
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 							UpperCAmelCase				(  unittest.TestCase  ):




 '''simple docstring'''





 def     UpperCamelCase_  (	self :			Dict							,A :			Dict							,A :			Tuple			):
     return f'''gaussian_noise_s={seed}_shape={'_'.join([str(A			) for s in shape]			)}.npy'''






 def     UpperCamelCase_  (	self :			Tuple			):
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()






 def     UpperCamelCase_  (	self :			Any							,A :			Any=0							,A :			str=(4, 4, 64, 64)							,A :			Tuple=False			):
     __A             =			jnp.bfloataa if fpaa else jnp.floataa
     __A             =			jnp.array(load_hf_numpy(self.get_file_format(A							,A			)			)							,dtype=A			)
     return image






 def     UpperCamelCase_  (	self :			Optional[int]							,A :			Dict=False							,A :			List[str]="CompVis/stable-diffusion-v1-4"			):
     __A             =			jnp.bfloataa if fpaa else jnp.floataa
     __A             =			"bf16" if fpaa else None

     __A      ,							__A             =			FlaxUNetaDConditionModel.from_pretrained(
         A							,subfolder="unet"							,dtype=A							,revision=A			)
     return model, params






 def     UpperCamelCase_  (	self :			Optional[Any]							,A :			str=0							,A :			Union[str, Any]=(4, 77, 7_68)							,A :			int=False			):
     __A             =			jnp.bfloataa if fpaa else jnp.floataa
     __A             =			jnp.array(load_hf_numpy(self.get_file_format(A							,A			)			)							,dtype=A			)
     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     UpperCamelCase_  (	self :			List[Any]							,A :			str							,A :			Any							,A :			Optional[int]			):
     __A      ,							__A             =			self.get_unet_model(model_id="CompVis/stable-diffusion-v1-4"							,fpaa=A			)
     __A             =			self.get_latents(A							,fpaa=A			)
     __A             =			self.get_encoder_hidden_states(A							,fpaa=A			)

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

     assert sample.shape == latents.shape

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

     # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
     assert jnp.allclose(A							,A							,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     UpperCamelCase_  (	self :			str							,A :			Tuple							,A :			List[Any]							,A :			List[str]			):
     __A      ,							__A             =			self.get_unet_model(model_id="stabilityai/stable-diffusion-2"							,fpaa=A			)
     __A             =			self.get_latents(A							,shape=(4, 4, 96, 96)							,fpaa=A			)
     __A             =			self.get_encoder_hidden_states(A							,shape=(4, 77, 10_24)							,fpaa=A			)

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

     assert sample.shape == latents.shape

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

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


import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


SCREAMING_SNAKE_CASE							:Any										=    logging.get_logger(__name__)





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	def __init__(	self :			int							,*A :			Dict							,**A :			Dict			):
					warnings.warn(
					    "The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
					    " use CLIPImageProcessor instead."							,A							,)
					super().__init__(*A							,**A			)
