


"""simple docstring"""







from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image


a__				:							List[Any]						=     ['''text''', '''image''', '''audio''']




def    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				[]

					for input_type in input_types:
										if input_type == "text":
															inputs.append("Text input"   )
										elif input_type == "image":
															inputs.append(
															    Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"   )   ) / "000000039769.png"   ).resize((512, 512)   )   )
										elif input_type == "audio":
															inputs.append(torch.ones(3000   )   )
										elif isinstance(lowerCAmelCase_  ,						lowerCAmelCase_   ):
															inputs.append(create_inputs(lowerCAmelCase_   )   )
										else:
															raise ValueError(f"""Invalid type requested: {input_type}"""   )

					return inputs




def    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				[]

					for output in outputs:
										if isinstance(lowerCAmelCase_  ,						(str, AgentText)   ):
															output_types.append("text"   )
										elif isinstance(lowerCAmelCase_  ,						(Image.Image, AgentImage)   ):
															output_types.append("image"   )
										elif isinstance(lowerCAmelCase_  ,						(torch.Tensor, AgentAudio)   ):
															output_types.append("audio"   )
										else:
															raise ValueError(f"""Invalid output: {output}"""   )

					return output_types




@is_tool_test
class  UpperCamelCase_	:

	"""simple docstring"""





	def   UpperCAmelCase_      ( self			:				Dict   )       ->	List[str]:
						self.assertTrue(hasattr(self.tool			,			"inputs"   )   )
						self.assertTrue(hasattr(self.tool			,			"outputs"   )   )

						__SCREAMING_SNAKE_CASE						   =				self.tool.inputs
						for _input in inputs:
											if isinstance(_input			,			UpperCAmelCase__   ):
																for __input in _input:
																					self.assertTrue(__input in authorized_types   )
											else:
																self.assertTrue(_input in authorized_types   )

						__SCREAMING_SNAKE_CASE						   =				self.tool.outputs
						for _output in outputs:
											self.assertTrue(_output in authorized_types   )




	def   UpperCAmelCase_      ( self			:				Dict   )       ->	List[Any]:
						__SCREAMING_SNAKE_CASE						   =				create_inputs(self.tool.inputs   )
						__SCREAMING_SNAKE_CASE						   =				self.tool(*UpperCAmelCase__   )

						# There is a single output
						if len(self.tool.outputs   ) == 1:
											__SCREAMING_SNAKE_CASE						   =				[outputs]

						self.assertListEqual(output_types(UpperCAmelCase__   )			,			self.tool.outputs   )




	def   UpperCAmelCase_      ( self			:				Union[str, Any]   )       ->	int:
						self.assertTrue(hasattr(self.tool			,			"description"   )   )
						self.assertTrue(hasattr(self.tool			,			"default_checkpoint"   )   )
						self.assertTrue(self.tool.description.startswith("This is a tool that"   )   )




	def   UpperCAmelCase_      ( self			:				Tuple   )       ->	Optional[int]:
						__SCREAMING_SNAKE_CASE						   =				create_inputs(self.tool.inputs   )
						__SCREAMING_SNAKE_CASE						   =				self.tool(*UpperCAmelCase__   )

						if not isinstance(UpperCAmelCase__			,			UpperCAmelCase__   ):
											__SCREAMING_SNAKE_CASE						   =				[outputs]

						self.assertEqual(len(UpperCAmelCase__   )			,			len(self.tool.outputs   )   )

						for output, output_type in zip(UpperCAmelCase__			,			self.tool.outputs   ):
											__SCREAMING_SNAKE_CASE						   =				AGENT_TYPE_MAPPING[output_type]
											self.assertTrue(isinstance(UpperCAmelCase__			,			UpperCAmelCase__   )   )




	def   UpperCAmelCase_      ( self			:				List[str]   )       ->	Tuple:
						__SCREAMING_SNAKE_CASE						   =				create_inputs(self.tool.inputs   )

						__SCREAMING_SNAKE_CASE						   =				[]

						for _input, input_type in zip(UpperCAmelCase__			,			self.tool.inputs   ):
											if isinstance(UpperCAmelCase__			,			UpperCAmelCase__   ):
																_inputs.append([AGENT_TYPE_MAPPING[_input_type](_input   ) for _input_type in input_type]   )
											else:
																_inputs.append(AGENT_TYPE_MAPPING[input_type](_input   )   )

        # Should not raise an error
						__SCREAMING_SNAKE_CASE						   =				self.tool(*UpperCAmelCase__   )

						if not isinstance(UpperCAmelCase__			,			UpperCAmelCase__   ):
											__SCREAMING_SNAKE_CASE						   =				[outputs]

						self.assertEqual(len(UpperCAmelCase__   )			,			len(self.tool.outputs   )   )




"""simple docstring"""







# 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


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




def    UpperCAmelCase__       (lowerCAmelCase_=None   ):




					'''simple docstring'''


					if subparsers is not None:
										__SCREAMING_SNAKE_CASE						   =				subparsers.add_parser("tpu-config"  ,						description=_description   )
					else:
										__SCREAMING_SNAKE_CASE						   =				argparse.ArgumentParser("Accelerate tpu-config command"  ,						description=_description   )
					# Core arguments
					__SCREAMING_SNAKE_CASE						   =				parser.add_argument_group(
					    "Config Arguments"  ,						"Arguments that can be configured through `accelerate config`."   )
					config_args.add_argument(
					    "--config_file"  ,						type=lowerCAmelCase_  ,						default=lowerCAmelCase_  ,						help="Path to the config file to use for accelerate."  ,						)
					config_args.add_argument(
					    "--tpu_name"  ,						default=lowerCAmelCase_  ,						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=lowerCAmelCase_  ,						help="The zone of the TPU to use. If not specified, will use the zone specified in the config file."  ,						)
					__SCREAMING_SNAKE_CASE						   =				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=lowerCAmelCase_  ,						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=lowerCAmelCase_   )
					return parser




def    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				None

					# Get the default from the config file if it exists.
					if args.config_file is not None or os.path.isfile(lowerCAmelCase_   ):
										__SCREAMING_SNAKE_CASE						   =				load_config_from_file(args.config_file   )
										if not args.command_file and defaults.command_file is not None and not args.command:
															__SCREAMING_SNAKE_CASE						   =				defaults.command_file
										if not args.command and defaults.commands is not None:
															__SCREAMING_SNAKE_CASE						   =				defaults.commands
										if not args.tpu_name:
															__SCREAMING_SNAKE_CASE						   =				defaults.tpu_name
										if not args.tpu_zone:
															__SCREAMING_SNAKE_CASE						   =				defaults.tpu_zone
					if args.accelerate_version == "dev":
										__SCREAMING_SNAKE_CASE						   =				"git+https://github.com/huggingface/accelerate.git"
					elif args.accelerate_version == "latest":
										__SCREAMING_SNAKE_CASE						   =				"accelerate -U"
					elif isinstance(parse(args.accelerate_version   )  ,						lowerCAmelCase_   ):
										__SCREAMING_SNAKE_CASE						   =				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:
															__SCREAMING_SNAKE_CASE						   =				[f.read().splitlines()]

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

					# Then send it to gcloud
					# Eventually try to use google-api-core to do this instead of subprocess
					__SCREAMING_SNAKE_CASE						   =				["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(lowerCAmelCase_   )}"""   )
										return
					subprocess.run(lowerCAmelCase_   )
					print("Successfully setup pod."   )




def    UpperCAmelCase__       ():




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				tpu_command_parser()
					__SCREAMING_SNAKE_CASE						   =				parser.parse_args()

					tpu_command_launcher(lowerCAmelCase_   )
