


'''simple docstring'''



import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
      import jax
      import jax.numpy as jnp





@slow
@require_flax
class    UpperCAmelCase_  (unittest.TestCase  ):





    """simple docstring"""






    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)				->     Optional[int]:
         return f'gaussian_noise_s={seed}_shape={"_".join([str(SCREAMING_SNAKE_CASE_	) for s in shape]	)}.npy'







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







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_=0			,     SCREAMING_SNAKE_CASE_=(4, 4, 64, 64)			,     SCREAMING_SNAKE_CASE_=False	)				->     str:
         __lowerCamelCase   :	List[str]								=		jnp.bfloataa if fpaa else jnp.floataa
         __lowerCamelCase   :	Tuple								=		jnp.array(load_hf_numpy(self.get_file_format(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)	)			,     dtype=SCREAMING_SNAKE_CASE_	)
         return image







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_=False			,     SCREAMING_SNAKE_CASE_="CompVis/stable-diffusion-v1-4"	)				->     Dict:
         __lowerCamelCase   :	Union[str, Any]								=		jnp.bfloataa if fpaa else jnp.floataa
         __lowerCamelCase   :	Optional[Any]								=		'bf16' if fpaa else None

         __lowerCamelCase				,       __lowerCamelCase   :	str								=		FlaxUNetaDConditionModel.from_pretrained(
             SCREAMING_SNAKE_CASE_			,     subfolder='unet'			,     dtype=SCREAMING_SNAKE_CASE_			,     revision=SCREAMING_SNAKE_CASE_	)
         return model, params







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_=0			,     SCREAMING_SNAKE_CASE_=(4, 77, 7_68)			,     SCREAMING_SNAKE_CASE_=False	)				->     Union[str, Any]:
         __lowerCamelCase   :	Any								=		jnp.bfloataa if fpaa else jnp.floataa
         __lowerCamelCase   :	Optional[Any]								=		jnp.array(load_hf_numpy(self.get_file_format(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)	)			,     dtype=SCREAMING_SNAKE_CASE_	)
         return hidden_states







    @parameterized.expand(
        [
            # fmt: off
            [83, 4, [-0.2_3_2_3, -0.1_3_0_4, 0.0_8_1_3, -0.3_0_9_3, -0.0_9_1_9, -0.1_5_7_1, -0.1_1_2_5, -0.5_8_0_6]],
            [17, 0.5_5, [-0.0_8_3_1, -0.2_4_4_3, 0.0_9_0_1, -0.0_9_1_9, 0.3_3_9_6, 0.0_1_0_3, -0.3_7_4_3, 0.0_7_0_1]],
            [8, 0.8_9, [-0.4_8_6_3, 0.0_8_5_9, 0.0_8_7_5, -0.1_6_5_8, 0.9_1_9_9, -0.0_1_1_4, 0.4_8_3_9, 0.4_6_3_9]],
            [3, 10_00, [-0.5_6_4_9, 0.2_4_0_2, -0.5_5_1_8, 0.1_2_4_8, 1.1_3_2_8, -0.2_4_4_3, -0.0_3_2_5, -1.0_0_7_8]],
            # fmt: on
        ]	)
    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)				->     Any:
         __lowerCamelCase				,       __lowerCamelCase   :	Tuple								=		self.get_unet_model(model_id='CompVis/stable-diffusion-v1-4'			,     fpaa=SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Any								=		self.get_latents(SCREAMING_SNAKE_CASE_			,     fpaa=SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	int								=		self.get_encoder_hidden_states(SCREAMING_SNAKE_CASE_			,     fpaa=SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	List[Any]								=		model.apply(
             {'params': params}			,     SCREAMING_SNAKE_CASE_			,     jnp.array(SCREAMING_SNAKE_CASE_			,     dtype=jnp.intaa	)			,     encoder_hidden_states=SCREAMING_SNAKE_CASE_			,     ).sample

         assert sample.shape == latents.shape

         __lowerCamelCase   :	Any								=		jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())	)			,     dtype=jnp.floataa	)
         __lowerCamelCase   :	Optional[Any]								=		jnp.array(SCREAMING_SNAKE_CASE_			,     dtype=jnp.floataa	)

         # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
         assert jnp.allclose(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     atol=1E-2	)







