
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


lowercase   =	['''text''', '''image''', '''audio''']



def   __lowerCAmelCase    (				UpperCAmelCase__					:   List[str]							)	->     int:
							lowerCamelCase_					=       []

							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((5_1_2, 5_1_2)							)							)
														elif input_type == "audio":
																					inputs.append(torch.ones(3_0_0_0							)							)
														elif isinstance(UpperCAmelCase__      ,					UpperCAmelCase__							):
																					inputs.append(create_inputs(UpperCAmelCase__							)							)
														else:
																					raise ValueError(F'''Invalid type requested: {input_type}'''							)

							return inputs



def   __lowerCAmelCase    (				UpperCAmelCase__					:   List							)	->     List[Any]:
							lowerCamelCase_					=       []

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

							return output_types





@is_tool_test
class    __A:

					def  lowercase__   (						self       :  Optional[int] ):
												self.assertTrue(hasattr(self.tool ,       """inputs""" ) )
												self.assertTrue(hasattr(self.tool ,       """outputs""" ) )

												lowerCamelCase_					=       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 )

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

					def  lowercase__   (						self       :  Union[str, Any] ):
												lowerCamelCase_					=       create_inputs(self.tool.inputs )
												lowerCamelCase_					=       self.tool(*__UpperCamelCase )

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

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

					def  lowercase__   (						self       :  Tuple ):
												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  lowercase__   (						self       :  Dict ):
												lowerCamelCase_					=       create_inputs(self.tool.inputs )
												lowerCamelCase_					=       self.tool(*__UpperCamelCase )

												if not isinstance(__UpperCamelCase ,       __UpperCamelCase ):
																			lowerCamelCase_					=       [outputs]

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

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



					def  lowercase__   (						self       :  List[Any] ):
												lowerCamelCase_					=       create_inputs(self.tool.inputs )

												lowerCamelCase_					=       []

												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
												lowerCamelCase_					=       self.tool(*__UpperCamelCase )

												if not isinstance(__UpperCamelCase ,       __UpperCamelCase ):
																			lowerCamelCase_					=       [outputs]

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







from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase   =	logging.get_logger(__name__)

lowercase   =	{
    '''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json''',
}





class    __A(					UpperCAmelCase							):
					SCREAMING_SNAKE_CASE           =     '''gpt_neox_japanese'''

					def __init__(						self       :  Union[str, Any] ,       __UpperCamelCase       :  str=3_2_0_0_0 ,       __UpperCamelCase       :  List[Any]=2_5_6_0 ,       __UpperCamelCase       :  Any=3_2 ,       __UpperCamelCase       :  List[str]=3_2 ,       __UpperCamelCase       :  List[str]=4 ,       __UpperCamelCase       :  Dict="gelu" ,       __UpperCamelCase       :  List[Any]=1.00 ,       __UpperCamelCase       :  Any=1_0_0_0_0 ,       __UpperCamelCase       :  Optional[Any]=2_0_4_8 ,       __UpperCamelCase       :  Tuple=0.02 ,       __UpperCamelCase       :  List[str]=1E-5 ,       __UpperCamelCase       :  str=True ,       __UpperCamelCase       :  str=3_1_9_9_6 ,       __UpperCamelCase       :  int=3_1_9_9_9 ,       __UpperCamelCase       :  Optional[Any]=0.1 ,       __UpperCamelCase       :  Tuple=0.0 ,       **__UpperCamelCase       :  List[str] ,       ):
												super().__init__(bos_token_id=__UpperCamelCase ,       eos_token_id=__UpperCamelCase ,       **__UpperCamelCase )
												lowerCamelCase_					=       vocab_size
												lowerCamelCase_					=       max_position_embeddings
												lowerCamelCase_					=       hidden_size
												lowerCamelCase_					=       num_hidden_layers
												lowerCamelCase_					=       num_attention_heads
												lowerCamelCase_					=       intermediate_multiple_size
												lowerCamelCase_					=       hidden_act
												lowerCamelCase_					=       rotary_pct
												lowerCamelCase_					=       rotary_emb_base
												lowerCamelCase_					=       initializer_range
												lowerCamelCase_					=       layer_norm_eps
												lowerCamelCase_					=       use_cache
												lowerCamelCase_					=       attention_dropout
												lowerCamelCase_					=       hidden_dropout





