
'''simple docstring'''




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 		lowerCAmelCase_				(   snake_case__		,				snake_case__						):




    '''simple docstring'''



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

    return flax_key_tuple, flax_tensor







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__						):




    '''simple docstring'''



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

    else:
        A       :   Union[str, Any]					=						layer.split('''/'''						)
        A       :   Optional[int]					=						'''/'''.join(split_layer[:-1]						)
        A       :   Optional[Any]					=						(split_layer[-1],)

    if "kvstore/path" in layer:
        A       :   int					=						F'{switch_checkpoint_path}/{checkpoint_info[layer]}'
    elif "kvstore/driver" in layer:
        A       :   int					=						'''file'''
    else:
        A       :   Any					=						checkpoint_info[layer]

    return curr_real_layer_name, split_layer, content







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__						):




    '''simple docstring'''



    A       :   Optional[int]					=						rename_keys(snake_case__						)
    A       :   Tuple					=						{}
    for k, v in current_block.items():
        A       :   Tuple					=						v
    A       :   List[str]					=						new_current_block
    torch.save(snake_case__		,				snake_case__						)







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__ = WEIGHTS_NAME						):




    '''simple docstring'''



    A       :   Dict					=						convert_file_size_to_int(snake_case__						)
    A       :   str					=						[]
    A       :   str					=						{}
    A       :   Any					=						0
    A       :   List[str]					=						0

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

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

    for key in all_layers.keys():
        # open tensorstore file
        A       :   Any					=						ts.open(unflatten_dict(all_layers[key]						)						).result().read().result()
        A       :   List[Any]					=						torch.tensor(snake_case__						)
        A       :   int					=						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('''/'''						)						)		,				snake_case__						)
        A       :   Union[str, Any]					=						'''/'''.join(snake_case__						)

        # 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(
                snake_case__		,				weights_name.replace('''.bin'''		,				F'-{len(snake_case__						)+1:05d}-of-???.bin'						)						)
            rename_and_save_block(snake_case__		,				snake_case__						)
            sharded_state_dicts.append(current_block.keys()						)
            del current_block
            A       :   Dict					=						{}
            A       :   List[Any]					=						0

        A       :   List[Any]					=						raw_weights.to(getattr(snake_case__		,				snake_case__						)						)
        current_block_size += weight_size
        total_size += weight_size

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

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

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

    # Add the metadata
    A       :   Tuple					=						{'''total_size''': total_size}
    A       :   Optional[int]					=						{'''metadata''': metadata, '''weight_map''': weight_map}

    with open(os.path.join(snake_case__		,				snake_case__						)		,				'''w'''		,				encoding='''utf-8'''						) as f:
        A       :   Union[str, Any]					=						json.dumps(snake_case__		,				indent=2		,				sort_keys=snake_case__						) + '''\n'''
        f.write(snake_case__						)

    return metadata, index


if __name__ == "__main__":
      lowercase			:				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.',
      )
      lowercase			:				List[str]    		=			parser.parse_args()
      shard_on_the_fly(
          args.switch_tax_checkpoint_path,
          args.pytorch_dump_folder_path,
          args.max_shard_size,
          args.dtype,
      )







def 		lowerCAmelCase_				(   ):




    '''simple docstring'''



    from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

    A       :   Optional[Any]					=						SwitchTransformersConfig.from_pretrained('''google/switch-base-8'''						)
    config.save_pretrained('''/home/arthur_huggingface_co/transformers/switch_converted'''						)
    A       :   Any					=						SwitchTransformersForConditionalGeneration.from_pretrained(
        '''/home/arthur_huggingface_co/transformers/switch_converted'''		,				device_map='''auto'''						)

    A       :   Any					=						TaTokenizer.from_pretrained('''t5-small'''						)
    A       :   Union[str, Any]					=						'''A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.'''

    A       :   List[Any]					=						tokenizer(snake_case__		,				return_tensors='''pt'''						).input_ids
    A       :   Optional[Any]					=						model.generate(snake_case__		,				decoder_start_token_id=0						)
    print(tokenizer.decode(out[0]						)						)








"""simple docstring"""


import sys
from collections import defaultdict
class   _UpperCamelCase  :
							'''simple docstring'''







							def __init__(     self	):
										__lowerCAmelCase				    =			[]

							def 			snake_case				(     self    ,   __a	):
										return self.node_position[vertex]

							def 			snake_case				(     self    ,   __a    ,   __a	):
										__lowerCAmelCase				    =			pos

							def 			snake_case				(     self    ,   __a    ,   __a    ,   __a    ,   __a	):
										if start > size // 2 - 1:
													return
										else:
													if 2 * start + 2 >= size:
																__lowerCAmelCase				    =			2 * start + 1
													else:
																if heap[2 * start + 1] < heap[2 * start + 2]:
																			__lowerCAmelCase				    =			2 * start + 1
																else:
																			__lowerCAmelCase				    =			2 * start + 2
													if heap[smallest_child] < heap[start]:
																__lowerCAmelCase    ,	__lowerCAmelCase				    =			heap[smallest_child], positions[smallest_child]
																__lowerCAmelCase    ,	__lowerCAmelCase				    =			(
																    heap[start],
																    positions[start],
																)
																__lowerCAmelCase    ,	__lowerCAmelCase				    =			temp, tempa

