






'''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


__A      :       Dict      =	logging.get_logger(__name__)

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

if is_torch_available():
       import torch

if is_soundfile_availble():
       import soundfile as sf






class 					a__    :
     '''simple docstring'''







     def __init__(self    :	str				,				lowercase__    :	Optional[Any]					)  ->       str:
           __A		   =      value






     def __str__(self    :	Tuple					)  ->       Any:
           return self.to_string()






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






     def     _UpperCamelCase   (self    :	Union[str, Any]					)  ->       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 					a__    (			lowerCamelCase ,						lowerCamelCase						):
     '''simple docstring'''







     def     _UpperCamelCase   (self    :	Optional[Any]					)  ->       List[str]:
           return self._value






     def     _UpperCamelCase   (self    :	List[Any]					)  ->       Optional[Any]:
           return self._value







class 					a__    (			lowerCamelCase ,						lowerCamelCase						):
     '''simple docstring'''







     def __init__(self    :	Optional[int]				,				lowercase__    :	Optional[Any]					)  ->       str:
           super().__init__(lowercase__					)

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

           __A		   =      None
           __A		   =      None
           __A		   =      None

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






     def     _UpperCamelCase   (self    :	List[Any]				,				lowercase__    :	Any=None				,				lowercase__    :	Union[str, Any]=None					)  ->       Optional[int]:
           from IPython.display import Image, display

           display(Image(self.to_string()					)					)






     def     _UpperCamelCase   (self    :	int					)  ->       List[Any]:
           if self._raw is not None:
                 return self._raw

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






     def     _UpperCamelCase   (self    :	Optional[int]					)  ->       Tuple:
           if self._path is not None:
                 return self._path

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

                 return self._path

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

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

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

                 img.save(self._path					)

                 return self._path







class 					a__    (			lowerCamelCase						):
     '''simple docstring'''







     def __init__(self    :	Optional[int]				,				lowercase__    :	Tuple				,				lowercase__    :	Optional[int]=16000					)  ->       Union[str, Any]:
           super().__init__(lowercase__					)

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

           __A		   =      None
           __A		   =      None

           __A		   =      samplerate

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






     def     _UpperCamelCase   (self    :	List[Any]				,				lowercase__    :	Optional[Any]=None				,				lowercase__    :	Union[str, Any]=None					)  ->       List[Any]:
           from IPython.display import Audio, display

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






     def     _UpperCamelCase   (self    :	List[str]					)  ->       Dict:
           if self._tensor is not None:
                 return self._tensor

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






     def     _UpperCamelCase   (self    :	List[Any]					)  ->       List[Any]:
           if self._path is not None:
                 return self._path

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







__A      :       Tuple      =	{'text': AgentText, 'image': AgentImage, 'audio': AgentAudio}
__A      :       Optional[int]      =	{str: AgentText}

if is_vision_available():
       __A      :       Dict      =	AgentImage


def 						_UpperCamelCase							(     *snake_case__							,			**snake_case__							):
      __A		   =      [(arg.to_raw() if isinstance(snake_case__							,			snake_case__							) else arg) for arg in args]
      __A		   =      {k: (v.to_raw() if isinstance(snake_case__							,			snake_case__							) else v) for k, v in kwargs.items()}
      return args, kwargs





def 						_UpperCamelCase							(     snake_case__							,			snake_case__=None							):
      if isinstance(snake_case__							,			snake_case__							):
            __A		   =      {}
            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:
                              __A		   =      AGENT_TYPE_MAPPING[output_types[i]](snake_case__							)
                        else:
                              __A		   =      AgentType(snake_case__							)

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

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

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

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

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

      return decoded_outputs










'''simple docstring'''







import copy
import inspect

from transformers.testing_utils import require_torch, torch_device
from transformers.utils.backbone_utils import BackboneType






@require_torch
class 					a__    :
     '''simple docstring'''







     _snake_case             =  ()
     _snake_case             =  True






     def     _UpperCamelCase   (self    :	Optional[Any]					)  ->       Union[str, Any]:
           __A		   =      self.config_class

