



"""simple docstring"""







import tempfile
import unittest

import transformers
from transformers import XGLMConfig, XGLMTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
							import jax
							import jax.numpy as jnp
							import numpy as np

							from transformers.modeling_flax_pytorch_utils import (
							    convert_pytorch_state_dict_to_flax,
							    load_flax_weights_in_pytorch_model,
							)
							from transformers.models.xglm.modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel


if is_torch_available():
							import torch






@require_flax
class   UpperCAmelCase__		:



		def __init__(			self	:     Optional[int]   ,				snake_case	:     List[str]   ,				snake_case	:     int=14   ,				snake_case	:     List[str]=7   ,				snake_case	:     Tuple=True   ,				snake_case	:     List[str]=True   ,				snake_case	:     str=True   ,				snake_case	:     Union[str, Any]=99   ,				snake_case	:     Tuple=32   ,				snake_case	:     Union[str, Any]=5   ,				snake_case	:     Tuple=4   ,				snake_case	:     Optional[int]=37   ,				snake_case	:     Tuple="gelu"   ,				snake_case	:     str=0.1   ,				snake_case	:     Dict=0.1   ,				snake_case	:     Any=512   ,				snake_case	:     List[str]=0.02   ,				snake_case	:     Dict=None   ,				)      ->    Tuple:






									'''simple docstring'''

									A    =       parent
									A    =       batch_size
									A    =       seq_length
									A    =       is_training
									A    =       use_input_mask
									A    =       use_labels
									A    =       vocab_size
									A    =       d_model
									A    =       num_hidden_layers
									A    =       num_attention_heads
									A    =       ffn_dim
									A    =       activation_function
									A    =       activation_dropout
									A    =       attention_dropout
									A    =       max_position_embeddings
									A    =       initializer_range
									A    =       None
									A    =       0
									A    =       2
									A    =       1



		def        A_		(			self	:     Optional[int]					)      ->    Union[str, Any]:






									'''simple docstring'''

									A    =       np.clip(ids_tensor([self.batch_size, self.seq_length]   ,				self.vocab_size					)   ,				3   ,				self.vocab_size					)

									A    =       None
									if self.use_input_mask:
																A    =       random_attention_mask([self.batch_size, self.seq_length]					)

									A    =       XGLMConfig(
									    vocab_size=self.vocab_size   ,				d_model=self.hidden_size   ,				num_layers=self.num_hidden_layers   ,				attention_heads=self.num_attention_heads   ,				ffn_dim=self.ffn_dim   ,				activation_function=self.activation_function   ,				activation_dropout=self.activation_dropout   ,				attention_dropout=self.attention_dropout   ,				max_position_embeddings=self.max_position_embeddings   ,				initializer_range=self.initializer_range   ,				use_cache=__UpperCamelCase   ,				bos_token_id=self.bos_token_id   ,				eos_token_id=self.eos_token_id   ,				pad_token_id=self.pad_token_id   ,				)

									return (config, input_ids, input_mask)



		def        A_		(			self	:     List[str]					)      ->    str:






									'''simple docstring'''

									A    =       self.prepare_config_and_inputs()
									A    =       config_and_inputs
									A    =       {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
									return config, inputs_dict



		def        A_		(			self	:     Optional[int]					)      ->    Dict:






									'''simple docstring'''

									A    =       self.prepare_config_and_inputs()

									A    =       floats_tensor([self.batch_size, self.seq_length, self.hidden_size]					)
									A    =       ids_tensor([self.batch_size, self.seq_length]   ,				vocab_size=2					)

									return (
									    config,
									    input_ids,
									    attention_mask,
									    encoder_hidden_states,
									    encoder_attention_mask,
									)



		def        A_		(			self	:     Tuple   ,				snake_case	:     Tuple   ,				snake_case	:     Optional[int]   ,				snake_case	:     Optional[Any]   ,				snake_case	:     Union[str, Any]					)      ->    str:






									'''simple docstring'''

									A    =       20
									A    =       model_class_name(__UpperCamelCase					)

