"""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   lowerCAmelCase_    (			unittest.TestCase					):



    """simple docstring"""

    def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase    ):






          """simple docstring"""

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

    def 		snake_case (				self    ):






          """simple docstring"""

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

    def 		snake_case (				self		,    lowerCAmelCase=0		,    lowerCAmelCase=(4, 4, 64, 64)		,    lowerCAmelCase=False    ):






          """simple docstring"""

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

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






          """simple docstring"""

          snake_case								=			jnp.bfloataa if fpaa else jnp.floataa
          snake_case								=			"bf16" if fpaa else None

          snake_case								=			FlaxUNetaDConditionModel.from_pretrained(
              lowerCAmelCase__		,    subfolder='unet'		,    dtype=lowerCAmelCase__		,    revision=lowerCAmelCase__    )
          return model, params

    def 		snake_case (				self		,    lowerCAmelCase=0		,    lowerCAmelCase=(4, 77, 7_68)		,    lowerCAmelCase=False    ):






          """simple docstring"""

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

    @parameterized.expand(
        [
            # fmt: off
            [83, 4, [-0.23_23, -0.13_04, 0.08_13, -0.30_93, -0.09_19, -0.15_71, -0.11_25, -0.58_06]],
            [17, 0.55, [-0.08_31, -0.24_43, 0.09_01, -0.09_19, 0.33_96, 0.01_03, -0.37_43, 0.07_01]],
            [8, 0.89, [-0.48_63, 0.08_59, 0.08_75, -0.16_58, 0.91_99, -0.01_14, 0.48_39, 0.46_39]],
            [3, 10_00, [-0.56_49, 0.24_02, -0.55_18, 0.12_48, 1.13_28, -0.24_43, -0.03_25, -1.00_78]],
            # fmt: on
        ]    )
    def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase    ):






          """simple docstring"""

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

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

          assert sample.shape == latents.shape

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

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

    @parameterized.expand(
        [
            # fmt: off
            [83, 4, [0.15_14, 0.08_07, 0.16_24, 0.10_16, -0.18_96, 0.02_63, 0.06_77, 0.23_10]],
            [17, 0.55, [0.11_64, -0.02_16, 0.01_70, 0.15_89, -0.31_20, 0.10_05, -0.05_81, -0.14_58]],
            [8, 0.89, [-0.17_58, -0.01_69, 0.10_04, -0.14_11, 0.13_12, 0.11_03, -0.19_96, 0.21_39]],
            [3, 10_00, [0.12_14, 0.03_52, -0.07_31, -0.15_62, -0.09_94, -0.09_06, -0.23_40, -0.05_39]],
            # fmt: on
        ]    )
    def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase    ):






          """simple docstring"""

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

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

          assert sample.shape == latents.shape

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

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



"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE__      						=  {"configuration_sew": ["SEW_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWConfig"]}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__      						=  [
					    "SEW_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "SEWForCTC",
					    "SEWForSequenceClassification",
					    "SEWModel",
					    "SEWPreTrainedModel",
					]

if TYPE_CHECKING:
					from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_sew import (
										    SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
										    SEWForCTC,
										    SEWForSequenceClassification,
										    SEWModel,
										    SEWPreTrainedModel,
										)


else:
					import sys

					SCREAMING_SNAKE_CASE__      						=  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


