

"""simple docstring"""



import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()



class     a  (				_lowerCamelCase,		unittest.TestCase ):



    """simple docstring"""



    UpperCAmelCase         =    PriorTransformer
    UpperCAmelCase         =    "hidden_states"

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






         """simple docstring"""


         A__								=     4
         A__								=     8
         A__								=     7

         A__								=     floats_tensor((batch_size, embedding_dim)				).to(UpperCamelCase				)

         A__								=     floats_tensor((batch_size, embedding_dim)				).to(UpperCamelCase				)
         A__								=     floats_tensor((batch_size, num_embeddings, embedding_dim)				).to(UpperCamelCase				)

         return {
             "hidden_states": hidden_states,
             "timestep": 2,
             "proj_embedding": proj_embedding,
             "encoder_hidden_states": encoder_hidden_states,
         }

    def  UpperCamelCase		(   self:       List[str]			,  UpperCamelCase:       Tuple=0				):






         """simple docstring"""


         torch.manual_seed(UpperCamelCase				)
         A__								=     4
         A__								=     8
         A__								=     7

         A__								=     torch.randn((batch_size, embedding_dim)				).to(UpperCamelCase				)

         A__								=     torch.randn((batch_size, embedding_dim)				).to(UpperCamelCase				)
         A__								=     torch.randn((batch_size, num_embeddings, embedding_dim)				).to(UpperCamelCase				)

         return {
             "hidden_states": hidden_states,
             "timestep": 2,
             "proj_embedding": proj_embedding,
             "encoder_hidden_states": encoder_hidden_states,
         }

    @property
    def  UpperCamelCase		(   self:       Tuple				):






         """simple docstring"""


         return (4, 8)

    @property
    def  UpperCamelCase		(   self:       Tuple				):






         """simple docstring"""


         return (4, 8)

    def  UpperCamelCase		(   self:       Dict				):






         """simple docstring"""


         A__								=     {
             'num_attention_heads': 2,
             'attention_head_dim': 4,
             'num_layers': 2,
             'embedding_dim': 8,
             'num_embeddings': 7,
             'additional_embeddings': 4,
         }
         A__								=     self.dummy_input
         return init_dict, inputs_dict

    def  UpperCamelCase		(   self:       int				):






         """simple docstring"""


         A__								=     PriorTransformer.from_pretrained(
             """hf-internal-testing/prior-dummy"""			,  output_loading_info=UpperCamelCase				)
         self.assertIsNotNone(UpperCamelCase				)
         self.assertEqual(len(loading_info["""missing_keys"""]				)			,  0				)

         model.to(UpperCamelCase				)
         A__								=     model(**self.dummy_input				)[0]

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

    def  UpperCamelCase		(   self:       Any				):






         """simple docstring"""


         A__								=     self.prepare_init_args_and_inputs_for_common()

         A__								=     self.model_class(**UpperCamelCase				)
         A__								=     inspect.signature(model.forward				)
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         A__								=     [*signature.parameters.keys()]

         A__								=     ['hidden_states', 'timestep']
         self.assertListEqual(arg_names[:2]			,  UpperCamelCase				)





    def  UpperCamelCase		(   self:       int				):






         """simple docstring"""


         A__								=     PriorTransformer.from_pretrained("""hf-internal-testing/prior-dummy"""				)
         A__								=     model.to(UpperCamelCase				)

         if hasattr(UpperCamelCase			,  """set_default_attn_processor"""				):
              model.set_default_attn_processor()

         A__								=     self.get_dummy_seed_input()

         with torch.no_grad():
              A__								=     model(**UpperCamelCase				)[0]

         A__								=     output[0, :5].flatten().cpu()
         print(UpperCamelCase				)

         # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
         # the expected output slices are not the same for CPU and GPU.
         A__								=     torch.tensor([-1.3_436, -0.2_870, 0.7_538, 0.4_368, -0.0_239]				)
         self.assertTrue(torch_all_close(UpperCamelCase			,  UpperCamelCase			,  rtol=1e-2				)				)



@slow
class     a  (				unittest.TestCase ):



    """simple docstring"""



    def  UpperCamelCase		(   self:       str			,  UpperCamelCase:       List[str]=1			,  UpperCamelCase:       Tuple=7_68			,  UpperCamelCase:       Union[str, Any]=77			,  UpperCamelCase:       int=0				):






         """simple docstring"""


         torch.manual_seed(UpperCamelCase				)
         A__								=     batch_size
         A__								=     embedding_dim
         A__								=     num_embeddings

         A__								=     torch.randn((batch_size, embedding_dim)				).to(UpperCamelCase				)

         A__								=     torch.randn((batch_size, embedding_dim)				).to(UpperCamelCase				)
         A__								=     torch.randn((batch_size, num_embeddings, embedding_dim)				).to(UpperCamelCase				)

         return {
             "hidden_states": hidden_states,
             "timestep": 2,
             "proj_embedding": proj_embedding,
             "encoder_hidden_states": encoder_hidden_states,
         }

    def  UpperCamelCase		(   self:       Union[str, Any]				):






         """simple docstring"""


         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()





    @parameterized.expand(
        [
            # fmt: off
            [13, [-0.5_861,  0.1_283, -0.0_931,  0.0_882,  0.4_476,  0.1_329, -0.0_498,  0.0_640]],
            [37, [-0.4_913,  0.0_110, -0.0_483,  0.0_541,  0.4_954, -0.0_170,  0.0_354,  0.1_651]],
            # fmt: on
        ]				)
    def  UpperCamelCase		(   self:       str			,  UpperCamelCase:       Any			,  UpperCamelCase:       Any				):






         """simple docstring"""


         A__								=     PriorTransformer.from_pretrained("""kandinsky-community/kandinsky-2-1-prior"""			,  subfolder="""prior"""				)
         model.to(UpperCamelCase				)
         A__								=     self.get_dummy_seed_input(seed=UpperCamelCase				)

         with torch.no_grad():
              A__								=     model(**UpperCamelCase				)[0]

         assert list(sample.shape				) == [1, 7_68]

         A__								=     sample[0, :8].flatten().cpu()
         print(UpperCamelCase				)
         A__								=     torch.tensor(UpperCamelCase				)

         assert torch_all_close(UpperCamelCase			,  UpperCamelCase			,  atol=1e-3				)








import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate







@dataclass(frozen=__magic_name__	)
class      A__							(     __magic_name__	):
				lowercase			    =  field(default='audio-classification'						,   metadata={'include_in_asdict_even_if_is_default': True}	)
				lowercase			    =  Features({'audio': Audio()}	)
				lowercase			    =  Features({'labels': ClassLabel}	)
				lowercase			    =  "audio"
				lowercase			    =  "labels"






				def 							_lowerCamelCase		(						self					:      Dict    ,     a					:      Tuple   ):






							'''simple docstring'''







							if self.label_column not in features:
										raise ValueError(f'''Column {self.label_column} is not present in features.'''   )
							if not isinstance(features[self.label_column]    ,     a   ):
										raise ValueError(f'''Column {self.label_column} is not a ClassLabel.'''   )
							lowerCAmelCase__      :      Tuple         =		copy.deepcopy(self   )
							lowerCAmelCase__      :      List[Any]         =		self.label_schema.copy()
							lowerCAmelCase__      :      List[Any]         =		features[self.label_column]
							lowerCAmelCase__      :      Optional[int]         =		label_schema
							return task_template






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






							'''simple docstring'''







							return {
							    self.audio_column: "audio",
							    self.label_column: "labels",
							}