
"""simple docstring"""

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()
class      _UpperCAmelCase							(		unittest.TestCase							):




      def     a_      (							self							)						->					Dict:
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()




      @property
      def     a_      (							self							)						->					Optional[Any]:
          UpperCAmelCase									=					1
          UpperCAmelCase									=					3
          UpperCAmelCase									=					(3_2, 3_2)

          UpperCAmelCase									=					floats_tensor((batch_size, num_channels) + sizes   ,       rng=random.Random(0							)							).to(lowercase_							)
          return image




      @property
      def     a_      (							self							)						->					Optional[Any]:
          torch.manual_seed(0							)
          UpperCAmelCase									=					UNetaDConditionModel(
              block_out_channels=(3_2, 3_2, 6_4)   ,       layers_per_block=2   ,       sample_size=3_2   ,       in_channels=7   ,       out_channels=4   ,       down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D', 'CrossAttnDownBlock2D')   ,       up_block_types=('CrossAttnUpBlock2D', 'CrossAttnUpBlock2D', 'UpBlock2D')   ,       cross_attention_dim=3_2   ,       attention_head_dim=8   ,       use_linear_projection=lowercase_   ,       only_cross_attention=(True, True, False)   ,       num_class_embeds=1_0_0   ,       )
          return model




      @property
      def     a_      (							self							)						->					Optional[Any]:
          torch.manual_seed(0							)
          UpperCAmelCase									=					AutoencoderKL(
              block_out_channels=[3_2, 3_2, 6_4]   ,       in_channels=3   ,       out_channels=3   ,       down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D', 'DownEncoderBlock2D']   ,       up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D']   ,       latent_channels=4   ,       )
          return model




      @property
      def     a_      (							self							)						->					Tuple:
          torch.manual_seed(0							)
          UpperCAmelCase									=					CLIPTextConfig(
              bos_token_id=0   ,       eos_token_id=2   ,       hidden_size=3_2   ,       intermediate_size=3_7   ,       layer_norm_eps=1E-05   ,       num_attention_heads=4   ,       num_hidden_layers=5   ,       pad_token_id=1   ,       vocab_size=1_0_0_0   ,       hidden_act='gelu'   ,       projection_dim=5_1_2   ,       )
          return CLIPTextModel(lowercase_							)




      def     a_      (							self							)						->					Optional[int]:
          UpperCAmelCase									=					'cpu'  # ensure determinism for the device-dependent torch.Generator
          UpperCAmelCase									=					self.dummy_cond_unet_upscale
          UpperCAmelCase									=					DDPMScheduler()
          UpperCAmelCase									=					DDIMScheduler(prediction_type='v_prediction'							)
          UpperCAmelCase									=					self.dummy_vae
          UpperCAmelCase									=					self.dummy_text_encoder
          UpperCAmelCase									=					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'							)

          UpperCAmelCase									=					self.dummy_image.cpu().permute(0   ,       2   ,       3   ,       1							)[0]
          UpperCAmelCase									=					Image.fromarray(np.uinta(lowercase_							)							).convert('RGB'							).resize((6_4, 6_4)							)

          # make sure here that pndm scheduler skips prk
          UpperCAmelCase									=					StableDiffusionUpscalePipeline(
              unet=lowercase_   ,       low_res_scheduler=lowercase_   ,       scheduler=lowercase_   ,       vae=lowercase_   ,       text_encoder=lowercase_   ,       tokenizer=lowercase_   ,       max_noise_level=3_5_0   ,       )
          UpperCAmelCase									=					sd_pipe.to(lowercase_							)
          sd_pipe.set_progress_bar_config(disable=lowercase_							)

          UpperCAmelCase									=					'A painting of a squirrel eating a burger'
          UpperCAmelCase									=					torch.Generator(device=lowercase_							).manual_seed(0							)
          UpperCAmelCase									=					sd_pipe(
              [prompt]   ,       image=lowercase_   ,       generator=lowercase_   ,       guidance_scale=6.0   ,       noise_level=2_0   ,       num_inference_steps=2   ,       output_type='np'   ,       )

