



'''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 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_   ,   unittest.TestCase     ):
  """simple docstring"""




  a_												=PriorTransformer
  a_												="""hidden_states"""




  @property
  def 						_lowercase     ( self      :	List[str]   )					->					Union[str, Any]:
    __lowerCamelCase       :       List[str]           = 4
    __lowerCamelCase       :       Tuple           = 8
    __lowerCamelCase       :       Tuple           = 7

    __lowerCamelCase       :       Union[str, Any]           = floats_tensor((batch_size, embedding_dim)   ).to(_a   )

    __lowerCamelCase       :       Tuple           = floats_tensor((batch_size, embedding_dim)   ).to(_a   )
    __lowerCamelCase       :       Optional[Any]           = floats_tensor((batch_size, num_embeddings, embedding_dim)   ).to(_a   )

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




  def 						_lowercase     ( self      :	List[Any]			,	_a      :	int=0   )					->					Optional[Any]:
    torch.manual_seed(_a   )
    __lowerCamelCase       :       List[Any]           = 4
    __lowerCamelCase       :       Tuple           = 8
    __lowerCamelCase       :       List[str]           = 7

    __lowerCamelCase       :       Union[str, Any]           = torch.randn((batch_size, embedding_dim)   ).to(_a   )

    __lowerCamelCase       :       Union[str, Any]           = torch.randn((batch_size, embedding_dim)   ).to(_a   )
    __lowerCamelCase       :       List[str]           = torch.randn((batch_size, num_embeddings, embedding_dim)   ).to(_a   )

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




  @property
  def 						_lowercase     ( self      :	Optional[int]   )					->					Tuple:
    return (4, 8)




  @property
  def 						_lowercase     ( self      :	int   )					->					int:
    return (4, 8)




  def 						_lowercase     ( self      :	List[Any]   )					->					Union[str, Any]:
    __lowerCamelCase       :       Any           = {
        'num_attention_heads': 2,
        'attention_head_dim': 4,
        'num_layers': 2,
        'embedding_dim': 8,
        'num_embeddings': 7,
        'additional_embeddings': 4,
    }
    __lowerCamelCase       :       Optional[int]           = self.dummy_input
    return init_dict, inputs_dict




  def 						_lowercase     ( self      :	Union[str, Any]   )					->					Any:
    __lowerCamelCase       ,__lowerCamelCase       :       Union[str, Any]           = PriorTransformer.from_pretrained(
        'hf-internal-testing/prior-dummy'			,	output_loading_info=_a   )
    self.assertIsNotNone(_a   )
    self.assertEqual(len(loading_info['missing_keys']   )			,	0   )

    model.to(_a   )
    __lowerCamelCase       :       Any           = model(**self.dummy_input   )[0]

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




  def 						_lowercase     ( self      :	Union[str, Any]   )					->					Any:
    __lowerCamelCase       ,__lowerCamelCase       :       Optional[Any]           = self.prepare_init_args_and_inputs_for_common()

    __lowerCamelCase       :       Any           = self.model_class(**_a   )
    __lowerCamelCase       :       str           = inspect.signature(model.forward   )
    # signature.parameters is an OrderedDict => so arg_names order is deterministic
    __lowerCamelCase       :       Optional[int]           = [*signature.parameters.keys()]

    __lowerCamelCase       :       Optional[int]           = ['hidden_states', 'timestep']
    self.assertListEqual(arg_names[:2]			,	_a   )




  def 						_lowercase     ( self      :	Tuple   )					->					List[Any]:
    __lowerCamelCase       :       str           = PriorTransformer.from_pretrained('hf-internal-testing/prior-dummy'   )
    __lowerCamelCase       :       Tuple           = model.to(_a   )

    if hasattr(_a			,	'set_default_attn_processor'   ):
      model.set_default_attn_processor()

    __lowerCamelCase       :       List[Any]           = self.get_dummy_seed_input()

    with torch.no_grad():
      __lowerCamelCase       :       Optional[int]           = model(**_a   )[0]

    __lowerCamelCase       :       Optional[int]           = output[0, :5].flatten().cpu()
    print(_a   )

    # 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.
    __lowerCamelCase       :       int           = torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239]   )
    self.assertTrue(torch_all_close(_a			,	_a			,	rtol=1e-2   )   )






@slow
class 					lowerCamelCase_    (				unittest.TestCase     ):
  """simple docstring"""




  def 						_lowercase     ( self      :	Any			,	_a      :	Optional[Any]=1			,	_a      :	Dict=768			,	_a      :	List[Any]=77			,	_a      :	List[str]=0   )					->					int:
    torch.manual_seed(_a   )
    __lowerCamelCase       :       int           = batch_size
    __lowerCamelCase       :       Optional[Any]           = embedding_dim
    __lowerCamelCase       :       Any           = num_embeddings

    __lowerCamelCase       :       Union[str, Any]           = torch.randn((batch_size, embedding_dim)   ).to(_a   )

    __lowerCamelCase       :       int           = torch.randn((batch_size, embedding_dim)   ).to(_a   )
    __lowerCamelCase       :       Tuple           = torch.randn((batch_size, num_embeddings, embedding_dim)   ).to(_a   )

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




  def 						_lowercase     ( self      :	Optional[int]   )					->					Any:
    # clean up the VRAM after each test
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()




  @parameterized.expand(
      [
          # fmt: off
          [13, [-0.5861,  0.1283, -0.0931,  0.0882,  0.4476,  0.1329, -0.0498,  0.0640]],
          [37, [-0.4913,  0.0110, -0.0483,  0.0541,  0.4954, -0.0170,  0.0354,  0.1651]],
          # fmt: on
      ]   )
  def 						_lowercase     ( self      :	Optional[int]			,	_a      :	Union[str, Any]			,	_a      :	Any   )					->					List[str]:
    __lowerCamelCase       :       Optional[Any]           = PriorTransformer.from_pretrained('kandinsky-community/kandinsky-2-1-prior'			,	subfolder='prior'   )
    model.to(_a   )
    __lowerCamelCase       :       List[str]           = self.get_dummy_seed_input(seed=_a   )

    with torch.no_grad():
      __lowerCamelCase       :       List[Any]           = model(**_a   )[0]

    assert list(sample.shape   ) == [1, 768]

    __lowerCamelCase       :       Tuple           = sample[0, :8].flatten().cpu()
    print(_a   )
    __lowerCamelCase       :       Dict           = torch.tensor(_a   )

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











'''simple docstring'''





from collections.abc import Sequence

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      float:
  return sum(c * (x**i) for i, c in enumerate(_lowerCAmelCase       )       )

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      float:
  __lowerCamelCase       :       Any           = 0.0
  for coeff in reversed(_lowerCAmelCase       ):
    __lowerCamelCase       :       Tuple           = result * x + coeff
  return result


if __name__ == "__main__":
   _UpperCamelCase											=						(0.0, 0.0, 5.0, 9.3, 7.0)
   _UpperCamelCase											=						1_0.0
   print(evaluate_poly(poly, x))
   print(horner(poly, x))






