






"""simple docstring"""






import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()




@slow
@require_torch_gpu
class 							lowerCAmelCase__       (    unittest.TestCase				):






      '''simple docstring'''

      def _SCREAMING_SNAKE_CASE				(		self  :						Any):




          '''simple docstring'''


          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()
      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Dict							=	StableDiffusionKDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4''')
          SCREAMING_SNAKE_CASE_   :						Dict							=	sd_pipe.to(lowercase_)
          sd_pipe.set_progress_bar_config(disable=lowercase_)

          sd_pipe.set_scheduler('''sample_euler''')

          SCREAMING_SNAKE_CASE_   :						List[Any]							=	'''A painting of a squirrel eating a burger'''
          SCREAMING_SNAKE_CASE_   :						int							=	torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	sd_pipe([prompt]		,			generator=lowercase_		,			guidance_scale=9.0		,			num_inference_steps=20		,			output_type='''np''')

          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	output.images

          SCREAMING_SNAKE_CASE_   :						List[str]							=	image[0, -3:, -3:, -1]

          assert image.shape == (1, 512, 512, 3)
          SCREAMING_SNAKE_CASE_   :						int							=	np.array([0.04_47, 0.04_92, 0.04_68, 0.04_08, 0.03_83, 0.04_08, 0.03_54, 0.03_80, 0.03_39])

          assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
      def _SCREAMING_SNAKE_CASE				(		self  :						List[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						int							=	StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''')
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	sd_pipe.to(lowercase_)
          sd_pipe.set_progress_bar_config(disable=lowercase_)

          sd_pipe.set_scheduler('''sample_euler''')

          SCREAMING_SNAKE_CASE_   :						Tuple							=	'''A painting of a squirrel eating a burger'''
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_   :						int							=	sd_pipe([prompt]		,			generator=lowercase_		,			guidance_scale=9.0		,			num_inference_steps=20		,			output_type='''np''')

          SCREAMING_SNAKE_CASE_   :						Dict							=	output.images

          SCREAMING_SNAKE_CASE_   :						int							=	image[0, -3:, -3:, -1]

          assert image.shape == (1, 512, 512, 3)
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	np.array([0.12_37, 0.13_20, 0.14_38, 0.13_59, 0.13_90, 0.11_32, 0.12_77, 0.11_75, 0.11_12])

          assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-1



      def _SCREAMING_SNAKE_CASE				(		self  :						List[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''')
          SCREAMING_SNAKE_CASE_   :						Dict							=	sd_pipe.to(lowercase_)
          sd_pipe.set_progress_bar_config(disable=lowercase_)

          sd_pipe.set_scheduler('''sample_dpmpp_2m''')

          SCREAMING_SNAKE_CASE_   :						List[str]							=	'''A painting of a squirrel eating a burger'''
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	sd_pipe(
              [prompt]		,			generator=lowercase_		,			guidance_scale=7.5		,			num_inference_steps=15		,			output_type='''np'''		,			use_karras_sigmas=lowercase_		,			)

          SCREAMING_SNAKE_CASE_   :						str							=	output.images

          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	image[0, -3:, -3:, -1]

          assert image.shape == (1, 512, 512, 3)
          SCREAMING_SNAKE_CASE_   :						Dict							=	np.array(
              [0.11_38_16_89, 0.12_11_29_21, 0.1_38_94_57, 0.12_54_96_06, 0.1_24_49_64, 0.10_83_15_17, 0.11_56_28_66, 0.10_86_78_16, 0.10_49_90_48])

          assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2










"""simple docstring"""






import unittest

import numpy as np

from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
    expand_dims,
    flatten_dict,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    reshape,
    squeeze,
    transpose,
)


if is_flax_available():
  import jax.numpy as jnp

if is_tf_available():
  import tensorflow as tf

if is_torch_available():
  import torch




class 							lowerCAmelCase__       (    unittest.TestCase				):






      '''simple docstring'''

      def _SCREAMING_SNAKE_CASE				(		self  :						str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						str							=	{
              '''task_specific_params''': {
                  '''summarization''': {'''length_penalty''': 1.0, '''max_length''': 128, '''min_length''': 12, '''num_beams''': 4},
                  '''summarization_cnn''': {'''length_penalty''': 2.0, '''max_length''': 142, '''min_length''': 56, '''num_beams''': 4},
                  '''summarization_xsum''': {'''length_penalty''': 1.0, '''max_length''': 62, '''min_length''': 11, '''num_beams''': 6},
              }
          }
          SCREAMING_SNAKE_CASE_   :						List[str]							=	{
              '''task_specific_params.summarization.length_penalty''': 1.0,
              '''task_specific_params.summarization.max_length''': 128,
              '''task_specific_params.summarization.min_length''': 12,
              '''task_specific_params.summarization.num_beams''': 4,
              '''task_specific_params.summarization_cnn.length_penalty''': 2.0,
              '''task_specific_params.summarization_cnn.max_length''': 142,
              '''task_specific_params.summarization_cnn.min_length''': 56,
              '''task_specific_params.summarization_cnn.num_beams''': 4,
              '''task_specific_params.summarization_xsum.length_penalty''': 1.0,
              '''task_specific_params.summarization_xsum.max_length''': 62,
              '''task_specific_params.summarization_xsum.min_length''': 11,
              '''task_specific_params.summarization_xsum.num_beams''': 6,
          }

