





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 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Dict       , UpperCamelCase__:				int     ):



		if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
				# expert layer
				SCREAMING_SNAKE_CASE__								=   flax_key_tuple[:-1] + ('weight',)
				SCREAMING_SNAKE_CASE__								=   torch.permute(__a       , (0, 2, 1)     )
		elif flax_key_tuple[-1] == "kernel" and ".".join(__a     ):
				# linear layer
				SCREAMING_SNAKE_CASE__								=   flax_key_tuple[:-1] + ('weight',)
				SCREAMING_SNAKE_CASE__								=   flax_tensor.T
		elif flax_key_tuple[-1] in ["scale", "embedding"]:
				SCREAMING_SNAKE_CASE__								=   flax_key_tuple[:-1] + ('weight',)

		return flax_key_tuple, flax_tensor






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Optional[Any]       , UpperCamelCase__:				int       , UpperCamelCase__:				Any     ):



		if "metadata" in layer:
				SCREAMING_SNAKE_CASE__								=   layer.split("""metadata"""     )
				SCREAMING_SNAKE_CASE__								=   ''.join(split_layer[0]     )[:-1]
				SCREAMING_SNAKE_CASE__								=   [tuple(("""metadata""" + split_layer[1]).split("""/"""     )     )]
		elif "kvstore" in layer:
				SCREAMING_SNAKE_CASE__								=   layer.split("""kvstore"""     )
				SCREAMING_SNAKE_CASE__								=   ''.join(split_layer[0]     )[:-1]
				SCREAMING_SNAKE_CASE__								=   [tuple(("""kvstore""" + split_layer[1]).split("""/"""     )     )]

		else:
				SCREAMING_SNAKE_CASE__								=   layer.split("""/"""     )
				SCREAMING_SNAKE_CASE__								=   '/'.join(split_layer[:-1]     )
				SCREAMING_SNAKE_CASE__								=   (split_layer[-1],)

		if "kvstore/path" in layer:
				SCREAMING_SNAKE_CASE__								=   f'''{switch_checkpoint_path}/{checkpoint_info[layer]}'''
		elif "kvstore/driver" in layer:
				SCREAMING_SNAKE_CASE__								=   'file'
		else:
				SCREAMING_SNAKE_CASE__								=   checkpoint_info[layer]

		return curr_real_layer_name, split_layer, content






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Dict       , UpperCamelCase__:				int     ):
		SCREAMING_SNAKE_CASE__								=   rename_keys(__a     )
		SCREAMING_SNAKE_CASE__								=   {}
		for k, v in current_block.items():
				SCREAMING_SNAKE_CASE__								=   v
		SCREAMING_SNAKE_CASE__								=   new_current_block
		torch.save(__a       , __a     )






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				List[Any]       , UpperCamelCase__:				str       , UpperCamelCase__:				Tuple       , UpperCamelCase__:				Optional[int]       , UpperCamelCase__:				str = WEIGHTS_NAME     ):
		SCREAMING_SNAKE_CASE__								=   convert_file_size_to_int(__a     )
		SCREAMING_SNAKE_CASE__								=   []
		SCREAMING_SNAKE_CASE__								=   {}
		SCREAMING_SNAKE_CASE__								=   0
		SCREAMING_SNAKE_CASE__								=   0

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

		SCREAMING_SNAKE_CASE__								=   {}
		for layer in checkpoint_info.keys():
				SCREAMING_SNAKE_CASE__								=   get_key_and_tensorstore_dict(
				    __a       , __a       , __a     )
				if curr_real_layer_name in all_layers:
						SCREAMING_SNAKE_CASE__								=   content
				else:
						SCREAMING_SNAKE_CASE__								=   {split_layer[-1]: content}

		for key in all_layers.keys():
				# open tensorstore file
				SCREAMING_SNAKE_CASE__								=   ts.open(unflatten_dict(all_layers[key]     )     ).result().read().result()
				SCREAMING_SNAKE_CASE__								=   torch.tensor(__a     )
				SCREAMING_SNAKE_CASE__								=   raw_weights.numel() * dtype_byte_size(raw_weights.dtype     )

