



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		:      Optional[int]      ,			UpperCamelCase		:      int )  ->List[str]:



			'''simple docstring'''







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

			return flax_key_tuple, flax_tensor






def      _A				(UpperCamelCase		:      Any      ,			UpperCamelCase		:      Union[str, Any]      ,			UpperCamelCase		:      Tuple )  ->str:



			'''simple docstring'''







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

			else:
						lowerCamelCase__ :      List[str]								=		layer.split("""/""" )
						lowerCamelCase__ :      int								=		'/'.join(split_layer[:-1] )
						lowerCamelCase__ :      List[str]								=		(split_layer[-1],)

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

			return curr_real_layer_name, split_layer, content






def      _A				(UpperCamelCase		:      Optional[Any]      ,			UpperCamelCase		:      List[str] )  ->Union[str, Any]:



			'''simple docstring'''







			lowerCamelCase__ :      Dict								=		rename_keys(lowerCAmelCase__ )
			lowerCamelCase__ :      Any								=		{}
			for k, v in current_block.items():
						lowerCamelCase__ :      Any								=		v
			lowerCamelCase__ :      Dict								=		new_current_block
			torch.save(lowerCAmelCase__      ,			lowerCAmelCase__ )






def      _A				(UpperCamelCase		:      str      ,			UpperCamelCase		:      Optional[Any]      ,			UpperCamelCase		:      List[str]      ,			UpperCamelCase		:      int      ,			UpperCamelCase		:      str = WEIGHTS_NAME )  ->Any:



			'''simple docstring'''







			lowerCamelCase__ :      str								=		convert_file_size_to_int(lowerCAmelCase__ )
			lowerCamelCase__ :      Tuple								=		[]
			lowerCamelCase__ :      List[Any]								=		{}
			lowerCamelCase__ :      List[Any]								=		0
			lowerCamelCase__ :      int								=		0

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

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

			for key in all_layers.keys():
						# open tensorstore file
						lowerCamelCase__ :      str								=		ts.open(unflatten_dict(all_layers[key] ) ).result().read().result()
						lowerCamelCase__ :      Union[str, Any]								=		torch.tensor(lowerCAmelCase__ )
						lowerCamelCase__ :      List[str]								=		raw_weights.numel() * dtype_byte_size(raw_weights.dtype )

						# use the renaming pattern from the small conversion scripts
						lowerCamelCase__ :      Optional[int]								=		rename_base_flax_keys(tuple(key.split("""/""" ) )      ,			lowerCAmelCase__ )
						lowerCamelCase__ :      Optional[Any]								=		'/'.join(lowerCAmelCase__ )

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

						lowerCamelCase__ :      Optional[int]								=		raw_weights.to(getattr(lowerCAmelCase__      ,			lowerCAmelCase__ ) )
						current_block_size += weight_size
						total_size += weight_size

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

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

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

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

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

			return metadata, index


if __name__ == "__main__":
				_lowercase     							=		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     							=		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]:



			'''simple docstring'''







			from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

			lowerCamelCase__ :      int								=		tokenizer(lowerCAmelCase__      ,			return_tensors="""pt""" ).input_ids
			lowerCamelCase__ :      List[Any]								=		model.generate(lowerCAmelCase__      ,			decoder_start_token_id=0 )
			print(tokenizer.decode(out[0] ) )








# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

_lowercase     							=						TypeVar('''T''')
class 				__A					( Generic[T]      ):





			def __init__(self							,    __magic_name__ = True				):
						lowerCamelCase__ :      dict[T, list[T]]								=		{}  # dictionary of lists
						lowerCamelCase__ :      List[str]								=		directed





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__				):

						if not self.directed:  # For undirected graphs
									# if both source vertex and destination vertex are both present in the
									# adjacency list, add destination vertex to source vertex list of adjacent
									# vertices and add source vertex to destination vertex list of adjacent
									# vertices.
									if source_vertex in self.adj_list and destination_vertex in self.adj_list:
												self.adj_list[source_vertex].append(__magic_name__				)
												self.adj_list[destination_vertex].append(__magic_name__				)
									# if only source vertex is present in adjacency list, add destination vertex
									# to source vertex list of adjacent vertices, then create a new vertex with
									# destination vertex as key and assign a list containing the source vertex
									# as it's first adjacent vertex.
									elif source_vertex in self.adj_list:
												self.adj_list[source_vertex].append(__magic_name__				)
												lowerCamelCase__ :      int								=		[source_vertex]
									# if only destination vertex is present in adjacency list, add source vertex
									# to destination vertex list of adjacent vertices, then create a new vertex
									# with source vertex as key and assign a list containing the source vertex
									# as it's first adjacent vertex.
									elif destination_vertex in self.adj_list:
												self.adj_list[destination_vertex].append(__magic_name__				)
												lowerCamelCase__ :      Dict								=		[destination_vertex]
									# if both source vertex and destination vertex are not present in adjacency
									# list, create a new vertex with source vertex as key and assign a list
									# containing the destination vertex as it's first adjacent vertex also
									# create a new vertex with destination vertex as key and assign a list
									# containing the source vertex as it's first adjacent vertex.
									else:
												lowerCamelCase__ :      Any								=		[destination_vertex]
												lowerCamelCase__ :      Optional[Any]								=		[source_vertex]
						else:  # For directed graphs
									# if both source vertex and destination vertex are present in adjacency
									# list, add destination vertex to source vertex list of adjacent vertices.
									if source_vertex in self.adj_list and destination_vertex in self.adj_list:
												self.adj_list[source_vertex].append(__magic_name__				)
									# if only source vertex is present in adjacency list, add destination
									# vertex to source vertex list of adjacent vertices and create a new vertex
									# with destination vertex as key, which has no adjacent vertex
									elif source_vertex in self.adj_list:
												self.adj_list[source_vertex].append(__magic_name__				)
												lowerCamelCase__ :      Optional[Any]								=		[]
									# if only destination vertex is present in adjacency list, create a new
									# vertex with source vertex as key and assign a list containing destination
									# vertex as first adjacent vertex
									elif destination_vertex in self.adj_list:
												lowerCamelCase__ :      Tuple								=		[destination_vertex]
									# if both source vertex and destination vertex are not present in adjacency
									# list, create a new vertex with source vertex as key and a list containing
									# destination vertex as it's first adjacent vertex. Then create a new vertex
									# with destination vertex as key, which has no adjacent vertex
									else:
												lowerCamelCase__ :      Optional[int]								=		[destination_vertex]
												lowerCamelCase__ :      List[Any]								=		[]

						return self





			def __repr__(self				):
						return pformat(self.adj_list				)



