'''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  a     (     unittest.TestCase       ):
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__						)					->							int:
      return f'gaussian_noise_s={seed}_shape={"_".join([str(__lowerCamelCase						) for s in shape]						)}.npy'
 def 							__UpperCAmelCase		( self						)					->							Optional[Any]:
      super().tearDown()
      gc.collect()
 def 							__UpperCAmelCase		( self      ,				__magic_name__=0      ,				__magic_name__=(4, 4, 64, 64)      ,				__magic_name__=False						)					->							Union[str, Any]:
      _a			      =							jnp.bfloataa if fpaa else jnp.floataa
      _a			      =							jnp.array(load_hf_numpy(self.get_file_format(__lowerCamelCase      ,				__lowerCamelCase						)						)      ,				dtype=__lowerCamelCase						)
      return image
 def 							__UpperCAmelCase		( self      ,				__magic_name__=False      ,				__magic_name__="CompVis/stable-diffusion-v1-4"						)					->							Optional[int]:
      _a			      =							jnp.bfloataa if fpaa else jnp.floataa
      _a			      =							"bf16" if fpaa else None

      _a			      =							FlaxUNetaDConditionModel.from_pretrained(
          __lowerCamelCase      ,				subfolder='unet'      ,				dtype=__lowerCamelCase      ,				revision=__lowerCamelCase						)
      return model, params
 def 							__UpperCAmelCase		( self      ,				__magic_name__=0      ,				__magic_name__=(4, 77, 7_68)      ,				__magic_name__=False						)					->							Tuple:
      _a			      =							jnp.bfloataa if fpaa else jnp.floataa
      _a			      =							jnp.array(load_hf_numpy(self.get_file_format(__lowerCamelCase      ,				__lowerCamelCase						)						)      ,				dtype=__lowerCamelCase						)
      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 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__						)					->							List[Any]:
      _a			      =							self.get_unet_model(model_id='CompVis/stable-diffusion-v1-4'      ,				fpaa=__lowerCamelCase						)
      _a			      =							self.get_latents(__lowerCamelCase      ,				fpaa=__lowerCamelCase						)
      _a			      =							self.get_encoder_hidden_states(__lowerCamelCase      ,				fpaa=__lowerCamelCase						)

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

      assert sample.shape == latents.shape

      _a			      =							jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())						)      ,				dtype=jnp.floataa						)
      _a			      =							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, [0.1_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 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__						)					->							List[str]:
      _a			      =							self.get_unet_model(model_id='stabilityai/stable-diffusion-2'      ,				fpaa=__lowerCamelCase						)
      _a			      =							self.get_latents(__lowerCamelCase      ,				shape=(4, 4, 96, 96)      ,				fpaa=__lowerCamelCase						)
      _a			      =							self.get_encoder_hidden_states(__lowerCamelCase      ,				shape=(4, 77, 10_24)      ,				fpaa=__lowerCamelCase						)

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

      assert sample.shape == latents.shape

      _a			      =							jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())						)      ,				dtype=jnp.floataa						)
      _a			      =							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'''






import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


a_					:    Optional[Any]          =    logging.get_logger(__name__)




class  a     (     _SCREAMING_SNAKE_CASE       ):
 def __init__( self      ,				*__magic_name__      ,				**__magic_name__						)					->							None:
      warnings.warn(
          'The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
          ' use DonutImageProcessor instead.'      ,				__magic_name__      ,				)
      super().__init__(*__magic_name__      ,				**__magic_name__						)

