



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 A    (							__UpperCamelCase			,						__UpperCamelCase       )    ->   Union[str, Any]:
			if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
						# expert layer
						A__	  =     flax_key_tuple[:-1] + ('weight',)
						A__	  =     torch.permute(__UpperCamelCase			,						(0, 2, 1)       )
			elif flax_key_tuple[-1] == "kernel" and ".".join(__UpperCamelCase       ):
						# linear layer
						A__	  =     flax_key_tuple[:-1] + ('weight',)
						A__	  =     flax_tensor.T
			elif flax_key_tuple[-1] in ["scale", "embedding"]:
						A__	  =     flax_key_tuple[:-1] + ('weight',)

			return flax_key_tuple, flax_tensor


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   List[Any]:
			if "metadata" in layer:
						A__	  =     layer.split('metadata'       )
						A__	  =     ''.join(split_layer[0]       )[:-1]
						A__	  =     [tuple(('metadata' + split_layer[1]).split('/'       )       )]
			elif "kvstore" in layer:
						A__	  =     layer.split('kvstore'       )
						A__	  =     ''.join(split_layer[0]       )[:-1]
						A__	  =     [tuple(('kvstore' + split_layer[1]).split('/'       )       )]

			else:
						A__	  =     layer.split('/'       )
						A__	  =     '/'.join(split_layer[:-1]       )
						A__	  =     (split_layer[-1],)

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

			return curr_real_layer_name, split_layer, content


def A    (							__UpperCamelCase			,						__UpperCamelCase       )    ->   Dict:
			A__	  =     rename_keys(__UpperCamelCase       )
			A__	  =     {}
			for k, v in current_block.items():
						A__	  =     v
			A__	  =     new_current_block
			torch.save(__UpperCamelCase			,						__UpperCamelCase       )


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase = WEIGHTS_NAME       )    ->   List[str]:
			A__	  =     convert_file_size_to_int(__UpperCamelCase       )
			A__	  =     []
			A__	  =     {}
			A__	  =     0
			A__	  =     0

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

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

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

						# use the renaming pattern from the small conversion scripts
						A__  ,			A__	  =     rename_base_flax_keys(tuple(key.split('/'       )       )			,						__UpperCamelCase       )
						A__	  =     '/'.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__	  =     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__	  =     {}
									A__	  =     0

						A__	  =     raw_weights.to(getattr(__UpperCamelCase			,						__UpperCamelCase       )       )
						current_block_size += weight_size
						total_size += weight_size

			# Add the last block
			A__	  =     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__	  =     {}
			A__	  =     {}
			for idx, shard in enumerate(__UpperCamelCase       ):
						A__	  =     weights_name.replace(
						    '.bin'			,						f'''-{idx+1:05d}-of-{len(__UpperCamelCase       ):05d}.bin'''       )  # len(sharded_state_dicts):05d}
						A__	  =     os.path.join(__UpperCamelCase			,						weights_name.replace('.bin'			,						f'''-{idx+1:05d}-of-???.bin'''       )       )
						os.rename(__UpperCamelCase			,						os.path.join(__UpperCamelCase			,						__UpperCamelCase       )       )
						A__	  =     shard
						for key in shard:
									A__	  =     shard_file

    # Add the metadata
			A__	  =     {'total_size': total_size}
			A__	  =     {'metadata': metadata, 'weight_map': weight_map}

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

			return metadata, index


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__   			=   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.''',
							)
							SCREAMING_SNAKE_CASE__   			=   parser.parse_args()
							shard_on_the_fly(
							    args.switch_tax_checkpoint_path,
							    args.pytorch_dump_folder_path,
							    args.max_shard_size,
							    args.dtype,
							)






def A    (							)    ->   Optional[Any]:
			from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

			A__	  =     tokenizer(__UpperCamelCase			,						return_tensors='pt'       ).input_ids
			A__	  =     model.generate(__UpperCamelCase			,						decoder_start_token_id=0       )
			print(tokenizer.decode(out[0]       )       )









from __future__ import annotations

from fractions import Fraction


def A    (							__UpperCamelCase			,						__UpperCamelCase       )    ->   bool:
			return (
			    num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
			)


def A    (							__UpperCamelCase       )    ->   list[str]:
			A__	  =     []
			A__	  =     11
			A__	  =     int('1' + '0' * digit_len       )
			for num in range(__UpperCamelCase			,						__UpperCamelCase       ):
						while den <= 99:
									if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
												if is_digit_cancelling(__UpperCamelCase			,						__UpperCamelCase       ):
															solutions.append(f'''{num}/{den}'''       )
									den += 1
						num += 1
						A__	  =     10
			return solutions


def A    (							__UpperCamelCase = 2       )    ->   int:
			A__	  =     1.0
			for fraction in fraction_list(__UpperCamelCase       ):
						A__	  =     Fraction(__UpperCamelCase       )
						result *= frac.denominator / frac.numerator
			return int(__UpperCamelCase       )


if __name__ == "__main__":
							print(solution())




