





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


__lowerCAmelCase 	=      logging.get_logger(__name__)

if is_vision_available():
				import PIL.Image
				from PIL import Image
				from PIL.Image import Image as ImageType
else:
				__lowerCAmelCase 	=      object

if is_torch_available():
				import torch

if is_soundfile_availble():
				import soundfile as sf




class 	_UpperCAmelCase						:




					def __init__(    self     ,   __lowerCAmelCase							)    ->	Optional[Any]:
							UpperCamelCase__								=						value




					def __str__(    self							)    ->	Dict:
							return self.to_string()




					def 						snake_case_		(    self							)    ->	Tuple:
							logger.error(
							    """This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable"""							)
							return self._value




					def 						snake_case_		(    self							)    ->	str:
							logger.error(
							    """This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable"""							)
							return str(self._value							)




class 	_UpperCAmelCase						( _lowercase			,							_lowercase       ):




					def 						snake_case_		(    self							)    ->	int:
							return self._value




					def 						snake_case_		(    self							)    ->	Any:
							return self._value




class 	_UpperCAmelCase						( _lowercase			,							_lowercase       ):




					def __init__(    self     ,   __lowerCAmelCase							)    ->	str:
							super().__init__(__lowerCAmelCase							)

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

							UpperCamelCase__								=						None
							UpperCamelCase__								=						None
							UpperCamelCase__								=						None

							if isinstance(__lowerCAmelCase     ,   __lowerCAmelCase							):
									UpperCamelCase__								=						value
							elif isinstance(__lowerCAmelCase     ,   (str, pathlib.Path)							):
									UpperCamelCase__								=						value
							elif isinstance(__lowerCAmelCase     ,   torch.Tensor							):
									UpperCamelCase__								=						value
							else:
									raise ValueError(F"""Unsupported type for {self.__class__.__name__}: {type(__lowerCAmelCase							)}"""							)




					def 						snake_case_		(    self     ,   __lowerCAmelCase=None     ,   __lowerCAmelCase=None							)    ->	str:
							from IPython.display import Image, display

							display(Image(self.to_string()							)							)




					def 						snake_case_		(    self							)    ->	int:
							if self._raw is not None:
									return self._raw

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




					def 						snake_case_		(    self							)    ->	Any:
							if self._path is not None:
									return self._path

							if self._raw is not None:
									UpperCamelCase__								=						tempfile.mkdtemp()
									UpperCamelCase__								=						os.path.join(__lowerCAmelCase     ,   str(uuid.uuida()							) + """.png"""							)
									self._raw.save(self._path							)

									return self._path

							if self._tensor is not None:
									UpperCamelCase__								=						self._tensor.cpu().detach().numpy()

									# There is likely simpler than load into image into save
									UpperCamelCase__								=						Image.fromarray((array * 255).astype(np.uinta							)							)

									UpperCamelCase__								=						tempfile.mkdtemp()
									UpperCamelCase__								=						os.path.join(__lowerCAmelCase     ,   str(uuid.uuida()							) + """.png"""							)

									img.save(self._path							)

									return self._path




class 	_UpperCAmelCase						( _lowercase       ):




					def __init__(    self     ,   __lowerCAmelCase     ,   __lowerCAmelCase=16_000							)    ->	str:
							super().__init__(__lowerCAmelCase							)

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

							UpperCamelCase__								=						None
							UpperCamelCase__								=						None

							UpperCamelCase__								=						samplerate

							if isinstance(__lowerCAmelCase     ,   (str, pathlib.Path)							):
									UpperCamelCase__								=						value
							elif isinstance(__lowerCAmelCase     ,   torch.Tensor							):
									UpperCamelCase__								=						value
							else:
									raise ValueError(F"""Unsupported audio type: {type(__lowerCAmelCase							)}"""							)




					def 						snake_case_		(    self     ,   __lowerCAmelCase=None     ,   __lowerCAmelCase=None							)    ->	str:
							from IPython.display import Audio, display

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




					def 						snake_case_		(    self							)    ->	Any:
							if self._tensor is not None:
									return self._tensor

							if self._path is not None:
									UpperCamelCase__	,      UpperCamelCase__								=						sf.read(self._path							)
									UpperCamelCase__								=						torch.tensor(__lowerCAmelCase							)
									return self._tensor




					def 						snake_case_		(    self							)    ->	Union[str, Any]:
							if self._path is not None:
									return self._path

							if self._tensor is not None:
									UpperCamelCase__								=						tempfile.mkdtemp()
									UpperCamelCase__								=						os.path.join(__lowerCAmelCase     ,   str(uuid.uuida()							) + """.wav"""							)
									sf.write(self._path     ,   self._tensor     ,   samplerate=self.samplerate							)
									return self._path



