



"""simple docstring"""






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
		from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


_lowercase : List[str]      						=						logging.get_logger(__name__)

_lowercase : int      						=						{
    "microsoft/deberta-v2-xlarge": "https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xxlarge": "https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"
    ),
    "microsoft/deberta-v2-xxlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"
    ),
}

class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					_a   			=							'deberta-v2'



					def __init__(       self							:  Optional[Any],  lowerCamelCase							:  Optional[int]=12_8100,  lowerCamelCase							:  List[Any]=1536,  lowerCamelCase							:  Dict=24,  lowerCamelCase							:  Any=24,  lowerCamelCase							:  Union[str, Any]=6144,  lowerCamelCase							:  List[Any]="gelu",  lowerCamelCase							:  int=0.1,  lowerCamelCase							:  Union[str, Any]=0.1,  lowerCamelCase							:  Union[str, Any]=512,  lowerCamelCase							:  Optional[Any]=0,  lowerCamelCase							:  Any=0.02,  lowerCamelCase							:  int=1E-7,  lowerCamelCase							:  Union[str, Any]=False,  lowerCamelCase							:  Union[str, Any]=-1,  lowerCamelCase							:  Tuple=0,  lowerCamelCase							:  Union[str, Any]=True,  lowerCamelCase							:  int=None,  lowerCamelCase							:  Dict=0,  lowerCamelCase							:  Tuple="gelu",  **lowerCamelCase							:  Optional[int],  )->					Union[str, Any]:
								super().__init__(**lowerCamelCase     )

								lowerCamelCase__		:		str			=hidden_size
								lowerCamelCase__		:		Optional[int]			=num_hidden_layers
								lowerCamelCase__		:		Optional[Any]			=num_attention_heads
								lowerCamelCase__		:		List[Any]			=intermediate_size
								lowerCamelCase__		:		int			=hidden_act
								lowerCamelCase__		:		Tuple			=hidden_dropout_prob
								lowerCamelCase__		:		Union[str, Any]			=attention_probs_dropout_prob
								lowerCamelCase__		:		Optional[Any]			=max_position_embeddings
								lowerCamelCase__		:		int			=type_vocab_size
								lowerCamelCase__		:		Tuple			=initializer_range
								lowerCamelCase__		:		Tuple			=relative_attention
								lowerCamelCase__		:		Optional[Any]			=max_relative_positions
								lowerCamelCase__		:		List[Any]			=pad_token_id
								lowerCamelCase__		:		int			=position_biased_input

								# Backwards compatibility
								if type(lowerCamelCase     ) == str:
											lowerCamelCase__		:		Union[str, Any]			=[x.strip() for x in pos_att_type.lower().split('''|'''     )]

								lowerCamelCase__		:		Tuple			=pos_att_type
								lowerCamelCase__		:		Union[str, Any]			=vocab_size
								lowerCamelCase__		:		Optional[int]			=layer_norm_eps

								lowerCamelCase__		:		Dict			=kwargs.get('''pooler_hidden_size''',  lowerCamelCase     )
								lowerCamelCase__		:		Tuple			=pooler_dropout
								lowerCamelCase__		:		List[Any]			=pooler_hidden_act





class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					@property
					def 		snake_case       (       self							:  List[str]     )->					Mapping[str, Mapping[int, str]]:
								if self.task == "multiple-choice":
											lowerCamelCase__		:		Union[str, Any]			={0: '''batch''', 1: '''choice''', 2: '''sequence'''}
								else:
											lowerCamelCase__		:		Any			={0: '''batch''', 1: '''sequence'''}
								if self._config.type_vocab_size > 0:
											return OrderedDict(
											    [('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis), ('''token_type_ids''', dynamic_axis)]     )
								else:
											return OrderedDict([('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis)]     )



					@property
					def 		snake_case       (       self							:  List[str]     )->					int:
								return 12




					def 		snake_case       (       self							:  str,  lowerCamelCase							:  Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"],  lowerCamelCase							:  int = -1,  lowerCamelCase							:  int = -1,  lowerCamelCase							:  int = -1,  lowerCamelCase							:  bool = False,  lowerCamelCase							:  Optional["TensorType"] = None,  lowerCamelCase							:  int = 3,  lowerCamelCase							:  int = 40,  lowerCamelCase							:  int = 40,  lowerCamelCase							:  "PreTrainedTokenizerBase" = None,  )->					Mapping[str, Any]:
								lowerCamelCase__		:		List[Any]			=super().generate_dummy_inputs(preprocessor=lowerCamelCase,  framework=lowerCamelCase     )
								if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
											del dummy_inputs["token_type_ids"]
								return dummy_inputs









"""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


_lowercase : Union[str, Any]      						=						["text", "image", "audio"]






def      snake_case__   (		__lowerCamelCase				:    List[str]	):
			"""simple docstring"""
			lowerCamelCase__		:		Union[str, Any]			=[]

			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      snake_case__   (		__lowerCamelCase				:    List	):
			"""simple docstring"""
			lowerCamelCase__		:		Tuple			=[]

			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        __SCREAMING_SNAKE_CASE	:


					'''simple docstring'''





					def 		snake_case       (       self							:  Any     )->					Optional[Any]:
								self.assertTrue(hasattr(self.tool,  '''inputs'''     )     )
								self.assertTrue(hasattr(self.tool,  '''outputs'''     )     )

								lowerCamelCase__		:		Tuple			=self.tool.inputs
								for _input in inputs:
											if isinstance(_input,  lowerCamelCase     ):
														for __input in _input:
																	self.assertTrue(__input in authorized_types     )
											else:
														self.assertTrue(_input in authorized_types     )

								lowerCamelCase__		:		Optional[Any]			=self.tool.outputs
								for _output in outputs:
											self.assertTrue(_output in authorized_types     )



					def 		snake_case       (       self							:  Optional[int]     )->					Union[str, Any]:
								lowerCamelCase__		:		Optional[int]			=create_inputs(self.tool.inputs     )
								lowerCamelCase__		:		List[Any]			=self.tool(*lowerCamelCase     )

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

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



					def 		snake_case       (       self							:  Union[str, Any]     )->					List[str]:
								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 		snake_case       (       self							:  Union[str, Any]     )->					str:
								lowerCamelCase__		:		List[str]			=create_inputs(self.tool.inputs     )
								lowerCamelCase__		:		Optional[Any]			=self.tool(*lowerCamelCase     )

								if not isinstance(lowerCamelCase,  lowerCamelCase     ):
											lowerCamelCase__		:		Any			=[outputs]

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

								for output, output_type in zip(lowerCamelCase,  self.tool.outputs     ):
											lowerCamelCase__		:		List[Any]			=AGENT_TYPE_MAPPING[output_type]
											self.assertTrue(isinstance(lowerCamelCase,  lowerCamelCase     )     )




					def 		snake_case       (       self							:  Optional[Any]     )->					List[Any]:
								lowerCamelCase__		:		Optional[Any]			=create_inputs(self.tool.inputs     )

								lowerCamelCase__		:		List[str]			=[]

								for _input, input_type in zip(lowerCamelCase,  self.tool.inputs     ):
											if isinstance(lowerCamelCase,  lowerCamelCase     ):
														_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__		:		Any			=self.tool(*lowerCamelCase     )

								if not isinstance(lowerCamelCase,  lowerCamelCase     ):
											lowerCamelCase__		:		Optional[int]			=[outputs]

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




