






import os
import random
import sys

from . import cryptomath_module as cryptomath
from . import rabin_miller

__lowerCamelCase   :     Any 		= 3







def 	SCREAMING_SNAKE_CASE							(					snake_case_   :				List[str]  ):
	print("Generating primitive root of p"  )
	while True:
		snake_case__					: Optional[int]  =    random.randrange(3	,    __UpperCamelCase  )
		if pow(__UpperCamelCase	,    2	,    __UpperCamelCase  ) == 1:
			continue
		if pow(__UpperCamelCase	,    __UpperCamelCase	,    __UpperCamelCase  ) == 1:
			continue
		return g







def 	SCREAMING_SNAKE_CASE							(					snake_case_   :				Dict  ):
	print("Generating prime p..."  )
	snake_case__					: Any  =    rabin_miller.generate_large_prime(__UpperCamelCase  )  # select large prime number.
	snake_case__					: Union[str, Any]  =    primitive_root(__UpperCamelCase  )  # one primitive root on modulo p.
	snake_case__					: Optional[int]  =    random.randrange(3	,    __UpperCamelCase  )  # private_key -> have to be greater than 2 for safety.
	snake_case__					: Optional[int]  =    cryptomath.find_mod_inverse(pow(__UpperCamelCase	,    __UpperCamelCase	,    __UpperCamelCase  )	,    __UpperCamelCase  )

	snake_case__					: int  =    (key_size, e_a, e_a, p)
	snake_case__					: int  =    (key_size, d)

	return public_key, private_key







def 	SCREAMING_SNAKE_CASE							(					snake_case_   :				Optional[int]	,    snake_case_   :				Optional[int]  ):
	if os.path.exists(F'''{name}_pubkey.txt'''  ) or os.path.exists(F'''{name}_privkey.txt'''  ):
		print("\nWARNING:"  )
		print(
		    F'''"{name}_pubkey.txt" or "{name}_privkey.txt" already exists. \n'''
		    "Use a different name or delete these files and re-run this program."  )
		sys.exit()

	snake_case__					: str  =    generate_key(__UpperCamelCase  )
	print(F'''\nWriting public key to file {name}_pubkey.txt...'''  )
	with open(F'''{name}_pubkey.txt'''	,    "w"  ) as fo:
		fo.write(F'''{public_key[0]},{public_key[1]},{public_key[2]},{public_key[3]}'''  )

	print(F'''Writing private key to file {name}_privkey.txt...'''  )
	with open(F'''{name}_privkey.txt'''	,    "w"  ) as fo:
		fo.write(F'''{private_key[0]},{private_key[1]}'''  )







def 	SCREAMING_SNAKE_CASE							(					):
	print("Making key files..."  )
	make_key_files("elgamal"	,    2048  )
	print("Key files generation successful"  )


if __name__ == "__main__":
	main()








import argparse
import json
import os

import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile

from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
    rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase )	->						Optional[Any]:
				if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
								# expert layer
								a__	:							List[str]      						= flax_key_tuple[:-1] + ("weight",)
								a__	:							Optional[int]      						= torch.permute(__UpperCamelCase	,				(0, 2, 1) )
				elif flax_key_tuple[-1] == "kernel" and ".".join(__UpperCamelCase ):
								# linear layer
								a__	:							Tuple      						= flax_key_tuple[:-1] + ("weight",)
								a__	:							Tuple      						= flax_tensor.T
				elif flax_key_tuple[-1] in ["scale", "embedding"]:
								a__	:							str      						= flax_key_tuple[:-1] + ("weight",)

				return flax_key_tuple, flax_tensor






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase )	->						List[Any]:
				if "metadata" in layer:
								a__	:							Union[str, Any]      						= layer.split("metadata" )
								a__	:							Any      						= "".join(split_layer[0] )[:-1]
								a__	:							Optional[int]      						= [tuple(("metadata" + split_layer[1]).split("/" ) )]
				elif "kvstore" in layer:
								a__	:							Optional[int]      						= layer.split("kvstore" )
								a__	:							Union[str, Any]      						= "".join(split_layer[0] )[:-1]
								a__	:							Optional[Any]      						= [tuple(("kvstore" + split_layer[1]).split("/" ) )]

				else:
								a__	:							Any      						= layer.split("/" )
								a__	:							Union[str, Any]      						= "/".join(split_layer[:-1] )
								a__	:							Tuple      						= (split_layer[-1],)

				if "kvstore/path" in layer:
								a__	:							Tuple      						= F'{switch_checkpoint_path}/{checkpoint_info[layer]}'
				elif "kvstore/driver" in layer:
								a__	:							int      						= "file"
				else:
								a__	:							List[str]      						= checkpoint_info[layer]

				return curr_real_layer_name, split_layer, content






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase )	->						Optional[int]:
				a__	:							List[Any]      						= rename_keys(__UpperCamelCase )
				a__	:							Tuple      						= {}
				for k, v in current_block.items():
								a__	:							str      						= v
				a__	:							Optional[int]      						= new_current_block
				torch.save(__UpperCamelCase	,				__UpperCamelCase )