          UpperCAmelCase									=					output.images

          UpperCAmelCase									=					torch.Generator(device=lowercase_							).manual_seed(0							)
          UpperCAmelCase									=					sd_pipe(
              [prompt]   ,       image=lowercase_   ,       generator=lowercase_   ,       guidance_scale=6.0   ,       noise_level=2_0   ,       num_inference_steps=2   ,       output_type='np'   ,       return_dict=lowercase_   ,       )[0]

          UpperCAmelCase									=					image[0, -3:, -3:, -1]
          UpperCAmelCase									=					image_from_tuple[0, -3:, -3:, -1]

          UpperCAmelCase									=					low_res_image.size[0] * 4
          assert image.shape == (1, expected_height_width, expected_height_width, 3)
          UpperCAmelCase									=					np.array([0.3_1_1_3, 0.3_9_1_0, 0.4_2_7_2, 0.4_8_5_9, 0.5_0_6_1, 0.4_6_5_2, 0.5_3_6_2, 0.5_7_1_5, 0.5_6_6_1]							)

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




      def     a_      (							self							)						->					int:
          UpperCAmelCase									=					'cpu'  # ensure determinism for the device-dependent torch.Generator
          UpperCAmelCase									=					self.dummy_cond_unet_upscale
          UpperCAmelCase									=					DDPMScheduler()
          UpperCAmelCase									=					DDIMScheduler(prediction_type='v_prediction'							)
          UpperCAmelCase									=					self.dummy_vae
          UpperCAmelCase									=					self.dummy_text_encoder
          UpperCAmelCase									=					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'							)

          UpperCAmelCase									=					self.dummy_image.cpu().permute(0   ,       2   ,       3   ,       1							)[0]
          UpperCAmelCase									=					Image.fromarray(np.uinta(lowercase_							)							).convert('RGB'							).resize((6_4, 6_4)							)

          # make sure here that pndm scheduler skips prk
          UpperCAmelCase									=					StableDiffusionUpscalePipeline(
              unet=lowercase_   ,       low_res_scheduler=lowercase_   ,       scheduler=lowercase_   ,       vae=lowercase_   ,       text_encoder=lowercase_   ,       tokenizer=lowercase_   ,       max_noise_level=3_5_0   ,       )
          UpperCAmelCase									=					sd_pipe.to(lowercase_							)
          sd_pipe.set_progress_bar_config(disable=lowercase_							)

          UpperCAmelCase									=					'A painting of a squirrel eating a burger'
          UpperCAmelCase									=					sd_pipe(
              2 * [prompt]   ,       image=2 * [low_res_image]   ,       guidance_scale=6.0   ,       noise_level=2_0   ,       num_inference_steps=2   ,       output_type='np'   ,       )
          UpperCAmelCase									=					output.images
          assert image.shape[0] == 2

          UpperCAmelCase									=					torch.Generator(device=lowercase_							).manual_seed(0							)
          UpperCAmelCase									=					sd_pipe(
              [prompt]   ,       image=lowercase_   ,       generator=lowercase_   ,       num_images_per_prompt=2   ,       guidance_scale=6.0   ,       noise_level=2_0   ,       num_inference_steps=2   ,       output_type='np'   ,       )
          UpperCAmelCase									=					output.images
          assert image.shape[0] == 2




      @unittest.skipIf(torch_device != 'cuda'   ,       'This test requires a GPU'							)
      def     a_      (							self							)						->					str:
          UpperCAmelCase									=					self.dummy_cond_unet_upscale
          UpperCAmelCase									=					DDPMScheduler()
          UpperCAmelCase									=					DDIMScheduler(prediction_type='v_prediction'							)
          UpperCAmelCase									=					self.dummy_vae
          UpperCAmelCase									=					self.dummy_text_encoder
          UpperCAmelCase									=					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'							)

          UpperCAmelCase									=					self.dummy_image.cpu().permute(0   ,       2   ,       3   ,       1							)[0]
          UpperCAmelCase									=					Image.fromarray(np.uinta(lowercase_							)							).convert('RGB'							).resize((6_4, 6_4)							)

          # put models in fp16, except vae as it overflows in fp16
          UpperCAmelCase									=					unet.half()
          UpperCAmelCase									=					text_encoder.half()

