


'''simple docstring'''

import gc
import random
import unittest

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

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, 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, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class   snake_case					(					lowercase    ,		lowercase    ,		unittest.TestCase    ):





  """simple docstring"""




  _lowerCamelCase						=						CycleDiffusionPipeline
  _lowerCamelCase						=						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
      "negative_prompt",
      "height",
      "width",
      "negative_prompt_embeds",
  }
  _lowerCamelCase						=						PipelineTesterMixin.required_optional_params - {"latents"}
  _lowerCamelCase						=						TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"source_prompt"}    )
  _lowerCamelCase						=						IMAGE_TO_IMAGE_IMAGE_PARAMS
  _lowerCamelCase						=						IMAGE_TO_IMAGE_IMAGE_PARAMS






  def 						snake_case	(					self  ):







   """simple docstring"""
   torch.manual_seed(0  )
   lowerCamelCase_  				=						UNetaDConditionModel(
       block_out_channels=(32, 64)    ,						layers_per_block=2    ,						sample_size=32    ,						in_channels=4    ,						out_channels=4    ,						down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")    ,						up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")    ,						cross_attention_dim=32    ,						)
   lowerCamelCase_  				=						DDIMScheduler(
       beta_start=0.00_085    ,						beta_end=0.012    ,						beta_schedule="scaled_linear"    ,						num_train_timesteps=1000    ,						clip_sample=UpperCamelCase    ,						set_alpha_to_one=UpperCamelCase    ,						)
   torch.manual_seed(0  )
   lowerCamelCase_  				=						AutoencoderKL(
       block_out_channels=[32, 64]    ,						in_channels=3    ,						out_channels=3    ,						down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]    ,						up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]    ,						latent_channels=4    ,						)
   torch.manual_seed(0  )
   lowerCamelCase_  				=						CLIPTextConfig(
       bos_token_id=0    ,						eos_token_id=2    ,						hidden_size=32    ,						intermediate_size=37    ,						layer_norm_eps=1e-05    ,						num_attention_heads=4    ,						num_hidden_layers=5    ,						pad_token_id=1    ,						vocab_size=1000    ,						)
   lowerCamelCase_  				=						CLIPTextModel(UpperCamelCase  )
   lowerCamelCase_  				=						CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"  )

   lowerCamelCase_  				=						{
       "unet": unet,
       "scheduler": scheduler,
       "vae": vae,
       "text_encoder": text_encoder,
       "tokenizer": tokenizer,
       "safety_checker": None,
       "feature_extractor": None,
   }
   return components






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase=0  ):







   """simple docstring"""
   lowerCamelCase_  				=						floats_tensor((1, 3, 32, 32)    ,						rng=random.Random(UpperCamelCase  )  ).to(UpperCamelCase  )
   lowerCamelCase_  				=						image / 2 + 0.5
   if str(UpperCamelCase  ).startswith("mps"  ):
    lowerCamelCase_  				=						torch.manual_seed(UpperCamelCase  )
   else:
    lowerCamelCase_  				=						torch.Generator(device=UpperCamelCase  ).manual_seed(UpperCamelCase  )
   lowerCamelCase_  				=						{
       "prompt": "An astronaut riding an elephant",
       "source_prompt": "An astronaut riding a horse",
       "image": image,
       "generator": generator,
       "num_inference_steps": 2,
       "eta": 0.1,
       "strength": 0.8,
       "guidance_scale": 3,
       "source_guidance_scale": 1,
       "output_type": "numpy",
   }
   return inputs






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						"cpu"  # ensure determinism for the device-dependent torch.Generator

   lowerCamelCase_  				=						self.get_dummy_components()
   lowerCamelCase_  				=						CycleDiffusionPipeline(**UpperCamelCase  )
   lowerCamelCase_  				=						pipe.to(UpperCamelCase  )
   pipe.set_progress_bar_config(disable=UpperCamelCase  )

   lowerCamelCase_  				=						self.get_dummy_inputs(UpperCamelCase  )
   lowerCamelCase_  				=						pipe(**UpperCamelCase  )
   lowerCamelCase_  				=						output.images

   lowerCamelCase_  				=						images[0, -3:, -3:, -1]

   assert images.shape == (1, 32, 32, 3)
   lowerCamelCase_  				=						np.array([0.4_459, 0.4_943, 0.4_544, 0.6_643, 0.5_474, 0.4_327, 0.5_701, 0.5_959, 0.5_179]  )

