






"""simple docstring"""







import inspect
import unittest

from transformers import ResNetConfig, 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.resnet.modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel

if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor





class      UpperCamelCase_       (				unittest.TestCase     ):



  def __init__(					self       ,						snake_case__       ,						snake_case__=3       ,						snake_case__=32       ,						snake_case__=3       ,						snake_case__=10       ,						snake_case__=[10, 20, 30, 40]       ,						snake_case__=[1, 1, 2, 1]       ,						snake_case__=True       ,						snake_case__=True       ,						snake_case__="relu"       ,						snake_case__=3       ,						snake_case__=None       ,						)	->	int:



         """simple docstring"""





         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(snake_case__ )



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



         """simple docstring"""





         UpperCAmelCase					=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

         UpperCAmelCase					=	self.get_config()

         return config, pixel_values



  def 						UpperCamelCase_ (					self )	->	Dict:



         """simple docstring"""





         return ResNetConfig(
             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 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__ )	->	Tuple:



         """simple docstring"""





         UpperCAmelCase					=	FlaxResNetModel(config=snake_case__ )
         UpperCAmelCase					=	model(snake_case__ )

         # Output shape (b, c, h, w)
         self.parent.assertEqual(
             result.last_hidden_state.shape       ,						(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)       ,						)



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__ )	->	Any:



         """simple docstring"""





         UpperCAmelCase					=	self.num_labels
         UpperCAmelCase					=	FlaxResNetForImageClassification(config=snake_case__ )
         UpperCAmelCase					=	model(snake_case__ )
         self.parent.assertEqual(result.logits.shape       ,						(self.batch_size, self.num_labels) )



  def 						UpperCamelCase_ (					self )	->	Optional[int]:



         """simple docstring"""





         UpperCAmelCase					=	self.prepare_config_and_inputs()
         UpperCAmelCase			,				UpperCAmelCase					=	config_and_inputs
         UpperCAmelCase					=	{"""pixel_values""": pixel_values}
         return config, inputs_dict





@require_flax
class      UpperCamelCase_       (				a_      ,    unittest.TestCase     ):
  _A				:    Tuple =		(FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

  _A				:    List[str] =		False
  _A				:    Optional[Any] =		False
  _A				:    List[str] =		False



  def 						UpperCamelCase_ (					self )	->	None:



         """simple docstring"""





         UpperCAmelCase					=	FlaxResNetModelTester(self )
         UpperCAmelCase					=	ConfigTester(self       ,						config_class=snake_case__       ,						has_text_modality=snake_case__ )



  def 						UpperCamelCase_ (					self )	->	Any:



         """simple docstring"""





         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 						UpperCamelCase_ (					self )	->	int:



         """simple docstring"""





         return



  def 						UpperCamelCase_ (					self )	->	Any:



         """simple docstring"""





         UpperCAmelCase					=	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*snake_case__ )



  def 						UpperCamelCase_ (					self )	->	str:



         """simple docstring"""





         UpperCAmelCase					=	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*snake_case__ )



  @unittest.skip(reason="""ResNet does not use inputs_embeds""" )
  def 						UpperCamelCase_ (					self )	->	int:



         """simple docstring"""





         pass



  @unittest.skip(reason="""ResNet does not support input and output embeddings""" )
  def 						UpperCamelCase_ (					self )	->	List[Any]:



         """simple docstring"""





         pass



  def 						UpperCamelCase_ (					self )	->	List[str]:



         """simple docstring"""





         UpperCAmelCase			,				UpperCAmelCase					=	self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
                UpperCAmelCase					=	model_class(snake_case__ )
                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]       ,						snake_case__ )



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



         """simple docstring"""





         def check_hidden_states_output(snake_case__       ,						snake_case__       ,						snake_case__ ):
                UpperCAmelCase					=	model_class(snake_case__ )

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

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

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



  @unittest.skip(reason="""ResNet does not use feedforward chunking""" )
  def 						UpperCamelCase_ (					self )	->	Optional[Any]:



         """simple docstring"""





         pass



  def 						UpperCamelCase_ (					self )	->	int:



         """simple docstring"""





         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(snake_case__       ,						snake_case__ )
                       UpperCAmelCase					=	model_class(snake_case__ )

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

                       with self.subTest("""JIT Enabled""" ):
                              UpperCAmelCase					=	model_jitted(**snake_case__ ).to_tuple()

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

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






def 					_lowerCAmelCase				(					):







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





@require_flax
class      UpperCamelCase_       (				unittest.TestCase     ):



  @cached_property
  def 						UpperCamelCase_ (					self )	->	Any:



         """simple docstring"""





         return AutoImageProcessor.from_pretrained("""microsoft/resnet-50""" ) if is_vision_available() else None



  @slow
  def 						UpperCamelCase_ (					self )	->	Tuple:



         """simple docstring"""





         UpperCAmelCase					=	FlaxResNetForImageClassification.from_pretrained("""microsoft/resnet-50""" )

