

"""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    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     int:
		if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
				# expert layer
				__lowerCAmelCase:	Optional[Any]   				=      flax_key_tuple[:-1] + ("weight",)
				__lowerCAmelCase:	Any   				=      torch.permute(__SCREAMING_SNAKE_CASE ,			(0, 2, 1)			)
		elif flax_key_tuple[-1] == "kernel" and ".".join(__SCREAMING_SNAKE_CASE			):
				# linear layer
				__lowerCAmelCase:	List[Any]   				=      flax_key_tuple[:-1] + ("weight",)
				__lowerCAmelCase:	Optional[int]   				=      flax_tensor.T
		elif flax_key_tuple[-1] in ["scale", "embedding"]:
				__lowerCAmelCase:	Union[str, Any]   				=      flax_key_tuple[:-1] + ("weight",)

		return flax_key_tuple, flax_tensor






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     Optional[Any]:
		if "metadata" in layer:
				__lowerCAmelCase:	Tuple   				=      layer.split("metadata"			)
				__lowerCAmelCase:	Optional[int]   				=      "".join(split_layer[0]			)[:-1]
				__lowerCAmelCase:	Dict   				=      [tuple(("metadata" + split_layer[1]).split("/"			)			)]
		elif "kvstore" in layer:
				__lowerCAmelCase:	str   				=      layer.split("kvstore"			)
				__lowerCAmelCase:	Any   				=      "".join(split_layer[0]			)[:-1]
				__lowerCAmelCase:	Optional[Any]   				=      [tuple(("kvstore" + split_layer[1]).split("/"			)			)]

		else:
				__lowerCAmelCase:	Any   				=      layer.split("/"			)
				__lowerCAmelCase:	int   				=      "/".join(split_layer[:-1]			)
				__lowerCAmelCase:	Union[str, Any]   				=      (split_layer[-1],)

		if "kvstore/path" in layer:
				__lowerCAmelCase:	Union[str, Any]   				=      F"{switch_checkpoint_path}/{checkpoint_info[layer]}"
		elif "kvstore/driver" in layer:
				__lowerCAmelCase:	Optional[Any]   				=      "file"
		else:
				__lowerCAmelCase:	int   				=      checkpoint_info[layer]

		return curr_real_layer_name, split_layer, content






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     Union[str, Any]:
		__lowerCAmelCase:	Any   				=      rename_keys(__SCREAMING_SNAKE_CASE			)
		__lowerCAmelCase:	Union[str, Any]   				=      {}
		for k, v in current_block.items():
				__lowerCAmelCase:	List[str]   				=      v
		__lowerCAmelCase:	int   				=      new_current_block
		torch.save(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE = WEIGHTS_NAME			)     ->     Optional[int]:
		__lowerCAmelCase:	List[str]   				=      convert_file_size_to_int(__SCREAMING_SNAKE_CASE			)
		__lowerCAmelCase:	Union[str, Any]   				=      []
		__lowerCAmelCase:	str   				=      {}
		__lowerCAmelCase:	Optional[int]   				=      0
		__lowerCAmelCase:	Union[str, Any]   				=      0

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

		__lowerCAmelCase:	List[Any]   				=      {}
		for layer in checkpoint_info.keys():
				__lowerCAmelCase				,			__lowerCAmelCase				,			__lowerCAmelCase:	List[str]   				=      get_key_and_tensorstore_dict(
				    __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)
				if curr_real_layer_name in all_layers:
						__lowerCAmelCase:	List[Any]   				=      content
				else:
						__lowerCAmelCase:	List[str]   				=      {split_layer[-1]: content}

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

				# use the renaming pattern from the small conversion scripts
				__lowerCAmelCase				,			__lowerCAmelCase:	Union[str, Any]   				=      rename_base_flax_keys(tuple(key.split("/"			)			) ,			__SCREAMING_SNAKE_CASE			)
				__lowerCAmelCase:	Union[str, Any]   				=      "/".join(__SCREAMING_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:
						__lowerCAmelCase:	str   				=      os.path.join(
						    __SCREAMING_SNAKE_CASE ,			weights_name.replace(".bin" ,			F"-{len(__SCREAMING_SNAKE_CASE			)+1:05d}-of-???.bin"			)			)
						rename_and_save_block(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)
						sharded_state_dicts.append(current_block.keys()			)
						del current_block
						__lowerCAmelCase:	int   				=      {}
						__lowerCAmelCase:	int   				=      0

