


'''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__(				_SCREAMING_SNAKE_CASE					: List[str],_SCREAMING_SNAKE_CASE					: Optional[Any] ):






			"""simple docstring"""




			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 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: Dict,_SCREAMING_SNAKE_CASE					: int,_SCREAMING_SNAKE_CASE					: Tuple ):






			"""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 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: Tuple,_SCREAMING_SNAKE_CASE					: Dict ):






			"""simple docstring"""




			__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 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: Union[str, Any],_SCREAMING_SNAKE_CASE					: Any,_SCREAMING_SNAKE_CASE					: Optional[int],_SCREAMING_SNAKE_CASE					: List[str],_SCREAMING_SNAKE_CASE					: str = WEIGHTS_NAME ):






			"""simple docstring"""




			__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__":
					UpperCAmelCase__  			=				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.''',
					)
					UpperCAmelCase__  			=				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__(				):






			"""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(_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 re

from filelock import FileLock


try:
					import nltk

					UpperCAmelCase__  			=				True
except (ImportError, ModuleNotFoundError):
					UpperCAmelCase__  			=				False

if NLTK_AVAILABLE:
					with FileLock('''.lock''') as lock:
										nltk.download('''punkt''', quiet=True)






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: str ):






			"""simple docstring"""




			re.sub('<n>','',_SCREAMING_SNAKE_CASE )  # remove pegasus newline char
			assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
			return "\n".join(nltk.sent_tokenize(_SCREAMING_SNAKE_CASE ) )



