






'''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 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase   )   -> str:
							if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
														# expert layer
														lowerCamelCase__      :			List[str] 						= flax_key_tuple[:-1] + ("""weight""",)
														lowerCamelCase__      :			Tuple 						= torch.permute(UpperCamelCase       ,    (0, 2, 1)   )
							elif flax_key_tuple[-1] == "kernel" and ".".join(UpperCamelCase   ):
														# linear layer
														lowerCamelCase__      :			List[str] 						= flax_key_tuple[:-1] + ("""weight""",)
														lowerCamelCase__      :			str 						= flax_tensor.T
							elif flax_key_tuple[-1] in ["scale", "embedding"]:
														lowerCamelCase__      :			Optional[Any] 						= flax_key_tuple[:-1] + ("""weight""",)

							return flax_key_tuple, flax_tensor





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )   -> Optional[Any]:
							if "metadata" in layer:
														lowerCamelCase__      :			Optional[Any] 						= layer.split("""metadata"""   )
														lowerCamelCase__      :			Any 						= """""".join(split_layer[0]   )[:-1]
														lowerCamelCase__      :			Optional[Any] 						= [tuple(("""metadata""" + split_layer[1]).split("""/"""   )   )]
							elif "kvstore" in layer:
														lowerCamelCase__      :			Dict 						= layer.split("""kvstore"""   )
														lowerCamelCase__      :			List[str] 						= """""".join(split_layer[0]   )[:-1]
														lowerCamelCase__      :			str 						= [tuple(("""kvstore""" + split_layer[1]).split("""/"""   )   )]

							else:
														lowerCamelCase__      :			Optional[int] 						= layer.split("""/"""   )
														lowerCamelCase__      :			str 						= """/""".join(split_layer[:-1]   )
														lowerCamelCase__      :			List[str] 						= (split_layer[-1],)

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

							return curr_real_layer_name, split_layer, content





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase   )   -> Dict:
							lowerCamelCase__      :			Tuple 						= rename_keys(UpperCamelCase   )
							lowerCamelCase__      :			Union[str, Any] 						= {}
							for k, v in current_block.items():
														lowerCamelCase__      :			int 						= v
							lowerCamelCase__      :			Optional[Any] 						= new_current_block
							torch.save(UpperCamelCase       ,    UpperCamelCase   )





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase = WEIGHTS_NAME   )   -> Tuple:
							lowerCamelCase__      :			Any 						= convert_file_size_to_int(UpperCamelCase   )
							lowerCamelCase__      :			List[Any] 						= []
							lowerCamelCase__      :			Optional[int] 						= {}
							lowerCamelCase__      :			Union[str, Any] 						= 0
							lowerCamelCase__      :			Optional[Any] 						= 0

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

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

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

														# use the renaming pattern from the small conversion scripts
														lowerCamelCase__      :			Optional[Any] 						= rename_base_flax_keys(tuple(key.split("""/"""   )   )       ,    UpperCamelCase   )
														lowerCamelCase__      :			List[str] 						= """/""".join(UpperCamelCase   )

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

														lowerCamelCase__      :			Optional[int] 						= raw_weights.to(getattr(UpperCamelCase       ,    UpperCamelCase   )   )
														current_block_size += weight_size
														total_size += weight_size

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

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

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

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

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

							return metadata, index


if __name__ == "__main__":
			_A			:    List[Any]					  =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			:    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 				SCREAMING_SNAKE_CASE_ ()   -> List[Any]:
							from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

							lowerCamelCase__      :			Tuple 						= tokenizer(UpperCamelCase       ,    return_tensors="""pt"""   ).input_ids
							lowerCamelCase__      :			int 						= model.generate(UpperCamelCase       ,    decoder_start_token_id=0   )
							print(tokenizer.decode(out[0]   )   )









'''simple docstring'''







from statistics import mean

import numpy as np





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )   -> list:
							lowerCamelCase__      :			Optional[int] 						= 0
							# Number of processes finished
							lowerCamelCase__      :			Union[str, Any] 						= 0
							# Displays the finished process.
							# If it is 0, the performance is completed if it is 1, before the performance.
							lowerCamelCase__      :			Tuple 						= [0] * no_of_process
							# List to include calculation results
							lowerCamelCase__      :			List[str] 						= [0] * no_of_process

							# Sort by arrival time.
							lowerCamelCase__      :			Union[str, Any] 						= [burst_time[i] for i in np.argsort(UpperCamelCase   )]
							lowerCamelCase__      :			List[Any] 						= [process_name[i] for i in np.argsort(UpperCamelCase   )]
							arrival_time.sort()

							while no_of_process > finished_process_count:
														lowerCamelCase__      :			str 						= 0
														while finished_process[i] == 1:
																					i += 1
														if current_time < arrival_time[i]:
																					lowerCamelCase__      :			Union[str, Any] 						= arrival_time[i]

														lowerCamelCase__      :			Any 						= 0
														# Index showing the location of the process being performed
														lowerCamelCase__      :			Union[str, Any] 						= 0
														# Saves the current response ratio.
														lowerCamelCase__      :			Any 						= 0
														for i in range(0       ,    UpperCamelCase   ):
																					if finished_process[i] == 0 and arrival_time[i] <= current_time:
																												lowerCamelCase__      :			Optional[int] 						= (burst_time[i] + (current_time - arrival_time[i])) / burst_time[
																												    i
																												]
																					if response_ratio < temp:
																												lowerCamelCase__      :			int 						= temp
																												lowerCamelCase__      :			str 						= i

        # Calculate the turn around time
														lowerCamelCase__      :			Optional[int] 						= current_time + burst_time[loc] - arrival_time[loc]
														current_time += burst_time[loc]
														# Indicates that the process has been performed.
														lowerCamelCase__      :			List[str] 						= 1
														# Increase finished_process_count by 1
														finished_process_count += 1

							return turn_around_time





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )   -> list:
							lowerCamelCase__      :			int 						= [0] * no_of_process
							for i in range(0       ,    UpperCamelCase   ):
														lowerCamelCase__      :			Optional[Any] 						= turn_around_time[i] - burst_time[i]
							return waiting_time


if __name__ == "__main__":
			_A			:    List[str]					  =5
			_A			:    Optional[Any]					  =['''A''', '''B''', '''C''', '''D''', '''E''']
			_A			:    Optional[int]					  =[1, 2, 3, 4, 5]
			_A			:    Dict					  =[1, 2, 3, 4, 5]

			_A			:    Any					  =calculate_turn_around_time(
			    process_name, arrival_time, burst_time, no_of_process
			)
			_A			:    Optional[int]					  =calculate_waiting_time(
			    process_name, turn_around_time, burst_time, no_of_process
			)

			print('''Process name \tArrival time \tBurst time \tTurn around time \tWaiting time''')
			for i in range(0, no_of_process):
						print(
						    F'{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t'
						    F'{turn_around_time[i]}\t\t\t{waiting_time[i]}'
						)

			print(F'average waiting time : {mean(waiting_time):.5f}')
			print(F'average turn around time : {mean(turn_around_time):.5f}')

