist!""")

   __lowercase								=						os.path.join(UpperCamelCase_,			VOCAB_FILES_NAMES["merges_file"])
   shutil.copyfile(UpperCamelCase_,			UpperCamelCase_)

   # model config
   __lowercase								=						os.path.join(UpperCamelCase_,			"config.json")

   __lowercase								=						{
       "activation_dropout": args["activation_dropout"],
       "architectures": ["BioGptForCausalLM"],
       "attention_probs_dropout_prob": args["attention_dropout"],
       "bos_token_id": 0,
       "eos_token_id": 2,
       "hidden_act": args["activation_fn"],
       "hidden_dropout_prob": args["dropout"],
       "hidden_size": args["decoder_embed_dim"],
       "initializer_range": 0.02,
       "intermediate_size": args["decoder_ffn_embed_dim"],
       "layer_norm_eps": 1E-12,
       "layerdrop": args["decoder_layerdrop"],
       "max_position_embeddings": args["max_target_positions"],
       "model_type": "biogpt",
       "num_attention_heads": args["decoder_attention_heads"],
       "num_hidden_layers": args["decoder_layers"],
       "pad_token_id": 1,
       "scale_embedding": not args["no_scale_embedding"],
       "tie_word_embeddings": args["share_decoder_input_output_embed"],
       "vocab_size": src_vocab_size,
   }

   # good hparam defaults to start with

   print(F"""Generating {biogpt_model_config_file}""")
   with open(UpperCamelCase_,			"w",			encoding="utf-8") as f:
      f.write(json.dumps(UpperCamelCase_,			ensure_ascii=UpperCamelCase_,			indent=UpperCamelCase_))

   # tokenizer config
   __lowercase								=						os.path.join(UpperCamelCase_,			UpperCamelCase_)

   __lowercase								=						{
       "bos_token": "<s>",
       "eos_token": "</s>",
       "model_max_length": 1024,
       "pad_token": "<pad>",
       "special_tokens_map_file": None,
       "tokenizer_class": "BioGptTokenizer",
       "unk_token": "<unk>",
   }

   print(F"""Generating {biogpt_tokenizer_config_file}""")
   with open(UpperCamelCase_,			"w",			encoding="utf-8") as f:
      f.write(json.dumps(UpperCamelCase_,			ensure_ascii=UpperCamelCase_,			indent=UpperCamelCase_))

   # model
   __lowercase								=						chkpt["model"]

   # remove unneeded keys
   __lowercase								=						[
       "decoder.version",
   ]
   for k in ignore_keys:
      model_state_dict.pop(UpperCamelCase_,			UpperCamelCase_)

   __lowercase								=						list(model_state_dict.keys())
   for layer_name in layer_names:
      if layer_name.endswith("output_projection.weight"):
         __lowercase								=						model_state_dict.pop(UpperCamelCase_)
      else:
         __lowercase								=						model_state_dict.pop(UpperCamelCase_)

   __lowercase								=						BioGptConfig.from_pretrained(UpperCamelCase_)
   __lowercase								=						BioGptForCausalLM(UpperCamelCase_)

   # check that it loads ok
   model_new.load_state_dict(UpperCamelCase_)

   # save
   __lowercase								=						os.path.join(UpperCamelCase_,			UpperCamelCase_)
   print(F"""Generating {pytorch_weights_dump_path}""")
   torch.save(UpperCamelCase_,			UpperCamelCase_)

   print("Conversion is done!")


if __name__ == "__main__":
   _a       =							argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--biogpt_checkpoint_path',
       default=None,
       type=str,
       required=True,
       help=(
           'Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,'
           ' bpecodes, etc.'
       ),
   )
   parser.add_argument(
       '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
   )
   _a       =							parser.parse_args()
   convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)




