





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					(							__a   :Union[str, Any]    ,     __a   :int    )		->				Tuple:






		"""simple docstring"""

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

		return flax_key_tuple, flax_tensor







def    __lowerCamelCase					(							__a   :str    ,     __a   :Tuple    ,     __a   :Tuple    )		->				Optional[int]:






		"""simple docstring"""

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

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

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

		return curr_real_layer_name, split_layer, content







def    __lowerCamelCase					(							__a   :Optional[int]    ,     __a   :Optional[int]    )		->				Optional[int]:






		"""simple docstring"""

		A__        =   rename_keys(__a    )
		A__        =   {}
		for k, v in current_block.items():
				A__        =   v
		A__        =   new_current_block
		torch.save(__a    ,     __a    )







def    __lowerCamelCase					(							__a   :Dict    ,     __a   :Optional[Any]    ,     __a   :Union[str, Any]    ,     __a   :Optional[Any]    ,     __a   :str = WEIGHTS_NAME    )		->				int:






		"""simple docstring"""

		A__        =   convert_file_size_to_int(__a    )
		A__        =   []
		A__        =   {}
		A__        =   0
		A__        =   0

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

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

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

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

				A__        =   raw_weights.to(getattr(__a    ,     __a    )    )
				current_block_size += weight_size
				total_size += weight_size

		# Add the last block
		A__        =   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
		A__        =   {}
		A__        =   {}
		for idx, shard in enumerate(__a    ):
				A__        =   weights_name.replace(
				    """.bin"""    ,     F'-{idx+1:05d}-of-{len(__a    ):05d}.bin'    )  # len(sharded_state_dicts):05d}
				A__        =   os.path.join(__a    ,     weights_name.replace(""".bin"""    ,     F'-{idx+1:05d}-of-???.bin'    )    )
				os.rename(__a    ,     os.path.join(__a    ,     __a    )    )
				A__        =   shard
				for key in shard:
						A__        =   shard_file

    # Add the metadata
		A__        =   {"""total_size""": total_size}
		A__        =   {"""metadata""": metadata, """weight_map""": weight_map}

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

		return metadata, index


if __name__ == "__main__":
					A    :					Dict =      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    :					List[Any] =      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					(							)		->				int:






		"""simple docstring"""

		from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

		A__        =   tokenizer(__a    ,     return_tensors="""pt"""    ).input_ids
		A__        =   model.generate(__a    ,     decoder_start_token_id=0    )
		print(tokenizer.decode(out[0]    )    )










from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
A    :					Dict =      Lock()







def    __lowerCamelCase					(							__a   :Dict    ,     __a   :List[str]    ,     __a   :Optional[int]    ,     __a   :Optional[int]    ,     __a   :Optional[Any]    ,     __a   :Optional[int]    ,     __a   :int    )		->				Dict:






		"""simple docstring"""

		global process_lock

		# we perform n swaps since after n swaps we know we are sorted
		# we *could* stop early if we are sorted already, but it takes as long to
		# find out we are sorted as it does to sort the list with this algorithm
		for i in range(0    ,     1_0    ):
				if (i + position) % 2 == 0 and r_send is not None:
						# send your value to your right neighbor
						process_lock.acquire()
						r_send[1].send(__a    )
						process_lock.release()

						# receive your right neighbor's value
						process_lock.acquire()
						A__        =   rr_cv[0].recv()
						process_lock.release()

						# take the lower value since you are on the left
						A__        =   min(__a    ,     __a    )
				elif (i + position) % 2 != 0 and l_send is not None:
						# send your value to your left neighbor
						process_lock.acquire()
						l_send[1].send(__a    )
						process_lock.release()

						# receive your left neighbor's value
						process_lock.acquire()
						A__        =   lr_cv[0].recv()
						process_lock.release()

						# take the higher value since you are on the right
						A__        =   max(__a    ,     __a    )
    # after all swaps are performed, send the values back to main
		result_pipe[1].send(__a    )







def    __lowerCamelCase					(							__a   :List[str]    )		->				int:






		"""simple docstring"""

		A__        =   []
		A__        =   []
		# initialize the list of pipes where the values will be retrieved
		for _ in arr:
				result_pipe.append(Pipe()    )
		# creates the processes
		# the first and last process only have one neighbor so they are made outside
		# of the loop
		A__        =   Pipe()
		A__        =   Pipe()
		process_array_.append(
		    Process(
		        target=__a    ,     args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0])    ,     )    )
		A__        =   temp_rs
		A__        =   temp_rr

		for i in range(1    ,     len(__a    ) - 1    ):
				A__        =   Pipe()
				A__        =   Pipe()
				process_array_.append(
				    Process(
				        target=__a    ,     args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i])    ,     )    )
				A__        =   temp_rs
				A__        =   temp_rr

		process_array_.append(
		    Process(
		        target=__a    ,     args=(
		            len(__a    ) - 1,
		            arr[len(__a    ) - 1],
		            temp_ls,
		            None,
		            temp_lr,
		            None,
		            result_pipe[len(__a    ) - 1],
		        )    ,     )    )

		# start the processes
		for p in process_array_:
				p.start()

		# wait for the processes to end and write their values to the list
		for p in range(0    ,     len(__a    )    ):
				A__        =   result_pipe[p][0].recv()
				process_array_[p].join()
		return arr







def    __lowerCamelCase					(							)		->				str:






		"""simple docstring"""

		A__        =   list(range(1_0    ,     0    ,     -1    )    )
		print("""Initial List"""    )
		print(*__a    )
		A__        =   odd_even_transposition(__a    )
		print("""Sorted List\n"""    )
		print(*__a    )


if __name__ == "__main__":
					main()



