

import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation

def 		UpperCamelCase      (lowercase_:				List[Any] = 8      )  ->   str:
	A__							:					Optional[int]       =	ascii_letters + digits + punctuation
	return "".join(secrets.choice(_SCREAMING_SNAKE_CASE      ) for _ in range(_SCREAMING_SNAKE_CASE      )      )

def 		UpperCamelCase      (lowercase_:				Union[str, Any]					,   lowercase_:				Tuple      )  ->   str:
	# Password Generator = full boot with random_number, random_letters, and
	# random_character FUNCTIONS
	# Put your code here...
	i -= len(_SCREAMING_SNAKE_CASE      )
	A__							:					Optional[int]       =	i // 3
	A__							:					List[Any]       =	i % 3
	# chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
	#     random_number(digits, i / 3) + random_characters(punctuation, i / 3)
	A__							:					Dict       =	(
	    chars_incl
	    + random(_SCREAMING_SNAKE_CASE					,   quotient + remainder      )
	    + random(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE      )
	    + random(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE      )
	)
	A__							:					Any       =	list(_SCREAMING_SNAKE_CASE      )
	shuffle(_SCREAMING_SNAKE_CASE      )
	return "".join(_SCREAMING_SNAKE_CASE      )

	# random is a generalised function for letters, characters and numbers

def 		UpperCamelCase      (lowercase_:				str					,   lowercase_:				int      )  ->   str:
	return "".join(secrets.choice(_SCREAMING_SNAKE_CASE      ) for _ in range(_SCREAMING_SNAKE_CASE      )      )

def 		UpperCamelCase      (lowercase_:				int					,   lowercase_:				List[str]      )  ->   Any:
	pass  # Put your code here...

def 		UpperCamelCase      (lowercase_:				Optional[int]					,   lowercase_:				Optional[Any]      )  ->   List[str]:
	pass  # Put your code here...

def 		UpperCamelCase      (lowercase_:				str					,   lowercase_:				Dict      )  ->   Optional[int]:
	pass  # Put your code here...

def 		UpperCamelCase      (lowercase_:				List[str]					,   lowercase_:				List[Any] = 8      )  ->   bool:

	if len(_SCREAMING_SNAKE_CASE      ) < min_length:
		# Your Password must be at least 8 characters long
		return False

	A__							:					Optional[int]       =	any(char in ascii_uppercase for char in password      )
	A__							:					List[Any]       =	any(char in ascii_lowercase for char in password      )
	A__							:					Optional[int]       =	any(char in digits for char in password      )
	A__							:					int       =	any(char in punctuation for char in password      )

	return upper and lower and num and spec_char
	# Passwords should contain UPPERCASE, lowerase
	# numbers, and special characters

def 		UpperCamelCase      ()  ->   str:
	A__							:					List[Any]       =	int(input("""Please indicate the max length of your password: """      ).strip()      )
	A__							:					Union[str, Any]       =	input(
	    """Please indicate the characters that must be in your password: """      ).strip()
	print("""Password generated:"""					,   password_generator(_SCREAMING_SNAKE_CASE      )      )
	print(
	    """Alternative Password generated:"""					,   alternative_password_generator(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE      )					,   )
	print("""[If you are thinking of using this passsword, You better save it.]"""      )


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

	return flax_key_tuple, flax_tensor

def 		UpperCamelCase      (lowercase_:				Tuple					,   lowercase_:				Optional[int]					,   lowercase_:				str      )  ->   Union[str, Any]:
	if "metadata" in layer:
		A__							:					Tuple       =	layer.split("""metadata"""      )
		A__							:					Optional[Any]       =	"""""".join(split_layer[0]      )[:-1]
		A__							:					Optional[Any]       =	[tuple(("""metadata""" + split_layer[1]).split("""/"""      )      )]
	elif "kvstore" in layer:
		A__							:					str       =	layer.split("""kvstore"""      )
		A__							:					int       =	"""""".join(split_layer[0]      )[:-1]
		A__							:					Optional[int]       =	[tuple(("""kvstore""" + split_layer[1]).split("""/"""      )      )]

	else:
		A__							:					Any       =	layer.split("""/"""      )
		A__							:					int       =	"""/""".join(split_layer[:-1]      )
		A__							:					str       =	(split_layer[-1],)

