
"""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							)							)





"""simple docstring"""

from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


SCREAMING_SNAKE_CASE_    					=						transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)




def     lowercase__						(					lowerCAmelCase				:					List[Any] ) ->      List[str]:






    """simple docstring"""




    if isinstance(lowerCAmelCase ,				torch.Tensor ):
        return image
    elif isinstance(lowerCAmelCase ,				PIL.Image.Image ):
        UpperCAmelCase									=					[image]

    UpperCAmelCase									=					[trans(img.convert('RGB' ) ) for img in image]
    UpperCAmelCase									=					torch.stack(lowerCAmelCase )
    return image







class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):




      def __init__(							self   ,       lowercase_   ,       lowercase_							)						->					List[str]:
          super().__init__()

          # make sure scheduler can always be converted to DDIM
          UpperCAmelCase									=					DDIMScheduler.from_config(scheduler.config							)

          self.register_modules(unet=lowercase_   ,       scheduler=lowercase_							)




      def     a_      (							self   ,       lowercase_							)						->					Tuple:
          if strength < 0 or strength > 1:
              raise ValueError(F"The value of strength should in [0.0, 1.0] but is {strength}"							)




      def     a_      (							self   ,       lowercase_   ,       lowercase_   ,       lowercase_							)						->					Any:
          # get the original timestep using init_timestep
          UpperCAmelCase									=					min(int(num_inference_steps * strength							)   ,       lowercase_							)

          UpperCAmelCase									=					max(num_inference_steps - init_timestep   ,       0							)
          UpperCAmelCase									=					self.scheduler.timesteps[t_start:]

          return timesteps, num_inference_steps - t_start




      def     a_      (							self   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_=None							)						->					Optional[Any]:
          if not isinstance(lowercase_   ,       (torch.Tensor, PIL.Image.Image, list)							):
              raise ValueError(
                  F"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(lowercase_							)}"							)

          UpperCAmelCase									=					image.to(device=lowercase_   ,       dtype=lowercase_							)

          if isinstance(lowercase_   ,       lowercase_							) and len(lowercase_							) != batch_size:
              raise ValueError(
                  F"You have passed a list of generators of length {len(lowercase_							)}, but requested an effective batch"
                  F" size of {batch_size}. Make sure the batch size matches the length of the generators."							)

          UpperCAmelCase									=					init_latents.shape
          UpperCAmelCase									=					randn_tensor(lowercase_   ,       generator=lowercase_   ,       device=lowercase_   ,       dtype=lowercase_							)

          # get latents
          print('add noise to latents at timestep'   ,       lowercase_							)
          UpperCAmelCase									=					self.scheduler.add_noise(lowercase_   ,       lowercase_   ,       lowercase_							)
          UpperCAmelCase									=					init_latents

          return latents




      @torch.no_grad()
      def __call__(							self   ,       lowercase_ = None   ,       lowercase_ = 0.8   ,       lowercase_ = 1   ,       lowercase_ = None   ,       lowercase_ = 0.0   ,       lowercase_ = 5_0   ,       lowercase_ = None   ,       lowercase_ = "pil"   ,       lowercase_ = True   ,       )						->					Union[ImagePipelineOutput, Tuple]:
          self.check_inputs(lowercase_							)

          # 2. Preprocess image
          UpperCAmelCase									=					preprocess(lowercase_							)

          # 3. set timesteps
          self.scheduler.set_timesteps(lowercase_   ,       device=self.device							)
          UpperCAmelCase			,							UpperCAmelCase									=					self.get_timesteps(lowercase_   ,       lowercase_   ,       self.device							)
          UpperCAmelCase									=					timesteps[:1].repeat(lowercase_							)

          # 4. Prepare latent variables
          UpperCAmelCase									=					self.prepare_latents(lowercase_   ,       lowercase_   ,       lowercase_   ,       self.unet.dtype   ,       self.device   ,       lowercase_							)
          UpperCAmelCase									=					latents

          # 5. Denoising loop
          for t in self.progress_bar(lowercase_							):
              # 1. predict noise model_output
              UpperCAmelCase									=					self.unet(lowercase_   ,       lowercase_							).sample

              # 2. predict previous mean of image x_t-1 and add variance depending on eta
              # eta corresponds to η in paper and should be between [0, 1]
              # do x_t -> x_t-1
              UpperCAmelCase									=					self.scheduler.step(
                  lowercase_   ,       lowercase_   ,       lowercase_   ,       eta=lowercase_   ,       use_clipped_model_output=lowercase_   ,       generator=lowercase_   ,       ).prev_sample

          UpperCAmelCase									=					(image / 2 + 0.5).clamp(0   ,       1							)
          UpperCAmelCase									=					image.cpu().permute(0   ,       2   ,       3   ,       1							).numpy()
          if output_type == "pil":
              UpperCAmelCase									=					self.numpy_to_pil(lowercase_							)

          if not return_dict:
              return (image, latent_timestep.item())

          return ImagePipelineOutput(images=lowercase_							)