				__lowerCAmelCase:	List[str]   				=      raw_weights.to(getattr(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)			)
				current_block_size += weight_size
				total_size += weight_size

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

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

		# Otherwise, let's build the index
		__lowerCAmelCase:	str   				=      {}
		__lowerCAmelCase:	List[Any]   				=      {}
		for idx, shard in enumerate(__SCREAMING_SNAKE_CASE			):
				__lowerCAmelCase:	Optional[Any]   				=      weights_name.replace(
				    ".bin" ,			F"-{idx+1:05d}-of-{len(__SCREAMING_SNAKE_CASE			):05d}.bin"			)  # len(sharded_state_dicts):05d}
				__lowerCAmelCase:	int   				=      os.path.join(__SCREAMING_SNAKE_CASE ,			weights_name.replace(".bin" ,			F"-{idx+1:05d}-of-???.bin"			)			)
				os.rename(__SCREAMING_SNAKE_CASE ,			os.path.join(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)			)
				__lowerCAmelCase:	Dict   				=      shard
				for key in shard:
						__lowerCAmelCase:	str   				=      shard_file

    # Add the metadata
		__lowerCAmelCase:	Any   				=      {"total_size": total_size}
		__lowerCAmelCase:	List[str]   				=      {"metadata": metadata, "weight_map": weight_map}

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

		return metadata, index


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






def    a__   (   )     ->     Optional[Any]:
		from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

		__lowerCAmelCase:	Union[str, Any]   				=      tokenizer(__SCREAMING_SNAKE_CASE ,			return_tensors="pt"			).input_ids
		__lowerCAmelCase:	Optional[int]   				=      model.generate(__SCREAMING_SNAKE_CASE ,			decoder_start_token_id=0			)
		print(tokenizer.decode(out[0]			)			)





"""simple docstring"""


import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel





class    snake_case     (    pl.LightningModule  ):

				def __init__(     self							:    str					,  UpperCamelCase__							:    List[str])->       str:



						'''simple docstring'''


						super().__init__()
						__lowerCAmelCase:	Optional[int]   				=      model
						__lowerCAmelCase:	Tuple   				=      2
						__lowerCAmelCase:	List[Any]   				=      nn.Linear(self.model.config.hidden_size					,  self.num_labels)




				def 							lowercase_					(     self							:    Optional[int])->       str:



						'''simple docstring'''


						pass






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     Optional[int]:
		# load longformer model from model identifier
		__lowerCAmelCase:	List[str]   				=      LongformerModel.from_pretrained(__SCREAMING_SNAKE_CASE			)
		__lowerCAmelCase:	int   				=      LightningModel(__SCREAMING_SNAKE_CASE			)

		__lowerCAmelCase:	Tuple   				=      torch.load(__SCREAMING_SNAKE_CASE ,			map_location=torch.device("cpu"			)			)
		lightning_model.load_state_dict(ckpt["state_dict"]			)

		# init longformer question answering model
		__lowerCAmelCase:	Optional[Any]   				=      LongformerForQuestionAnswering.from_pretrained(__SCREAMING_SNAKE_CASE			)

		# transfer weights
		longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict()			)
		longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict()			)
		longformer_for_qa.eval()

		# save model
		longformer_for_qa.save_pretrained(__SCREAMING_SNAKE_CASE			)

		print(F"Conversion successful. Model saved under {pytorch_dump_folder_path}"			)


if __name__ == "__main__":
			__A						       =      argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--longformer_model",
			    default=None,
			    type=str,
			    required=True,
			    help="model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.",
			)
			parser.add_argument(
			    "--longformer_question_answering_ckpt_path",
			    default=None,
			    type=str,
			    required=True,
			    help="Path the official PyTorch Lightning Checkpoint.",
			)
			parser.add_argument(
			    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
			)
			__A						       =      parser.parse_args()
			convert_longformer_qa_checkpoint_to_pytorch(
			    args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
			)


