






"""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					(					lowerCAmelCase__       ,lowerCAmelCase__				):
				if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
								# expert layer
								A__							 =       flax_key_tuple[:-1] + ('weight',)
								A__							 =       torch.permute(lowerCAmelCase__       ,(0, 2, 1)				)
				elif flax_key_tuple[-1] == "kernel" and ".".join(lowerCAmelCase__				):
								# 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  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				):
				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  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__				):
				A__							 =       rename_keys(lowerCAmelCase__				)
				A__							 =       {}
				for k, v in current_block.items():
								A__							 =       v
				A__							 =       new_current_block
				torch.save(lowerCAmelCase__       ,lowerCAmelCase__				)



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__ = WEIGHTS_NAME				):
				A__							 =       convert_file_size_to_int(lowerCAmelCase__				)
				A__							 =       []
				A__							 =       {}
				A__							 =       0
				A__							 =       0

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

				A__							 =       {}
				for layer in checkpoint_info.keys():
								A__ ,			A__ ,			A__							 =       get_key_and_tensorstore_dict(
								    lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				)
								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(lowerCAmelCase__				)
								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('/'				)				)       ,lowerCAmelCase__				)
								A__							 =       '/'.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:
												A__							 =       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
												A__							 =       {}
												A__							 =       0

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

				# Add the last block
				A__							 =       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
				A__							 =       {}
				A__							 =       {}
				for idx, shard in enumerate(lowerCAmelCase__				):
								A__							 =       weights_name.replace(
								    '.bin'       ,f'''-{idx+1:05d}-of-{len(lowerCAmelCase__				):05d}.bin'''				)  # len(sharded_state_dicts):05d}
								A__							 =       os.path.join(lowerCAmelCase__       ,weights_name.replace('.bin'       ,f'''-{idx+1:05d}-of-???.bin'''				)				)
								os.rename(lowerCAmelCase__       ,os.path.join(lowerCAmelCase__       ,lowerCAmelCase__				)				)
								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(lowerCAmelCase__       ,lowerCAmelCase__				)       ,'w'       ,encoding='utf-8'				) as f:
								A__							 =       json.dumps(lowerCAmelCase__       ,indent=2       ,sort_keys=lowerCAmelCase__				) + '\n'
								f.write(lowerCAmelCase__				)

				return metadata, index


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE						:						str					   =       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						:						Optional[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					(					):
				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(lowerCAmelCase__       ,return_tensors='pt'				).input_ids
				A__							 =       model.generate(lowerCAmelCase__       ,decoder_start_token_id=0				)
				print(tokenizer.decode(out[0]				)				)










"""simple docstring"""






print((lambda quine: quine % quine)('''print((lambda quine: quine %% quine)(%r))'''))