         UpperCAmelCase					=	self.default_image_processor
         UpperCAmelCase					=	prepare_img()
         UpperCAmelCase					=	image_processor(images=snake_case__       ,						return_tensors="""np""" )

         UpperCAmelCase					=	model(**snake_case__ )

         # verify the logits
         UpperCAmelCase					=	(1, 10_00)
         self.assertEqual(outputs.logits.shape       ,						snake_case__ )

         UpperCAmelCase					=	jnp.array([-11.1_069, -9.7_877, -8.3_777] )

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








"""simple docstring"""







from typing import Optional

import torch
from PIL import Image
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNetaDConditionModel
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import (
    deprecate,
)





class      UpperCamelCase_       (				a_     ):



  def __init__(					self       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__ = 0.93       ,						snake_case__ = True       ,						)	->	Optional[Any]:



         """simple docstring"""





         UpperCAmelCase					=	mixing_coeff
         UpperCAmelCase					=	leapfrog_steps

         super().__init__()
         self.register_modules(
             vae=snake_case__       ,						text_encoder=snake_case__       ,						tokenizer=snake_case__       ,						unet=snake_case__       ,						scheduler=snake_case__       ,						)

         UpperCAmelCase					=	2 ** (len(self.vae.config.block_out_channels ) - 1)
         UpperCAmelCase					=	VaeImageProcessor(vae_scale_factor=self.vae_scale_factor )



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__ = None       ,						snake_case__ = False )	->	Dict:



         """simple docstring"""





         UpperCAmelCase					=	self.tokenizer(
             snake_case__       ,						padding="""max_length"""       ,						max_length=self.tokenizer.model_max_length       ,						truncation=snake_case__       ,						return_tensors="""pt"""       ,						)

         UpperCAmelCase					=	self.text_encoder(text_inputs.input_ids.to(self.device ) ).last_hidden_state

         UpperCAmelCase					=	prompt_embeds.to(dtype=self.text_encoder.dtype       ,						device=self.device )

         if do_classifier_free_guidance:
                UpperCAmelCase					=	"""""" if negative_prompt is None else negative_prompt

                UpperCAmelCase					=	self.tokenizer(
                    snake_case__       ,						padding="""max_length"""       ,						max_length=self.tokenizer.model_max_length       ,						truncation=snake_case__       ,						return_tensors="""pt"""       ,						)

                UpperCAmelCase					=	self.text_encoder(uncond_input.input_ids.to(self.device ) ).last_hidden_state

                UpperCAmelCase					=	torch.cat([negative_prompt_embeds, prompt_embeds] )

         return prompt_embeds



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__ )	->	List[Any]:



         """simple docstring"""





         UpperCAmelCase					=	self.mixing_coeff * x + (1 - self.mixing_coeff) * y
         UpperCAmelCase					=	self.mixing_coeff * y + (1 - self.mixing_coeff) * x

         return [x, y]



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__ )	->	Dict:



         """simple docstring"""





         UpperCAmelCase					=	(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
         UpperCAmelCase					=	(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

         return [x, y]



  def 						UpperCamelCase_ (					self       ,						snake_case__ )	->	Dict:



         """simple docstring"""





         UpperCAmelCase					=	int(snake_case__ )

         UpperCAmelCase					=	self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

         return alpha_prod, 1 - alpha_prod



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						)	->	int:



         """simple docstring"""





         UpperCAmelCase					=	timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

         UpperCAmelCase			,				UpperCAmelCase					=	self._get_alpha_and_beta(snake_case__ )
         UpperCAmelCase			,				UpperCAmelCase					=	self._get_alpha_and_beta(snake_case__ )

         UpperCAmelCase					=	(alpha_prod_t_prev / alpha_prod_t) ** 0.5
         UpperCAmelCase					=	-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

         UpperCAmelCase					=	(base - b_t * model_output) / a_t

         return model_input, next_model_input.to(base.dtype )



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						)	->	str:



         """simple docstring"""





         UpperCAmelCase					=	timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

         UpperCAmelCase			,				UpperCAmelCase					=	self._get_alpha_and_beta(snake_case__ )
         UpperCAmelCase			,				UpperCAmelCase					=	self._get_alpha_and_beta(snake_case__ )

         UpperCAmelCase					=	(alpha_prod_t_prev / alpha_prod_t) ** 0.5
         UpperCAmelCase					=	-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
         UpperCAmelCase					=	a_t * base + b_t * model_output

         return model_input, next_model_input.to(base.dtype )



  @torch.no_grad()
  def 						UpperCamelCase_ (					self       ,						snake_case__ )	->	str:



         """simple docstring"""





         UpperCAmelCase					=	1 / self.vae.config.scaling_factor * latents
         UpperCAmelCase					=	self.vae.decode(snake_case__ ).sample
         UpperCAmelCase					=	(image / 2 + 0.5).clamp(0       ,						1 )
         return image



  @torch.no_grad()
  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__ = None       ,						)	->	List[Any]:



         """simple docstring"""