          self.assertEqual(flatten_dict(lowercase_)		,			lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						List[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	np.random.randn(3		,			4)
          self.assertTrue(np.allclose(transpose(lowercase_)		,			x.transpose()))

          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	np.random.randn(3		,			4		,			5)
          self.assertTrue(np.allclose(transpose(lowercase_		,			axes=(1, 2, 0))		,			x.transpose((1, 2, 0))))
      @require_torch
      def _SCREAMING_SNAKE_CASE				(		self  :						Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						str							=	np.random.randn(3		,			4)
          SCREAMING_SNAKE_CASE_   :						str							=	torch.tensor(lowercase_)
          self.assertTrue(np.allclose(transpose(lowercase_)		,			transpose(lowercase_).numpy()))

          SCREAMING_SNAKE_CASE_   :						List[Any]							=	np.random.randn(3		,			4		,			5)
          SCREAMING_SNAKE_CASE_   :						Dict							=	torch.tensor(lowercase_)
          self.assertTrue(np.allclose(transpose(lowercase_		,			axes=(1, 2, 0))		,			transpose(lowercase_		,			axes=(1, 2, 0)).numpy()))
      @require_tf
      def _SCREAMING_SNAKE_CASE				(		self  :						Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	np.random.randn(3		,			4)
          SCREAMING_SNAKE_CASE_   :						str							=	tf.constant(lowercase_)
          self.assertTrue(np.allclose(transpose(lowercase_)		,			transpose(lowercase_).numpy()))

          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	np.random.randn(3		,			4		,			5)
          SCREAMING_SNAKE_CASE_   :						int							=	tf.constant(lowercase_)
          self.assertTrue(np.allclose(transpose(lowercase_		,			axes=(1, 2, 0))		,			transpose(lowercase_		,			axes=(1, 2, 0)).numpy()))
      @require_flax
      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Tuple							=	np.random.randn(3		,			4)
          SCREAMING_SNAKE_CASE_   :						Any							=	jnp.array(lowercase_)
          self.assertTrue(np.allclose(transpose(lowercase_)		,			np.asarray(transpose(lowercase_))))

          SCREAMING_SNAKE_CASE_   :						Dict							=	np.random.randn(3		,			4		,			5)
          SCREAMING_SNAKE_CASE_   :						Any							=	jnp.array(lowercase_)
          self.assertTrue(np.allclose(transpose(lowercase_		,			axes=(1, 2, 0))		,			np.asarray(transpose(lowercase_		,			axes=(1, 2, 0)))))
      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[int]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Dict							=	np.random.randn(3		,			4)
          self.assertTrue(np.allclose(reshape(lowercase_		,			(4, 3))		,			np.reshape(lowercase_		,			(4, 3))))

          SCREAMING_SNAKE_CASE_   :						Dict							=	np.random.randn(3		,			4		,			5)
          self.assertTrue(np.allclose(reshape(lowercase_		,			(12, 5))		,			np.reshape(lowercase_		,			(12, 5))))
      @require_torch
      def _SCREAMING_SNAKE_CASE				(		self  :						Union[str, Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	np.random.randn(3		,			4)
          SCREAMING_SNAKE_CASE_   :						List[str]							=	torch.tensor(lowercase_)
          self.assertTrue(np.allclose(reshape(lowercase_		,			(4, 3))		,			reshape(lowercase_		,			(4, 3)).numpy()))

          SCREAMING_SNAKE_CASE_   :						int							=	np.random.randn(3		,			4		,			5)
          SCREAMING_SNAKE_CASE_   :						int							=	torch.tensor(lowercase_)
          self.assertTrue(np.allclose(reshape(lowercase_		,			(12, 5))		,			reshape(lowercase_		,			(12, 5)).numpy()))
      @require_tf
      def _SCREAMING_SNAKE_CASE				(		self  :						Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	np.random.randn(3		,			4)
          SCREAMING_SNAKE_CASE_   :						int							=	tf.constant(lowercase_)
          self.assertTrue(np.allclose(reshape(lowercase_		,			(4, 3))		,			reshape(lowercase_		,			(4, 3)).numpy()))