    @parameterized.expand(
        [
            # fmt: off
            [83, 4, [0.1_5_1_4, 0.0_8_0_7, 0.1_6_2_4, 0.1_0_1_6, -0.1_8_9_6, 0.0_2_6_3, 0.0_6_7_7, 0.2_3_1_0]],
            [17, 0.5_5, [0.1_1_6_4, -0.0_2_1_6, 0.0_1_7_0, 0.1_5_8_9, -0.3_1_2_0, 0.1_0_0_5, -0.0_5_8_1, -0.1_4_5_8]],
            [8, 0.8_9, [-0.1_7_5_8, -0.0_1_6_9, 0.1_0_0_4, -0.1_4_1_1, 0.1_3_1_2, 0.1_1_0_3, -0.1_9_9_6, 0.2_1_3_9]],
            [3, 10_00, [0.1_2_1_4, 0.0_3_5_2, -0.0_7_3_1, -0.1_5_6_2, -0.0_9_9_4, -0.0_9_0_6, -0.2_3_4_0, -0.0_5_3_9]],
            # fmt: on
        ]	)
    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)				->     Optional[int]:
         __lowerCamelCase				,       __lowerCamelCase   :	List[str]								=		self.get_unet_model(model_id='stabilityai/stable-diffusion-2'			,     fpaa=SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	int								=		self.get_latents(SCREAMING_SNAKE_CASE_			,     shape=(4, 4, 96, 96)			,     fpaa=SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Optional[int]								=		self.get_encoder_hidden_states(SCREAMING_SNAKE_CASE_			,     shape=(4, 77, 10_24)			,     fpaa=SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	List[str]								=		model.apply(
             {'params': params}			,     SCREAMING_SNAKE_CASE_			,     jnp.array(SCREAMING_SNAKE_CASE_			,     dtype=jnp.intaa	)			,     encoder_hidden_states=SCREAMING_SNAKE_CASE_			,     ).sample

         assert sample.shape == latents.shape

         __lowerCamelCase   :	Optional[int]								=		jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())	)			,     dtype=jnp.floataa	)
         __lowerCamelCase   :	Tuple								=		jnp.array(SCREAMING_SNAKE_CASE_			,     dtype=jnp.floataa	)

         # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
         assert jnp.allclose(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     atol=1E-2	)










'''simple docstring'''



A__		:					Optional[int]												=  """ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"""



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				bytes							)     ->		bytes:
     # Make sure the supplied data is a bytes-like object
     if not isinstance(UpperCAmelCase_      ,    UpperCAmelCase_							):
          __lowerCamelCase   :	Dict								=		F'a bytes-like object is required, not \'{data.__class__.__name__}\''
          raise TypeError(UpperCAmelCase_							)

     __lowerCamelCase   :	Optional[int]								=		''.join(bin(UpperCAmelCase_							)[2:].zfill(8							) for byte in data							)

     __lowerCamelCase   :	Any								=		len(UpperCAmelCase_							) % 6 != 0

     if padding_needed:
          # The padding that will be added later
          __lowerCamelCase   :	int								=		B'=' * ((6 - len(UpperCAmelCase_							) % 6) // 2)

          # Append binary_stream with arbitrary binary digits (0's by default) to make its
          # length a multiple of 6.
          binary_stream += "0" * (6 - len(UpperCAmelCase_							) % 6)
     else:
          __lowerCamelCase   :	str								=		B''

     # Encode every 6 binary digits to their corresponding Base64 character
     return (
         "".join(
             B64_CHARSET[int(binary_stream[index : index + 6]      ,    2							)]
             for index in range(0      ,    len(UpperCAmelCase_							)      ,    6							)							).encode()
         + padding
     )



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				str							)     ->		bytes:
     # Make sure encoded_data is either a string or a bytes-like object
     if not isinstance(UpperCAmelCase_      ,    UpperCAmelCase_							) and not isinstance(UpperCAmelCase_      ,    UpperCAmelCase_							):
          __lowerCamelCase   :	Dict								=		(
              'argument should be a bytes-like object or ASCII string, '
              F'not \'{encoded_data.__class__.__name__}\''
          )
          raise TypeError(UpperCAmelCase_							)

     # In case encoded_data is a bytes-like object, make sure it contains only
     # ASCII characters so we convert it to a string object
     if isinstance(UpperCAmelCase_      ,    UpperCAmelCase_							):
          try:
               __lowerCamelCase   :	int								=		encoded_data.decode('utf-8'							)
          except UnicodeDecodeError:
               raise ValueError('base64 encoded data should only contain ASCII characters'							)

     __lowerCamelCase   :	Union[str, Any]								=		encoded_data.count('='							)

     # Check if the encoded string contains non base64 characters
     if padding:
          assert all(
              char in B64_CHARSET for char in encoded_data[:-padding]							), "Invalid base64 character(s) found."
     else:
          assert all(
              char in B64_CHARSET for char in encoded_data							), "Invalid base64 character(s) found."

     # Check the padding
     assert len(UpperCAmelCase_							) % 4 == 0 and padding < 3, "Incorrect padding"

     if padding:
          # Remove padding if there is one
          __lowerCamelCase   :	Any								=		encoded_data[:-padding]

          __lowerCamelCase   :	Optional[Any]								=		''.join(
              bin(B64_CHARSET.index(UpperCAmelCase_							)							)[2:].zfill(6							) for char in encoded_data							)[: -padding * 2]
     else:
          __lowerCamelCase   :	Any								=		''.join(
              bin(B64_CHARSET.index(UpperCAmelCase_							)							)[2:].zfill(6							) for char in encoded_data							)

     __lowerCamelCase   :	str								=		[
         int(binary_stream[index : index + 8]      ,    2							)
         for index in range(0      ,    len(UpperCAmelCase_							)      ,    8							)
     ]

     return bytes(UpperCAmelCase_							)


if __name__ == "__main__":
      import doctest

      doctest.testmod()