									A    =       model.init_cache(input_ids.shape[0]   ,				__UpperCamelCase					)
									A    =       jnp.ones((input_ids.shape[0], max_decoder_length)   ,				dtype='i4'					)

									A    =       jnp.broadcast_to(
									    jnp.arange(input_ids.shape[-1] - 1					)[None, :]   ,				(input_ids.shape[0], input_ids.shape[-1] - 1)					)
									A    =       model(
									    input_ids[:, :-1]   ,				attention_mask=__UpperCamelCase   ,				past_key_values=__UpperCamelCase   ,				position_ids=__UpperCamelCase   ,				)

									A    =       jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]   ,				dtype='i4'					)
									A    =       model(
									    input_ids[:, -1:]   ,				attention_mask=__UpperCamelCase   ,				past_key_values=outputs_cache.past_key_values   ,				position_ids=__UpperCamelCase   ,				)

									A    =       model(__UpperCamelCase					)

									A    =       np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])					)					)
									self.parent.assertTrue(diff < 1E-3   ,				msg=f"""Max diff is {diff}"""					)



		def        A_		(			self	:     List[str]   ,				snake_case	:     Dict   ,				snake_case	:     str   ,				snake_case	:     Optional[Any]   ,				snake_case	:     List[Any]					)      ->    Union[str, Any]:






									'''simple docstring'''

									A    =       20
									A    =       model_class_name(__UpperCamelCase					)

									A    =       jnp.concatenate(
									    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])					)]   ,				axis=-1   ,				)

									A    =       model.init_cache(input_ids.shape[0]   ,				__UpperCamelCase					)
									A    =       jnp.broadcast_to(
									    jnp.arange(input_ids.shape[-1] - 1					)[None, :]   ,				(input_ids.shape[0], input_ids.shape[-1] - 1)					)

									A    =       model(
									    input_ids[:, :-1]   ,				attention_mask=__UpperCamelCase   ,				past_key_values=__UpperCamelCase   ,				position_ids=__UpperCamelCase   ,				)
									A    =       jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]   ,				dtype='i4'					)
									A    =       model(
									    input_ids[:, -1:]   ,				past_key_values=outputs_cache.past_key_values   ,				attention_mask=__UpperCamelCase   ,				position_ids=__UpperCamelCase   ,				)

									A    =       model(__UpperCamelCase   ,				attention_mask=__UpperCamelCase					)
									A    =       np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])					)					)
									self.parent.assertTrue(diff < 1E-3   ,				msg=f"""Max diff is {diff}"""					)






@require_sentencepiece
@require_flax
class   UpperCAmelCase__		(   lowercase__ ,lowercase__ ,unittest.TestCase  ):
		lowerCAmelCase_							:						str							=  (FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
		lowerCAmelCase_							:						str							=  (FlaxXGLMForCausalLM,) if is_flax_available() else ()



		def        A_		(			self	:     Optional[int]					)      ->    List[str]:






									'''simple docstring'''

									A    =       FlaxXGLMModelTester(self					)



		def        A_		(			self	:     Any					)      ->    str:






									'''simple docstring'''

									for model_class_name in self.all_model_classes:
																A    =       self.model_tester.prepare_config_and_inputs()
																self.model_tester.check_use_cache_forward(__UpperCamelCase   ,				__UpperCamelCase   ,				__UpperCamelCase   ,				__UpperCamelCase					)



		def        A_		(			self	:     List[Any]					)      ->    List[Any]:






									'''simple docstring'''

									for model_class_name in self.all_model_classes:
																A    =       self.model_tester.prepare_config_and_inputs()
																self.model_tester.check_use_cache_forward_with_attn_mask(
																    __UpperCamelCase   ,				__UpperCamelCase   ,				__UpperCamelCase   ,				__UpperCamelCase					)



		@slow
		def        A_		(			self	:     str					)      ->    Any:






									'''simple docstring'''

									A    =       XGLMTokenizer.from_pretrained('XGLM'   ,				padding_side='left'					)
									A    =       tokenizer(['Hello this is a long string', 'Hey']   ,				return_tensors='np'   ,				padding=__UpperCamelCase   ,				truncation=__UpperCamelCase					)