         UpperCAmelCase					=	guidance_scale > 1.0

         UpperCAmelCase					=	image.to(device=self.device       ,						dtype=text_embeds.dtype )
         UpperCAmelCase					=	self.vae.encode(snake_case__ ).latent_dist.sample(snake_case__ )

         UpperCAmelCase					=	self.vae.config.scaling_factor * latent

         UpperCAmelCase					=	[latent.clone(), latent.clone()]

         for i, t in tqdm(enumerate(snake_case__ )       ,						total=len(snake_case__ ) ):
                UpperCAmelCase					=	self.noise_mixing_layer(x=coupled_latents[0]       ,						y=coupled_latents[1] )

                # j - model_input index, k - base index
                for j in range(2 ):
                       UpperCAmelCase					=	j ^ 1

                       if self.leapfrog_steps:
                              if i % 2 == 0:
                                     UpperCAmelCase			,				UpperCAmelCase					=	j, k

                       UpperCAmelCase					=	coupled_latents[j]
                       UpperCAmelCase					=	coupled_latents[k]

                       UpperCAmelCase					=	torch.cat([model_input] * 2 ) if do_classifier_free_guidance else model_input

                       UpperCAmelCase					=	self.unet(snake_case__       ,						snake_case__       ,						encoder_hidden_states=snake_case__ ).sample

                       if do_classifier_free_guidance:
                              UpperCAmelCase			,				UpperCAmelCase					=	noise_pred.chunk(2 )
                              UpperCAmelCase					=	noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                       UpperCAmelCase			,				UpperCAmelCase					=	self.noise_step(
                           base=snake_case__       ,						model_input=snake_case__       ,						model_output=snake_case__       ,						timestep=snake_case__       ,						)

                       UpperCAmelCase					=	model_input

         return coupled_latents



  @torch.no_grad()
  def __call__(					self       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__ = 3.0       ,						snake_case__ = 50       ,						snake_case__ = 0.8       ,						snake_case__ = None       ,						snake_case__ = None       ,						snake_case__ = "pil"       ,						)	->	str:



         """simple docstring"""





         UpperCAmelCase					=	guidance_scale > 1.0

         UpperCAmelCase					=	self.image_processor.preprocess(snake_case__ )

         UpperCAmelCase					=	self._encode_prompt(snake_case__       ,						snake_case__       ,						snake_case__ )
         UpperCAmelCase					=	self._encode_prompt(snake_case__       ,						snake_case__       ,						snake_case__ )

         self.scheduler.set_timesteps(snake_case__       ,						self.device )

         UpperCAmelCase					=	num_inference_steps - int(num_inference_steps * strength )
         UpperCAmelCase					=	self.scheduler.timesteps[t_limit:]
         UpperCAmelCase					=	fwd_timesteps.flip(0 )

         UpperCAmelCase					=	self.prepare_latents(snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__ )

         for i, t in tqdm(enumerate(snake_case__ )       ,						total=len(snake_case__ ) ):
                # j - model_input index, k - base index
                for k in range(2 ):
                       UpperCAmelCase					=	k ^ 1

                       if self.leapfrog_steps:
                              if i % 2 == 1:
                                     UpperCAmelCase			,				UpperCAmelCase					=	j, k

                       UpperCAmelCase					=	coupled_latents[j]
                       UpperCAmelCase					=	coupled_latents[k]

                       UpperCAmelCase					=	torch.cat([model_input] * 2 ) if do_classifier_free_guidance else model_input

                       UpperCAmelCase					=	self.unet(snake_case__       ,						snake_case__       ,						encoder_hidden_states=snake_case__ ).sample

                       if do_classifier_free_guidance:
                              UpperCAmelCase			,				UpperCAmelCase					=	noise_pred.chunk(2 )
                              UpperCAmelCase					=	noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                       UpperCAmelCase			,				UpperCAmelCase					=	self.denoise_step(
                           base=snake_case__       ,						model_input=snake_case__       ,						model_output=snake_case__       ,						timestep=snake_case__       ,						)

                       UpperCAmelCase					=	model_input

                UpperCAmelCase					=	self.denoise_mixing_layer(x=coupled_latents[0]       ,						y=coupled_latents[1] )

         # either one is fine
         UpperCAmelCase					=	coupled_latents[0]

         if output_type not in ["latent", "pt", "np", "pil"]:
                UpperCAmelCase					=	(
                    f'''the output_type {output_type} is outdated. Please make sure to set it to one of these instead: '''
                    """`pil`, `np`, `pt`, `latent`"""
                )
                deprecate("""Unsupported output_type"""       ,						"""1.0.0"""       ,						snake_case__       ,						standard_warn=snake_case__ )
                UpperCAmelCase					=	"""np"""

         if output_type == "latent":
                UpperCAmelCase					=	final_latent
         else:
                UpperCAmelCase					=	self.decode_latents(snake_case__ )
                UpperCAmelCase					=	self.image_processor.postprocess(snake_case__       ,						output_type=snake_case__ )

         return image
