

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			(				UpperCamelCase__     ,							UpperCamelCase__     ):







							'''simple docstring'''






							if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
														# expert layer
														snake_case_           =					flax_key_tuple[:-1] + ('weight',)
														snake_case_           =					torch.permute(UpperCamelCase__     ,							(0, 2, 1)     )
							elif flax_key_tuple[-1] == "kernel" and ".".join(UpperCamelCase__     ):
														# linear layer
														snake_case_           =					flax_key_tuple[:-1] + ('weight',)
														snake_case_           =					flax_tensor.T
							elif flax_key_tuple[-1] in ["scale", "embedding"]:
														snake_case_           =					flax_key_tuple[:-1] + ('weight',)

							return flax_key_tuple, flax_tensor





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ):







							'''simple docstring'''






							if "metadata" in layer:
														snake_case_           =					layer.split('metadata'     )
														snake_case_           =					''.join(split_layer[0]     )[:-1]
														snake_case_           =					[tuple(('metadata' + split_layer[1]).split('/'     )     )]
							elif "kvstore" in layer:
														snake_case_           =					layer.split('kvstore'     )
														snake_case_           =					''.join(split_layer[0]     )[:-1]
														snake_case_           =					[tuple(('kvstore' + split_layer[1]).split('/'     )     )]

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

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

							return curr_real_layer_name, split_layer, content





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ):







							'''simple docstring'''






							snake_case_           =					rename_keys(UpperCamelCase__     )
							snake_case_           =					{}
							for k, v in current_block.items():
														snake_case_           =					v
							snake_case_           =					new_current_block
							torch.save(UpperCamelCase__     ,							UpperCamelCase__     )





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__ = WEIGHTS_NAME     ):







							'''simple docstring'''






							snake_case_           =					convert_file_size_to_int(UpperCamelCase__     )
							snake_case_           =					[]
							snake_case_           =					{}
							snake_case_           =					0
							snake_case_           =					0

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

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

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

														# use the renaming pattern from the small conversion scripts
														snake_case_       ,					snake_case_           =					rename_base_flax_keys(tuple(key.split('/'     )     )     ,							UpperCamelCase__     )
														snake_case_           =					'/'.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:
																					snake_case_           =					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
																					snake_case_           =					{}
																					snake_case_           =					0

														snake_case_           =					raw_weights.to(getattr(UpperCamelCase__     ,							UpperCamelCase__     )     )
														current_block_size += weight_size
														total_size += weight_size

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

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

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

							return metadata, index


if __name__ == "__main__":
					_UpperCAmelCase	:     Tuple		       = 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.""",
					)
					_UpperCAmelCase	:     List[Any]		       = 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

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

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

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







from __future__ import annotations

from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any







@dataclass
class 				lowercase       :
				__SCREAMING_SNAKE_CASE      :				int
				__SCREAMING_SNAKE_CASE      :				Node | None     =    None
				__SCREAMING_SNAKE_CASE      :				Node | None     =    None





def       __lowerCamelCase			(				):







							'''simple docstring'''






							snake_case_           =					Node(1   )
							snake_case_           =					Node(2   )
							snake_case_           =					Node(3   )
							snake_case_           =					Node(4   )
							snake_case_           =					Node(5   )
							return tree





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							return [root.data, *preorder(root.left   ), *preorder(root.right   )] if root else []





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							return postorder(root.left   ) + postorder(root.right   ) + [root.data] if root else []





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							return [*inorder(root.left   ), root.data, *inorder(root.right   )] if root else []





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							return (max(height(root.left   )     ,							height(root.right   )   ) + 1) if root else 0





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					[]

							if root is None:
														return output

							snake_case_           =					deque([root]   )

							while process_queue:
														snake_case_           =					process_queue.popleft()
														output.append(node.data   )

														if node.left:
																					process_queue.append(node.left   )
														if node.right:
																					process_queue.append(node.right   )
							return output





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					[]

							def populate_output(UpperCamelCase__     ,							UpperCamelCase__   ) -> None:
														if not root:
																					return
														if level == 1:
																					output.append(root.data   )
														elif level > 1:
																					populate_output(root.left     ,							level - 1   )
																					populate_output(root.right     ,							level - 1   )

							populate_output(UpperCamelCase__     ,							UpperCamelCase__   )
							return output





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					[]

							def populate_output(UpperCamelCase__     ,							UpperCamelCase__   ) -> None:
														if root is None:
																					return
														if level == 1:
																					output.append(root.data   )
														elif level > 1:
																					populate_output(root.right     ,							level - 1   )
																					populate_output(root.left     ,							level - 1   )

							populate_output(UpperCamelCase__     ,							UpperCamelCase__   )
							return output





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							if root is None:
														return []

							snake_case_           =					[]

							snake_case_           =					0
							snake_case_           =					height(UpperCamelCase__   )

							for h in range(1     ,							height_tree + 1   ):
														if not flag:
																					output.append(get_nodes_from_left_to_right(UpperCamelCase__     ,							UpperCamelCase__   )   )
																					snake_case_           =					1
														else:
																					output.append(get_nodes_from_right_to_left(UpperCamelCase__     ,							UpperCamelCase__   )   )
																					snake_case_           =					0

							return output





def       __lowerCamelCase			(				):  # Main function for testing.







							'''simple docstring'''






							snake_case_           =					make_tree()
							print(F'''In-order Traversal: {inorder(UpperCamelCase__   )}'''   )
							print(F'''Pre-order Traversal: {preorder(UpperCamelCase__   )}'''   )
							print(F'''Post-order Traversal: {postorder(UpperCamelCase__   )}'''     ,							'\n'   )

							print(F'''Height of Tree: {height(UpperCamelCase__   )}'''     ,							'\n'   )

							print('Complete Level Order Traversal: '   )
							print(level_order(UpperCamelCase__   )     ,							'\n'   )

							print('Level-wise order Traversal: '   )

							for level in range(1     ,							height(UpperCamelCase__   ) + 1   ):
														print(F'''Level {level}:'''     ,							get_nodes_from_left_to_right(UpperCamelCase__     ,							level=UpperCamelCase__   )   )

							print('\nZigZag order Traversal: '   )
							print(zigzag(UpperCamelCase__   )   )


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					main()




