


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 	__lowerCamelCase						(							lowerCamelCase__		:						Tuple  ,							lowerCamelCase__		:						Dict      ):





			'''simple docstring'''




			if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
						# expert layer
						lowerCamelCase    		=  flax_key_tuple[:-1] + ("""weight""",)
						lowerCamelCase    		=  torch.permute(lowerCamelCase__  ,							(0, 2, 1)      )
			elif flax_key_tuple[-1] == "kernel" and ".".join(lowerCamelCase__      ):
						# linear layer
						lowerCamelCase    		=  flax_key_tuple[:-1] + ("""weight""",)
						lowerCamelCase    		=  flax_tensor.T
			elif flax_key_tuple[-1] in ["scale", "embedding"]:
						lowerCamelCase    		=  flax_key_tuple[:-1] + ("""weight""",)

			return flax_key_tuple, flax_tensor






def 	__lowerCamelCase						(							lowerCamelCase__		:						List[str]  ,							lowerCamelCase__		:						List[Any]  ,							lowerCamelCase__		:						List[Any]      ):





			'''simple docstring'''




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

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

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

			return curr_real_layer_name, split_layer, content






def 	__lowerCamelCase						(							lowerCamelCase__		:						Any  ,							lowerCamelCase__		:						Optional[Any]      ):





			'''simple docstring'''




			lowerCamelCase    		=  rename_keys(lowerCamelCase__      )
			lowerCamelCase    		=  {}
			for k, v in current_block.items():
						lowerCamelCase    		=  v
			lowerCamelCase    		=  new_current_block
			torch.save(lowerCamelCase__  ,							lowerCamelCase__      )






def 	__lowerCamelCase						(							lowerCamelCase__		:						int  ,							lowerCamelCase__		:						Optional[Any]  ,							lowerCamelCase__		:						Optional[Any]  ,							lowerCamelCase__		:						Dict  ,							lowerCamelCase__		:						str = WEIGHTS_NAME      ):





			'''simple docstring'''




			lowerCamelCase    		=  convert_file_size_to_int(lowerCamelCase__      )
			lowerCamelCase    		=  []
			lowerCamelCase    		=  {}
			lowerCamelCase    		=  0
			lowerCamelCase    		=  0

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

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

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

						# use the renaming pattern from the small conversion scripts
						lowerCamelCase		,		lowerCamelCase    		=  rename_base_flax_keys(tuple(key.split("""/"""      )      )  ,							lowerCamelCase__      )
						lowerCamelCase    		=  """/""".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:
									lowerCamelCase    		=  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
									lowerCamelCase    		=  {}
									lowerCamelCase    		=  0

						lowerCamelCase    		=  raw_weights.to(getattr(lowerCamelCase__  ,							lowerCamelCase__      )      )
						current_block_size += weight_size
						total_size += weight_size

			# Add the last block
			lowerCamelCase    		=  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
			lowerCamelCase    		=  {}
			lowerCamelCase    		=  {}
			for idx, shard in enumerate(lowerCamelCase__      ):
						lowerCamelCase    		=  weights_name.replace(
						    """.bin"""  ,							f'-{idx+1:05d}-of-{len(lowerCamelCase__      ):05d}.bin'      )  # len(sharded_state_dicts):05d}
						lowerCamelCase    		=  os.path.join(lowerCamelCase__  ,							weights_name.replace(""".bin"""  ,							f'-{idx+1:05d}-of-???.bin'      )      )
						os.rename(lowerCamelCase__  ,							os.path.join(lowerCamelCase__  ,							lowerCamelCase__      )      )
						lowerCamelCase    		=  shard
						for key in shard:
									lowerCamelCase    		=  shard_file

    # Add the metadata
			lowerCamelCase    		=  {"""total_size""": total_size}
			lowerCamelCase    		=  {"""metadata""": metadata, """weight_map""": weight_map}

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

			return metadata, index


if __name__ == "__main__":
		UpperCAmelCase   :    Tuple     =		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   :    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 	__lowerCamelCase						(							):