																__lowerCAmelCase				    =			self.get_position(positions[smallest_child]	)
																self.set_position(
																    positions[smallest_child]    ,   self.get_position(positions[start]	)	)
																self.set_position(positions[start]    ,   __a	)

																self.top_to_bottom(__a    ,   __a    ,   __a    ,   __a	)

							def 			snake_case				(     self    ,   __a    ,   __a    ,   __a    ,   __a	):
										__lowerCAmelCase				    =			position[index]

										while index != 0:
													__lowerCAmelCase				    =			int((index - 2) / 2	) if index % 2 == 0 else int((index - 1) / 2	)

													if val < heap[parent]:
																__lowerCAmelCase				    =			heap[parent]
																__lowerCAmelCase				    =			position[parent]
																self.set_position(position[parent]    ,   __a	)
													else:
																__lowerCAmelCase				    =			val
																__lowerCAmelCase				    =			temp
																self.set_position(__a    ,   __a	)
																break
													__lowerCAmelCase				    =			parent
										else:
													__lowerCAmelCase				    =			val
													__lowerCAmelCase				    =			temp
													self.set_position(__a    ,   0	)

							def 			snake_case				(     self    ,   __a    ,   __a	):
										__lowerCAmelCase				    =			len(__a	) // 2 - 1
										for i in range(__a    ,   -1    ,   -1	):
													self.top_to_bottom(__a    ,   __a    ,   len(__a	)    ,   __a	)



							def 			snake_case				(     self    ,   __a    ,   __a	):
										__lowerCAmelCase				    =			positions[0]
										__lowerCAmelCase				    =			sys.maxsize
										self.top_to_bottom(__a    ,   0    ,   len(__a	)    ,   __a	)
										return temp


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			Heap()

			__lowerCAmelCase				    =			[0] * len(_UpperCamelCase      )
			__lowerCAmelCase				    =			[-1] * len(_UpperCamelCase      )  # Neighboring Tree Vertex of selected vertex
			# Minimum Distance of explored vertex with neighboring vertex of partial tree
			# formed in graph
			__lowerCAmelCase				    =			[]  # Heap of Distance of vertices from their neighboring vertex
			__lowerCAmelCase				    =			[]

			for vertex in range(len(_UpperCamelCase      )      ):
						distance_tv.append(sys.maxsize      )
						positions.append(_UpperCamelCase      )
						heap.node_position.append(_UpperCamelCase      )

			__lowerCAmelCase				    =			[]
			__lowerCAmelCase				    =			1
			__lowerCAmelCase				    =			sys.maxsize
			for neighbor, distance in adjacency_list[0]:
						__lowerCAmelCase				    =			0
						__lowerCAmelCase				    =			distance
			heap.heapify(_UpperCamelCase    , _UpperCamelCase      )

			for _ in range(1    , len(_UpperCamelCase      )      ):
						__lowerCAmelCase				    =			heap.delete_minimum(_UpperCamelCase    , _UpperCamelCase      )
						if visited[vertex] == 0:
									tree_edges.append((nbr_tv[vertex], vertex)      )
									__lowerCAmelCase				    =			1
									for neighbor, distance in adjacency_list[vertex]:
												if (
												    visited[neighbor] == 0
												    and distance < distance_tv[heap.get_position(_UpperCamelCase      )]
												):
															__lowerCAmelCase				    =			distance
															heap.bottom_to_top(
															    _UpperCamelCase    , heap.get_position(_UpperCamelCase      )    , _UpperCamelCase    , _UpperCamelCase      )
															__lowerCAmelCase				    =			vertex
			return tree_edges


if __name__ == "__main__":  # pragma: no cover
	# < --------- Prims Algorithm --------- >
	A     :   Optional[Any]			    =			int(input("Enter number of edges: ").strip())
	A     :   Dict			    =			defaultdict(list)
	for _ in range(edges_number):
		A     :   str			    =			[int(x) for x in input().strip().split()]
		adjacency_list[edge[0]].append([edge[1], edge[2]])
		adjacency_list[edge[1]].append([edge[0], edge[2]])
	print(prisms_algorithm(adjacency_list))




