
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, slow, torch_device

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin





class   __UpperCamelCase				(    lowerCAmelCase__				,		lowerCAmelCase__				,		unittest.TestCase   ):






     """simple docstring"""







     lowerCAmelCase_     =	UNetaDModel
     lowerCAmelCase_     =	'''sample'''



     @property
     def 				UpperCAmelCase__    (							self    :    int				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					int			=						4
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						14
       __SCREAMING_SNAKE_CASE     :					Dict			=						16

       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						floats_tensor((batch_size, num_features, seq_len)				).to(_A				)
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						torch.tensor([10] * batch_size				).to(_A				)

       return {"sample": noise, "timestep": time_step}



     @property
     def 				UpperCAmelCase__    (							self    :    Dict				):







       """simple docstring"""




       return (4, 14, 16)



     @property
     def 				UpperCAmelCase__    (							self    :    int				):







       """simple docstring"""




       return (4, 14, 16)



     def 				UpperCAmelCase__    (							self    :    List[Any]				):







       """simple docstring"""




       pass



     def 				UpperCAmelCase__    (							self    :    Dict				):







       """simple docstring"""




       pass



     @unittest.skipIf(torch_device == '''mps'''      ,		'''mish op not supported in MPS'''				)
     def 				UpperCAmelCase__    (							self    :    Optional[int]				):







       """simple docstring"""




       super().test_determinism()



     @unittest.skipIf(torch_device == '''mps'''      ,		'''mish op not supported in MPS'''				)
     def 				UpperCAmelCase__    (							self    :    Optional[Any]				):







       """simple docstring"""




       super().test_outputs_equivalence()



     @unittest.skipIf(torch_device == '''mps'''      ,		'''mish op not supported in MPS'''				)
     def 				UpperCAmelCase__    (							self    :    Any				):







       """simple docstring"""




       super().test_from_save_pretrained()



     @unittest.skipIf(torch_device == '''mps'''      ,		'''mish op not supported in MPS'''				)
     def 				UpperCAmelCase__    (							self    :    List[Any]				):







       """simple docstring"""




       super().test_from_save_pretrained_variant()



     @unittest.skipIf(torch_device == '''mps'''      ,		'''mish op not supported in MPS'''				)
     def 				UpperCAmelCase__    (							self    :    Optional[Any]				):







       """simple docstring"""




       super().test_model_from_pretrained()



     @unittest.skipIf(torch_device == '''mps'''      ,		'''mish op not supported in MPS'''				)
     def 				UpperCAmelCase__    (							self    :    Tuple				):







       """simple docstring"""




       super().test_output()



     def 				UpperCAmelCase__    (							self    :    List[str]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					str			=						{
           '''block_out_channels''': (32, 64, 128, 256),
           '''in_channels''': 14,
           '''out_channels''': 14,
           '''time_embedding_type''': '''positional''',
           '''use_timestep_embedding''': True,
           '''flip_sin_to_cos''': False,
           '''freq_shift''': 1.0,
           '''out_block_type''': '''OutConv1DBlock''',
           '''mid_block_type''': '''MidResTemporalBlock1D''',
           '''down_block_types''': ('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D'''),
           '''up_block_types''': ('''UpResnetBlock1D''', '''UpResnetBlock1D''', '''UpResnetBlock1D'''),
           '''act_fn''': '''mish''',
       }
       __SCREAMING_SNAKE_CASE     :					int			=						self.dummy_input
       return init_dict, inputs_dict



     @unittest.skipIf(torch_device == '''mps'''      ,		'''mish op not supported in MPS'''				)
     def 				UpperCAmelCase__    (							self    :    Optional[int]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						UNetaDModel.from_pretrained(
           '''bglick13/hopper-medium-v2-value-function-hor32'''      ,		output_loading_info=_A      ,		subfolder='''unet'''				)
       self.assertIsNotNone(_A				)
       self.assertEqual(len(loading_info['''missing_keys''']				)      ,		0				)

       model.to(_A				)
       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						model(**self.dummy_input				)

       assert image is not None, "Make sure output is not None"



     @unittest.skipIf(torch_device == '''mps'''      ,		'''mish op not supported in MPS'''				)
     def 				UpperCAmelCase__    (							self    :    Optional[int]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[str]			=						UNetaDModel.from_pretrained('''bglick13/hopper-medium-v2-value-function-hor32'''      ,		subfolder='''unet'''				)
       torch.manual_seed(0				)
       if torch.cuda.is_available():
         torch.cuda.manual_seed_all(0				)