          # make sure here that pndm scheduler skips prk
          UpperCAmelCase									=					StableDiffusionUpscalePipeline(
              unet=lowercase_   ,       low_res_scheduler=lowercase_   ,       scheduler=lowercase_   ,       vae=lowercase_   ,       text_encoder=lowercase_   ,       tokenizer=lowercase_   ,       max_noise_level=3_5_0   ,       )
          UpperCAmelCase									=					sd_pipe.to(lowercase_							)
          sd_pipe.set_progress_bar_config(disable=lowercase_							)

          UpperCAmelCase									=					'A painting of a squirrel eating a burger'
          UpperCAmelCase									=					torch.manual_seed(0							)
          UpperCAmelCase									=					sd_pipe(
              [prompt]   ,       image=lowercase_   ,       generator=lowercase_   ,       num_inference_steps=2   ,       output_type='np'   ,       ).images

          UpperCAmelCase									=					low_res_image.size[0] * 4
          assert image.shape == (1, expected_height_width, expected_height_width, 3)

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




      def     a_      (							self							)						->					Dict:
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()




      def     a_      (							self							)						->					Any:
          UpperCAmelCase									=					load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
              '/sd2-upscale/low_res_cat.png'							)
          UpperCAmelCase									=					load_numpy(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
              '/upsampled_cat.npy'							)

          UpperCAmelCase									=					'stabilityai/stable-diffusion-x4-upscaler'
          UpperCAmelCase									=					StableDiffusionUpscalePipeline.from_pretrained(lowercase_							)
          pipe.to(lowercase_							)
          pipe.set_progress_bar_config(disable=lowercase_							)
          pipe.enable_attention_slicing()

          UpperCAmelCase									=					'a cat sitting on a park bench'

          UpperCAmelCase									=					torch.manual_seed(0							)
          UpperCAmelCase									=					pipe(
              prompt=lowercase_   ,       image=lowercase_   ,       generator=lowercase_   ,       output_type='np'   ,       )
          UpperCAmelCase									=					output.images[0]

          assert image.shape == (5_1_2, 5_1_2, 3)
          assert np.abs(expected_image - image							).max() < 1E-3




      def     a_      (							self							)						->					Optional[int]:
          UpperCAmelCase									=					load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
              '/sd2-upscale/low_res_cat.png'							)
          UpperCAmelCase									=					load_numpy(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
              '/upsampled_cat_fp16.npy'							)

          UpperCAmelCase									=					'stabilityai/stable-diffusion-x4-upscaler'
          UpperCAmelCase									=					StableDiffusionUpscalePipeline.from_pretrained(
              lowercase_   ,       torch_dtype=torch.floataa   ,       )
          pipe.to(lowercase_							)
          pipe.set_progress_bar_config(disable=lowercase_							)
          pipe.enable_attention_slicing()

          UpperCAmelCase									=					'a cat sitting on a park bench'

          UpperCAmelCase									=					torch.manual_seed(0							)
          UpperCAmelCase									=					pipe(
              prompt=lowercase_   ,       image=lowercase_   ,       generator=lowercase_   ,       output_type='np'   ,       )
          UpperCAmelCase									=					output.images[0]

          assert image.shape == (5_1_2, 5_1_2, 3)
          assert np.abs(expected_image - image							).max() < 5E-1




      def     a_      (							self							)						->					Any:
          torch.cuda.empty_cache()
          torch.cuda.reset_max_memory_allocated()
          torch.cuda.reset_peak_memory_stats()

          UpperCAmelCase									=					load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
              '/sd2-upscale/low_res_cat.png'							)

          UpperCAmelCase									=					'stabilityai/stable-diffusion-x4-upscaler'
          UpperCAmelCase									=					StableDiffusionUpscalePipeline.from_pretrained(
              lowercase_   ,       torch_dtype=torch.floataa   ,       )
          pipe.to(lowercase_							)
          pipe.set_progress_bar_config(disable=lowercase_							)
          pipe.enable_attention_slicing(1							)
          pipe.enable_sequential_cpu_offload()

          UpperCAmelCase									=					'a cat sitting on a park bench'

          UpperCAmelCase									=					torch.manual_seed(0							)
          UpperCAmelCase									=					pipe(
              prompt=lowercase_   ,       image=lowercase_   ,       generator=lowercase_   ,       num_inference_steps=5   ,       output_type='np'   ,       )

