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

				return flax_key_tuple, flax_tensor





def    UpperCAmelCase							(  UpperCAmelCase__							:							Optional[int]       ,							UpperCAmelCase__							:							Optional[Any]       ,							UpperCAmelCase__							:							Optional[int]):
				if "metadata" in layer:
								lowerCamelCase					:			List[str]									= layer.split('metadata')
								lowerCamelCase					:			Dict									= ''.join(split_layer[0])[:-1]
								lowerCamelCase					:			Tuple									= [tuple(('metadata' + split_layer[1]).split('/'))]
				elif "kvstore" in layer:
								lowerCamelCase					:			List[str]									= layer.split('kvstore')
								lowerCamelCase					:			Optional[Any]									= ''.join(split_layer[0])[:-1]
								lowerCamelCase					:			Optional[Any]									= [tuple(('kvstore' + split_layer[1]).split('/'))]

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

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

				return curr_real_layer_name, split_layer, content





def    UpperCAmelCase							(  UpperCAmelCase__							:							List[str]       ,							UpperCAmelCase__							:							Dict):
				lowerCamelCase					:			Tuple									= rename_keys(UpperCAmelCase__)
				lowerCamelCase					:			Union[str, Any]									= {}
				for k, v in current_block.items():
								lowerCamelCase					:			Union[str, Any]									= v
				lowerCamelCase					:			Optional[int]									= new_current_block
				torch.save(UpperCAmelCase__       ,							UpperCAmelCase__)





def    UpperCAmelCase							(  UpperCAmelCase__							:							Optional[int]       ,							UpperCAmelCase__							:							List[Any]       ,							UpperCAmelCase__							:							Optional[Any]       ,							UpperCAmelCase__							:							Tuple       ,							UpperCAmelCase__							:							str = WEIGHTS_NAME):
				lowerCamelCase					:			int									= convert_file_size_to_int(UpperCAmelCase__)
				lowerCamelCase					:			List[Any]									= []
				lowerCamelCase					:			Optional[Any]									= {}
				lowerCamelCase					:			Any									= 0
				lowerCamelCase					:			List[Any]									= 0

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

				lowerCamelCase					:			str									= {}
				for layer in checkpoint_info.keys():
								lowerCamelCase					:			Union[str, Any]									= get_key_and_tensorstore_dict(
								    UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__)
								if curr_real_layer_name in all_layers:
												lowerCamelCase					:			str									= content
								else:
												lowerCamelCase					:			List[str]									= {split_layer[-1]: content}

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

								# use the renaming pattern from the small conversion scripts
								lowerCamelCase					:			Dict									= rename_base_flax_keys(tuple(key.split('/'))       ,							UpperCAmelCase__)
								lowerCamelCase					:			int									= '/'.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:
												lowerCamelCase					:			List[Any]									= 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
												lowerCamelCase					:			List[Any]									= {}
												lowerCamelCase					:			Any									= 0

								lowerCamelCase					:			Union[str, Any]									= raw_weights.to(getattr(UpperCAmelCase__       ,							UpperCAmelCase__))
								current_block_size += weight_size
								total_size += weight_size

				# Add the last block
				lowerCamelCase					:			Any									= 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
				lowerCamelCase					:			List[Any]									= {}
				lowerCamelCase					:			Tuple									= {}
				for idx, shard in enumerate(UpperCAmelCase__):
								lowerCamelCase					:			str									= weights_name.replace(
								    '.bin'       ,							F'''-{idx+1:05d}-of-{len(UpperCAmelCase__):05d}.bin''')  # len(sharded_state_dicts):05d}
								lowerCamelCase					:			List[Any]									= os.path.join(UpperCAmelCase__       ,							weights_name.replace('.bin'       ,							F'''-{idx+1:05d}-of-???.bin'''))
								os.rename(UpperCAmelCase__       ,							os.path.join(UpperCAmelCase__       ,							UpperCAmelCase__))
								lowerCamelCase					:			Union[str, Any]									= shard
								for key in shard:
												lowerCamelCase					:			Dict									= shard_file

    # Add the metadata
				lowerCamelCase					:			List[str]									= {'total_size': total_size}
				lowerCamelCase					:			int									= {'metadata': metadata, 'weight_map': weight_map}

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

				return metadata, index


if __name__ == "__main__":
					A			      =      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.',
					)
					A			      =      parser.parse_args()
					shard_on_the_fly(
					    args.switch_tax_checkpoint_path,
					    args.pytorch_dump_folder_path,
					    args.max_shard_size,
					    args.dtype,
					)





def    UpperCAmelCase							(  ):
				from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

				lowerCamelCase					:			Union[str, 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					:			Tuple									= TaTokenizer.from_pretrained('t5-small')
				lowerCamelCase					:			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>.'

				lowerCamelCase					:			Union[str, Any]									= tokenizer(UpperCAmelCase__       ,							return_tensors='pt').input_ids
				lowerCamelCase					:			Optional[Any]									= model.generate(UpperCAmelCase__       ,							decoder_start_token_id=0)
				print(tokenizer.decode(out[0]))


'''simple docstring'''






def    UpperCAmelCase							(  UpperCAmelCase__							:							list[int]):
				if not nums:  # Makes sure that the list is not empty
								raise ValueError('List is empty')

				lowerCamelCase					:			int									= sum(UpperCAmelCase__) / len(UpperCAmelCase__)  # Calculate the average
				return sum(abs(x - average) for x in nums) / len(UpperCAmelCase__)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