       __SCREAMING_SNAKE_CASE     :					Any			=						model.config.in_channels
       __SCREAMING_SNAKE_CASE     :					Dict			=						16
       __SCREAMING_SNAKE_CASE     :					int			=						torch.randn((1, seq_len, num_features)				).permute(
           0      ,		2      ,		1				)  # match original, we can update values and remove
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						torch.full((num_features,)      ,		0				)

       with torch.no_grad():
         __SCREAMING_SNAKE_CASE     :					int			=						model(_A      ,		_A				).sample.permute(0      ,		2      ,		1				)

       __SCREAMING_SNAKE_CASE     :					List[Any]			=						output[0, -3:, -3:].flatten()
       # fmt: off
       __SCREAMING_SNAKE_CASE     :					int			=						torch.tensor([-2.13_71_72, 1.1_42_60_16, 0.3_68_86_87, -0.76_69_22, 0.7_30_31_46, 0.11_03_88_64, -0.4_76_06_33, 0.13_27_01_72, 0.02_59_13_48]				)
       # fmt: on
       self.assertTrue(torch.allclose(_A      ,		_A      ,		rtol=1e-3				)				)



     def 				UpperCAmelCase__    (							self    :    int				):







       """simple docstring"""




       pass




     @slow
     def 				UpperCAmelCase__    (							self    :    Optional[Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Any			=						'''harmonai/maestro-150k'''
       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						UNetaDModel.from_pretrained(_A      ,		subfolder='''unet'''				)
       model.to(_A				)

       __SCREAMING_SNAKE_CASE     :					Any			=						6_5536
       __SCREAMING_SNAKE_CASE     :					Any			=						torch.sin(torch.arange(_A				)[None, None, :].repeat(1      ,		2      ,		1				)				).to(_A				)
       __SCREAMING_SNAKE_CASE     :					int			=						torch.tensor([1]				).to(_A				)

       with torch.no_grad():
         __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						model(_A      ,		_A				).sample

       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						output.abs().sum()
       __SCREAMING_SNAKE_CASE     :					Any			=						output.abs().max()

       assert (output_sum - 2_24.08_96).abs() < 0.5
       assert (output_max - 0.06_07).abs() < 4e-4






class   __UpperCamelCase				(    lowerCAmelCase__				,		lowerCAmelCase__				,		unittest.TestCase   ):






     """simple docstring"""







     lowerCAmelCase_     =	UNetaDModel
     lowerCAmelCase_     =	'''sample'''



     @property
     def 				UpperCAmelCase__    (							self    :    Union[str, Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[str]			=						4
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						14
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						16

       __SCREAMING_SNAKE_CASE     :					List[Any]			=						floats_tensor((batch_size, num_features, seq_len)				).to(_A				)
       __SCREAMING_SNAKE_CASE     :					Tuple			=						torch.tensor([10] * batch_size				).to(_A				)

       return {"sample": noise, "timestep": time_step}



     @property
     def 				UpperCAmelCase__    (							self    :    Any				):







       """simple docstring"""




       return (4, 14, 16)



     @property
     def 				UpperCAmelCase__    (							self    :    List[str]				):







       """simple docstring"""




       return (4, 14, 1)



     @unittest.skipIf(torch_device == '''mps'''      ,		'''mish op not supported in MPS'''				)
     def 				UpperCAmelCase__    (							self    :    Tuple				):







       """simple docstring"""




       super().test_determinism()



     @unittest.skipIf(torch_device == '''mps'''      ,		'''mish op not supported in MPS'''				)
     def 				UpperCAmelCase__    (							self    :    List[Any]				):







       """simple docstring"""




       super().test_outputs_equivalence()



     @unittest.skipIf(torch_device == '''mps'''      ,		'''mish op not supported in MPS'''				)
     def 				UpperCAmelCase__    (							self    :    str				):







       """simple docstring"""




       super().test_from_save_pretrained()



     @unittest.skipIf(torch_device == '''mps'''      ,		'''mish op not supported in MPS'''				)
     def 				UpperCAmelCase__    (							self    :    Optional[int]				):







       """simple docstring"""




       super().test_from_save_pretrained_variant()



     @unittest.skipIf(torch_device == '''mps'''      ,		'''mish op not supported in MPS'''				)
     def 				UpperCAmelCase__    (							self    :    int				):







       """simple docstring"""




       super().test_model_from_pretrained()



