




'''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_(     snake_case							:	List[str]							,    snake_case							:	Optional[Any]					):

							'''simple docstring'''






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

							return flax_key_tuple, flax_tensor


def 					UpperCamelCase_(     snake_case							:	int							,    snake_case							:	Tuple							,    snake_case							:	int					):

							'''simple docstring'''






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

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

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

							return curr_real_layer_name, split_layer, content


def 					UpperCamelCase_(     snake_case							:	Tuple							,    snake_case							:	List[Any]					):

							'''simple docstring'''






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


def 					UpperCamelCase_(     snake_case							:	Optional[Any]							,    snake_case							:	Any							,    snake_case							:	Optional[int]							,    snake_case							:	List[str]							,    snake_case							:	str = WEIGHTS_NAME					):

							'''simple docstring'''






							snake_case_      =				convert_file_size_to_int(snake_case					)
							snake_case_      =				[]
							snake_case_      =				{}
							snake_case_      =				0
							snake_case_      =				0

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

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

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

														# use the renaming pattern from the small conversion scripts
														snake_case_ ,				snake_case_      =				rename_base_flax_keys(tuple(key.split("/"					)					)							,    snake_case					)
														snake_case_      =				"/".join(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:
																					snake_case_      =				os.path.join(
																					    snake_case							,    weights_name.replace(".bin"							,    f'-{len(snake_case					)+1:05d}-of-???.bin'					)					)
																					rename_and_save_block(snake_case							,    snake_case					)
																					sharded_state_dicts.append(current_block.keys()					)
																					del current_block
																					snake_case_      =				{}
																					snake_case_      =				0

														snake_case_      =				raw_weights.to(getattr(snake_case							,    snake_case					)					)
														current_block_size += weight_size
														total_size += weight_size

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

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

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

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

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

							return metadata, index


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE							:       Union[str, 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.",
		)
		_SCREAMING_SNAKE_CASE							:       Optional[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 					UpperCamelCase_(     ):

							'''simple docstring'''






							from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

							snake_case_      =				TaTokenizer.from_pretrained("t5-small"					)
							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>."

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










'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE							:       Any						=			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							:       int						=			{
    "alibaba-damo/mgp-str-base": "https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json",
}




class 			_snake_case		(     lowercase_							):
			lowerCAmelCase_					:				Union[str, Any]							=   "mgp-str"




			def __init__( self    ,							a__=[32, 128]    ,							a__=4    ,							a__=3    ,							a__=27    ,							a__=38    ,							a__=50_257    ,							a__=30_522    ,							a__=768    ,							a__=12    ,							a__=12    ,							a__=4.0    ,							a__=True    ,							a__=False    ,							a__=1e-5    ,							a__=0.0    ,							a__=0.0    ,							a__=0.0    ,							a__=False    ,							a__=0.0_2    ,							**a__    ,							)    ->    Union[str, Any]:

										'''simple docstring'''




										super().__init__(**a__							)

										snake_case_      =				image_size
										snake_case_      =				patch_size
										snake_case_      =				num_channels
										snake_case_      =				max_token_length
										snake_case_      =				num_character_labels
										snake_case_      =				num_bpe_labels
										snake_case_      =				num_wordpiece_labels
										snake_case_      =				hidden_size
										snake_case_      =				num_hidden_layers
										snake_case_      =				num_attention_heads
										snake_case_      =				mlp_ratio
										snake_case_      =				distilled
										snake_case_      =				layer_norm_eps
										snake_case_      =				drop_rate
										snake_case_      =				qkv_bias
										snake_case_      =				attn_drop_rate
										snake_case_      =				drop_path_rate
										snake_case_      =				output_aa_attentions
										snake_case_      =				initializer_range




