



"""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		(			lowerCamelCase_:							Optional[int]     ,		lowerCamelCase_:							Union[str, Any] ):
						"""simple docstring"""






						if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
												# expert layer
												snake_case   :			List[str]						    = flax_key_tuple[:-1] + ("weight",)
												snake_case   :			Union[str, Any]						    = torch.permute(lowerCamelCase_     ,		(0, 2, 1) )
						elif flax_key_tuple[-1] == "kernel" and ".".join(lowerCamelCase_ ):
												# linear layer
												snake_case   :			int						    = flax_key_tuple[:-1] + ("weight",)
												snake_case   :			Dict						    = flax_tensor.T
						elif flax_key_tuple[-1] in ["scale", "embedding"]:
												snake_case   :			Any						    = flax_key_tuple[:-1] + ("weight",)

						return flax_key_tuple, flax_tensor

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Union[str, Any]     ,		lowerCamelCase_:							Union[str, Any]     ,		lowerCamelCase_:							str ):
						"""simple docstring"""






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

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

						if "kvstore/path" in layer:
												snake_case   :			str						    = f'''{switch_checkpoint_path}/{checkpoint_info[layer]}'''
						elif "kvstore/driver" in layer:
												snake_case   :			Tuple						    = "file"
						else:
												snake_case   :			int						    = checkpoint_info[layer]

						return curr_real_layer_name, split_layer, content

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Optional[Any]     ,		lowerCamelCase_:							Dict ):
						"""simple docstring"""






						snake_case   :			Optional[int]						    = rename_keys(lowerCamelCase_ )
						snake_case   :			str						    = {}
						for k, v in current_block.items():
												snake_case   :			List[str]						    = v
						snake_case   :			List[str]						    = new_current_block
						torch.save(lowerCamelCase_     ,		lowerCamelCase_ )

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Tuple     ,		lowerCamelCase_:							Optional[Any]     ,		lowerCamelCase_:							Dict     ,		lowerCamelCase_:							int     ,		lowerCamelCase_:							str = WEIGHTS_NAME ):
						"""simple docstring"""






						snake_case   :			List[str]						    = convert_file_size_to_int(lowerCamelCase_ )
						snake_case   :			List[Any]						    = []
						snake_case   :			Dict						    = {}
						snake_case   :			str						    = 0
						snake_case   :			List[str]						    = 0

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

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

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

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

												snake_case   :			Any						    = raw_weights.to(getattr(lowerCamelCase_     ,		lowerCamelCase_ ) )
												current_block_size += weight_size
												total_size += weight_size

						# Add the last block
						snake_case   :			List[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
						snake_case   :			List[Any]						    = {}
						snake_case   :			Dict						    = {}
						for idx, shard in enumerate(lowerCamelCase_ ):
												snake_case   :			List[Any]						    = weights_name.replace(
												    ".bin"     ,		f'''-{idx+1:05d}-of-{len(lowerCamelCase_ ):05d}.bin''' )  # len(sharded_state_dicts):05d}
												snake_case   :			Tuple						    = os.path.join(lowerCamelCase_     ,		weights_name.replace(".bin"     ,		f'''-{idx+1:05d}-of-???.bin''' ) )
												os.rename(lowerCamelCase_     ,		os.path.join(lowerCamelCase_     ,		lowerCamelCase_ ) )
												snake_case   :			Union[str, Any]						    = shard
												for key in shard:
																		snake_case   :			List[Any]						    = shard_file

    # Add the metadata
						snake_case   :			Optional[int]						    = {"total_size": total_size}
						snake_case   :			List[str]						    = {"metadata": metadata, "weight_map": weight_map}

						with open(os.path.join(lowerCamelCase_     ,		lowerCamelCase_ )     ,		"w"     ,		encoding="utf-8" ) as f:
												snake_case   :			Tuple						    = 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  __SCREAMING_SNAKE_CASE		(			):
						"""simple docstring"""






						from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

						snake_case   :			Dict						    = tokenizer(lowerCamelCase_     ,		return_tensors="pt" ).input_ids
						snake_case   :			Optional[int]						    = model.generate(lowerCamelCase_     ,		decoder_start_token_id=0 )
						print(tokenizer.decode(out[0] ) )








"""simple docstring"""







from random import randint, random

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							int     ,		lowerCamelCase_:							int     ,		lowerCamelCase_:							int     ,		lowerCamelCase_:							bool = False     ,		lowerCamelCase_:							bool = False     ,		lowerCamelCase_:							int = 5     ,		):
						"""simple docstring"""






						snake_case   :			str						    = [[-1] * number_of_cells]  # Create a highway without any car
						snake_case   :			str						    = 0
						snake_case   :			Any						    = max(lowerCamelCase_     ,		0 )
						while i < number_of_cells:
												snake_case   :			Optional[int]						    = (
												    randint(0     ,		lowerCamelCase_ ) if random_speed else initial_speed
												)  # Place the cars
												i += (
												    randint(1     ,		max_speed * 2 ) if random_frequency else frequency
												)  # Arbitrary number, may need tuning
						return highway

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							list     ,		lowerCamelCase_:							int ):
						"""simple docstring"""






						snake_case   :			Any						    = 0
						snake_case   :			int						    = highway_now[car_index + 1 :]
						for cell in range(len(lowerCamelCase_ ) ):  # May need a better name for this
												if cells[cell] != -1:  # If the cell is not empty then
																		return distance  # we have the distance we wanted
												distance += 1
						# Here if the car is near the end of the highway
						return distance + get_distance(lowerCamelCase_     ,		-1 )

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							list     ,		lowerCamelCase_:							float     ,		lowerCamelCase_:							int ):
						"""simple docstring"""






						snake_case   :			List[str]						    = len(lowerCamelCase_ )
						# Beforce calculations, the highway is empty
						snake_case   :			Dict						    = [-1] * number_of_cells

						for car_index in range(lowerCamelCase_ ):
												if highway_now[car_index] != -1:
																		# Add 1 to the current speed of the car and cap the speed
																		snake_case   :			Tuple						    = min(highway_now[car_index] + 1     ,		lowerCamelCase_ )
																		# Number of empty cell before the next car
																		snake_case   :			int						    = get_distance(lowerCamelCase_     ,		lowerCamelCase_ ) - 1
																		# We can't have the car causing an accident
																		snake_case   :			str						    = min(next_highway[car_index]     ,		lowerCamelCase_ )
																		if random() < probability:
																								# Randomly, a driver will slow down
																								snake_case   :			Optional[Any]						    = max(next_highway[car_index] - 1     ,		0 )
						return next_highway

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							list     ,		lowerCamelCase_:							int     ,		lowerCamelCase_:							float     ,		lowerCamelCase_:							int ):
						"""simple docstring"""






						snake_case   :			Optional[Any]						    = len(highway[0] )

						for i in range(lowerCamelCase_ ):
												snake_case   :			Union[str, Any]						    = update(highway[i]     ,		lowerCamelCase_     ,		lowerCamelCase_ )
												snake_case   :			Optional[int]						    = [-1] * number_of_cells

												for car_index in range(lowerCamelCase_ ):
																		snake_case   :			List[Any]						    = next_speeds_calculated[car_index]
																		if speed != -1:
																								# Change the position based on the speed (with % to create the loop)
																								snake_case   :			Optional[Any]						    = (car_index + speed) % number_of_cells
																								# Commit the change of position
																								snake_case   :			Tuple						    = speed
												highway.append(lowerCamelCase_ )

						return highway


if __name__ == "__main__":
			import doctest

			doctest.testmod()



