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


__UpperCamelCase	: Optional[Any]    		=  ['text', 'image', 'audio']
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Dict           =							[]

						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(3_000		)		)
												elif isinstance(_lowercase	,						_lowercase		):
																		inputs.append(create_inputs(_lowercase		)		)
												else:
																		raise ValueError(f"""Invalid type requested: {input_type}"""		)

						return inputs
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Dict           =							[]

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

						return output_types





@is_tool_test
class       lowercase__		:


						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												self.assertTrue(hasattr(self.tool					,	'''inputs'''		)		)
												self.assertTrue(hasattr(self.tool					,	'''outputs'''		)		)

												SCREAMING_SNAKE_CASE							:					List[str]           =							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							:					Dict           =							self.tool.outputs
												for _output in outputs:
																		self.assertTrue(_output in authorized_types		)


						def  __A ( self			:       Any		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[str]           =							create_inputs(self.tool.inputs		)
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							self.tool(*UpperCamelCase__		)

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

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


						def  __A ( self			:       Any		):





												'''simple docstring'''





												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  __A ( self			:       Any		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					int           =							create_inputs(self.tool.inputs		)
												SCREAMING_SNAKE_CASE							:					str           =							self.tool(*UpperCamelCase__		)

												if not isinstance(UpperCamelCase__					,	UpperCamelCase__		):
																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							[outputs]

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

												for output, output_type in zip(UpperCamelCase__					,	self.tool.outputs		):
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							AGENT_TYPE_MAPPING[output_type]
																		self.assertTrue(isinstance(UpperCamelCase__					,	UpperCamelCase__		)		)



						def  __A ( self			:       str		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							create_inputs(self.tool.inputs		)

												SCREAMING_SNAKE_CASE							:					Dict           =							[]

												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							:					List[Any]           =							self.tool(*UpperCamelCase__		)

												if not isinstance(UpperCamelCase__					,	UpperCamelCase__		):
																		SCREAMING_SNAKE_CASE							:					Tuple           =							[outputs]

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






# Copyright 2021 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

from ...utils.dataclasses import (
    ComputeEnvironment,
    DistributedType,
    DynamoBackend,
    PrecisionType,
    SageMakerDistributedType,
)
from ..menu import BulletMenu


__UpperCamelCase	: Dict    		=  [
    'EAGER',
    'AOT_EAGER',
    'INDUCTOR',
    'NVFUSER',
    'AOT_NVFUSER',
    'AOT_CUDAGRAPHS',
    'OFI',
    'FX2TRT',
    'ONNXRT',
    'IPEX',
]
def    A			(					_lowercase	,						_lowercase=None	,						_lowercase=None	,						_lowercase=None		):
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							True
						while ask_again:
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							input(_lowercase		)
												try:
																		if default is not None and len(_lowercase		) == 0:
																								return default
																		return convert_value(_lowercase		) if convert_value is not None else result
												except Exception:
																		if error_message is not None:
																								print(_lowercase		)
def    A			(					_lowercase	,						_lowercase=[]	,						_lowercase=None	,						_lowercase=0		):
						SCREAMING_SNAKE_CASE							:					Dict           =							BulletMenu(_lowercase	,						_lowercase		)
						SCREAMING_SNAKE_CASE							:					str           =							menu.run(default_choice=_lowercase		)
						return convert_value(_lowercase		) if convert_value is not None else result
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Dict           =							int(_lowercase		)
						return ComputeEnvironment(['''LOCAL_MACHINE''', '''AMAZON_SAGEMAKER'''][value]		)
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Any           =							int(_lowercase		)
						return DistributedType(['''NO''', '''MULTI_CPU''', '''MULTI_XPU''', '''MULTI_GPU''', '''MULTI_NPU''', '''TPU'''][value]		)
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							int(_lowercase		)
						return DynamoBackend(DYNAMO_BACKENDS[value]		).value
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							int(_lowercase		)
						return PrecisionType(['''no''', '''fp16''', '''bf16''', '''fp8'''][value]		)
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Dict           =							int(_lowercase		)
						return SageMakerDistributedType(['''NO''', '''DATA_PARALLEL''', '''MODEL_PARALLEL'''][value]		)
def    A			(					_lowercase		):
						return {"yes": True, "no": False}[value.lower()]





class       lowercase__		(     argparse.RawDescriptionHelpFormatter):


						def  __A ( self			:       List[str]					,	UpperCamelCase__			:       List[str]					,	UpperCamelCase__			:       Dict					,	UpperCamelCase__			:       Dict					,	UpperCamelCase__			:       Dict		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					str           =							super()._format_usage(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							usage.replace('''<command> [<args>] '''					,	''''''		)
												return usage





