



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 __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					) ->    List[Any]:
			if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
						# expert layer
						lowercase__   					=			flax_key_tuple[:-1] + ('''weight''',)
						lowercase__   					=			torch.permute(SCREAMING_SNAKE_CASE_  ,					(0, 2, 1)					)
			elif flax_key_tuple[-1] == "kernel" and ".".join(SCREAMING_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 __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					) ->    int:
			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 __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					) ->    Optional[Any]:
			lowercase__   					=			rename_keys(SCREAMING_SNAKE_CASE_					)
			lowercase__   					=			{}
			for k, v in current_block.items():
						lowercase__   					=			v
			lowercase__   					=			new_current_block
			torch.save(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_ = WEIGHTS_NAME					) ->    Dict:
			lowercase__   					=			convert_file_size_to_int(SCREAMING_SNAKE_CASE_					)
			lowercase__   					=			[]
			lowercase__   					=			{}
			lowercase__   					=			0
			lowercase__   					=			0

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

			lowercase__   					=			{}
			for layer in checkpoint_info.keys():
						lowercase__   					=			get_key_and_tensorstore_dict(
						    SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_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(SCREAMING_SNAKE_CASE_					)
						lowercase__   					=			raw_weights.numel() * dtype_byte_size(raw_weights.dtype					)

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

						lowercase__   					=			raw_weights.to(getattr(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)					)
						current_block_size += weight_size
						total_size += weight_size

			# Add the last block
			lowercase__   					=			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
			lowercase__   					=			{}
			lowercase__   					=			{}
			for idx, shard in enumerate(SCREAMING_SNAKE_CASE_					):
						lowercase__   					=			weights_name.replace(
						    ".bin"  ,					f'''-{idx+1:05d}-of-{len(SCREAMING_SNAKE_CASE_					):05d}.bin'''					)  # len(sharded_state_dicts):05d}
						lowercase__   					=			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_					)					)
						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(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)  ,					"w"  ,					encoding="utf-8"					) as f:
						lowercase__   					=			json.dumps(SCREAMING_SNAKE_CASE_  ,					indent=2  ,					sort_keys=SCREAMING_SNAKE_CASE_					) + '''\n'''
						f.write(SCREAMING_SNAKE_CASE_					)

			return metadata, index


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




def __lowerCAmelCase	(       ) ->    str:
			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(SCREAMING_SNAKE_CASE_  ,					return_tensors="pt"					).input_ids
			lowercase__   					=			model.generate(SCREAMING_SNAKE_CASE_  ,					decoder_start_token_id=0					)
			print(tokenizer.decode(out[0]					)					)









def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			len(SCREAMING_SNAKE_CASE_					)
			for i in range(SCREAMING_SNAKE_CASE_					):
						for j in range(i + 1  ,					SCREAMING_SNAKE_CASE_					):
									if numbers[j] < numbers[i]:
												lowercase__					,    lowercase__   					=			numbers[j], numbers[i]
			return numbers


if __name__ == "__main__":
							lowercase_						=       input("""Enter numbers separated by a comma:\n""").strip()
							lowercase_						=       [int(item) for item in user_input.split(""",""")]
							print(exchange_sort(unsorted))




