



"""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 __lowercase    (							_a	,    _a					):







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

				return flax_key_tuple, flax_tensor




def __lowercase    (							_a	,    _a	,    _a					):







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

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

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

				return curr_real_layer_name, split_layer, content




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




def __lowercase    (							_a	,    _a	,    _a	,    _a	,    _a = WEIGHTS_NAME					):
				snake_case_				:  str       =			convert_file_size_to_int(lowerCAmelCase__					)
				snake_case_				:  Tuple       =			[]
				snake_case_				:  List[Any]       =			{}
				snake_case_				:  List[Any]       =			0
				snake_case_				:  int       =			0

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

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

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

								# use the renaming pattern from the small conversion scripts
								snake_case_				:  Optional[int]       =			rename_base_flax_keys(tuple(key.split('''/'''					)					)	,    lowerCAmelCase__					)
								snake_case_				:  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:
												snake_case_				:  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
												snake_case_				:  Union[str, Any]       =			{}
												snake_case_				:  str       =			0

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

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

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

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

				return metadata, index


if __name__ == "__main__":
					lowercase__					:						Optional[int]												=			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__					:						Dict												=			parser.parse_args()
					shard_on_the_fly(
					    args.switch_tax_checkpoint_path,
					    args.pytorch_dump_folder_path,
					    args.max_shard_size,
					    args.dtype,
					)






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

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

				snake_case_				:  Any       =			TaTokenizer.from_pretrained('''t5-small'''					)
				snake_case_				:  Dict       =			'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_				:  int       =			tokenizer(lowerCAmelCase__	,    return_tensors='''pt'''					).input_ids
				snake_case_				:  List[Any]       =			model.generate(lowerCAmelCase__	,    decoder_start_token_id=0					)
				print(tokenizer.decode(out[0]					)					)










"""simple docstring"""

def __lowercase    (							_a					):
    snake_case_				:  Optional[Any]       =			int(_a					)
    if decimal in (0, 1):  # Exit cases for the recursion
        return str(_a					)
    snake_case_,			snake_case_				:  Optional[int]       =			divmod(_a	,    2					)
    return binary_recursive(_a					) + str(_a					)




def __lowercase    (							_a					):
    snake_case_				:  Dict       =			str(_a					).strip()
    if not number:
        raise ValueError('''No input value was provided'''					)
    snake_case_				:  List[str]       =			'''-''' if number.startswith('''-'''					) else ''''''
    snake_case_				:  Optional[Any]       =			number.lstrip('''-'''					)
    if not number.isnumeric():
        raise ValueError('''Input value is not an integer'''					)
    return f"{negative}0b{binary_recursive(int(_a					)					)}"


if __name__ == "__main__":
     from doctest import testmod

     testmod()