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






  @unittest.skipIf(torch_device != "cuda"    ,						"This test requires a GPU"  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.get_dummy_components()
   for name, module in components.items():
    if hasattr(UpperCamelCase    ,						"half"  ):
     lowerCamelCase_  				=						module.half()
   lowerCamelCase_  				=						CycleDiffusionPipeline(**UpperCamelCase  )
   lowerCamelCase_  				=						pipe.to(UpperCamelCase  )
   pipe.set_progress_bar_config(disable=UpperCamelCase  )

   lowerCamelCase_  				=						self.get_dummy_inputs(UpperCamelCase  )
   lowerCamelCase_  				=						pipe(**UpperCamelCase  )
   lowerCamelCase_  				=						output.images

   lowerCamelCase_  				=						images[0, -3:, -3:, -1]

   assert images.shape == (1, 32, 32, 3)
   lowerCamelCase_  				=						np.array([0.3_506, 0.4_543, 0.446, 0.4_575, 0.5_195, 0.4_155, 0.5_273, 0.518, 0.4_116]  )

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






  @skip_mps
  def 						snake_case	(					self  ):







   """simple docstring"""
   return super().test_save_load_local()






  @unittest.skip("non-deterministic pipeline"  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   return super().test_inference_batch_single_identical()






  @skip_mps
  def 						snake_case	(					self  ):







   """simple docstring"""
   return super().test_dict_tuple_outputs_equivalent()






  @skip_mps
  def 						snake_case	(					self  ):







   """simple docstring"""
   return super().test_save_load_optional_components()







  @skip_mps
  def 						snake_case	(					self  ):







   """simple docstring"""
   return super().test_attention_slicing_forward_pass()



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





  """simple docstring"""




  def 						snake_case	(					self  ):







   """simple docstring"""
   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						load_image(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
       "/cycle-diffusion/black_colored_car.png"  )
   lowerCamelCase_  				=						load_numpy(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy"  )
   lowerCamelCase_  				=						init_image.resize((512, 512)  )

   lowerCamelCase_  				=						"CompVis/stable-diffusion-v1-4"
   lowerCamelCase_  				=						DDIMScheduler.from_pretrained(UpperCamelCase    ,						subfolder="scheduler"  )
   lowerCamelCase_  				=						CycleDiffusionPipeline.from_pretrained(
       UpperCamelCase    ,						scheduler=UpperCamelCase    ,						safety_checker=UpperCamelCase    ,						torch_dtype=torch.floataa    ,						revision="fp16"  )

   pipe.to(UpperCamelCase  )
   pipe.set_progress_bar_config(disable=UpperCamelCase  )
   pipe.enable_attention_slicing()

   lowerCamelCase_  				=						"A black colored car"
   lowerCamelCase_  				=						"A blue colored car"

   lowerCamelCase_  				=						torch.manual_seed(0  )
   lowerCamelCase_  				=						pipe(
       prompt=UpperCamelCase    ,						source_prompt=UpperCamelCase    ,						image=UpperCamelCase    ,						num_inference_steps=100    ,						eta=0.1    ,						strength=0.85    ,						guidance_scale=3    ,						source_guidance_scale=1    ,						generator=UpperCamelCase    ,						output_type="np"    ,						)
   lowerCamelCase_  				=						output.images

   # the values aren't exactly equal, but the images look the same visually
   assert np.abs(image - expected_image  ).max() < 5e-1







  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						load_image(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
       "/cycle-diffusion/black_colored_car.png"  )
   lowerCamelCase_  				=						load_numpy(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy"  )
   lowerCamelCase_  				=						init_image.resize((512, 512)  )

   lowerCamelCase_  				=						"CompVis/stable-diffusion-v1-4"
   lowerCamelCase_  				=						DDIMScheduler.from_pretrained(UpperCamelCase    ,						subfolder="scheduler"  )
   lowerCamelCase_  				=						CycleDiffusionPipeline.from_pretrained(UpperCamelCase    ,						scheduler=UpperCamelCase    ,						safety_checker=UpperCamelCase  )

   pipe.to(UpperCamelCase  )
   pipe.set_progress_bar_config(disable=UpperCamelCase  )
   pipe.enable_attention_slicing()

