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 			__magic_name__	(		unittest.TestCase     ):





   """simple docstring"""




   def 	lowerCAmelCase    (						self       :	Optional[Any]		,	_lowercase       :	List[str]		,	_lowercase       :	int				):



    """simple docstring"""



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



   def 	lowerCAmelCase    (						self       :	List[Any]				):



    """simple docstring"""



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



   def 	lowerCAmelCase    (						self       :	Tuple		,	_lowercase       :	int=0		,	_lowercase       :	Tuple=(4, 4, 64, 64)		,	_lowercase       :	Optional[int]=False				):



    """simple docstring"""



    _UpperCamelCase:  List[str]    						=			jnp.bfloataa if fpaa else jnp.floataa
    _UpperCamelCase:  Any    						=			jnp.array(load_hf_numpy(self.get_file_format(_lowercase		,	_lowercase				)				)		,	dtype=_lowercase				)
    return image



   def 	lowerCAmelCase    (						self       :	List[Any]		,	_lowercase       :	str=False		,	_lowercase       :	str="CompVis/stable-diffusion-v1-4"				):



    """simple docstring"""



    _UpperCamelCase:  Union[str, Any]    						=			jnp.bfloataa if fpaa else jnp.floataa
    _UpperCamelCase:  str    						=			'''bf16''' if fpaa else None

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



   def 	lowerCAmelCase    (						self       :	Union[str, Any]		,	_lowercase       :	List[str]=0		,	_lowercase       :	Union[str, Any]=(4, 77, 768)		,	_lowercase       :	Union[str, Any]=False				):



    """simple docstring"""



    _UpperCamelCase:  str    						=			jnp.bfloataa if fpaa else jnp.floataa
    _UpperCamelCase:  Tuple    						=			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, 1_000, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
           # fmt: on
       ]				)
   def 	lowerCAmelCase    (						self       :	str		,	_lowercase       :	Any		,	_lowercase       :	List[Any]		,	_lowercase       :	Optional[int]				):



    """simple docstring"""



    _UpperCamelCase						,				_UpperCamelCase:  Optional[Any]    						=			self.get_unet_model(model_id='''CompVis/stable-diffusion-v1-4'''		,	fpaa=_lowercase				)
    _UpperCamelCase:  Optional[Any]    						=			self.get_latents(_lowercase		,	fpaa=_lowercase				)
    _UpperCamelCase:  Any    						=			self.get_encoder_hidden_states(_lowercase		,	fpaa=_lowercase				)

    _UpperCamelCase:  Optional[Any]    						=			model.apply(
        {'''params''': params}		,	_lowercase		,	jnp.array(_lowercase		,	dtype=jnp.intaa				)		,	encoder_hidden_states=_lowercase		,	).sample

    assert sample.shape == latents.shape

    _UpperCamelCase:  List[str]    						=			jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())				)		,	dtype=jnp.floataa				)
    _UpperCamelCase:  Any    						=			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, 1_000, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
           # fmt: on
       ]				)
   def 	lowerCAmelCase    (						self       :	Union[str, Any]		,	_lowercase       :	Tuple		,	_lowercase       :	Union[str, Any]		,	_lowercase       :	Tuple				):



    """simple docstring"""



    _UpperCamelCase						,				_UpperCamelCase:  Any    						=			self.get_unet_model(model_id='''stabilityai/stable-diffusion-2'''		,	fpaa=_lowercase				)
    _UpperCamelCase:  Dict    						=			self.get_latents(_lowercase		,	shape=(4, 4, 96, 96)		,	fpaa=_lowercase				)
    _UpperCamelCase:  int    						=			self.get_encoder_hidden_states(_lowercase		,	shape=(4, 77, 1_024)		,	fpaa=_lowercase				)

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

    assert sample.shape == latents.shape

    _UpperCamelCase:  Optional[Any]    						=			jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())				)		,	dtype=jnp.floataa				)
    _UpperCamelCase:  str    						=			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__)