
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		(				snake_case__:   List[Any]		,  snake_case__:   Optional[Any]   ):


				'''simple docstring'''
				if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
								# expert layer
								lowercase_	    =   flax_key_tuple[:-1] + ('''weight''',)
								lowercase_	    =   torch.permute(snake_case__		,  (0, 2, 1)   )
				elif flax_key_tuple[-1] == "kernel" and ".".join(snake_case__   ):
								# linear layer
								lowercase_	    =   flax_key_tuple[:-1] + ('''weight''',)
								lowercase_	    =   flax_tensor.T
				elif flax_key_tuple[-1] in ["scale", "embedding"]:
								lowercase_	    =   flax_key_tuple[:-1] + ('''weight''',)

				return flax_key_tuple, flax_tensor




def 							a		(				snake_case__:   Optional[int]		,  snake_case__:   Optional[int]		,  snake_case__:   Any   ):


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

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

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

				return curr_real_layer_name, split_layer, content




def 							a		(				snake_case__:   Optional[Any]		,  snake_case__:   Any   ):


				'''simple docstring'''
				lowercase_	    =   rename_keys(snake_case__   )
				lowercase_	    =   {}
				for k, v in current_block.items():
								lowercase_	    =   v
				lowercase_	    =   new_current_block
				torch.save(snake_case__		,  snake_case__   )




def 							a		(				snake_case__:   Optional[int]		,  snake_case__:   Tuple		,  snake_case__:   Union[str, Any]		,  snake_case__:   Dict		,  snake_case__:   str = WEIGHTS_NAME   ):


				'''simple docstring'''
				lowercase_	    =   convert_file_size_to_int(snake_case__   )
				lowercase_	    =   []
				lowercase_	    =   {}
				lowercase_	    =   0
				lowercase_	    =   0

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

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

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

								# use the renaming pattern from the small conversion scripts
								lowercase_    ,      lowercase_	    =   rename_base_flax_keys(tuple(key.split('''/'''   )   )		,  snake_case__   )
								lowercase_	    =   '''/'''.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:
												lowercase_	    =   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
												lowercase_	    =   {}
												lowercase_	    =   0

								lowercase_	    =   raw_weights.to(getattr(snake_case__		,  snake_case__   )   )
								current_block_size += weight_size
								total_size += weight_size

				# Add the last block
				lowercase_	    =   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
				lowercase_	    =   {}
				lowercase_	    =   {}
				for idx, shard in enumerate(snake_case__   ):
								lowercase_	    =   weights_name.replace(
								    '''.bin'''		,  F'''-{idx+1:05d}-of-{len(snake_case__   ):05d}.bin'''   )  # len(sharded_state_dicts):05d}
								lowercase_	    =   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__   )   )
								lowercase_	    =   shard
								for key in shard:
												lowercase_	    =   shard_file

    # Add the metadata
				lowercase_	    =   {'''total_size''': total_size}
				lowercase_	    =   {'''metadata''': metadata, '''weight_map''': weight_map}

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


				'''simple docstring'''
				from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

				lowercase_	    =   tokenizer(snake_case__		,  return_tensors='''pt'''   ).input_ids
				lowercase_	    =   model.generate(snake_case__		,  decoder_start_token_id=0   )
				print(tokenizer.decode(out[0]   )   )






from __future__ import annotations




def 							a		(				snake_case__:   list[list[int]]   ):


				'''simple docstring'''

				# preprocessing the first row
				for i in range(1		,  len(matrix[0]   )   ):
								matrix[0][i] += matrix[0][i - 1]

				# preprocessing the first column
				for i in range(1		,  len(snake_case__   )   ):
								matrix[i][0] += matrix[i - 1][0]

				# updating the path cost for current position
				for i in range(1		,  len(snake_case__   )   ):
								for j in range(1		,  len(matrix[0]   )   ):
												matrix[i][j] += min(matrix[i - 1][j]		,  matrix[i][j - 1]   )

				return matrix[-1][-1]


if __name__ == "__main__":
						import doctest

						doctest.testmod()




