






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 		SCREAMING_SNAKE_CASE__	(  unittest.TestCase     ):

  '''simple docstring'''







  def  lowerCamelCase       (      self		:			Optional[Any]       ,				lowerCamelCase		:			Union[str, Any]       ,				lowerCamelCase		:			List[Any]							)						->	Tuple:


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


  def  lowerCamelCase       (      self		:			List[Any]							)						->	Any:


         """simple docstring"""
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()


  def  lowerCamelCase       (      self		:			int       ,				lowerCamelCase		:			List[Any]=0       ,				lowerCamelCase		:			Tuple=(4, 4, 64, 64)       ,				lowerCamelCase		:			Tuple=False							)						->	Tuple:


         """simple docstring"""
         _UpperCAmelCase							=    jnp.bfloataa if fpaa else jnp.floataa
         _UpperCAmelCase							=    jnp.array(load_hf_numpy(self.get_file_format(lowerCamelCase       ,				lowerCamelCase							)							)       ,				dtype=lowerCamelCase							)
         return image


  def  lowerCamelCase       (      self		:			Optional[int]       ,				lowerCamelCase		:			Tuple=False       ,				lowerCamelCase		:			str="CompVis/stable-diffusion-v1-4"							)						->	Any:


         """simple docstring"""
         _UpperCAmelCase							=    jnp.bfloataa if fpaa else jnp.floataa
         _UpperCAmelCase							=    """bf16""" if fpaa else None

         _UpperCAmelCase	,			_UpperCAmelCase							=    FlaxUNetaDConditionModel.from_pretrained(
             lowerCamelCase       ,				subfolder="""unet"""       ,				dtype=lowerCamelCase       ,				revision=lowerCamelCase							)
         return model, params


  def  lowerCamelCase       (      self		:			Tuple       ,				lowerCamelCase		:			Any=0       ,				lowerCamelCase		:			Any=(4, 77, 768)       ,				lowerCamelCase		:			Optional[Any]=False							)						->	Optional[int]:


         """simple docstring"""
         _UpperCAmelCase							=    jnp.bfloataa if fpaa else jnp.floataa
         _UpperCAmelCase							=    jnp.array(load_hf_numpy(self.get_file_format(lowerCamelCase       ,				lowerCamelCase							)							)       ,				dtype=lowerCamelCase							)
         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, 1000, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
          # fmt: on
      ]							)
  def  lowerCamelCase       (      self		:			List[str]       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			List[str]							)						->	Union[str, Any]:


         """simple docstring"""
         _UpperCAmelCase	,			_UpperCAmelCase							=    self.get_unet_model(model_id="""CompVis/stable-diffusion-v1-4"""       ,				fpaa=lowerCamelCase							)
         _UpperCAmelCase							=    self.get_latents(lowerCamelCase       ,				fpaa=lowerCamelCase							)
         _UpperCAmelCase							=    self.get_encoder_hidden_states(lowerCamelCase       ,				fpaa=lowerCamelCase							)

         _UpperCAmelCase							=    model.apply(
             {"""params""": params}       ,				lowerCamelCase       ,				jnp.array(lowerCamelCase       ,				dtype=jnp.intaa							)       ,				encoder_hidden_states=lowerCamelCase       ,				).sample

         assert sample.shape == latents.shape

         _UpperCAmelCase							=    jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())							)       ,				dtype=jnp.floataa							)
         _UpperCAmelCase							=    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, [0.1514, 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, 1000, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
          # fmt: on
      ]							)
  def  lowerCamelCase       (      self		:			Any       ,				lowerCamelCase		:			Any       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			List[str]							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase	,			_UpperCAmelCase							=    self.get_unet_model(model_id="""stabilityai/stable-diffusion-2"""       ,				fpaa=lowerCamelCase							)
         _UpperCAmelCase							=    self.get_latents(lowerCamelCase       ,				shape=(4, 4, 96, 96)       ,				fpaa=lowerCamelCase							)
         _UpperCAmelCase							=    self.get_encoder_hidden_states(lowerCamelCase       ,				shape=(4, 77, 1024)       ,				fpaa=lowerCamelCase							)

         _UpperCAmelCase							=    model.apply(
             {"""params""": params}       ,				lowerCamelCase       ,				jnp.array(lowerCamelCase       ,				dtype=jnp.intaa							)       ,				encoder_hidden_states=lowerCamelCase       ,				).sample

         assert sample.shape == latents.shape

         _UpperCAmelCase							=    jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())							)       ,				dtype=jnp.floataa							)
         _UpperCAmelCase							=    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 copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=lowerCAmelCase__						)
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       str							 =field(default="""image-classification"""			,metadata={"""include_in_asdict_even_if_is_default""": True}						)
							__UpperCAmelCase		:       ClassVar[Features]							 =Features({"""image""": Image()}						)
							__UpperCAmelCase		:       ClassVar[Features]							 =Features({"""labels""": ClassLabel}						)
							__UpperCAmelCase		:       str							 ="image"
							__UpperCAmelCase		:       str							 ="labels"

							def      snake_case							(	self       ,  __a      ):
										if self.label_column not in features:
													raise ValueError(f"Column {self.label_column} is not present in features."      )
										if not isinstance(features[self.label_column]       ,  __a      ):
													raise ValueError(f"Column {self.label_column} is not a ClassLabel."      )
										__lowerCAmelCase							       =     copy.deepcopy(self      )
										__lowerCAmelCase							       =     self.label_schema.copy()
										__lowerCAmelCase							       =     features[self.label_column]
										__lowerCAmelCase							       =     label_schema
										return task_template



							@property
							def      snake_case							(	self      ):
										return {
										    self.image_column: "image",
										    self.label_column: "labels",
										}




