




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


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

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

if is_torch_available():
       import torch

if is_soundfile_availble():
       import soundfile as sf




class 	UpperCamelCase__			:

    '''simple docstring'''



    def __init__(					self  ,						UpperCamelCase__  )  ->   Dict:
           lowerCamelCase     :   List[str]    				=      value







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







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







    def 					_lowercase  (					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__			(lowerCAmelCase__					,       lowerCAmelCase__  ):

    '''simple docstring'''



    def 					_lowercase  (					self  )  ->   Union[str, Any]:
           return self._value







    def 					_lowercase  (					self  )  ->   Dict:
           return self._value




class 	UpperCamelCase__			(lowerCAmelCase__					,       lowerCAmelCase__  ):

    '''simple docstring'''



    def __init__(					self  ,						UpperCamelCase__  )  ->   Any:
           super().__init__(UpperCamelCase__  )

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

           lowerCamelCase     :   Union[str, Any]    				=      None
           lowerCamelCase     :   Dict    				=      None
           lowerCamelCase     :   int    				=      None

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







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

           display(Image(self.to_string()  )  )







    def 					_lowercase  (					self  )  ->   Dict:
           if self._raw is not None:
                  return self._raw

           if self._path is not None:
                  lowerCamelCase     :   Optional[int]    				=      Image.open(self._path  )
                  return self._raw







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

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

                  return self._path

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

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

                  lowerCamelCase     :   Optional[int]    				=      tempfile.mkdtemp()
                  lowerCamelCase     :   int    				=      os.path.join(UpperCamelCase__  ,						str(uuid.uuida()  ) + ".png"  )

                  img.save(self._path  )

                  return self._path




class 	UpperCamelCase__			(lowerCAmelCase__  ):

    '''simple docstring'''



    def __init__(					self  ,						UpperCamelCase__  ,						UpperCamelCase__=1_6000  )  ->   List[str]:
           super().__init__(UpperCamelCase__  )

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

           lowerCamelCase     :   List[Any]    				=      None
           lowerCamelCase     :   List[str]    				=      None

           lowerCamelCase     :   Any    				=      samplerate

           if isinstance(UpperCamelCase__  ,						(str, pathlib.Path)  ):
                  lowerCamelCase     :   Union[str, Any]    				=      value
           elif isinstance(UpperCamelCase__  ,						torch.Tensor  ):
                  lowerCamelCase     :   List[str]    				=      value
           else:
                  raise ValueError(F'''Unsupported audio type: {type(UpperCamelCase__  )}'''  )







    def 					_lowercase  (					self  ,						UpperCamelCase__=None  ,						UpperCamelCase__=None  )  ->   int:
           from IPython.display import Audio, display

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







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

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







    def 					_lowercase  (					self  )  ->   Dict:
           if self._path is not None:
                  return self._path

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




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

if is_vision_available():
       SCREAMING_SNAKE_CASE__ :   str						=     AgentImage

def   A   (   *_SCREAMING_SNAKE_CASE				,**_SCREAMING_SNAKE_CASE					)			->				int:
       lowerCamelCase     :   Tuple    				=      [(arg.to_raw() if isinstance(_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE					) else arg) for arg in args]
       lowerCamelCase     :   Dict    				=      {k: (v.to_raw() if isinstance(_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE					) else v) for k, v in kwargs.items()}
       return args, kwargs


def   A   (   _SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE=None					)			->				Tuple:
       if isinstance(_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE					):
              lowerCamelCase     :   Optional[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:
                                   lowerCamelCase     :   List[Any]    				=      AGENT_TYPE_MAPPING[output_types[i]](_SCREAMING_SNAKE_CASE					)
                            else:
                                   lowerCamelCase     :   List[str]    				=      AgentType(_SCREAMING_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(_SCREAMING_SNAKE_CASE				,_k					):
                                          lowerCamelCase     :   int    				=      _v(_SCREAMING_SNAKE_CASE					)
                            if k not in decoded_outputs:
                                   lowerCamelCase     :   str    				=      AgentType[v]

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

                            if not found:
                                   decoded_outputs.append(AgentType(_SCREAMING_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
                     lowerCamelCase     :   List[Any]    				=      AGENT_TYPE_MAPPING[output_types[0]](_SCREAMING_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(_SCREAMING_SNAKE_CASE				,_k					):
                                   return _v(_SCREAMING_SNAKE_CASE					)
                     return AgentType(_SCREAMING_SNAKE_CASE					)

       return decoded_outputs












import os
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
       import torch

       from transformers import GPTaLMHeadModel




@require_torch
@require_sentencepiece
@require_tokenizers
class 	UpperCamelCase__			(unittest.TestCase  ):

    '''simple docstring'''



    @slow
    @unittest.skip("Model is not available."  )
    def 					_lowercase  (					self  )  ->   List[Any]:
           lowerCamelCase     :   List[Any]    				=      "nvidia/megatron-gpt2-345m/"
           if "MYDIR" in os.environ:
                  lowerCamelCase     :   Union[str, Any]    				=      os.path.join(os.environ["MYDIR"]  ,						UpperCamelCase__  )
           lowerCamelCase     :   Any    				=      GPTaLMHeadModel.from_pretrained(UpperCamelCase__  )
           model.to(UpperCamelCase__  )
           model.half()

           lowerCamelCase     :   Dict    				=      torch.tensor(
               [[101, 7110, 1005, 1056, 2023, 1_1333, 1_7413, 1029, 102]]  ,						device=UpperCamelCase__  ,						dtype=torch.long  ,						)

           with torch.no_grad():
                  lowerCamelCase     :   Any    				=      model(UpperCamelCase__  ).logits

           lowerCamelCase     :   Union[str, Any]    				=      torch.Size((1, 9, 5_0257)  )
           self.assertEqual(output.shape  ,						UpperCamelCase__  )

           lowerCamelCase     :   List[str]    				=      torch.tensor(
               [
                   4.9414,
                   -0.2920,
                   -1.2148,
                   -4.0273,
                   -0.5161,
                   -5.2109,
                   -1.2412,
                   -1.8301,
                   -1.7734,
                   -4.7148,
                   -0.2317,
                   -1.0811,
                   -2.1777,
                   0.4141,
                   -3.7969,
                   -4.0586,
                   -2.5332,
                   -3.3809,
                   4.3867,
               ]  ,						device=UpperCamelCase__  ,						dtype=torch.half  ,						)

           for i in range(19  ):
                  lowerCamelCase				,   lowerCamelCase     :   Optional[int]    				=      8 * i // 17, 2792 * i  # along the diagonal
                  lowerCamelCase				,   lowerCamelCase     :   Tuple    				=      output[0, r, c], expected_diag[i]
                  lowerCamelCase     :   List[str]    				=      F'''row={r} col={c} computed={computed} expected={expected}'''
                  self.assertAlmostEqual(UpperCamelCase__  ,						UpperCamelCase__  ,						delta=1e-4  ,						msg=UpperCamelCase__  )






