





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

					return flax_key_tuple, flax_tensor







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

					else:
										_snake_case       :					Any				       =   layer.split("/"				)
										_snake_case       :					List[Any]				       =   "/".join(split_layer[:-1]				)
										_snake_case       :					Tuple				       =   (split_layer[-1],)

					if "kvstore/path" in layer:
										_snake_case       :					Optional[Any]				       =   F"""{switch_checkpoint_path}/{checkpoint_info[layer]}"""
					elif "kvstore/driver" in layer:
										_snake_case       :					List[str]				       =   "file"
					else:
										_snake_case       :					Tuple				       =   checkpoint_info[layer]

					return curr_real_layer_name, split_layer, content







def    A				(							UpperCAmelCase  ,  UpperCAmelCase				):
					_snake_case       :					Tuple				       =   rename_keys(UpperCAmelCase				)
					_snake_case       :					List[str]				       =   {}
					for k, v in current_block.items():
										_snake_case       :					Optional[int]				       =   v
					_snake_case       :					Tuple				       =   new_current_block
					torch.save(UpperCAmelCase  ,  UpperCAmelCase				)







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase = WEIGHTS_NAME				):
					_snake_case       :					Tuple				       =   convert_file_size_to_int(UpperCAmelCase				)
					_snake_case       :					str				       =   []
					_snake_case       :					Union[str, Any]				       =   {}
					_snake_case       :					Dict				       =   0
					_snake_case       :					Any				       =   0

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

					_snake_case       :					List[str]				       =   {}
					for layer in checkpoint_info.keys():
										_snake_case       :					Any				       =   get_key_and_tensorstore_dict(
										    UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase				)
										if curr_real_layer_name in all_layers:
															_snake_case       :					str				       =   content
										else:
															_snake_case       :					str				       =   {split_layer[-1]: content}

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

										# use the renaming pattern from the small conversion scripts
										_snake_case       :					Tuple				       =   rename_base_flax_keys(tuple(key.split("/"				)				)  ,  UpperCAmelCase				)
										_snake_case       :					Dict				       =   "/".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:
															_snake_case       :					Dict				       =   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
															_snake_case       :					Dict				       =   {}
															_snake_case       :					Any				       =   0

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

					# Add the last block
					_snake_case       :					List[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()				)

					# 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
					_snake_case       :					Dict				       =   {}
					_snake_case       :					str				       =   {}
					for idx, shard in enumerate(UpperCAmelCase				):
										_snake_case       :					Dict				       =   weights_name.replace(
										    ".bin"  ,  F"""-{idx+1:05d}-of-{len(UpperCAmelCase				):05d}.bin"""				)  # len(sharded_state_dicts):05d}
										_snake_case       :					Tuple				       =   os.path.join(UpperCAmelCase  ,  weights_name.replace(".bin"  ,  F"""-{idx+1:05d}-of-???.bin"""				)				)
										os.rename(UpperCAmelCase  ,  os.path.join(UpperCAmelCase  ,  UpperCAmelCase				)				)
										_snake_case       :					Dict				       =   shard
										for key in shard:
															_snake_case       :					int				       =   shard_file

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

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

					return metadata, index


if __name__ == "__main__":
				__lowerCAmelCase :List[str]     =    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.',
				)
				__lowerCAmelCase :Tuple     =    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				(							):
					from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

					_snake_case       :					Tuple				       =   TaTokenizer.from_pretrained("t5-small"				)
					_snake_case       :					str				       =   "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       :					Tuple				       =   tokenizer(UpperCAmelCase  ,  return_tensors="pt"				).input_ids
					_snake_case       :					Any				       =   model.generate(UpperCAmelCase  ,  decoder_start_token_id=0				)
					print(tokenizer.decode(out[0]				)				)






import math

from numpy import inf
from scipy.integrate import quad







def    A				(							UpperCAmelCase				):
					if num <= 0:
										raise ValueError("math domain error"				)

					return quad(UpperCAmelCase  ,  0  ,  UpperCAmelCase  ,  args=(UpperCAmelCase)				)[0]







def    A				(							UpperCAmelCase  ,  UpperCAmelCase				):
					return math.pow(UpperCAmelCase  ,  z - 1				) * math.exp(-x				)


if __name__ == "__main__":
							from doctest import testmod

							testmod()