




'''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 	snake_case_					(UpperCamelCase :						Dict				,		UpperCamelCase :						Tuple   ):





							'''simple docstring'''


							if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
														# expert layer
														_a			  =       flax_key_tuple[:-1] + ('''weight''',)
														_a			  =       torch.permute(UpperCamelCase				,		(0, 2, 1)   )
							elif flax_key_tuple[-1] == "kernel" and ".".join(UpperCamelCase   ):
														# 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 	snake_case_					(UpperCamelCase :						int				,		UpperCamelCase :						int				,		UpperCamelCase :						Dict   ):





							'''simple docstring'''


							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 	snake_case_					(UpperCamelCase :						List[Any]				,		UpperCamelCase :						List[Any]   ):





							'''simple docstring'''


							_a			  =       rename_keys(UpperCamelCase   )
							_a			  =       {}
							for k, v in current_block.items():
														_a			  =       v
							_a			  =       new_current_block
							torch.save(UpperCamelCase				,		UpperCamelCase   )
def 	snake_case_					(UpperCamelCase :						Union[str, Any]				,		UpperCamelCase :						Optional[int]				,		UpperCamelCase :						Tuple				,		UpperCamelCase :						List[str]				,		UpperCamelCase :						str = WEIGHTS_NAME   ):





							'''simple docstring'''


							_a			  =       convert_file_size_to_int(UpperCamelCase   )
							_a			  =       []
							_a			  =       {}
							_a			  =       0
							_a			  =       0

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

							_a			  =       {}
							for layer in checkpoint_info.keys():
														_a  ,							_a  ,							_a			  =       get_key_and_tensorstore_dict(
														    UpperCamelCase				,		UpperCamelCase				,		UpperCamelCase   )
														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(UpperCamelCase   )
														_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('''/'''   )   )				,		UpperCamelCase   )
														_a			  =       '''/'''.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:
																					_a			  =       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
																					_a			  =       {}
																					_a			  =       0

														_a			  =       raw_weights.to(getattr(UpperCamelCase				,		UpperCamelCase   )   )
														current_block_size += weight_size
														total_size += weight_size

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

							return metadata, index


if __name__ == "__main__":
				_snake_case   :   Any     							=	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.',
				)
				_snake_case   :   Union[str, 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 	snake_case_					():





							'''simple docstring'''


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







'''simple docstring'''


from ..utils import DummyObject, requires_backends

class     A					(	metaclass=_a					):
							lowercase_           =       ['torch', 'torchsde']







							def __init__(						self :						Optional[Any]							,    *lowerCAmelCase_ :						Any							,    **lowerCAmelCase_ :						Tuple      )    ->							Dict:


														"""simple docstring"""


														requires_backends(self							,    ['''torch''', '''torchsde''']      )







							@classmethod
							def      __lowerCAmelCase    (						cls :						Dict							,    *lowerCAmelCase_ :						Optional[int]							,    **lowerCAmelCase_ :						Optional[Any]      )    ->							int:


														"""simple docstring"""


														requires_backends(cls							,    ['''torch''', '''torchsde''']      )







							@classmethod
							def      __lowerCAmelCase    (						cls :						List[str]							,    *lowerCAmelCase_ :						str							,    **lowerCAmelCase_ :						Optional[Any]      )    ->							Any:


														"""simple docstring"""


														requires_backends(cls							,    ['''torch''', '''torchsde''']      )