           # test default config
           __A		   =      config_class()
           self.assertIsNotNone(lowercase__					)
           __A		   =      ["stem"] + [f"stage{idx}" for idx in range(1				,				len(config.depths					) + 1					)]
           self.assertEqual(config.stage_names				,				lowercase__					)
           self.assertTrue(set(config.out_features					).issubset(set(config.stage_names					)					)					)

           # Test out_features and out_indices are correctly set
           # out_features and out_indices both None
           __A		   =      config_class(out_features=lowercase__				,				out_indices=lowercase__					)
           self.assertEqual(config.out_features				,				[config.stage_names[-1]]					)
           self.assertEqual(config.out_indices				,				[len(config.stage_names					) - 1]					)

           # out_features and out_indices both set
           __A		   =      config_class(out_features=["stem", "stage1"]				,				out_indices=[0, 1]					)
           self.assertEqual(config.out_features				,				["stem", "stage1"]					)
           self.assertEqual(config.out_indices				,				[0, 1]					)

           # Only out_features set
           __A		   =      config_class(out_features=["stage1", "stage3"]					)
           self.assertEqual(config.out_features				,				["stage1", "stage3"]					)
           self.assertEqual(config.out_indices				,				[1, 3]					)

           # Only out_indices set
           __A		   =      config_class(out_indices=[0, 2]					)
           self.assertEqual(config.out_features				,				[config.stage_names[0], config.stage_names[2]]					)
           self.assertEqual(config.out_indices				,				[0, 2]					)

           # Error raised when out_indices do not correspond to out_features
           with self.assertRaises(lowercase__					):
                 __A		   =      config_class(out_features=["stage1", "stage2"]				,				out_indices=[0, 2]					)






     def     _UpperCamelCase   (self    :	Optional[int]					)  ->       Optional[Any]:
           __A   ,				__A		   =      self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 __A		   =      model_class(lowercase__					)
                 __A		   =      inspect.signature(model.forward					)
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 __A		   =      [*signature.parameters.keys()]
                 __A		   =      ["pixel_values"]
                 self.assertListEqual(arg_names[:1]				,				lowercase__					)






     def     _UpperCamelCase   (self    :	Dict					)  ->       Union[str, Any]:
           __A   ,				__A		   =      self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 __A		   =      model_class(lowercase__					)
                 self.assertEqual(len(model.channels					)				,				len(config.out_features					)					)
                 __A		   =      model.num_features
                 __A		   =      [config.stage_names.index(lowercase__					) for feat in config.out_features]
                 __A		   =      [num_features[idx] for idx in out_indices]
                 self.assertListEqual(model.channels				,				lowercase__					)

                 __A		   =      copy.deepcopy(lowercase__					)
                 __A		   =      None
                 __A		   =      model_class(lowercase__					)
                 self.assertEqual(len(model.channels					)				,				1					)
                 self.assertListEqual(model.channels				,				[num_features[-1]]					)

                 __A		   =      copy.deepcopy(lowercase__					)
                 __A		   =      None
                 __A		   =      model_class(lowercase__					)
                 self.assertEqual(len(model.channels					)				,				1					)
                 self.assertListEqual(model.channels				,				[num_features[-1]]					)






     def     _UpperCamelCase   (self    :	Optional[Any]					)  ->       Optional[Any]:
           __A   ,				__A		   =      self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 __A		   =      model_class(lowercase__					)
                 model.to(lowercase__					)
                 model.eval()
                 __A		   =      model(**lowercase__					)

                 self.assertEqual(len(result.feature_maps					)				,				len(config.out_features					)					)
                 self.assertEqual(len(model.channels					)				,				len(config.out_features					)					)
                 self.assertEqual(len(result.feature_maps					)				,				len(config.out_indices					)					)
                 self.assertEqual(len(model.channels					)				,				len(config.out_indices					)					)