									A    =       FlaxXGLMForCausalLM.from_pretrained('facebook/xglm-564M'					)
									A    =       1
									A    =       False

									A    =       jax.jit(model.generate					)

									A    =       jit_generate(inputs['input_ids']   ,				attention_mask=inputs['attention_mask']					).sequences

									A    =       tokenizer.batch_decode(__UpperCamelCase   ,				skip_special_tokens=__UpperCamelCase					)

									A    =       [
									    '''Hello this is a long string of questions, but I\'m not sure if I\'m''',
									    '''Hey, I\'m a newbie to the forum and I\'''',
									]

									self.assertListEqual(__UpperCamelCase   ,				__UpperCamelCase					)



		@is_pt_flax_cross_test
		def        A_		(			self	:     Optional[Any]					)      ->    str:






									'''simple docstring'''

									A    =       self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
																with self.subTest(model_class.__name__					):
																							# prepare inputs
																							A    =       self._prepare_for_class(__UpperCamelCase   ,				__UpperCamelCase					)
																							A    =       {k: torch.tensor(v.tolist()					) for k, v in prepared_inputs_dict.items()}

																							# load corresponding PyTorch class
																							A    =       model_class.__name__[4:]  # Skip the "Flax" at the beginning
																							A    =       getattr(__UpperCamelCase   ,				__UpperCamelCase					)

																							A    =       pt_inputs['''input_ids'''].shape
																							A    =       np.random.randint(0   ,				seq_length - 1   ,				size=(batch_size,)					)
																							for batch_idx, start_index in enumerate(__UpperCamelCase					):
																														A    =       0
																														A    =       1
																														A    =       0
																														A    =       1
																							A    =       pt_model_class(__UpperCamelCase					).eval()
																							# Flax models don't use the `use_cache` option and cache is not returned as a default.
																							# So we disable `use_cache` here for PyTorch model.
																							A    =       False
																							A    =       model_class(__UpperCamelCase   ,				dtype=jnp.floataa					)
																							A    =       convert_pytorch_state_dict_to_flax(pt_model.state_dict()   ,				__UpperCamelCase					)
																							A    =       fx_state

																							with torch.no_grad():
																														A    =       pt_model(**__UpperCamelCase					).to_tuple()

																							A    =       fx_model(**__UpperCamelCase					).to_tuple()
																							self.assertEqual(len(__UpperCamelCase					)   ,				len(__UpperCamelCase					)   ,				'Output lengths differ between Flax and PyTorch'					)
																							for fx_output, pt_output in zip(__UpperCamelCase   ,				__UpperCamelCase					):
																														self.assert_almost_equals(fx_output[:, -1]   ,				pt_output[:, -1].numpy()   ,				4E-2					)

																							with tempfile.TemporaryDirectory() as tmpdirname:
																														pt_model.save_pretrained(__UpperCamelCase					)
																														A    =       model_class.from_pretrained(__UpperCamelCase   ,				from_pt=__UpperCamelCase					)

																							A    =       fx_model_loaded(**__UpperCamelCase					).to_tuple()
																							self.assertEqual(
																							    len(__UpperCamelCase					)   ,				len(__UpperCamelCase					)   ,				'Output lengths differ between Flax and PyTorch'					)
																							for fx_output_loaded, pt_output in zip(__UpperCamelCase   ,				__UpperCamelCase					):
																														self.assert_almost_equals(fx_output_loaded[:, -1]   ,				pt_output[:, -1].numpy()   ,				4E-2					)



		@is_pt_flax_cross_test
		def        A_		(			self	:     Optional[Any]					)      ->    Tuple:






									'''simple docstring'''

									A    =       self.model_tester.prepare_config_and_inputs_for_common()
									for model_class in self.all_model_classes:
																with self.subTest(model_class.__name__					):
																							# prepare inputs
																							A    =       self._prepare_for_class(__UpperCamelCase   ,				__UpperCamelCase					)
																							A    =       {k: torch.tensor(v.tolist()					) for k, v in prepared_inputs_dict.items()}

																							# load corresponding PyTorch class
																							A    =       model_class.__name__[4:]  # Skip the "Flax" at the beginning
																							A    =       getattr(__UpperCamelCase   ,				__UpperCamelCase					)

