






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


def __A    (      __lowerCAmelCase	)->  Optional[int]:





						"""simple docstring"""



						_UpperCAmelCase									=			[]

						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(__lowerCAmelCase ,		__lowerCAmelCase	):
																		inputs.append(create_inputs(__lowerCAmelCase	)	)
												else:
																		raise ValueError(F"""Invalid type requested: {input_type}"""	)

						return inputs


def __A    (      __lowerCAmelCase	)->  Optional[Any]:





						"""simple docstring"""



						_UpperCAmelCase									=			[]

						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 __lowerCamelCase :





	"""simple docstring"""






	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







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

							_UpperCAmelCase									=			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    )

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

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			create_inputs(self.tool.inputs    )
							_UpperCAmelCase									=			self.tool(*UpperCAmelCase    )

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

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

	def        UpperCamelCase    (							self    ):
							"""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        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			create_inputs(self.tool.inputs    )
							_UpperCAmelCase									=			self.tool(*UpperCAmelCase    )

							if not isinstance(UpperCAmelCase       ,    UpperCAmelCase    ):
													_UpperCAmelCase									=			[outputs]

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

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

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			create_inputs(self.tool.inputs    )

							_UpperCAmelCase									=			[]

							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
							_UpperCAmelCase									=			self.tool(*UpperCAmelCase    )

							if not isinstance(UpperCAmelCase       ,    UpperCAmelCase    ):
													_UpperCAmelCase									=			[outputs]

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


from collections import OrderedDict
from typing import Mapping

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


__snake_case								=  logging.get_logger(__name__)

__snake_case								=  {
    """facebook/xmod-base""": """https://huggingface.co/facebook/xmod-base/resolve/main/config.json""",
    """facebook/xmod-large-prenorm""": """https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json""",
    """facebook/xmod-base-13-125k""": """https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-125k""": """https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-195k""": """https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json""",
    """facebook/xmod-base-60-125k""": """https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json""",
    """facebook/xmod-base-60-265k""": """https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json""",
    """facebook/xmod-base-75-125k""": """https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json""",
    """facebook/xmod-base-75-269k""": """https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json""",
}




class 						lowercase__  (						_UpperCAmelCase  ):
	A__    :   int				       ="""xmod"""
	def __init__(			self							:   Optional[Any]       ,   UpperCAmelCase_							:   Optional[int]=30522       ,   UpperCAmelCase_							:   Union[str, Any]=768       ,   UpperCAmelCase_							:   List[str]=12       ,   UpperCAmelCase_							:   List[str]=12       ,   UpperCAmelCase_							:   List[str]=3072       ,   UpperCAmelCase_							:   Dict="gelu"       ,   UpperCAmelCase_							:   str=0.1       ,   UpperCAmelCase_							:   Dict=0.1       ,   UpperCAmelCase_							:   Optional[Any]=512       ,   UpperCAmelCase_							:   Any=2       ,   UpperCAmelCase_							:   Optional[Any]=0.02       ,   UpperCAmelCase_							:   Optional[Any]=1e-1_2       ,   UpperCAmelCase_							:   Any=1       ,   UpperCAmelCase_							:   Dict=0       ,   UpperCAmelCase_							:   Optional[int]=2       ,   UpperCAmelCase_							:   Tuple="absolute"       ,   UpperCAmelCase_							:   Union[str, Any]=True       ,   UpperCAmelCase_							:   Dict=None       ,   UpperCAmelCase_							:   Union[str, Any]=False       ,   UpperCAmelCase_							:   int=2       ,   UpperCAmelCase_							:   int=False       ,   UpperCAmelCase_							:   Optional[int]=True       ,   UpperCAmelCase_							:   List[Any]=True       ,   UpperCAmelCase_							:   Union[str, Any]=("en_XX",)       ,   UpperCAmelCase_							:   int=None       ,   **UpperCAmelCase_							:   str       ,   ):
						super().__init__(pad_token_id=UpperCAmelCase_       ,   bos_token_id=UpperCAmelCase_       ,   eos_token_id=UpperCAmelCase_       ,   **UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= vocab_size
						SCREAMING_SNAKE_CASE__													= hidden_size
						SCREAMING_SNAKE_CASE__													= num_hidden_layers
						SCREAMING_SNAKE_CASE__													= num_attention_heads
						SCREAMING_SNAKE_CASE__													= hidden_act
						SCREAMING_SNAKE_CASE__													= intermediate_size
						SCREAMING_SNAKE_CASE__													= hidden_dropout_prob
						SCREAMING_SNAKE_CASE__													= attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE__													= max_position_embeddings
						SCREAMING_SNAKE_CASE__													= type_vocab_size
						SCREAMING_SNAKE_CASE__													= initializer_range
						SCREAMING_SNAKE_CASE__													= layer_norm_eps
						SCREAMING_SNAKE_CASE__													= position_embedding_type
						SCREAMING_SNAKE_CASE__													= use_cache
						SCREAMING_SNAKE_CASE__													= classifier_dropout
						SCREAMING_SNAKE_CASE__													= pre_norm
						SCREAMING_SNAKE_CASE__													= adapter_reduction_factor
						SCREAMING_SNAKE_CASE__													= adapter_layer_norm
						SCREAMING_SNAKE_CASE__													= adapter_reuse_layer_norm
						SCREAMING_SNAKE_CASE__													= ln_before_adapter
						SCREAMING_SNAKE_CASE__													= list(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= default_language





class 						lowercase__  (						_UpperCAmelCase  ):
	@property
	def      A_				(			self							:   List[Any]							):
						if self.task == "multiple-choice":
											SCREAMING_SNAKE_CASE__													= {0: 'batch', 1: 'choice', 2: 'sequence'}
						else:
											SCREAMING_SNAKE_CASE__													= {0: 'batch', 1: 'sequence'}
						return OrderedDict(
						    [
						        ('input_ids', dynamic_axis),
						        ('attention_mask', dynamic_axis),
						    ]							)





