


from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings

@maybe_allow_in_graph
class 		__a				(    nn.Module      ):
  def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=0.0      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = "geglu"      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = False      ,   _SCREAMING_SNAKE_CASE = False      ,   _SCREAMING_SNAKE_CASE = False      ,   _SCREAMING_SNAKE_CASE = False      ,   _SCREAMING_SNAKE_CASE = True      ,   _SCREAMING_SNAKE_CASE = "layer_norm"      ,   _SCREAMING_SNAKE_CASE = False      ,   )			->	Optional[Any]:




         """simple docstring"""





         super().__init__()
         _UpperCAmelCase					=      only_cross_attention

         _UpperCAmelCase					=      (num_embeds_ada_norm is not None) and norm_type == 'ada_norm_zero'
         _UpperCAmelCase					=      (num_embeds_ada_norm is not None) and norm_type == 'ada_norm'

         if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
                raise ValueError(
                    f'''`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to'''
                    f''' define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}.'''						)

         # Define 3 blocks. Each block has its own normalization layer.
         # 1. Self-Attn
         if self.use_ada_layer_norm:
                _UpperCAmelCase					=      AdaLayerNorm(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
         elif self.use_ada_layer_norm_zero:
                _UpperCAmelCase					=      AdaLayerNormZero(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
         else:
                _UpperCAmelCase					=      nn.LayerNorm(_SCREAMING_SNAKE_CASE      ,   elementwise_affine=_SCREAMING_SNAKE_CASE						)
         _UpperCAmelCase					=      Attention(
             query_dim=_SCREAMING_SNAKE_CASE      ,   heads=_SCREAMING_SNAKE_CASE      ,   dim_head=_SCREAMING_SNAKE_CASE      ,   dropout=_SCREAMING_SNAKE_CASE      ,   bias=_SCREAMING_SNAKE_CASE      ,   cross_attention_dim=cross_attention_dim if only_cross_attention else None      ,   upcast_attention=_SCREAMING_SNAKE_CASE      ,   )

         # 2. Cross-Attn
         if cross_attention_dim is not None or double_self_attention:
                # We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
                # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
                # the second cross attention block.
                _UpperCAmelCase					=      (
                    AdaLayerNorm(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
                    if self.use_ada_layer_norm
                    else nn.LayerNorm(_SCREAMING_SNAKE_CASE      ,   elementwise_affine=_SCREAMING_SNAKE_CASE						)
                )
                _UpperCAmelCase					=      Attention(
                    query_dim=_SCREAMING_SNAKE_CASE      ,   cross_attention_dim=cross_attention_dim if not double_self_attention else None      ,   heads=_SCREAMING_SNAKE_CASE      ,   dim_head=_SCREAMING_SNAKE_CASE      ,   dropout=_SCREAMING_SNAKE_CASE      ,   bias=_SCREAMING_SNAKE_CASE      ,   upcast_attention=_SCREAMING_SNAKE_CASE      ,   )  # is self-attn if encoder_hidden_states is none
         else:
                _UpperCAmelCase					=      None
                _UpperCAmelCase					=      None

         # 3. Feed-forward
         _UpperCAmelCase					=      nn.LayerNorm(_SCREAMING_SNAKE_CASE      ,   elementwise_affine=_SCREAMING_SNAKE_CASE						)
         _UpperCAmelCase					=      FeedForward(_SCREAMING_SNAKE_CASE      ,   dropout=_SCREAMING_SNAKE_CASE      ,   activation_fn=_SCREAMING_SNAKE_CASE      ,   final_dropout=_SCREAMING_SNAKE_CASE						)

         # let chunk size default to None
         _UpperCAmelCase					=      None
         _UpperCAmelCase					=      0
  def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	Dict:




         """simple docstring"""





         _UpperCAmelCase					=      chunk_size
         _UpperCAmelCase					=      dim






  def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   )			->	Dict:




         """simple docstring"""





         if self.use_ada_layer_norm:
                _UpperCAmelCase					=      self.norma(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
         elif self.use_ada_layer_norm_zero:
                _UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase					=      self.norma(
                    _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   hidden_dtype=hidden_states.dtype						)
         else:
                _UpperCAmelCase					=      self.norma(_SCREAMING_SNAKE_CASE						)

         _UpperCAmelCase					=      cross_attention_kwargs if cross_attention_kwargs is not None else {}

         _UpperCAmelCase					=      self.attna(
             _SCREAMING_SNAKE_CASE      ,   encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None      ,   attention_mask=_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE      ,   )
         if self.use_ada_layer_norm_zero:
                _UpperCAmelCase					=      gate_msa.unsqueeze(1						) * attn_output
         _UpperCAmelCase					=      attn_output + hidden_states