          UpperCAmelCase									=					torch.cuda.max_memory_allocated()
          # make sure that less than 2.9 GB is allocated
          assert mem_bytes < 2.9 * 1_0**9





"""simple docstring"""

import inspect
import unittest

from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
   import jax
   import jax.numpy as jnp

   from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel

if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor
class      _UpperCAmelCase							(		unittest.TestCase							):




      def __init__(							self   ,       lowercase_   ,       lowercase_=3   ,       lowercase_=3_2   ,       lowercase_=3   ,       lowercase_=1_0   ,       lowercase_=[1_0, 2_0, 3_0, 4_0]   ,       lowercase_=[1, 1, 2, 1]   ,       lowercase_=True   ,       lowercase_=True   ,       lowercase_="relu"   ,       lowercase_=3   ,       lowercase_=None   ,       )						->					str:
          UpperCAmelCase									=					parent
          UpperCAmelCase									=					batch_size
          UpperCAmelCase									=					image_size
          UpperCAmelCase									=					num_channels
          UpperCAmelCase									=					embeddings_size
          UpperCAmelCase									=					hidden_sizes
          UpperCAmelCase									=					depths
          UpperCAmelCase									=					is_training
          UpperCAmelCase									=					use_labels
          UpperCAmelCase									=					hidden_act
          UpperCAmelCase									=					num_labels
          UpperCAmelCase									=					scope
          UpperCAmelCase									=					len(lowercase_							)




      def     a_      (							self							)						->					Optional[int]:
          UpperCAmelCase									=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

          UpperCAmelCase									=					self.get_config()

          return config, pixel_values




      def     a_      (							self							)						->					Optional[Any]:
          return RegNetConfig(
              num_channels=self.num_channels   ,       embeddings_size=self.embeddings_size   ,       hidden_sizes=self.hidden_sizes   ,       depths=self.depths   ,       hidden_act=self.hidden_act   ,       num_labels=self.num_labels   ,       image_size=self.image_size   ,       )




      def     a_      (							self   ,       lowercase_   ,       lowercase_							)						->					List[Any]:
          UpperCAmelCase									=					FlaxRegNetModel(config=lowercase_							)
          UpperCAmelCase									=					model(lowercase_							)

          # Output shape (b, c, h, w)
          self.parent.assertEqual(
              result.last_hidden_state.shape   ,       (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)   ,       )




      def     a_      (							self   ,       lowercase_   ,       lowercase_							)						->					Union[str, Any]:
          UpperCAmelCase									=					self.num_labels
          UpperCAmelCase									=					FlaxRegNetForImageClassification(config=lowercase_							)
          UpperCAmelCase									=					model(lowercase_							)
          self.parent.assertEqual(result.logits.shape   ,       (self.batch_size, self.num_labels)							)




      def     a_      (							self							)						->					List[Any]:
          UpperCAmelCase									=					self.prepare_config_and_inputs()
          UpperCAmelCase			,							UpperCAmelCase									=					config_and_inputs
          UpperCAmelCase									=					{'pixel_values': pixel_values}
          return config, inputs_dict

@require_flax
class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_     ,			unittest.TestCase							):
      __SCREAMING_SNAKE_CASE   :							Tuple							      =	(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

      __SCREAMING_SNAKE_CASE   :							List[Any]							      =	False
      __SCREAMING_SNAKE_CASE   :							Optional[int]							      =	False
      __SCREAMING_SNAKE_CASE   :							Tuple							      =	False




      def     a_      (							self							)						->					None:
          UpperCAmelCase									=					FlaxRegNetModelTester(self							)
          UpperCAmelCase									=					ConfigTester(self   ,       config_class=lowercase_   ,       has_text_modality=lowercase_							)




      def     a_      (							self							)						->					List[Any]:
          self.create_and_test_config_common_properties()
          self.config_tester.create_and_test_config_to_json_string()
          self.config_tester.create_and_test_config_to_json_file()
          self.config_tester.create_and_test_config_from_and_save_pretrained()
          self.config_tester.create_and_test_config_with_num_labels()
          self.config_tester.check_config_can_be_init_without_params()
          self.config_tester.check_config_arguments_init()




      def     a_      (							self							)						->					Optional[Any]:
          return




      def     a_      (							self							)						->					Any:
          UpperCAmelCase									=					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowercase_							)




      def     a_      (							self							)						->					List[str]:
          UpperCAmelCase									=					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*lowercase_							)