                 # Check output of last stage is taken if out_features=None, out_indices=None
                 __A		   =      copy.deepcopy(lowercase__					)
                 __A		   =      None
                 __A		   =      model_class(lowercase__					)
                 model.to(lowercase__					)
                 model.eval()
                 __A		   =      model(**lowercase__					)

                 self.assertEqual(len(result.feature_maps					)				,				1					)
                 self.assertEqual(len(model.channels					)				,				1					)

                 __A		   =      copy.deepcopy(lowercase__					)
                 __A		   =      None
                 __A		   =      model_class(lowercase__					)
                 model.to(lowercase__					)
                 model.eval()
                 __A		   =      model(**lowercase__					)

                 self.assertEqual(len(result.feature_maps					)				,				1					)
                 self.assertEqual(len(model.channels					)				,				1					)

                 # Check backbone can be initialized with fresh weights
                 __A		   =      copy.deepcopy(lowercase__					)
                 __A		   =      False
                 __A		   =      model_class(lowercase__					)
                 model.to(lowercase__					)
                 model.eval()
                 __A		   =      model(**lowercase__					)






     def     _UpperCamelCase   (self    :	Dict					)  ->       Optional[int]:
           __A   ,				__A		   =      self.model_tester.prepare_config_and_inputs_for_common()

           for backbone_class in self.all_model_classes:
                 __A		   =      backbone_class(lowercase__					)

                 self.assertTrue(hasattr(lowercase__				,				"backbone_type"					)					)
                 self.assertTrue(hasattr(lowercase__				,				"stage_names"					)					)
                 self.assertTrue(hasattr(lowercase__				,				"num_features"					)					)
                 self.assertTrue(hasattr(lowercase__				,				"out_indices"					)					)
                 self.assertTrue(hasattr(lowercase__				,				"out_features"					)					)
                 self.assertTrue(hasattr(lowercase__				,				"out_feature_channels"					)					)
                 self.assertTrue(hasattr(lowercase__				,				"channels"					)					)

                 self.assertIsInstance(backbone.backbone_type				,				lowercase__					)
                 # Verify num_features has been initialized in the backbone init
                 self.assertIsNotNone(backbone.num_features					)
                 self.assertTrue(len(backbone.channels					) == len(backbone.out_indices					)					)
                 self.assertTrue(len(backbone.stage_names					) == len(backbone.num_features					)					)
                 self.assertTrue(len(backbone.channels					) <= len(backbone.num_features					)					)
                 self.assertTrue(len(backbone.out_feature_channels					) == len(backbone.stage_names					)					)






     def     _UpperCamelCase   (self    :	Tuple					)  ->       int:
           __A   ,				__A		   =      self.model_tester.prepare_config_and_inputs_for_common()
           __A		   =      inputs_dict["pixel_values"].shape[0]

           for backbone_class in self.all_model_classes:
                 __A		   =      backbone_class(lowercase__					)
                 backbone.to(lowercase__					)
                 backbone.eval()

                 __A		   =      backbone(**lowercase__					)

                 # Test default outputs and verify feature maps
                 self.assertIsInstance(outputs.feature_maps				,				lowercase__					)
                 self.assertTrue(len(outputs.feature_maps					) == len(backbone.channels					)					)
                 for feature_map, n_channels in zip(outputs.feature_maps				,				backbone.channels					):
                       self.assertTrue(feature_map.shape[:2]				,				(batch_size, n_channels)					)
                 self.assertIsNone(outputs.hidden_states					)
                 self.assertIsNone(outputs.attentions					)

                 # Test output_hidden_states=True
                 __A		   =      backbone(**lowercase__				,				output_hidden_states=lowercase__					)
                 self.assertIsNotNone(outputs.hidden_states					)
                 self.assertTrue(len(outputs.hidden_states					)				,				len(backbone.stage_names					)					)
                 for hidden_state, n_channels in zip(outputs.hidden_states				,				backbone.channels					):
                       self.assertTrue(hidden_state.shape[:2]				,				(batch_size, n_channels)					)

                 # Test output_attentions=True
                 if self.has_attentions:
                       __A		   =      backbone(**lowercase__				,				output_attentions=lowercase__					)
                       self.assertIsNotNone(outputs.attentions					)