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase = WEIGHTS_NAME )	->						Any:
				a__	:							Optional[Any]      						= convert_file_size_to_int(__UpperCamelCase )
				a__	:							Tuple      						= []
				a__	:							Dict      						= {}
				a__	:							str      						= 0
				a__	:							Tuple      						= 0

				os.makedirs(__UpperCamelCase	,				exist_ok=__UpperCamelCase )
				with gfile.GFile(switch_checkpoint_path + "/checkpoint"	,				"rb" ) as fp:
								a__	:							Any      						= serialization.msgpack_restore(fp.read() )["optimizer"]["target"]
								a__	:							Tuple      						= flatten_dict(__UpperCamelCase	,				sep="/" )

				a__	:							Any      						= {}
				for layer in checkpoint_info.keys():
								a__						,   a__						,   a__	:							Optional[Any]      						= get_key_and_tensorstore_dict(
								    __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase )
								if curr_real_layer_name in all_layers:
												a__	:							int      						= content
								else:
												a__	:							List[Any]      						= {split_layer[-1]: content}

				for key in all_layers.keys():
								# open tensorstore file
								a__	:							Tuple      						= ts.open(unflatten_dict(all_layers[key] ) ).result().read().result()
								a__	:							Tuple      						= torch.tensor(__UpperCamelCase )
								a__	:							str      						= raw_weights.numel() * dtype_byte_size(raw_weights.dtype )

								# use the renaming pattern from the small conversion scripts
								a__						,   a__	:							int      						= rename_base_flax_keys(tuple(key.split("/" ) )	,				__UpperCamelCase )
								a__	:							Union[str, Any]      						= "/".join(__UpperCamelCase )

								# If this weight is going to tip up over the maximal size, we split.
								if current_block_size + weight_size > max_shard_size:
												a__	:							int      						= os.path.join(
												    __UpperCamelCase	,				weights_name.replace(".bin"	,				F'-{len(__UpperCamelCase )+1:05d}-of-???.bin' ) )
												rename_and_save_block(__UpperCamelCase	,				__UpperCamelCase )
												sharded_state_dicts.append(current_block.keys() )
												del current_block
												a__	:							int      						= {}
												a__	:							Optional[int]      						= 0

								a__	:							List[str]      						= raw_weights.to(getattr(__UpperCamelCase	,				__UpperCamelCase ) )
								current_block_size += weight_size
								total_size += weight_size

				# Add the last block
				a__	:							Optional[int]      						= os.path.join(__UpperCamelCase	,				weights_name.replace(".bin"	,				F'-{len(__UpperCamelCase )+1:05d}-of-???.bin' ) )
				rename_and_save_block(__UpperCamelCase	,				__UpperCamelCase )
				sharded_state_dicts.append(current_block.keys() )

				# If we only have one shard, we return it
				if len(__UpperCamelCase ) == 1:
								return {weights_name: sharded_state_dicts[0]}, None

				# Otherwise, let's build the index
				a__	:							int      						= {}
				a__	:							Tuple      						= {}
				for idx, shard in enumerate(__UpperCamelCase ):
								a__	:							Tuple      						= weights_name.replace(
								    ".bin"	,				F'-{idx+1:05d}-of-{len(__UpperCamelCase ):05d}.bin' )  # len(sharded_state_dicts):05d}
								a__	:							Optional[int]      						= os.path.join(__UpperCamelCase	,				weights_name.replace(".bin"	,				F'-{idx+1:05d}-of-???.bin' ) )
								os.rename(__UpperCamelCase	,				os.path.join(__UpperCamelCase	,				__UpperCamelCase ) )
								a__	:							List[str]      						= shard
								for key in shard:
												a__	:							Tuple      						= shard_file

    # Add the metadata
				a__	:							int      						= {"total_size": total_size}
				a__	:							List[str]      						= {"metadata": metadata, "weight_map": weight_map}

				with open(os.path.join(__UpperCamelCase	,				__UpperCamelCase )	,				"w"	,				encoding="utf-8" ) as f:
								a__	:							List[str]      						= json.dumps(__UpperCamelCase	,				indent=2	,				sort_keys=__UpperCamelCase ) + "\n"
								f.write(__UpperCamelCase )

				return metadata, index


if __name__ == "__main__":
						lowerCamelCase		       =      argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--switch_t5x_checkpoint_path""",
						    default="""/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600""",
						    type=str,
						    required=False,
						    help="""Path to a directory containing a folder per layer. Follows the original Google format.""",
						)
						parser.add_argument("""--max_shard_size""", default="""10GB""", required=False, help="""Max shard size""")
						parser.add_argument("""--dtype""", default="""bfloat16""", type=str, required=False, help="""dtype of the saved model""")
						parser.add_argument(
						    """--pytorch_dump_folder_path""",
						    default="""/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted""",
						    type=str,
						    required=False,
						    help="""Path to the output pytorch model.""",
						)
						lowerCamelCase		       =      parser.parse_args()
						shard_on_the_fly(
						    args.switch_tax_checkpoint_path,
						    args.pytorch_dump_folder_path,
						    args.max_shard_size,
						    args.dtype,
						)






def     SCREAMING_SNAKE_CASE(      )	->						int:
				from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

				a__	:							Any      						= SwitchTransformersConfig.from_pretrained("google/switch-base-8" )
				config.save_pretrained("/home/arthur_huggingface_co/transformers/switch_converted" )
				a__	:							List[str]      						= SwitchTransformersForConditionalGeneration.from_pretrained(
				    "/home/arthur_huggingface_co/transformers/switch_converted"	,				device_map="auto" )

				a__	:							str      						= TaTokenizer.from_pretrained("t5-small" )
				a__	:							Tuple      						= "A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."

				a__	:							Optional[int]      						= tokenizer(__UpperCamelCase	,				return_tensors="pt" ).input_ids
				a__	:							Optional[Any]      						= model.generate(__UpperCamelCase	,				decoder_start_token_id=0 )
				print(tokenizer.decode(out[0] ) )


