"""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    __snake_case       (     __lowerCAmelCase       ,      unittest.TestCase				):
      a__        =		PriorTransformer
      a__        =		"""hidden_states"""



      @property
      def   lowerCamelCase_   (						self)	->			Optional[Any]:





        '''simple docstring'''





        a__:     Any										=	4
        a__:     Tuple										=	8
        a__:     Dict										=	7

        a__:     List[Any]										=	floats_tensor((batch_size, embedding_dim)).to(lowercase)

        a__:     List[Any]										=	floats_tensor((batch_size, embedding_dim)).to(lowercase)
        a__:     List[Any]										=	floats_tensor((batch_size, num_embeddings, embedding_dim)).to(lowercase)

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



      def   lowerCamelCase_   (						self    ,			lowercase=0)	->			Any:





        '''simple docstring'''





        torch.manual_seed(lowercase)
        a__:     str										=	4
        a__:     Tuple										=	8
        a__:     Union[str, Any]										=	7

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

        a__:     str										=	torch.randn((batch_size, embedding_dim)).to(lowercase)
        a__:     Optional[int]										=	torch.randn((batch_size, num_embeddings, embedding_dim)).to(lowercase)

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



      @property
      def   lowerCamelCase_   (						self)	->			List[str]:





        '''simple docstring'''





        return (4, 8)



      @property
      def   lowerCamelCase_   (						self)	->			Optional[Any]:





        '''simple docstring'''





        return (4, 8)



      def   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        a__:     List[Any]										=	{
            'num_attention_heads': 2,
            'attention_head_dim': 4,
            'num_layers': 2,
            'embedding_dim': 8,
            'num_embeddings': 7,
            'additional_embeddings': 4,
        }
        a__:     str										=	self.dummy_input
        return init_dict, inputs_dict



      def   lowerCamelCase_   (						self)	->			Optional[Any]:





        '''simple docstring'''





        a__							,    a__:     str										=	PriorTransformer.from_pretrained(
            'hf-internal-testing/prior-dummy'    ,			output_loading_info=lowercase)
        self.assertIsNotNone(lowercase)
        self.assertEqual(len(loading_info['missing_keys'])    ,			0)

        model.to(lowercase)
        a__:     Optional[int]										=	model(**self.dummy_input)[0]

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



      def   lowerCamelCase_   (						self)	->			List[str]:





        '''simple docstring'''





        a__							,    a__:     Optional[int]										=	self.prepare_init_args_and_inputs_for_common()

        a__:     Dict										=	self.model_class(**lowercase)
        a__:     List[str]										=	inspect.signature(model.forward)
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        a__:     List[str]										=	[*signature.parameters.keys()]

        a__:     Tuple										=	['hidden_states', 'timestep']
        self.assertListEqual(arg_names[:2]    ,			lowercase)



      def   lowerCamelCase_   (						self)	->			List[str]:





        '''simple docstring'''





        a__:     Tuple										=	PriorTransformer.from_pretrained('hf-internal-testing/prior-dummy')
        a__:     Any										=	model.to(lowercase)

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

        a__:     Optional[int]										=	self.get_dummy_seed_input()

        with torch.no_grad():
          a__:     Any										=	model(**lowercase)[0]

        a__:     int										=	output[0, :5].flatten().cpu()
        print(lowercase)

        # 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__:     Union[str, Any]										=	torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239])
        self.assertTrue(torch_all_close(lowercase    ,			lowercase    ,			rtol=1e-2))







@slow
class    __snake_case       (     unittest.TestCase				):



      def   lowerCamelCase_   (						self    ,			lowercase=1    ,			lowercase=7_68    ,			lowercase=77    ,			lowercase=0)	->			List[str]:





        '''simple docstring'''





        torch.manual_seed(lowercase)
        a__:     Optional[Any]										=	batch_size
        a__:     int										=	embedding_dim
        a__:     Union[str, Any]										=	num_embeddings

        a__:     Tuple										=	torch.randn((batch_size, embedding_dim)).to(lowercase)

        a__:     Tuple										=	torch.randn((batch_size, embedding_dim)).to(lowercase)
        a__:     Union[str, Any]										=	torch.randn((batch_size, num_embeddings, embedding_dim)).to(lowercase)

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



      def   lowerCamelCase_   (						self)	->			Optional[Any]:





        '''simple docstring'''





        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   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase)	->			Optional[int]:





        '''simple docstring'''





        a__:     Optional[int]										=	PriorTransformer.from_pretrained('kandinsky-community/kandinsky-2-1-prior'    ,			subfolder='prior')
        model.to(lowercase)
        a__:     Any										=	self.get_dummy_seed_input(seed=lowercase)

        with torch.no_grad():
          a__:     Tuple										=	model(**lowercase)[0]

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

        a__:     Any										=	sample[0, :8].flatten().cpu()
        print(lowercase)
        a__:     Optional[Any]										=	torch.tensor(lowercase)

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



"""simple docstring"""
def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE = False   )	->str:

  if not isinstance(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   ):
    a__:     Optional[int]										=	F'Expected string as input, found {type(_SCREAMING_SNAKE_CASE   )}'
    raise ValueError(_SCREAMING_SNAKE_CASE   )
  if not isinstance(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   ):
    a__:     List[str]										=	F'Expected boolean as use_pascal parameter, found {type(_SCREAMING_SNAKE_CASE   )}'
    raise ValueError(_SCREAMING_SNAKE_CASE   )

  a__:     int										=	input_str.split('_'   )

  a__:     List[str]										=	0 if use_pascal else 1

  a__:     List[str]										=	words[start_index:]

  a__:     List[str]										=	[word[0].upper() + word[1:] for word in words_to_capitalize]

  a__:     List[str]										=	'' if use_pascal else words[0]

  return "".join([initial_word, *capitalized_words]   )


if __name__ == "__main__":
 from doctest import testmod

 testmod()


