





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 					__snake_case	(  unittest.TestCase    ):


    '''simple docstring'''







    def  lowercase_					(       self   ,				A_   ,				A_     ):





        '''simple docstring'''

        return f'''gaussian_noise_s={seed}_shape={"_".join([str(_lowercase     ) for s in shape]     )}.npy'''



    def  lowercase_					(       self     ):





        '''simple docstring'''

        super().tearDown()
        gc.collect()



    def  lowercase_					(       self   ,				A_=0   ,				A_=(4, 4, 64, 64)   ,				A_=False     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			jnp.bfloataa if fpaa else jnp.floataa
        SCREAMING_SNAKE_CASE__											=			jnp.array(load_hf_numpy(self.get_file_format(_lowercase   ,				_lowercase     )     )   ,				dtype=_lowercase     )
        return image



    def  lowercase_					(       self   ,				A_=False   ,				A_="CompVis/stable-diffusion-v1-4"     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			jnp.bfloataa if fpaa else jnp.floataa
        SCREAMING_SNAKE_CASE__											=			'''bf16''' if fpaa else None

        SCREAMING_SNAKE_CASE__											=			FlaxUNetaDConditionModel.from_pretrained(
            _lowercase   ,				subfolder='''unet'''   ,				dtype=_lowercase   ,				revision=_lowercase     )
        return model, params



    def  lowercase_					(       self   ,				A_=0   ,				A_=(4, 77, 7_68)   ,				A_=False     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			jnp.bfloataa if fpaa else jnp.floataa
        SCREAMING_SNAKE_CASE__											=			jnp.array(load_hf_numpy(self.get_file_format(_lowercase   ,				_lowercase     )     )   ,				dtype=_lowercase     )
        return hidden_states



    @parameterized.expand(
        [
            # fmt: off
            [83, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]],
            [17, 0.55, [-0.0831, -0.2443, 0.0901, -0.0919, 0.3396, 0.0103, -0.3743, 0.0701]],
            [8, 0.89, [-0.4863, 0.0859, 0.0875, -0.1658, 0.9199, -0.0114, 0.4839, 0.4639]],
            [3, 10_00, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
            # fmt: on
        ]     )
    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.get_unet_model(model_id='''CompVis/stable-diffusion-v1-4'''   ,				fpaa=_lowercase     )
        SCREAMING_SNAKE_CASE__											=			self.get_latents(_lowercase   ,				fpaa=_lowercase     )
        SCREAMING_SNAKE_CASE__											=			self.get_encoder_hidden_states(_lowercase   ,				fpaa=_lowercase     )

        SCREAMING_SNAKE_CASE__											=			model.apply(
            {'''params''': params}   ,				_lowercase   ,				jnp.array(_lowercase   ,				dtype=jnp.intaa     )   ,				encoder_hidden_states=_lowercase   ,				).sample

        assert sample.shape == latents.shape

        SCREAMING_SNAKE_CASE__											=			jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())     )   ,				dtype=jnp.floataa     )
        SCREAMING_SNAKE_CASE__											=			jnp.array(_lowercase   ,				dtype=jnp.floataa     )

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




    @parameterized.expand(
        [
            # fmt: off
            [83, 4, [0.1514, 0.0807, 0.1624, 0.1016, -0.1896, 0.0263, 0.0677, 0.2310]],
            [17, 0.55, [0.1164, -0.0216, 0.0170, 0.1589, -0.3120, 0.1005, -0.0581, -0.1458]],
            [8, 0.89, [-0.1758, -0.0169, 0.1004, -0.1411, 0.1312, 0.1103, -0.1996, 0.2139]],
            [3, 10_00, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
            # fmt: on
        ]     )
    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.get_unet_model(model_id='''stabilityai/stable-diffusion-2'''   ,				fpaa=_lowercase     )
        SCREAMING_SNAKE_CASE__											=			self.get_latents(_lowercase   ,				shape=(4, 4, 96, 96)   ,				fpaa=_lowercase     )
        SCREAMING_SNAKE_CASE__											=			self.get_encoder_hidden_states(_lowercase   ,				shape=(4, 77, 10_24)   ,				fpaa=_lowercase     )

        SCREAMING_SNAKE_CASE__											=			model.apply(
            {'''params''': params}   ,				_lowercase   ,				jnp.array(_lowercase   ,				dtype=jnp.intaa     )   ,				encoder_hidden_states=_lowercase   ,				).sample

        assert sample.shape == latents.shape

        SCREAMING_SNAKE_CASE__											=			jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())     )   ,				dtype=jnp.floataa     )
        SCREAMING_SNAKE_CASE__											=			jnp.array(_lowercase   ,				dtype=jnp.floataa     )

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

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


UpperCAmelCase_           =				{'''configuration_encoder_decoder''': ['''EncoderDecoderConfig''']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_           =				['''EncoderDecoderModel''']

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_           =				['''TFEncoderDecoderModel''']

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_           =				['''FlaxEncoderDecoderModel''']

if TYPE_CHECKING:
  from .configuration_encoder_decoder import EncoderDecoderConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_encoder_decoder import EncoderDecoderModel

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_encoder_decoder import TFEncoderDecoderModel

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_encoder_decoder import FlaxEncoderDecoderModel

else:
  import sys

  UpperCAmelCase_           =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)