	if "kvstore/path" in layer:
		A__							:					Dict       =	f"""{switch_checkpoint_path}/{checkpoint_info[layer]}"""
	elif "kvstore/driver" in layer:
		A__							:					Optional[int]       =	"""file"""
	else:
		A__							:					str       =	checkpoint_info[layer]

	return curr_real_layer_name, split_layer, content

def 		UpperCamelCase      (lowercase_:				str					,   lowercase_:				List[Any]      )  ->   int:
	A__							:					int       =	rename_keys(lowercase_      )
	A__							:					Any       =	{}
	for k, v in current_block.items():
		A__							:					Dict       =	v
	A__							:					str       =	new_current_block
	torch.save(lowercase_					,   lowercase_      )

def 		UpperCamelCase      (lowercase_:				Dict					,   lowercase_:				Optional[Any]					,   lowercase_:				Optional[Any]					,   lowercase_:				Optional[int]					,   lowercase_:				str = WEIGHTS_NAME      )  ->   Tuple:
	A__							:					Optional[int]       =	convert_file_size_to_int(lowercase_      )
	A__							:					List[Any]       =	[]
	A__							:					int       =	{}
	A__							:					List[str]       =	0
	A__							:					Any       =	0

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

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

	for key in all_layers.keys():
		# open tensorstore file
		A__							:					Optional[Any]       =	ts.open(unflatten_dict(all_layers[key]      )      ).result().read().result()
		A__							:					List[Any]       =	torch.tensor(lowercase_      )
		A__							:					List[Any]       =	raw_weights.numel() * dtype_byte_size(raw_weights.dtype      )

		# use the renaming pattern from the small conversion scripts
		A__     ,     A__							:					Any       =	rename_base_flax_keys(tuple(key.split("""/"""      )      )					,   lowercase_      )
		A__							:					Any       =	"""/""".join(lowercase_      )

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

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

	# Add the last block
	A__							:					Union[str, Any]       =	os.path.join(lowercase_					,   weights_name.replace(""".bin"""					,   f"""-{len(lowercase_      )+1:05d}-of-???.bin"""      )      )
	rename_and_save_block(lowercase_					,   lowercase_      )
	sharded_state_dicts.append(current_block.keys()      )

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

	# Otherwise, let's build the index
	A__							:					str       =	{}
	A__							:					Any       =	{}
	for idx, shard in enumerate(lowercase_      ):
		A__							:					Any       =	weights_name.replace(
		    """.bin"""					,   f"""-{idx+1:05d}-of-{len(lowercase_      ):05d}.bin"""      )  # len(sharded_state_dicts):05d}
		A__							:					Dict       =	os.path.join(lowercase_					,   weights_name.replace(""".bin"""					,   f"""-{idx+1:05d}-of-???.bin"""      )      )
		os.rename(lowercase_					,   os.path.join(lowercase_					,   lowercase_      )      )
		A__							:					str       =	shard
		for key in shard:
			A__							:					Any       =	shard_file

    # Add the metadata
	A__							:					Tuple       =	{"""total_size""": total_size}
	A__							:					Union[str, Any]       =	{"""metadata""": metadata, """weight_map""": weight_map}

	with open(os.path.join(lowercase_					,   lowercase_      )					,   """w"""					,   encoding="""utf-8"""      ) as f:
		A__							:					Dict       =	json.dumps(lowercase_					,   indent=2					,   sort_keys=lowercase_      ) + """\n"""
		f.write(lowercase_      )

	return metadata, index


if __name__ == "__main__":
	A_ :				Optional[int]						 =    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.',
	)
	A_ :				Dict						 =    parser.parse_args()
	shard_on_the_fly(
	    args.switch_tax_checkpoint_path,
	    args.pytorch_dump_folder_path,
	    args.max_shard_size,
	    args.dtype,
	)


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

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

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

	A__							:					Union[str, Any]       =	tokenizer(lowercase_					,   return_tensors="""pt"""      ).input_ids
	A__							:					Tuple       =	model.generate(lowercase_					,   decoder_start_token_id=0      )
	print(tokenizer.decode(out[0]      )      )