      @unittest.skip(reason='RegNet does not use inputs_embeds'							)
      def     a_      (							self							)						->					List[str]:
          pass




      @unittest.skip(reason='RegNet does not support input and output embeddings'							)
      def     a_      (							self							)						->					Optional[int]:
          pass




      def     a_      (							self							)						->					Dict:
          UpperCAmelCase			,							UpperCAmelCase									=					self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              UpperCAmelCase									=					model_class(lowercase_							)
              UpperCAmelCase									=					inspect.signature(model.__call__							)
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              UpperCAmelCase									=					[*signature.parameters.keys()]

              UpperCAmelCase									=					['pixel_values']
              self.assertListEqual(arg_names[:1]   ,       lowercase_							)




      def     a_      (							self							)						->					int:
          def check_hidden_states_output(lowercase_   ,       lowercase_   ,       lowercase_							):
              UpperCAmelCase									=					model_class(lowercase_							)

              UpperCAmelCase									=					model(**self._prepare_for_class(lowercase_   ,       lowercase_							)							)

              UpperCAmelCase									=					outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

              UpperCAmelCase									=					self.model_tester.num_stages
              self.assertEqual(len(lowercase_							)   ,       expected_num_stages + 1							)

          UpperCAmelCase			,							UpperCAmelCase									=					self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              UpperCAmelCase									=					True
              check_hidden_states_output(lowercase_   ,       lowercase_   ,       lowercase_							)

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              UpperCAmelCase									=					True

              check_hidden_states_output(lowercase_   ,       lowercase_   ,       lowercase_							)




      def     a_      (							self							)						->					List[Any]:
          UpperCAmelCase			,							UpperCAmelCase									=					self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__							):
                  UpperCAmelCase									=					self._prepare_for_class(lowercase_   ,       lowercase_							)
                  UpperCAmelCase									=					model_class(lowercase_							)

                  @jax.jit
                  def model_jitted(lowercase_   ,       **lowercase_							):
                      return model(pixel_values=lowercase_   ,       **lowercase_							)

                  with self.subTest('JIT Enabled'							):
                      UpperCAmelCase									=					model_jitted(**lowercase_							).to_tuple()

                  with self.subTest('JIT Disabled'							):
                      with jax.disable_jit():
                          UpperCAmelCase									=					model_jitted(**lowercase_							).to_tuple()

                  self.assertEqual(len(lowercase_							)   ,       len(lowercase_							)							)
                  for jitted_output, output in zip(lowercase_   ,       lowercase_							):
                      self.assertEqual(jitted_output.shape   ,       output.shape							)




def     lowercase__						(					) ->      Tuple:






    """simple docstring"""




    UpperCAmelCase									=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
    return image







@require_flax
class      _UpperCAmelCase							(		unittest.TestCase							):




      @cached_property
      def     a_      (							self							)						->					List[str]:
          return AutoImageProcessor.from_pretrained('facebook/regnet-y-040'							) if is_vision_available() else None




      @slow
      def     a_      (							self							)						->					Union[str, Any]:
          UpperCAmelCase									=					FlaxRegNetForImageClassification.from_pretrained('facebook/regnet-y-040'							)

          UpperCAmelCase									=					self.default_image_processor
          UpperCAmelCase									=					prepare_img()
          UpperCAmelCase									=					image_processor(images=lowercase_   ,       return_tensors='np'							)

          UpperCAmelCase									=					model(**lowercase_							)

          # verify the logits
          UpperCAmelCase									=					(1, 1_0_0_0)
          self.assertEqual(outputs.logits.shape   ,       lowercase_							)

          UpperCAmelCase									=					jnp.array([-0.4_1_8_0, -1.5_0_5_1, -3.4_8_3_6]							)

          self.assertTrue(jnp.allclose(outputs.logits[0, :3]   ,       lowercase_   ,       atol=1E-4							)							)