																							A    =       pt_model_class(__UpperCamelCase					).eval()
																							A    =       False
																							A    =       model_class(__UpperCamelCase   ,				dtype=jnp.floataa					)

																							A    =       load_flax_weights_in_pytorch_model(__UpperCamelCase   ,				fx_model.params					)
																							A    =       pt_inputs['''input_ids'''].shape
																							A    =       np.random.randint(0   ,				seq_length - 1   ,				size=(batch_size,)					)
																							for batch_idx, start_index in enumerate(__UpperCamelCase					):
																														A    =       0
																														A    =       1
																														A    =       0
																														A    =       1

																							# make sure weights are tied in PyTorch
																							pt_model.tie_weights()

																							with torch.no_grad():
																														A    =       pt_model(**__UpperCamelCase					).to_tuple()

																							A    =       fx_model(**__UpperCamelCase					).to_tuple()
																							self.assertEqual(len(__UpperCamelCase					)   ,				len(__UpperCamelCase					)   ,				'Output lengths differ between Flax and PyTorch'					)
																							for fx_output, pt_output in zip(__UpperCamelCase   ,				__UpperCamelCase					):
																														self.assert_almost_equals(fx_output[:, -1]   ,				pt_output[:, -1].numpy()   ,				4E-2					)

																							with tempfile.TemporaryDirectory() as tmpdirname:
																														fx_model.save_pretrained(__UpperCamelCase					)
																														A    =       pt_model_class.from_pretrained(__UpperCamelCase   ,				from_flax=__UpperCamelCase					)

																							with torch.no_grad():
																														A    =       pt_model_loaded(**__UpperCamelCase					).to_tuple()

																							self.assertEqual(
																							    len(__UpperCamelCase					)   ,				len(__UpperCamelCase					)   ,				'Output lengths differ between Flax and PyTorch'					)
																							for fx_output, pt_output in zip(__UpperCamelCase   ,				__UpperCamelCase					):
																														self.assert_almost_equals(fx_output[:, -1]   ,				pt_output[:, -1].numpy()   ,				4E-2					)



		@slow
		def        A_		(			self	:     Optional[int]					)      ->    List[Any]:






									'''simple docstring'''

									for model_class_name in self.all_model_classes:
																A    =       model_class_name.from_pretrained('facebook/xglm-564M'					)
																A    =       model(np.ones((1, 1)					)					)
																self.assertIsNotNone(__UpperCamelCase					)








'''simple docstring'''



import os
import pathlib
import tempfile
import uuid

import numpy as np

from ..utils import is_soundfile_availble, is_torch_available, is_vision_available, logging


_lowercase       :						Dict						=logging.get_logger(__name__)

if is_vision_available():
				import PIL.Image
				from PIL import Image
				from PIL.Image import Image as ImageType
else:
				_lowercase       :						List[Any]						=object

if is_torch_available():
				import torch

if is_soundfile_availble():
				import soundfile as sf






class 			_SCREAMING_SNAKE_CASE				:



			def __init__(     self		:						List[str]    ,			__UpperCamelCase		:						Optional[int]			)      ->      List[str]:
							"""simple docstring"""





							snake_case__      :   int      =    value



			def __str__(     self		:						int			)      ->      str:
							"""simple docstring"""





							return self.to_string()



			def 				lowerCAmelCase (     self		:						List[Any]			)      ->      List[str]:
							"""simple docstring"""





							logger.error(
							    '''This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'''			)
							return self._value







			def 				lowerCAmelCase (     self		:						Optional[Any]			)      ->      str:
							"""simple docstring"""





							logger.error(
							    '''This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'''			)
							return str(self._value			)






class 			_SCREAMING_SNAKE_CASE				(lowercase__,	lowercase__      ):



			def 				lowerCAmelCase (     self		:						str			)      ->      Tuple:
							"""simple docstring"""





							return self._value







			def 				lowerCAmelCase (     self		:						Optional[int]			)      ->      Union[str, Any]:
							"""simple docstring"""





