



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

						return flax_key_tuple, flax_tensor

def    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_    ) ->       int:
						if "metadata" in layer:
												UpperCAmelCase         =    layer.split("metadata"    )
												UpperCAmelCase         =    "".join(split_layer[0]    )[:-1]
												UpperCAmelCase         =    [tuple(("metadata" + split_layer[1]).split("/"    )    )]
						elif "kvstore" in layer:
												UpperCAmelCase         =    layer.split("kvstore"    )
												UpperCAmelCase         =    "".join(split_layer[0]    )[:-1]
												UpperCAmelCase         =    [tuple(("kvstore" + split_layer[1]).split("/"    )    )]

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

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

						return curr_real_layer_name, split_layer, content

def    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_    ) ->       List[str]:
						UpperCAmelCase         =    rename_keys(lowerCamelCase_    )
						UpperCAmelCase         =    {}
						for k, v in current_block.items():
												UpperCAmelCase         =    v
						UpperCAmelCase         =    new_current_block
						torch.save(lowerCamelCase_    ,   lowerCamelCase_    )

def    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_ = WEIGHTS_NAME    ) ->       List[str]:
						UpperCAmelCase         =    convert_file_size_to_int(lowerCamelCase_    )
						UpperCAmelCase         =    []
						UpperCAmelCase         =    {}
						UpperCAmelCase         =    0
						UpperCAmelCase         =    0

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

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

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

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

												UpperCAmelCase         =    raw_weights.to(getattr(lowerCamelCase_    ,   lowerCamelCase_    )    )
												current_block_size += weight_size
												total_size += weight_size

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

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

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

						return metadata, index


if __name__ == "__main__":
		__lowerCamelCase     :							Optional[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.",
		)
		__lowerCamelCase     :							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    lowerCamelCase_() ->       Dict:
						from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

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








import unittest

import torch
from torch import nn

from diffusers.models.activations import get_activation





class  __magic_name__   (							unittest.TestCase						):






		def SCREAMING_SNAKE_CASE_			(						self      :     int      )				->					str:
								'''simple docstring'''







								UpperCAmelCase         =     get_activation("swish"      )

								self.assertIsInstance(UpperCamelCase__				,		nn.SiLU      )

								self.assertEqual(act(torch.tensor(-1_00				,		dtype=torch.floataa      )      ).item()				,		0      )
								self.assertNotEqual(act(torch.tensor(-1				,		dtype=torch.floataa      )      ).item()				,		0      )
								self.assertEqual(act(torch.tensor(0				,		dtype=torch.floataa      )      ).item()				,		0      )
								self.assertEqual(act(torch.tensor(20				,		dtype=torch.floataa      )      ).item()				,		20      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple      )				->					List[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     get_activation("silu"      )

								self.assertIsInstance(UpperCamelCase__				,		nn.SiLU      )

								self.assertEqual(act(torch.tensor(-1_00				,		dtype=torch.floataa      )      ).item()				,		0      )
								self.assertNotEqual(act(torch.tensor(-1				,		dtype=torch.floataa      )      ).item()				,		0      )
								self.assertEqual(act(torch.tensor(0				,		dtype=torch.floataa      )      ).item()				,		0      )
								self.assertEqual(act(torch.tensor(20				,		dtype=torch.floataa      )      ).item()				,		20      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]      )				->					int:
								'''simple docstring'''







								UpperCAmelCase         =     get_activation("mish"      )

								self.assertIsInstance(UpperCamelCase__				,		nn.Mish      )

								self.assertEqual(act(torch.tensor(-2_00				,		dtype=torch.floataa      )      ).item()				,		0      )
								self.assertNotEqual(act(torch.tensor(-1				,		dtype=torch.floataa      )      ).item()				,		0      )
								self.assertEqual(act(torch.tensor(0				,		dtype=torch.floataa      )      ).item()				,		0      )
								self.assertEqual(act(torch.tensor(20				,		dtype=torch.floataa      )      ).item()				,		20      )







		def SCREAMING_SNAKE_CASE_			(						self      :     str      )				->					List[str]:
								'''simple docstring'''







								UpperCAmelCase         =     get_activation("gelu"      )

								self.assertIsInstance(UpperCamelCase__				,		nn.GELU      )

								self.assertEqual(act(torch.tensor(-1_00				,		dtype=torch.floataa      )      ).item()				,		0      )
								self.assertNotEqual(act(torch.tensor(-1				,		dtype=torch.floataa      )      ).item()				,		0      )
								self.assertEqual(act(torch.tensor(0				,		dtype=torch.floataa      )      ).item()				,		0      )
								self.assertEqual(act(torch.tensor(20				,		dtype=torch.floataa      )      ).item()				,		20      )