         # 2. Cross-Attention
         if self.attna is not None:
                _UpperCAmelCase					=      (
                    self.norma(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						) if self.use_ada_layer_norm else self.norma(_SCREAMING_SNAKE_CASE						)
                )

                _UpperCAmelCase					=      self.attna(
                    _SCREAMING_SNAKE_CASE      ,   encoder_hidden_states=_SCREAMING_SNAKE_CASE      ,   attention_mask=_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE      ,   )
                _UpperCAmelCase					=      attn_output + hidden_states

         # 3. Feed-forward
         _UpperCAmelCase					=      self.norma(_SCREAMING_SNAKE_CASE						)

         if self.use_ada_layer_norm_zero:
                _UpperCAmelCase					=      norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]

         if self._chunk_size is not None:
                # "feed_forward_chunk_size" can be used to save memory
                if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
                       raise ValueError(
                           f'''`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`.'''						)

                _UpperCAmelCase					=      norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
                _UpperCAmelCase					=      torch.cat(
                    [self.ff(_SCREAMING_SNAKE_CASE						) for hid_slice in norm_hidden_states.chunk(_SCREAMING_SNAKE_CASE      ,   dim=self._chunk_dim						)]      ,   dim=self._chunk_dim      ,   )
         else:
                _UpperCAmelCase					=      self.ff(_SCREAMING_SNAKE_CASE						)

         if self.use_ada_layer_norm_zero:
                _UpperCAmelCase					=      gate_mlp.unsqueeze(1						) * ff_output

         _UpperCAmelCase					=      ff_output + hidden_states

         return hidden_states



class 		__a				(    nn.Module      ):
  def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = 4      ,   _SCREAMING_SNAKE_CASE = 0.0      ,   _SCREAMING_SNAKE_CASE = "geglu"      ,   _SCREAMING_SNAKE_CASE = False      ,   )			->	Any:




         """simple docstring"""





         super().__init__()
         _UpperCAmelCase					=      int(dim * mult						)
         _UpperCAmelCase					=      dim_out if dim_out is not None else dim

         if activation_fn == "gelu":
                _UpperCAmelCase					=      GELU(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
         if activation_fn == "gelu-approximate":
                _UpperCAmelCase					=      GELU(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   approximate='tanh'						)
         elif activation_fn == "geglu":
                _UpperCAmelCase					=      GEGLU(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
         elif activation_fn == "geglu-approximate":
                _UpperCAmelCase					=      ApproximateGELU(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)

         _UpperCAmelCase					=      nn.ModuleList([]						)
         # project in
         self.net.append(_SCREAMING_SNAKE_CASE						)
         # project dropout
         self.net.append(nn.Dropout(_SCREAMING_SNAKE_CASE						)						)
         # project out
         self.net.append(nn.Linear(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)						)
         # FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
         if final_dropout:
                self.net.append(nn.Dropout(_SCREAMING_SNAKE_CASE						)						)






  def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	str:




         """simple docstring"""





         for module in self.net:
                _UpperCAmelCase					=      module(_SCREAMING_SNAKE_CASE						)
         return hidden_states



class 		__a				(    nn.Module      ):
  def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = "none"						)			->	List[Any]:




         """simple docstring"""





         super().__init__()
         _UpperCAmelCase					=      nn.Linear(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
         _UpperCAmelCase					=      approximate
  def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	Optional[Any]:




         """simple docstring"""





         if gate.device.type != "mps":
                return F.gelu(_SCREAMING_SNAKE_CASE      ,   approximate=self.approximate						)
         # mps: gelu is not implemented for float16
         return F.gelu(gate.to(dtype=torch.floataa						)      ,   approximate=self.approximate						).to(dtype=gate.dtype						)






  def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	List[str]:




         """simple docstring"""





         _UpperCAmelCase					=      self.proj(_SCREAMING_SNAKE_CASE						)
         _UpperCAmelCase					=      self.gelu(_SCREAMING_SNAKE_CASE						)
         return hidden_states



class 		__a				(    nn.Module      ):
  def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	str:




         """simple docstring"""





         super().__init__()
         _UpperCAmelCase					=      nn.Linear(_SCREAMING_SNAKE_CASE      ,   dim_out * 2						)
  def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	Tuple:




         """simple docstring"""





         if gate.device.type != "mps":
                return F.gelu(_SCREAMING_SNAKE_CASE						)
         # mps: gelu is not implemented for float16
         return F.gelu(gate.to(dtype=torch.floataa						)						).to(dtype=gate.dtype						)






  def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	Optional[int]:




         """simple docstring"""