     @unittest.skipIf(torch_device == '''mps'''      ,		'''mish op not supported in MPS'''				)
     def 				UpperCAmelCase__    (							self    :    Union[str, Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					Optional[int]			=						self.prepare_init_args_and_inputs_for_common()
       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						self.model_class(**_A				)
       model.to(_A				)
       model.eval()

       with torch.no_grad():
         __SCREAMING_SNAKE_CASE     :					Any			=						model(**_A				)

         if isinstance(_A      ,		_A				):
           __SCREAMING_SNAKE_CASE     :					str			=						output.sample

       self.assertIsNotNone(_A				)
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						torch.Size((inputs_dict['''sample'''].shape[0], 1)				)
       self.assertEqual(output.shape      ,		_A      ,		'''Input and output shapes do not match'''				)



     def 				UpperCAmelCase__    (							self    :    Any				):







       """simple docstring"""




       pass



     def 				UpperCAmelCase__    (							self    :    Tuple				):







       """simple docstring"""




       pass



     def 				UpperCAmelCase__    (							self    :    str				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						{
           '''in_channels''': 14,
           '''out_channels''': 14,
           '''down_block_types''': ['''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D'''],
           '''up_block_types''': [],
           '''out_block_type''': '''ValueFunction''',
           '''mid_block_type''': '''ValueFunctionMidBlock1D''',
           '''block_out_channels''': [32, 64, 128, 256],
           '''layers_per_block''': 1,
           '''downsample_each_block''': True,
           '''use_timestep_embedding''': True,
           '''freq_shift''': 1.0,
           '''flip_sin_to_cos''': False,
           '''time_embedding_type''': '''positional''',
           '''act_fn''': '''mish''',
       }
       __SCREAMING_SNAKE_CASE     :					int			=						self.dummy_input
       return init_dict, inputs_dict



     @unittest.skipIf(torch_device == '''mps'''      ,		'''mish op not supported in MPS'''				)
     def 				UpperCAmelCase__    (							self    :    int				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					Any			=						UNetaDModel.from_pretrained(
           '''bglick13/hopper-medium-v2-value-function-hor32'''      ,		output_loading_info=_A      ,		subfolder='''value_function'''				)
       self.assertIsNotNone(_A				)
       self.assertEqual(len(vf_loading_info['''missing_keys''']				)      ,		0				)

       value_function.to(_A				)
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						value_function(**self.dummy_input				)

       assert image is not None, "Make sure output is not None"



     @unittest.skipIf(torch_device == '''mps'''      ,		'''mish op not supported in MPS'''				)
     def 				UpperCAmelCase__    (							self    :    Dict				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					List[Any]			=						UNetaDModel.from_pretrained(
           '''bglick13/hopper-medium-v2-value-function-hor32'''      ,		output_loading_info=_A      ,		subfolder='''value_function'''				)
       torch.manual_seed(0				)
       if torch.cuda.is_available():
         torch.cuda.manual_seed_all(0				)

       __SCREAMING_SNAKE_CASE     :					Any			=						value_function.config.in_channels
       __SCREAMING_SNAKE_CASE     :					int			=						14
       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						torch.randn((1, seq_len, num_features)				).permute(
           0      ,		2      ,		1				)  # match original, we can update values and remove
       __SCREAMING_SNAKE_CASE     :					List[str]			=						torch.full((num_features,)      ,		0				)

       with torch.no_grad():
         __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						value_function(_A      ,		_A				).sample

       # fmt: off
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						torch.tensor([1_65.25] * seq_len				)
       # fmt: on
       self.assertTrue(torch.allclose(_A      ,		_A      ,		rtol=1e-3				)				)




     def 				UpperCAmelCase__    (							self    :    Optional[Any]				):







       """simple docstring"""




       pass


from typing import Optional

from ..utils.logging import get_logger
from .audio_classificiation import AudioClassification
from .automatic_speech_recognition import AutomaticSpeechRecognition
from .base import TaskTemplate
from .image_classification import ImageClassification
from .language_modeling import LanguageModeling
from .question_answering import QuestionAnsweringExtractive
from .summarization import Summarization
from .text_classification import TextClassification


lowercase_											=	[
    """AutomaticSpeechRecognition""",
    """AudioClassification""",
    """ImageClassification""",
    """LanguageModeling""",
    """QuestionAnsweringExtractive""",
    """Summarization""",
    """TaskTemplate""",
    """TextClassification""",
]

lowercase_											=	get_logger(__name__)


lowercase_											=	{
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}


def     a__			(    snake_case    ):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE     :					str			=						task_template_dict.get('''task'''    )
  if task_name is None:
    logger.warning(F'''Couldn\'t find template for task \'{task_name}\'. Available templates: {list(snake_case    )}'''    )
    return None
  __SCREAMING_SNAKE_CASE     :					Any			=						NAMEaTEMPLATE.get(snake_case    )
  return template.from_dict(snake_case    )
