




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





def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_ = WEIGHTS_NAME     )		->		Tuple:
			A_									= convert_file_size_to_int(SCREAMING_SNAKE_CASE_     )
			A_									= []
			A_									= {}
			A_									= 0
			A_									= 0

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

			A_									= {}
			for layer in checkpoint_info.keys():
						A_		, A_		, A_									= get_key_and_tensorstore_dict(
						    SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )
						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(SCREAMING_SNAKE_CASE_     )
						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('/'     )     )       ,SCREAMING_SNAKE_CASE_     )
						A_									= '/'.join(SCREAMING_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_									= os.path.join(
									    SCREAMING_SNAKE_CASE_       ,weights_name.replace('.bin'       ,F'-{len(SCREAMING_SNAKE_CASE_     )+1:05d}-of-???.bin'     )     )
									rename_and_save_block(SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )
									sharded_state_dicts.append(current_block.keys()     )
									del current_block
									A_									= {}
									A_									= 0

						A_									= raw_weights.to(getattr(SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )     )
						current_block_size += weight_size
						total_size += weight_size

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

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

			# Otherwise, let's build the index
			A_									= {}
			A_									= {}
			for idx, shard in enumerate(SCREAMING_SNAKE_CASE_     ):
						A_									= weights_name.replace(
						    '.bin'       ,F'-{idx+1:05d}-of-{len(SCREAMING_SNAKE_CASE_     ):05d}.bin'     )  # len(sharded_state_dicts):05d}
						A_									= os.path.join(SCREAMING_SNAKE_CASE_       ,weights_name.replace('.bin'       ,F'-{idx+1:05d}-of-???.bin'     )     )
						os.rename(SCREAMING_SNAKE_CASE_       ,os.path.join(SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )     )
						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(SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )       ,'w'       ,encoding='utf-8'     ) as f:
						A_									= json.dumps(SCREAMING_SNAKE_CASE_       ,indent=2       ,sort_keys=SCREAMING_SNAKE_CASE_     ) + '\n'
						f.write(SCREAMING_SNAKE_CASE_     )

			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 			lowerCamelCase(							)		->		int:
			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(SCREAMING_SNAKE_CASE_       ,return_tensors='pt'     ).input_ids
			A_									= model.generate(SCREAMING_SNAKE_CASE_       ,decoder_start_token_id=0     )
			print(tokenizer.decode(out[0]     )     )










'''simple docstring'''
import numpy as np





def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_     )		->		np.array:
			return 1 / (1 + np.exp(-vector     ))





def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_     )		->		np.array:
			return vector * sigmoid(1.702 * vector     )


if __name__ == "__main__":
							import doctest

							doctest.testmod()