							return self._value






class 			_SCREAMING_SNAKE_CASE				(lowercase__,	lowercase__      ):



			def __init__(     self		:						str    ,			__UpperCamelCase		:						List[Any]			)      ->      Dict:
							"""simple docstring"""





							super().__init__(__UpperCamelCase			)

							if not is_vision_available():
											raise ImportError('''PIL must be installed in order to handle images.'''			)

							snake_case__      :   str      =    None
							snake_case__      :   List[Any]      =    None
							snake_case__      :   Tuple      =    None

							if isinstance(__UpperCamelCase    ,			__UpperCamelCase			):
											snake_case__      :   List[Any]      =    value
							elif isinstance(__UpperCamelCase    ,			(str, pathlib.Path)			):
											snake_case__      :   int      =    value
							elif isinstance(__UpperCamelCase    ,			torch.Tensor			):
											snake_case__      :   Union[str, Any]      =    value
							else:
											raise ValueError(F'''Unsupported type for {self.__class__.__name__}: {type(__UpperCamelCase			)}'''			)



			def 				lowerCAmelCase (     self		:						str    ,			__UpperCamelCase		:						Dict=None    ,			__UpperCamelCase		:						Any=None			)      ->      List[Any]:
							"""simple docstring"""





							from IPython.display import Image, display

							display(Image(self.to_string()			)			)



			def 				lowerCAmelCase (     self		:						List[str]			)      ->      str:
							"""simple docstring"""





							if self._raw is not None:
											return self._raw

							if self._path is not None:
											snake_case__      :   str      =    Image.open(self._path			)
											return self._raw







			def 				lowerCAmelCase (     self		:						Dict			)      ->      Optional[Any]:
							"""simple docstring"""





							if self._path is not None:
											return self._path

							if self._raw is not None:
											snake_case__      :   Any      =    tempfile.mkdtemp()
											snake_case__      :   Dict      =    os.path.join(__UpperCamelCase    ,			str(uuid.uuida()			) + '''.png'''			)
											self._raw.save(self._path			)

											return self._path

							if self._tensor is not None:
											snake_case__      :   List[str]      =    self._tensor.cpu().detach().numpy()

											# There is likely simpler than load into image into save
											snake_case__      :   List[Any]      =    Image.fromarray((array * 255).astype(np.uinta			)			)

											snake_case__      :   str      =    tempfile.mkdtemp()
											snake_case__      :   Dict      =    os.path.join(__UpperCamelCase    ,			str(uuid.uuida()			) + '''.png'''			)

											img.save(self._path			)

											return self._path






class 			_SCREAMING_SNAKE_CASE				(lowercase__      ):



			def __init__(     self		:						Optional[Any]    ,			__UpperCamelCase		:						List[Any]    ,			__UpperCamelCase		:						str=16000			)      ->      Union[str, Any]:
							"""simple docstring"""





							super().__init__(__UpperCamelCase			)

							if not is_soundfile_availble():
											raise ImportError('''soundfile must be installed in order to handle audio.'''			)

							snake_case__      :   List[str]      =    None
							snake_case__      :   Optional[int]      =    None

							snake_case__      :   Any      =    samplerate

							if isinstance(__UpperCamelCase    ,			(str, pathlib.Path)			):
											snake_case__      :   int      =    value
							elif isinstance(__UpperCamelCase    ,			torch.Tensor			):
											snake_case__      :   int      =    value
							else:
											raise ValueError(F'''Unsupported audio type: {type(__UpperCamelCase			)}'''			)



			def 				lowerCAmelCase (     self		:						Tuple    ,			__UpperCamelCase		:						List[Any]=None    ,			__UpperCamelCase		:						Optional[Any]=None			)      ->      Any:
							"""simple docstring"""





							from IPython.display import Audio, display

							display(Audio(self.to_string()    ,			rate=self.samplerate			)			)



			def 				lowerCAmelCase (     self		:						Union[str, Any]			)      ->      Union[str, Any]:
							"""simple docstring"""





							if self._tensor is not None:
											return self._tensor

							if self._path is not None:
											snake_case__  ,      snake_case__      :   Tuple      =    sf.read(self._path			)
											snake_case__      :   Tuple      =    torch.tensor(__UpperCamelCase			)
											return self._tensor







			def 				lowerCAmelCase (     self		:						Any			)      ->      Dict:
							"""simple docstring"""





							if self._path is not None:
											return self._path

							if self._tensor is not None:
											snake_case__      :   Tuple      =    tempfile.mkdtemp()
											snake_case__      :   int      =    os.path.join(__UpperCamelCase    ,			str(uuid.uuida()			) + '''.wav'''			)
											sf.write(self._path    ,			self._tensor    ,			samplerate=self.samplerate			)
											return self._path