         _UpperCAmelCase		,	_UpperCAmelCase					=      self.proj(_SCREAMING_SNAKE_CASE						).chunk(2      ,   dim=-1						)
         return hidden_states * self.gelu(_SCREAMING_SNAKE_CASE						)



class 		__a				(    nn.Module      ):
  def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	Optional[int]:




         """simple docstring"""





         super().__init__()
         _UpperCAmelCase					=      nn.Linear(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)






  def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	int:




         """simple docstring"""





         _UpperCAmelCase					=      self.proj(_SCREAMING_SNAKE_CASE						)
         return x * torch.sigmoid(1.702 * x						)



class 		__a				(    nn.Module      ):
  def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	int:




         """simple docstring"""





         super().__init__()
         _UpperCAmelCase					=      nn.Embedding(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
         _UpperCAmelCase					=      nn.SiLU()
         _UpperCAmelCase					=      nn.Linear(_SCREAMING_SNAKE_CASE      ,   embedding_dim * 2						)
         _UpperCAmelCase					=      nn.LayerNorm(_SCREAMING_SNAKE_CASE      ,   elementwise_affine=_SCREAMING_SNAKE_CASE						)






  def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	str:




         """simple docstring"""





         _UpperCAmelCase					=      self.linear(self.silu(self.emb(_SCREAMING_SNAKE_CASE						)						)						)
         _UpperCAmelCase		,	_UpperCAmelCase					=      torch.chunk(_SCREAMING_SNAKE_CASE      ,   2						)
         _UpperCAmelCase					=      self.norm(_SCREAMING_SNAKE_CASE						) * (1 + scale) + shift
         return x



class 		__a				(    nn.Module      ):
  def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	Tuple:




         """simple docstring"""





         super().__init__()

         _UpperCAmelCase					=      CombinedTimestepLabelEmbeddings(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)

         _UpperCAmelCase					=      nn.SiLU()
         _UpperCAmelCase					=      nn.Linear(_SCREAMING_SNAKE_CASE      ,   6 * embedding_dim      ,   bias=_SCREAMING_SNAKE_CASE						)
         _UpperCAmelCase					=      nn.LayerNorm(_SCREAMING_SNAKE_CASE      ,   elementwise_affine=_SCREAMING_SNAKE_CASE      ,   eps=1e-6						)






  def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=None						)			->	int:




         """simple docstring"""





         _UpperCAmelCase					=      self.linear(self.silu(self.emb(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   hidden_dtype=_SCREAMING_SNAKE_CASE						)						)						)
         _UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase					=      emb.chunk(6      ,   dim=1						)
         _UpperCAmelCase					=      self.norm(_SCREAMING_SNAKE_CASE						) * (1 + scale_msa[:, None]) + shift_msa[:, None]
         return x, gate_msa, shift_mlp, scale_mlp, gate_mlp



class 		__a				(    nn.Module      ):
  def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = 1e-5						)			->	str:




         """simple docstring"""





         super().__init__()
         _UpperCAmelCase					=      num_groups
         _UpperCAmelCase					=      eps

         if act_fn is None:
                _UpperCAmelCase					=      None
         else:
                _UpperCAmelCase					=      get_activation(_SCREAMING_SNAKE_CASE						)

         _UpperCAmelCase					=      nn.Linear(_SCREAMING_SNAKE_CASE      ,   out_dim * 2						)






  def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	Union[str, Any]:




         """simple docstring"""





         if self.act:
                _UpperCAmelCase					=      self.act(_SCREAMING_SNAKE_CASE						)
         _UpperCAmelCase					=      self.linear(_SCREAMING_SNAKE_CASE						)
         _UpperCAmelCase					=      emb[:, :, None, None]
         _UpperCAmelCase		,	_UpperCAmelCase					=      emb.chunk(2      ,   dim=1						)

         _UpperCAmelCase					=      F.group_norm(_SCREAMING_SNAKE_CASE      ,   self.num_groups      ,   eps=self.eps						)
         _UpperCAmelCase					=      x * (1 + scale) + shift
         return x




# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os
import subprocess

from packaging.version import Version, parse

from accelerate.commands.config.config_args import default_config_file, load_config_from_file


lowerCAmelCase__  :Dict             =							'''Run commands across TPU VMs for initial setup before running `accelerate launch`.'''

def 	lowerCAmelCase__			(      a__:		Optional[Any]=None	)  ->		List[Any]:


							'''simple docstring'''





							if subparsers is not None:
														_UpperCAmelCase					=      subparsers.add_parser('tpu-config'      ,  description=_description	)
							else:
														_UpperCAmelCase					=      argparse.ArgumentParser('Accelerate tpu-config command'      ,  description=_description	)
							# Core arguments
							_UpperCAmelCase					=      parser.add_argument_group(
							    'Config Arguments'      ,  'Arguments that can be configured through `accelerate config`.'	)
							config_args.add_argument(
							    '--config_file'      ,  type=a__      ,  default=a__      ,  help='Path to the config file to use for accelerate.'      ,  )
							config_args.add_argument(
							    '--tpu_name'      ,  default=a__      ,  help='The name of the TPU to use. If not specified, will use the TPU specified in the config file.'      ,  )
							config_args.add_argument(
							    '--tpu_zone'      ,  default=a__      ,  help='The zone of the TPU to use. If not specified, will use the zone specified in the config file.'      ,  )
							_UpperCAmelCase					=      parser.add_argument_group('TPU Arguments'      ,  'Arguments for options ran inside the TPU.'	)
							pod_args.add_argument(
							    '--use_alpha'      ,  action='store_true'      ,  help='Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`.'      ,  )
							pod_args.add_argument(
							    '--command_file'      ,  default=a__      ,  help='The path to the file containing the commands to run on the pod on startup.'      ,  )
							pod_args.add_argument(
							    '--command'      ,  action='append'      ,  nargs='+'      ,  help='A command to run on the pod. Can be passed multiple times.'      ,  )
							pod_args.add_argument(
							    '--install_accelerate'      ,  action='store_true'      ,  help='Whether to install accelerate on the pod. Defaults to False.'      ,  )
							pod_args.add_argument(
							    '--accelerate_version'      ,  default='latest'      ,  help='The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify \'dev\' to install from GitHub.'      ,  )
							pod_args.add_argument(
							    '--debug'      ,  action='store_true'      ,  help='If set, will print the command that would be run instead of running it.'	)

							if subparsers is not None:
														parser.set_defaults(func=a__	)
							return parser

def 	lowerCAmelCase__			(      a__:		str	)  ->		Any:


							'''simple docstring'''





							_UpperCAmelCase					=      None

							# Get the default from the config file if it exists.
							if args.config_file is not None or os.path.isfile(a__	):
														_UpperCAmelCase					=      load_config_from_file(args.config_file	)
														if not args.command_file and defaults.command_file is not None and not args.command:
																					_UpperCAmelCase					=      defaults.command_file
														if not args.command and defaults.commands is not None:
																					_UpperCAmelCase					=      defaults.commands
														if not args.tpu_name:
																					_UpperCAmelCase					=      defaults.tpu_name
														if not args.tpu_zone:
																					_UpperCAmelCase					=      defaults.tpu_zone
							if args.accelerate_version == "dev":
														_UpperCAmelCase					=      'git+https://github.com/huggingface/accelerate.git'
							elif args.accelerate_version == "latest":
														_UpperCAmelCase					=      'accelerate -U'
							elif isinstance(parse(args.accelerate_version	)      ,  a__	):
														_UpperCAmelCase					=      F'''accelerate=={args.accelerate_version}'''

							if not args.command_file and not args.command:
														raise ValueError('You must specify either a command file or a command to run on the pod.'	)

							if args.command_file:
														with open(args.command_file      ,  'r'	) as f:
																					_UpperCAmelCase					=      [f.read().splitlines()]

    # To turn list of lists into list of strings
							if isinstance(args.command[0]      ,  a__	):
														_UpperCAmelCase					=      [line for cmd in args.command for line in cmd]
							# Default to the shared folder and install accelerate
							_UpperCAmelCase					=      ['cd /usr/share']
							if args.install_accelerate:
														new_cmd += [F'''pip install {args.accelerate_version}''']
							new_cmd += args.command
							_UpperCAmelCase					=      '; '.join(a__	)

							# Then send it to gcloud
							# Eventually try to use google-api-core to do this instead of subprocess
							_UpperCAmelCase					=      ['gcloud']
							if args.use_alpha:
														cmd += ["alpha"]
							cmd += [
							    "compute",
							    "tpus",
							    "tpu-vm",
							    "ssh",
							    args.tpu_name,
							    "--zone",
							    args.tpu_zone,
							    "--command",
							    args.command,
							    "--worker",
							    "all",
							]
							if args.debug:
														print(F'''Running {" ".join(a__	)}'''	)
														return
							subprocess.run(a__	)
							print('Successfully setup pod.'	)







def 	lowerCAmelCase__			(      )  ->		Optional[Any]:


							'''simple docstring'''





							_UpperCAmelCase					=      tpu_command_parser()
							_UpperCAmelCase					=      parser.parse_args()

							tpu_command_launcher(a__	)
