





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      (	_lowerCamelCase				:    Optional[int]		,						_lowerCamelCase				:    int)      ->					List[Any]:






							'''simple docstring'''





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

							return flax_key_tuple, flax_tensor




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Any		,						_lowerCamelCase				:    Union[str, Any]		,						_lowerCamelCase				:    Tuple)      ->					Any:






							'''simple docstring'''





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

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

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

							return curr_real_layer_name, split_layer, content




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Optional[Any]		,						_lowerCamelCase				:    List[str])      ->					Dict:






							'''simple docstring'''





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




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    str		,						_lowerCamelCase				:    Optional[Any]		,						_lowerCamelCase				:    List[str]		,						_lowerCamelCase				:    int		,						_lowerCamelCase				:    str = WEIGHTS_NAME)      ->					List[str]:






							'''simple docstring'''





							__UpperCamelCase			:     str         =      convert_file_size_to_int(lowerCAmelCase__)
							__UpperCamelCase			:     Tuple         =      []
							__UpperCamelCase			:     List[Any]         =      {}
							__UpperCamelCase			:     List[Any]         =      0
							__UpperCamelCase			:     int         =      0

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

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

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

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

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

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

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

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

							return metadata, index


if __name__ == "__main__":
		lowercase						:      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 			_SCREAMING_SNAKE_CASE      (	)      ->					List[str]:






							'''simple docstring'''





							from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

							__UpperCamelCase			:     int         =      tokenizer(lowerCAmelCase__		,						return_tensors="pt").input_ids
							__UpperCamelCase			:     List[Any]         =      model.generate(lowerCAmelCase__		,						decoder_start_token_id=0)
							print(tokenizer.decode(out[0]))

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase						:      Tuple									= {
    'configuration_swinv2': ['SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Swinv2Config'],
}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase						:      Union[str, Any]									= [
		    'SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'Swinv2ForImageClassification',
		    'Swinv2ForMaskedImageModeling',
		    'Swinv2Model',
		    'Swinv2PreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_swinva import (
				    SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SwinvaForImageClassification,
				    SwinvaForMaskedImageModeling,
				    SwinvaModel,
				    SwinvaPreTrainedModel,
				)


else:
		import sys

		lowercase						:      Optional[Any]									= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)