"""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    lowercase_   (   _lowerCamelCase:	int						,   _lowerCamelCase:	Any      ) ->      Any:



							'''simple docstring'''

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

							return flax_key_tuple, flax_tensor


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



							'''simple docstring'''

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

							else:
														__lowerCamelCase					:		List[str]		   =     layer.split("/"      )
														__lowerCamelCase					:		List[str]		   =     "/".join(split_layer[:-1]      )
														__lowerCamelCase					:		Tuple		   =     (split_layer[-1],)

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

							return curr_real_layer_name, split_layer, content


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



							'''simple docstring'''

							__lowerCamelCase					:		List[str]		   =     rename_keys(_lowerCamelCase      )
							__lowerCamelCase					:		List[str]		   =     {}
							for k, v in current_block.items():
														__lowerCamelCase					:		List[Any]		   =     v
							__lowerCamelCase					:		Any		   =     new_current_block
							torch.save(_lowerCamelCase						,   _lowerCamelCase      )


def    lowercase_   (   _lowerCamelCase:	Tuple						,   _lowerCamelCase:	Any						,   _lowerCamelCase:	Tuple						,   _lowerCamelCase:	Any						,   _lowerCamelCase:	str = WEIGHTS_NAME      ) ->      Union[str, Any]:



							'''simple docstring'''

							__lowerCamelCase					:		Optional[Any]		   =     convert_file_size_to_int(_lowerCamelCase      )
							__lowerCamelCase					:		Dict		   =     []
							__lowerCamelCase					:		Tuple		   =     {}
							__lowerCamelCase					:		Union[str, Any]		   =     0
							__lowerCamelCase					:		Dict		   =     0

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

							__lowerCamelCase					:		Dict		   =     {}
							for layer in checkpoint_info.keys():
														__lowerCamelCase   ,  __lowerCamelCase   ,  __lowerCamelCase					:		Any		   =     get_key_and_tensorstore_dict(
														    _lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase      )
														if curr_real_layer_name in all_layers:
																					__lowerCamelCase					:		Tuple		   =     content
														else:
																					__lowerCamelCase					:		Tuple		   =     {split_layer[-1]: content}

							for key in all_layers.keys():
														# open tensorstore file
														__lowerCamelCase					:		Optional[Any]		   =     ts.open(unflatten_dict(all_layers[key]      )      ).result().read().result()
														__lowerCamelCase					:		Any		   =     torch.tensor(_lowerCamelCase      )
														__lowerCamelCase					:		List[Any]		   =     raw_weights.numel() * dtype_byte_size(raw_weights.dtype      )

														# use the renaming pattern from the small conversion scripts
														__lowerCamelCase   ,  __lowerCamelCase					:		Any		   =     rename_base_flax_keys(tuple(key.split("/"      )      )						,   _lowerCamelCase      )
														__lowerCamelCase					:		List[str]		   =     "/".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:
																					__lowerCamelCase					:		Optional[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
																					__lowerCamelCase					:		Dict		   =     {}
																					__lowerCamelCase					:		List[str]		   =     0

														__lowerCamelCase					:		Dict		   =     raw_weights.to(getattr(_lowerCamelCase						,   _lowerCamelCase      )      )
														current_block_size += weight_size
														total_size += weight_size

							# Add the last block
							__lowerCamelCase					:		Optional[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()      )

							# 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
							__lowerCamelCase					:		str		   =     {}
							__lowerCamelCase					:		str		   =     {}
							for idx, shard in enumerate(_lowerCamelCase      ):
														__lowerCamelCase					:		str		   =     weights_name.replace(
														    ".bin"						,   F"""-{idx+1:05d}-of-{len(_lowerCamelCase      ):05d}.bin"""      )  # len(sharded_state_dicts):05d}
														__lowerCamelCase					:		Optional[Any]		   =     os.path.join(_lowerCamelCase						,   weights_name.replace(".bin"						,   F"""-{idx+1:05d}-of-???.bin"""      )      )
														os.rename(_lowerCamelCase						,   os.path.join(_lowerCamelCase						,   _lowerCamelCase      )      )
														__lowerCamelCase					:		List[Any]		   =     shard
														for key in shard:
																					__lowerCamelCase					:		Tuple		   =     shard_file

    # Add the metadata
							__lowerCamelCase					:		Union[str, Any]		   =     {"total_size": total_size}
							__lowerCamelCase					:		Dict		   =     {"metadata": metadata, "weight_map": weight_map}

							with open(os.path.join(_lowerCamelCase						,   _lowerCamelCase      )						,   "w"						,   encoding="utf-8"      ) as f:
														__lowerCamelCase					:		List[Any]		   =     json.dumps(_lowerCamelCase						,   indent=2						,   sort_keys=_lowerCamelCase      ) + "\n"
														f.write(_lowerCamelCase      )

							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    lowercase_   (   ) ->      Any:



							'''simple docstring'''

							from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

							__lowerCamelCase					:		Optional[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					:		Dict		   =     tokenizer(_lowerCamelCase						,   return_tensors="pt"      ).input_ids
							__lowerCamelCase					:		Any		   =     model.generate(_lowerCamelCase						,   decoder_start_token_id=0      )
							print(tokenizer.decode(out[0]      )      )
"""simple docstring"""





from __future__ import annotations


def    lowercase_   (   _lowerCamelCase:	list[list[int]]      ) ->      bool:



							'''simple docstring'''

							__lowerCamelCase					:		str		   =     len(_lowerCamelCase      )
							# We need to create solution object to save path.
							__lowerCamelCase					:		List[Any]		   =     [[0 for _ in range(_lowerCamelCase      )] for _ in range(_lowerCamelCase      )]
							__lowerCamelCase					:		Dict		   =     run_maze(_lowerCamelCase						,   0						,   0						,   _lowerCamelCase      )
							if solved:
														print("\n".join(str(_lowerCamelCase      ) for row in solutions      )      )
							else:
														print("No solution exists!"      )
							return solved


def    lowercase_   (   _lowerCamelCase:	list[list[int]]						,   _lowerCamelCase:	int						,   _lowerCamelCase:	int						,   _lowerCamelCase:	list[list[int]]      ) ->      bool:



							'''simple docstring'''

							__lowerCamelCase					:		Dict		   =     len(_lowerCamelCase      )
							# Final check point.
							if i == j == (size - 1):
														__lowerCamelCase					:		Optional[Any]		   =     1
														return True

							__lowerCamelCase					:		int		   =     (not i < 0) and (not j < 0)  # Check lower bounds
							__lowerCamelCase					:		Union[str, Any]		   =     (i < size) and (j < size)  # Check upper bounds

							if lower_flag and upper_flag:
														# check for already visited and block points.
														__lowerCamelCase					:		List[Any]		   =     (not solutions[i][j]) and (not maze[i][j])
														if block_flag:
																					# check visited
																					__lowerCamelCase					:		Dict		   =     1

																					# check for directions
																					if (
																					    run_maze(_lowerCamelCase						,   i + 1						,   _lowerCamelCase						,   _lowerCamelCase      )
																					    or run_maze(_lowerCamelCase						,   _lowerCamelCase						,   j + 1						,   _lowerCamelCase      )
																					    or run_maze(_lowerCamelCase						,   i - 1						,   _lowerCamelCase						,   _lowerCamelCase      )
																					    or run_maze(_lowerCamelCase						,   _lowerCamelCase						,   j - 1						,   _lowerCamelCase      )
																					):
																												return True

																					__lowerCamelCase					:		List[str]		   =     0
																					return False
							return False


if __name__ == "__main__":
				import doctest

				doctest.testmod()