



"""simple docstring"""
def    _snake_case		(		UpperCamelCase		:	List[Any] = "The quick brown fox jumps over the lazy dog"    ,	):
 UpperCAmelCase			:      Any			=			set()

 # Replace all the whitespace in our sentence
 UpperCAmelCase			:      str			=			input_str.replace(""" """    ,	""""""		)
 for alpha in input_str:
  if "a" <= alpha.lower() <= "z":
   frequency.add(alpha.lower()		)
 return len(lowerCAmelCase_		) == 26

def    _snake_case		(		UpperCamelCase		:	Tuple = "The quick brown fox jumps over the lazy dog"    ,	):
 UpperCAmelCase			:      List[Any]			=			[False] * 26
 for char in input_str:
  if char.islower():
   UpperCAmelCase			:      List[Any]			=			True
  elif char.isupper():
   UpperCAmelCase			:      Optional[int]			=			True
 return all(lowerCAmelCase_		)

def    _snake_case		(		UpperCamelCase		:	Optional[int] = "The quick brown fox jumps over the lazy dog"    ,	):
 return len({char for char in input_str.lower() if char.isalpha()}		) == 26

def    _snake_case		(		):
 from timeit import timeit

 UpperCAmelCase			:      Optional[int]			=			"from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"
 print(timeit("""is_pangram()"""    ,	setup=lowerCAmelCase_		)		)
 print(timeit("""is_pangram_faster()"""    ,	setup=lowerCAmelCase_		)		)
 print(timeit("""is_pangram_fastest()"""    ,	setup=lowerCAmelCase_		)		)
 # 5.348480500048026, 2.6477354579837993, 1.8470395830227062
 # 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      benchmark()





"""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    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__   ,    unittest.TestCase				):
   __lowerCAmelCase						:     int		=		PriorTransformer
   __lowerCAmelCase						:     Dict		=		'hidden_states'

   @property
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   List[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Tuple			=			4
    UpperCAmelCase			:      Optional[Any]			=			8
    UpperCAmelCase			:      Union[str, Any]			=			7

    UpperCAmelCase			:      Any			=			floats_tensor((batch_size, embedding_dim)		).to(_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      List[Any]			=			floats_tensor((batch_size, embedding_dim)		).to(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      List[Any]			=			floats_tensor((batch_size, num_embeddings, embedding_dim)		).to(_SCREAMING_SNAKE_CASE		)

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

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE=0		)						->   Any:





    '''simple docstring'''




    torch.manual_seed(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Any			=			4
    UpperCAmelCase			:      Any			=			8
    UpperCAmelCase			:      List[Any]			=			7

    UpperCAmelCase			:      Any			=			torch.randn((batch_size, embedding_dim)		).to(_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      List[Any]			=			torch.randn((batch_size, embedding_dim)		).to(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      str			=			torch.randn((batch_size, num_embeddings, embedding_dim)		).to(_SCREAMING_SNAKE_CASE		)

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

   @property
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   List[Any]:





    '''simple docstring'''




    return (4, 8)

   @property
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Dict:





    '''simple docstring'''




    return (4, 8)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   int:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[int]			=			{
        """num_attention_heads""": 2,
        """attention_head_dim""": 4,
        """num_layers""": 2,
        """embedding_dim""": 8,
        """num_embeddings""": 7,
        """additional_embeddings""": 4,
    }
    UpperCAmelCase			:      Tuple			=			self.dummy_input
    return init_dict, inputs_dict

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Dict:





    '''simple docstring'''




    UpperCAmelCase			,	UpperCAmelCase			:      Optional[Any]			=			PriorTransformer.from_pretrained(
        """hf-internal-testing/prior-dummy"""   ,  output_loading_info=_SCREAMING_SNAKE_CASE		)
    self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)
    self.assertEqual(len(loading_info["""missing_keys"""]		)   ,  0		)

    model.to(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Dict			=			model(**self.dummy_input		)[0]

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

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Any:





    '''simple docstring'''




    UpperCAmelCase			,	UpperCAmelCase			:      Tuple			=			self.prepare_init_args_and_inputs_for_common()

    UpperCAmelCase			:      str			=			self.model_class(**_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      List[str]			=			inspect.signature(model.forward		)
    # signature.parameters is an OrderedDict => so arg_names order is deterministic
    UpperCAmelCase			:      Union[str, Any]			=			[*signature.parameters.keys()]

    UpperCAmelCase			:      int			=			["""hidden_states""", """timestep"""]
    self.assertListEqual(arg_names[:2]   ,  _SCREAMING_SNAKE_CASE		)




   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Any:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[Any]			=			PriorTransformer.from_pretrained("""hf-internal-testing/prior-dummy"""		)
    UpperCAmelCase			:      Dict			=			model.to(_SCREAMING_SNAKE_CASE		)

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

    UpperCAmelCase			:      str			=			self.get_dummy_seed_input()

    with torch.no_grad():
     UpperCAmelCase			:      str			=			model(**_SCREAMING_SNAKE_CASE		)[0]

    UpperCAmelCase			:      Dict			=			output[0, :5].flatten().cpu()
    print(_SCREAMING_SNAKE_CASE		)

    # 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.
    UpperCAmelCase			:      Dict			=			torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239]		)
    self.assertTrue(torch_all_close(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  rtol=1E-2		)		)



@slow
class    SCREAMING_SNAKE_CASE__						(      unittest.TestCase				):

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE=1   ,  _SCREAMING_SNAKE_CASE=768   ,  _SCREAMING_SNAKE_CASE=77   ,  _SCREAMING_SNAKE_CASE=0		)						->   int:





    '''simple docstring'''




    torch.manual_seed(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Optional[Any]			=			batch_size
    UpperCAmelCase			:      int			=			embedding_dim
    UpperCAmelCase			:      Tuple			=			num_embeddings

    UpperCAmelCase			:      Optional[Any]			=			torch.randn((batch_size, embedding_dim)		).to(_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      str			=			torch.randn((batch_size, embedding_dim)		).to(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Dict			=			torch.randn((batch_size, num_embeddings, embedding_dim)		).to(_SCREAMING_SNAKE_CASE		)

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

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   int:





    '''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 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   Any:





    '''simple docstring'''




    UpperCAmelCase			:      Dict			=			PriorTransformer.from_pretrained("""kandinsky-community/kandinsky-2-1-prior"""   ,  subfolder="""prior"""		)
    model.to(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Any			=			self.get_dummy_seed_input(seed=_SCREAMING_SNAKE_CASE		)

    with torch.no_grad():
     UpperCAmelCase			:      Any			=			model(**_SCREAMING_SNAKE_CASE		)[0]

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

    UpperCAmelCase			:      int			=			sample[0, :8].flatten().cpu()
    print(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Optional[int]			=			torch.tensor(_SCREAMING_SNAKE_CASE		)

    assert torch_all_close(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  atol=1E-3		)