__lowerCAmelCase 	=      {"text": AgentText, "image": AgentImage, "audio": AgentAudio}
__lowerCAmelCase 	=      {str: AgentText}

if is_vision_available():
				__lowerCAmelCase 	=      AgentImage

def      a    (   *_lowercase :  Union[str, Any]			,    **_lowercase :  Dict							)  ->str:
		UpperCamelCase__								=						[(arg.to_raw() if isinstance(_lowercase			,    _lowercase							) else arg) for arg in args]
		UpperCamelCase__								=						{k: (v.to_raw() if isinstance(_lowercase			,    _lowercase							) else v) for k, v in kwargs.items()}
		return args, kwargs

def      a    (   _lowercase :  Any			,    _lowercase :  Tuple=None							)  ->Optional[int]:
		if isinstance(_lowercase			,    _lowercase							):
				UpperCamelCase__								=						{}
				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:
										UpperCamelCase__								=						AGENT_TYPE_MAPPING[output_types[i]](_lowercase							)
								else:
										UpperCamelCase__								=						AgentType(_lowercase							)

						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(_lowercase			,    _k							):
												UpperCamelCase__								=						_v(_lowercase							)
								if k not in decoded_outputs:
										UpperCamelCase__								=						AgentType[v]

		elif isinstance(_lowercase			,    (list, tuple)							):
				UpperCamelCase__								=						type(_lowercase							)()
				for i, v in enumerate(_lowercase							):
						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]](_lowercase							)							)
								else:
										decoded_outputs.append(AgentType(_lowercase							)							)
						else:
								# If the class does not have defined output, then we map according to the type
								UpperCamelCase__								=						False
								for _k, _v in INSTANCE_TYPE_MAPPING.items():
										if isinstance(_lowercase			,    _k							):
												decoded_outputs.append(_v(_lowercase							)							)
												UpperCamelCase__								=						True

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

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

				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(_lowercase			,    _k							):
										return _v(_lowercase							)
						return AgentType(_lowercase							)

		return decoded_outputs












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


__lowerCAmelCase 	=      logging.get_logger(__name__)

__lowerCAmelCase 	=      {
    "MCG-NJU/videomae-base": "https://huggingface.co/MCG-NJU/videomae-base/resolve/main/config.json",
}




class 	_UpperCAmelCase						( _lowercase       ):
					__A        = "videomae"




					def __init__(    self     ,   __lowerCAmelCase=224     ,   __lowerCAmelCase=16     ,   __lowerCAmelCase=3     ,   __lowerCAmelCase=16     ,   __lowerCAmelCase=2     ,   __lowerCAmelCase=768     ,   __lowerCAmelCase=12     ,   __lowerCAmelCase=12     ,   __lowerCAmelCase=3_072     ,   __lowerCAmelCase="gelu"     ,   __lowerCAmelCase=0.0     ,   __lowerCAmelCase=0.0     ,   __lowerCAmelCase=0.0_2     ,   __lowerCAmelCase=1E-1_2     ,   __lowerCAmelCase=True     ,   __lowerCAmelCase=True     ,   __lowerCAmelCase=6     ,   __lowerCAmelCase=384     ,   __lowerCAmelCase=4     ,   __lowerCAmelCase=1_536     ,   __lowerCAmelCase=True     ,   **__lowerCAmelCase     ,   )    ->	Any:
							super().__init__(**__lowerCAmelCase							)

							UpperCamelCase__								=						image_size
							UpperCamelCase__								=						patch_size
							UpperCamelCase__								=						num_channels
							UpperCamelCase__								=						num_frames
							UpperCamelCase__								=						tubelet_size

							UpperCamelCase__								=						hidden_size
							UpperCamelCase__								=						num_hidden_layers
							UpperCamelCase__								=						num_attention_heads
							UpperCamelCase__								=						intermediate_size
							UpperCamelCase__								=						hidden_act
							UpperCamelCase__								=						hidden_dropout_prob
							UpperCamelCase__								=						attention_probs_dropout_prob
							UpperCamelCase__								=						initializer_range
							UpperCamelCase__								=						layer_norm_eps
							UpperCamelCase__								=						qkv_bias
							UpperCamelCase__								=						use_mean_pooling

							UpperCamelCase__								=						decoder_num_attention_heads
							UpperCamelCase__								=						decoder_hidden_size
							UpperCamelCase__								=						decoder_num_hidden_layers
							UpperCamelCase__								=						decoder_intermediate_size
							UpperCamelCase__								=						norm_pix_loss