   lowerCamelCase_  				=						"A black colored car"
   lowerCamelCase_  				=						"A blue colored car"

   lowerCamelCase_  				=						torch.manual_seed(0  )
   lowerCamelCase_  				=						pipe(
       prompt=UpperCamelCase    ,						source_prompt=UpperCamelCase    ,						image=UpperCamelCase    ,						num_inference_steps=100    ,						eta=0.1    ,						strength=0.85    ,						guidance_scale=3    ,						source_guidance_scale=1    ,						generator=UpperCamelCase    ,						output_type="np"    ,						)
   lowerCamelCase_  				=						output.images

   assert np.abs(image - expected_image  ).max() < 2e-2







'''simple docstring'''

import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
     import torch

     from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin





class   snake_case					:





  """simple docstring"""




  def __init__(					self    ,						UpperCamelCase    ,						UpperCamelCase=None    ,						UpperCamelCase=None    ,						UpperCamelCase=None    ,						UpperCamelCase="resnet50"    ,						UpperCamelCase=3    ,						UpperCamelCase=32    ,						UpperCamelCase=3    ,						UpperCamelCase=True    ,						UpperCamelCase=True    ,						):







   """simple docstring"""
   lowerCamelCase_  				=						parent
   lowerCamelCase_  				=						out_indices if out_indices is not None else [4]
   lowerCamelCase_  				=						stage_names
   lowerCamelCase_  				=						out_features
   lowerCamelCase_  				=						backbone
   lowerCamelCase_  				=						batch_size
   lowerCamelCase_  				=						image_size
   lowerCamelCase_  				=						num_channels
   lowerCamelCase_  				=						use_pretrained_backbone
   lowerCamelCase_  				=						is_training






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )
   lowerCamelCase_  				=						self.get_config()

   return config, pixel_values






  def 						snake_case	(					self  ):







   """simple docstring"""
   return TimmBackboneConfig(
       image_size=self.image_size    ,						num_channels=self.num_channels    ,						out_features=self.out_features    ,						out_indices=self.out_indices    ,						stage_names=self.stage_names    ,						use_pretrained_backbone=self.use_pretrained_backbone    ,						backbone=self.backbone    ,						)






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						TimmBackbone(config=UpperCamelCase  )
   model.to(UpperCamelCase  )
   model.eval()
   with torch.no_grad():
    lowerCamelCase_  				=						model(UpperCamelCase  )
   self.parent.assertEqual(
       result.feature_map[-1].shape    ,						(self.batch_size, model.channels[-1], 14, 14)    ,						)







  def 						snake_case	(					self  ):







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





@require_torch
@require_timm
class   snake_case					(					lowercase    ,		lowercase    ,		lowercase    ,		unittest.TestCase    ):





  """simple docstring"""




  _lowerCamelCase						=						(TimmBackbone,) if is_torch_available() else ()
  _lowerCamelCase						=						{"feature-extraction": TimmBackbone} if is_torch_available() else {}
  _lowerCamelCase						=						False
  _lowerCamelCase						=						False
  _lowerCamelCase						=						False
  _lowerCamelCase						=						False






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						TimmBackboneModelTester(self  )
   lowerCamelCase_  				=						ConfigTester(self    ,						config_class=UpperCamelCase    ,						has_text_modality=UpperCamelCase  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   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 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						"resnet18"
   lowerCamelCase_  				=						"microsoft/resnet-18"

   lowerCamelCase_  				=						AutoBackbone.from_pretrained(UpperCamelCase    ,						use_timm_backbone=UpperCamelCase  )
   lowerCamelCase_  				=						AutoBackbone.from_pretrained(UpperCamelCase  )

   self.assertEqual(len(timm_model.out_features  )    ,						len(transformers_model.out_features  )  )
   self.assertEqual(len(timm_model.stage_names  )    ,						len(transformers_model.stage_names  )  )
   self.assertEqual(timm_model.channels    ,						transformers_model.channels  )
   # Out indices are set to the last layer by default. For timm models, we don't know
   # the number of layers in advance, so we set it to (-1,), whereas for transformers
   # models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
   self.assertEqual(timm_model.out_indices    ,						(-1,)  )
   self.assertEqual(transformers_model.out_indices    ,						[len(timm_model.stage_names  ) - 1]  )

