





'''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    (				a_    :	Optional[Any]   ,a_    :	Optional[Any]						):






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

		return flax_key_tuple, flax_tensor






def   __A    (				a_    :	Tuple   ,a_    :	Any   ,a_    :	Any						):






		if "metadata" in layer:
				lowerCAmelCase  :     Tuple       						= layer.split("metadata"						)
				lowerCAmelCase  :     Optional[Any]       						= "".join(split_layer[0]						)[:-1]
				lowerCAmelCase  :     Optional[Any]       						= [tuple(("metadata" + split_layer[1]).split("/"						)						)]
		elif "kvstore" in layer:
				lowerCAmelCase  :     List[Any]       						= layer.split("kvstore"						)
				lowerCAmelCase  :     str       						= "".join(split_layer[0]						)[:-1]
				lowerCAmelCase  :     int       						= [tuple(("kvstore" + split_layer[1]).split("/"						)						)]

		else:
				lowerCAmelCase  :     Optional[Any]       						= layer.split("/"						)
				lowerCAmelCase  :     Dict       						= "/".join(split_layer[:-1]						)
				lowerCAmelCase  :     Optional[Any]       						= (split_layer[-1],)

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

		return curr_real_layer_name, split_layer, content






def   __A    (				a_    :	Optional[Any]   ,a_    :	Optional[int]						):
		lowerCAmelCase  :     Dict       						= rename_keys(_a						)
		lowerCAmelCase  :     List[str]       						= {}
		for k, v in current_block.items():
				lowerCAmelCase  :     Optional[int]       						= v
		lowerCAmelCase  :     int       						= new_current_block
		torch.save(_a   ,_a						)






def   __A    (				a_    :	str   ,a_    :	Optional[Any]   ,a_    :	int   ,a_    :	Optional[int]   ,a_    :	str = WEIGHTS_NAME						):
		lowerCAmelCase  :     Dict       						= convert_file_size_to_int(_a						)
		lowerCAmelCase  :     List[str]       						= []
		lowerCAmelCase  :     Union[str, Any]       						= {}
		lowerCAmelCase  :     Any       						= 0
		lowerCAmelCase  :     Tuple       						= 0

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

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

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

				# use the renaming pattern from the small conversion scripts
				lowerCAmelCase						,		lowerCAmelCase  :     int       						= rename_base_flax_keys(tuple(key.split("/"						)						)   ,_a						)
				lowerCAmelCase  :     str       						= "/".join(_a						)

				# If this weight is going to tip up over the maximal size, we split.
				if current_block_size + weight_size > max_shard_size:
						lowerCAmelCase  :     Dict       						= os.path.join(
						    _a   ,weights_name.replace(".bin"   ,f'''-{len(_a						)+1:05d}-of-???.bin'''						)						)
						rename_and_save_block(_a   ,_a						)
						sharded_state_dicts.append(current_block.keys()						)
						del current_block
						lowerCAmelCase  :     Union[str, Any]       						= {}
						lowerCAmelCase  :     List[Any]       						= 0

				lowerCAmelCase  :     int       						= raw_weights.to(getattr(_a   ,_a						)						)
				current_block_size += weight_size
				total_size += weight_size

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

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

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

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

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

		return metadata, index


if __name__ == "__main__":
					lowerCAmelCase									=   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									=   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    (				):
		from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

		lowerCAmelCase  :     Optional[Any]       						= tokenizer(_a   ,return_tensors="pt"						).input_ids
		lowerCAmelCase  :     Optional[int]       						= model.generate(_a   ,decoder_start_token_id=0						)
		print(tokenizer.decode(out[0]						)						)






"""simple docstring"""


from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate





@dataclass(frozen=SCREAMING_SNAKE_CASE    )
class lowerCamelCase__   (				SCREAMING_SNAKE_CASE    ):



    '''simple docstring'''




    _lowerCamelCase					=   field(default='''summarization'''     ,   metadata={'''include_in_asdict_even_if_is_default''': True}    )
    _lowerCamelCase					=   Features({'''text''': Value('''string'''    )}    )
    _lowerCamelCase					=   Features({'''summary''': Value('''string'''    )}    )
    _lowerCamelCase					=   "text"
    _lowerCamelCase					=   "summary"




    @property
    def UpperCamelCase__		( self    )    ->      Dict[str, str]:
          return {self.text_column: "text", self.summary_column: "summary"}