			'''simple docstring'''




			from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

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





# Lint as: python3
import itertools
import os
import re


UpperCAmelCase   :    Any     =		re.compile(r"([A-Z]+)([A-Z][a-z])")
UpperCAmelCase   :    Optional[Any]     =		re.compile(r"([a-z\d])([A-Z])")

UpperCAmelCase   :    Optional[Any]     =		re.compile(r"(?<!_)_(?!_)")
UpperCAmelCase   :    Any     =		re.compile(r"(_{2,})")

UpperCAmelCase   :    List[Any]     =		r"^\w+(\.\w+)*$"

UpperCAmelCase   :    Any     =		r"<>:/\|?*"






def 	__lowerCamelCase						(							lowerCamelCase__		:						Tuple      ):





			'''simple docstring'''




			lowerCamelCase    		=  _uppercase_uppercase_re.sub(R"""\1_\2"""  ,							lowerCamelCase__      )
			lowerCamelCase    		=  _lowercase_uppercase_re.sub(R"""\1_\2"""  ,							lowerCamelCase__      )
			return name.lower()






def 	__lowerCamelCase						(							lowerCamelCase__		:						List[str]      ):





			'''simple docstring'''




			lowerCamelCase    		=  _single_underscore_re.split(lowerCamelCase__      )
			lowerCamelCase    		=  [_multiple_underscores_re.split(lowerCamelCase__      ) for n in name]
			return "".join(n.capitalize() for n in itertools.chain.from_iterable(lowerCamelCase__      ) if n != """"""      )






def 	__lowerCamelCase						(							lowerCamelCase__		:						Any      ):





			'''simple docstring'''




			if os.path.basename(lowerCamelCase__      ) != name:
						raise ValueError(f'Should be a dataset name, not a path: {name}'      )
			return camelcase_to_snakecase(lowerCamelCase__      )






def 	__lowerCamelCase						(							lowerCamelCase__		:						List[str]  ,							lowerCamelCase__		:						int      ):





			'''simple docstring'''




			if os.path.basename(lowerCamelCase__      ) != name:
						raise ValueError(f'Should be a dataset name, not a path: {name}'      )
			if not re.match(_split_re  ,							lowerCamelCase__      ):
						raise ValueError(f'Split name should match \'{_split_re}\'\' but got \'{split}\'.'      )
			return f'{filename_prefix_for_name(lowerCamelCase__      )}-{split}'






def 	__lowerCamelCase						(							lowerCamelCase__		:						List[Any]  ,							lowerCamelCase__		:						Optional[Any]  ,							lowerCamelCase__		:						List[Any]  ,							lowerCamelCase__		:						int=None      ):





			'''simple docstring'''




			lowerCamelCase    		=  filename_prefix_for_split(lowerCamelCase__  ,							lowerCamelCase__      )
			if filetype_suffix:
						prefix += f'.{filetype_suffix}'
			lowerCamelCase    		=  os.path.join(lowerCamelCase__  ,							lowerCamelCase__      )
			return f'{filepath}*'






def 	__lowerCamelCase						(							lowerCamelCase__		:						Dict  ,							lowerCamelCase__		:						List[Any]  ,							lowerCamelCase__		:						List[Any]  ,							lowerCamelCase__		:						int=None  ,							lowerCamelCase__		:						str=None      ):





			'''simple docstring'''




			lowerCamelCase    		=  filename_prefix_for_split(lowerCamelCase__  ,							lowerCamelCase__      )
			lowerCamelCase    		=  os.path.join(lowerCamelCase__  ,							lowerCamelCase__      )

			if shard_lengths:
						lowerCamelCase    		=  len(lowerCamelCase__      )
						lowerCamelCase    		=  [f'{prefix}-{shard_id:05d}-of-{num_shards:05d}' for shard_id in range(lowerCamelCase__      )]
						if filetype_suffix:
									lowerCamelCase    		=  [filename + f'.{filetype_suffix}' for filename in filenames]
						return filenames
			else:
						lowerCamelCase    		=  prefix
						if filetype_suffix:
									filename += f'.{filetype_suffix}'
						return [filename]