          SCREAMING_SNAKE_CASE_   :						List[Any]							=	np.random.randn(3		,			4		,			5)
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	tf.constant(lowercase_)
          self.assertTrue(np.allclose(reshape(lowercase_		,			(12, 5))		,			reshape(lowercase_		,			(12, 5)).numpy()))
      @require_flax
      def _SCREAMING_SNAKE_CASE				(		self  :						Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Any							=	np.random.randn(3		,			4)
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	jnp.array(lowercase_)
          self.assertTrue(np.allclose(reshape(lowercase_		,			(4, 3))		,			np.asarray(reshape(lowercase_		,			(4, 3)))))

          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	np.random.randn(3		,			4		,			5)
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	jnp.array(lowercase_)
          self.assertTrue(np.allclose(reshape(lowercase_		,			(12, 5))		,			np.asarray(reshape(lowercase_		,			(12, 5)))))
      def _SCREAMING_SNAKE_CASE				(		self  :						List[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[Any]							=	np.random.randn(1		,			3		,			4)
          self.assertTrue(np.allclose(squeeze(lowercase_)		,			np.squeeze(lowercase_)))

          SCREAMING_SNAKE_CASE_   :						str							=	np.random.randn(1		,			4		,			1		,			5)
          self.assertTrue(np.allclose(squeeze(lowercase_		,			axis=2)		,			np.squeeze(lowercase_		,			axis=2)))
      @require_torch
      def _SCREAMING_SNAKE_CASE				(		self  :						Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	np.random.randn(1		,			3		,			4)
          SCREAMING_SNAKE_CASE_   :						int							=	torch.tensor(lowercase_)
          self.assertTrue(np.allclose(squeeze(lowercase_)		,			squeeze(lowercase_).numpy()))

          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	np.random.randn(1		,			4		,			1		,			5)
          SCREAMING_SNAKE_CASE_   :						str							=	torch.tensor(lowercase_)
          self.assertTrue(np.allclose(squeeze(lowercase_		,			axis=2)		,			squeeze(lowercase_		,			axis=2).numpy()))
      @require_tf
      def _SCREAMING_SNAKE_CASE				(		self  :						List[str]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						int							=	np.random.randn(1		,			3		,			4)
          SCREAMING_SNAKE_CASE_   :						Dict							=	tf.constant(lowercase_)
          self.assertTrue(np.allclose(squeeze(lowercase_)		,			squeeze(lowercase_).numpy()))

          SCREAMING_SNAKE_CASE_   :						List[Any]							=	np.random.randn(1		,			4		,			1		,			5)
          SCREAMING_SNAKE_CASE_   :						Any							=	tf.constant(lowercase_)
          self.assertTrue(np.allclose(squeeze(lowercase_		,			axis=2)		,			squeeze(lowercase_		,			axis=2).numpy()))
      @require_flax
      def _SCREAMING_SNAKE_CASE				(		self  :						int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	np.random.randn(1		,			3		,			4)
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	jnp.array(lowercase_)
          self.assertTrue(np.allclose(squeeze(lowercase_)		,			np.asarray(squeeze(lowercase_))))

          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	np.random.randn(1		,			4		,			1		,			5)
          SCREAMING_SNAKE_CASE_   :						str							=	jnp.array(lowercase_)
          self.assertTrue(np.allclose(squeeze(lowercase_		,			axis=2)		,			np.asarray(squeeze(lowercase_		,			axis=2))))
      def _SCREAMING_SNAKE_CASE				(		self  :						Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						int							=	np.random.randn(3		,			4)
          self.assertTrue(np.allclose(expand_dims(lowercase_		,			axis=1)		,			np.expand_dims(lowercase_		,			axis=1)))
      @require_torch
      def _SCREAMING_SNAKE_CASE				(		self  :						Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						int							=	np.random.randn(3		,			4)
          SCREAMING_SNAKE_CASE_   :						int							=	torch.tensor(lowercase_)
          self.assertTrue(np.allclose(expand_dims(lowercase_		,			axis=1)		,			expand_dims(lowercase_		,			axis=1).numpy()))
      @require_tf
      def _SCREAMING_SNAKE_CASE				(		self  :						str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[str]							=	np.random.randn(3		,			4)
          SCREAMING_SNAKE_CASE_   :						Any							=	tf.constant(lowercase_)
          self.assertTrue(np.allclose(expand_dims(lowercase_		,			axis=1)		,			expand_dims(lowercase_		,			axis=1).numpy()))



      @require_flax
      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Tuple							=	np.random.randn(3		,			4)
          SCREAMING_SNAKE_CASE_   :						int							=	jnp.array(lowercase_)
          self.assertTrue(np.allclose(expand_dims(lowercase_		,			axis=1)		,			np.asarray(expand_dims(lowercase_		,			axis=1))))