   lowerCamelCase_  				=						AutoBackbone.from_pretrained(UpperCamelCase    ,						use_timm_backbone=UpperCamelCase    ,						out_indices=[1, 2, 3]  )
   lowerCamelCase_  				=						AutoBackbone.from_pretrained(UpperCamelCase    ,						out_indices=[1, 2, 3]  )

   self.assertEqual(timm_model.out_indices    ,						transformers_model.out_indices  )
   self.assertEqual(len(timm_model.out_features  )    ,						len(transformers_model.out_features  )  )
   self.assertEqual(timm_model.channels    ,						transformers_model.channels  )






  @unittest.skip("TimmBackbone doesn't support feed forward chunking"  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  @unittest.skip("TimmBackbone doesn't have num_hidden_layers attribute"  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  @unittest.skip("TimmBackbone initialization is managed on the timm side"  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  @unittest.skip("TimmBackbone models doesn't have inputs_embeds"  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  @unittest.skip("TimmBackbone models doesn't have inputs_embeds"  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  @unittest.skip("TimmBackbone model cannot be created without specifying a backbone checkpoint"  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  @unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone"  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  @unittest.skip("model weights aren't tied in TimmBackbone."  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  @unittest.skip("model weights aren't tied in TimmBackbone."  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  @unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone"  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  @unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone"  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  @unittest.skip("TimmBackbone doesn't have hidden size info in its configuration."  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  @unittest.skip("TimmBackbone doesn't support output_attentions."  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  @unittest.skip("Safetensors is not supported by timm."  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_				,lowerCamelCase_  				=						self.model_tester.prepare_config_and_inputs_for_common()

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

    lowerCamelCase_  				=						["pixel_values"]
    self.assertListEqual(arg_names[:1]    ,						UpperCamelCase  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_				,lowerCamelCase_  				=						self.model_tester.prepare_config_and_inputs_for_common()
   lowerCamelCase_  				=						True
   lowerCamelCase_  				=						self.has_attentions

   # no need to test all models as different heads yield the same functionality
   lowerCamelCase_  				=						self.all_model_classes[0]
   lowerCamelCase_  				=						model_class(UpperCamelCase  )
   model.to(UpperCamelCase  )

   lowerCamelCase_  				=						self._prepare_for_class(UpperCamelCase    ,						UpperCamelCase  )
   lowerCamelCase_  				=						model(**UpperCamelCase  )
   lowerCamelCase_  				=						outputs[0][-1]

   # Encoder-/Decoder-only models
   lowerCamelCase_  				=						outputs.hidden_states[0]
   hidden_states.retain_grad()

   if self.has_attentions:
    lowerCamelCase_  				=						outputs.attentions[0]
    attentions.retain_grad()

   output.flatten()[0].backward(retain_graph=UpperCamelCase  )

   self.assertIsNotNone(hidden_states.grad  )

   if self.has_attentions:
    self.assertIsNotNone(attentions.grad  )







  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_				,lowerCamelCase_  				=						self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    lowerCamelCase_  				=						model_class(UpperCamelCase  )
    model.to(UpperCamelCase  )
    model.eval()
    lowerCamelCase_  				=						model(**UpperCamelCase  )

    self.assertEqual(len(result.feature_maps  )    ,						len(config.out_indices  )  )
    self.assertEqual(len(model.channels  )    ,						len(config.out_indices  )  )

    # Check output of last stage is taken if out_features=None, out_indices=None
    lowerCamelCase_  				=						copy.deepcopy(UpperCamelCase  )
    lowerCamelCase_  				=						None
    lowerCamelCase_  				=						model_class(UpperCamelCase  )
    model.to(UpperCamelCase  )
    model.eval()
    lowerCamelCase_  				=						model(**UpperCamelCase  )

    self.assertEqual(len(result.feature_maps  )    ,						1  )
    self.assertEqual(len(model.channels  )    ,						1  )

    # Check backbone can be initialized with fresh weights
    lowerCamelCase_  				=						copy.deepcopy(UpperCamelCase  )
    lowerCamelCase_  				=						False
    lowerCamelCase_  				=						model_class(UpperCamelCase  )
    model.to(UpperCamelCase  )
    model.eval()
    lowerCamelCase_  				=						model(**UpperCamelCase  )