				# use the renaming pattern from the small conversion scripts
				SCREAMING_SNAKE_CASE__								=   rename_base_flax_keys(tuple(key.split("""/"""     )     )       , __a     )
				SCREAMING_SNAKE_CASE__								=   '/'.join(__a     )

				# If this weight is going to tip up over the maximal size, we split.
				if current_block_size + weight_size > max_shard_size:
						SCREAMING_SNAKE_CASE__								=   os.path.join(
						    __a       , weights_name.replace(""".bin"""       , f'''-{len(__a     )+1:05d}-of-???.bin'''     )     )
						rename_and_save_block(__a       , __a     )
						sharded_state_dicts.append(current_block.keys()     )
						del current_block
						SCREAMING_SNAKE_CASE__								=   {}
						SCREAMING_SNAKE_CASE__								=   0

				SCREAMING_SNAKE_CASE__								=   raw_weights.to(getattr(__a       , __a     )     )
				current_block_size += weight_size
				total_size += weight_size

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

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

		# Otherwise, let's build the index
		SCREAMING_SNAKE_CASE__								=   {}
		SCREAMING_SNAKE_CASE__								=   {}
		for idx, shard in enumerate(__a     ):
				SCREAMING_SNAKE_CASE__								=   weights_name.replace(
				    """.bin"""       , f'''-{idx+1:05d}-of-{len(__a     ):05d}.bin'''     )  # len(sharded_state_dicts):05d}
				SCREAMING_SNAKE_CASE__								=   os.path.join(__a       , weights_name.replace(""".bin"""       , f'''-{idx+1:05d}-of-???.bin'''     )     )
				os.rename(__a       , os.path.join(__a       , __a     )     )
				SCREAMING_SNAKE_CASE__								=   shard
				for key in shard:
						SCREAMING_SNAKE_CASE__								=   shard_file

    # Add the metadata
		SCREAMING_SNAKE_CASE__								=   {'total_size': total_size}
		SCREAMING_SNAKE_CASE__								=   {'metadata': metadata, 'weight_map': weight_map}

		with open(os.path.join(__a       , __a     )       , """w"""       , encoding="""utf-8"""     ) as f:
				SCREAMING_SNAKE_CASE__								=   json.dumps(__a       , indent=2       , sort_keys=__a     ) + '\n'
				f.write(__a     )

		return metadata, index


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






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

		SCREAMING_SNAKE_CASE__								=   SwitchTransformersConfig.from_pretrained("""google/switch-base-8"""     )
		config.save_pretrained("""/home/arthur_huggingface_co/transformers/switch_converted"""     )
		SCREAMING_SNAKE_CASE__								=   SwitchTransformersForConditionalGeneration.from_pretrained(
		    """/home/arthur_huggingface_co/transformers/switch_converted"""       , device_map="""auto"""     )

		SCREAMING_SNAKE_CASE__								=   TaTokenizer.from_pretrained("""t5-small"""     )
		SCREAMING_SNAKE_CASE__								=   'A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.'

		SCREAMING_SNAKE_CASE__								=   tokenizer(__a       , return_tensors="""pt"""     ).input_ids
		SCREAMING_SNAKE_CASE__								=   model.generate(__a       , decoder_start_token_id=0     )
		print(tokenizer.decode(out[0]     )     )




"""simple docstring"""







def     UpperCAmelCase_					(				__a   :			float      ,							__a   :			float						):




			'''simple docstring'''



			if density <= 0:
						raise ValueError('Impossible fluid density'						)
			if bulk_modulus <= 0:
						raise ValueError('Impossible bulk modulus'						)

			return (bulk_modulus / density) ** 0.5


if __name__ == "__main__":
	import doctest

	doctest.testmod()