_lowercase       :						Union[str, Any]						={"text": AgentText, "image": AgentImage, "audio": AgentAudio}
_lowercase       :						List[Any]						={str: AgentText}

if is_vision_available():
				_lowercase       :						Optional[Any]						=AgentImage


def 							__UpperCAmelCase				(				*UpperCamelCase__				:Optional[int]   ,				**UpperCamelCase__				:List[Any]			)		->						Optional[Any]:
				snake_case__      :   List[str]      =    [(arg.to_raw() if isinstance(UpperCamelCase__   ,				UpperCamelCase__			) else arg) for arg in args]
				snake_case__      :   Any      =    {k: (v.to_raw() if isinstance(UpperCamelCase__   ,				UpperCamelCase__			) else v) for k, v in kwargs.items()}
				return args, kwargs



def 							__UpperCAmelCase				(				UpperCamelCase__				:Optional[Any]   ,				UpperCamelCase__				:Tuple=None			)		->						Dict:
				if isinstance(UpperCamelCase__   ,				UpperCamelCase__			):
								snake_case__      :   Any      =    {}
								for i, (k, v) in enumerate(outputs.items()			):
												if output_types is not None:
																# If the class has defined outputs, we can map directly according to the class definition
																if output_types[i] in AGENT_TYPE_MAPPING:
																				snake_case__      :   List[Any]      =    AGENT_TYPE_MAPPING[output_types[i]](UpperCamelCase__			)
																else:
																				snake_case__      :   Optional[int]      =    AgentType(UpperCamelCase__			)

												else:
																# If the class does not have defined output, then we map according to the type
																for _k, _v in INSTANCE_TYPE_MAPPING.items():
																				if isinstance(UpperCamelCase__   ,				_k			):
																								snake_case__      :   str      =    _v(UpperCamelCase__			)
																if k not in decoded_outputs:
																				snake_case__      :   Any      =    AgentType[v]

				elif isinstance(UpperCamelCase__   ,				(list, tuple)			):
								snake_case__      :   List[str]      =    type(UpperCamelCase__			)()
								for i, v in enumerate(UpperCamelCase__			):
												if output_types is not None:
																# If the class has defined outputs, we can map directly according to the class definition
																if output_types[i] in AGENT_TYPE_MAPPING:
																				decoded_outputs.append(AGENT_TYPE_MAPPING[output_types[i]](UpperCamelCase__			)			)
																else:
																				decoded_outputs.append(AgentType(UpperCamelCase__			)			)
												else:
																# If the class does not have defined output, then we map according to the type
																snake_case__      :   Any      =    False
																for _k, _v in INSTANCE_TYPE_MAPPING.items():
																				if isinstance(UpperCamelCase__   ,				_k			):
																								decoded_outputs.append(_v(UpperCamelCase__			)			)
																								snake_case__      :   str      =    True

																if not found:
																				decoded_outputs.append(AgentType(UpperCamelCase__			)			)

				else:
								if output_types[0] in AGENT_TYPE_MAPPING:
												# If the class has defined outputs, we can map directly according to the class definition
												snake_case__      :   Tuple      =    AGENT_TYPE_MAPPING[output_types[0]](UpperCamelCase__			)

								else:
												# If the class does not have defined output, then we map according to the type
												for _k, _v in INSTANCE_TYPE_MAPPING.items():
																if isinstance(UpperCamelCase__   ,				_k			):
																				return _v(UpperCamelCase__			)
												return AgentType(UpperCamelCase__			)

				return decoded_outputs




