



'''simple docstring'''






import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def 				lowerCamelCase__	(						_A    ):
  a						: List[str]					       =					filter(lambda _A    : p.requires_grad			,      model.parameters()    )
  a						: int					       =					sum([np.prod(p.size()    ) for p in model_parameters]    )
  return params


lowerCAmelCase: Dict							=						logging.getLogger(__name__)
def 				lowerCamelCase__	(						_A			,      _A    ):
  if metric == "rouge2":
    a						: List[Any]					       =					'{val_avg_rouge2:.4f}-{step_count}'
  elif metric == "bleu":
    a						: Any					       =					'{val_avg_bleu:.4f}-{step_count}'
  elif metric == "em":
    a						: Optional[int]					       =					'{val_avg_em:.4f}-{step_count}'
  elif metric == "loss":
    a						: List[Any]					       =					'{val_avg_loss:.4f}-{step_count}'
  else:
    raise NotImplementedError(
        f"""seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this"""
        ' function.'    )

  a						: int					       =					ModelCheckpoint(
      dirpath=_A			,      filename=_A			,      monitor=f"""val_{metric}"""			,      mode='max'			,      save_top_k=1			,      every_n_epochs=1			,      )

  return checkpoint_callback
def 				lowerCamelCase__	(						_A			,      _A    ):
  return EarlyStopping(
      monitor=f"""val_{metric}"""			,      mode='min' if 'loss' in metric else 'max'			,      patience=_A			,      verbose=_A			,      )

class 							a__( pl.Callback     ):







    def       lowercase_	(				self			:  Dict						,      __snake_case			:  List[Any]						,      __snake_case			:  str     ):
      a						: Optional[int]					       =					{F"""lr_group_{i}""": param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups     )}
      pl_module.logger.log_metrics(__snake_case     )







    @rank_zero_only
    def       lowercase_	(				self			:  Optional[Any]						,      __snake_case			:  pl.Trainer						,      __snake_case			:  pl.LightningModule						,      __snake_case			:  str						,      __snake_case			:  Optional[int]=True     ):
      logger.info(F"""***** {type_path} results at step {trainer.global_step:05d} *****"""     )
      a						: Optional[int]					       =					trainer.callback_metrics
      trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['log', 'progress_bar', 'preds']}     )
      # Log results
      a						: int					       =					Path(pl_module.hparams.output_dir     )
      if type_path == "test":
        a						: Optional[int]					       =					od / 'test_results.txt'
        a						: List[str]					       =					od / 'test_generations.txt'
      else:
        # this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
        # If people want this it will be easy enough to add back.
        a						: List[Any]					       =					od / F"""{type_path}_results/{trainer.global_step:05d}.txt"""
        a						: Any					       =					od / F"""{type_path}_generations/{trainer.global_step:05d}.txt"""
        results_file.parent.mkdir(exist_ok=__snake_case     )
        generations_file.parent.mkdir(exist_ok=__snake_case     )
      with open(__snake_case						,      'a+'     ) as writer:
        for key in sorted(__snake_case     ):
          if key in ["log", "progress_bar", "preds"]:
            continue
          a						: Optional[int]					       =					metrics[key]
          if isinstance(__snake_case						,      torch.Tensor     ):
            a						: List[str]					       =					val.item()
          a						: int					       =					F"""{key}: {val:.6f}\n"""
          writer.write(__snake_case     )

      if not save_generations:
        return

      if "preds" in metrics:
        a						: Union[str, Any]					       =					'\n'.join(metrics['preds']     )
        generations_file.open('w+'     ).write(__snake_case     )







    @rank_zero_only
    def       lowercase_	(				self			:  Optional[Any]						,      __snake_case			:  List[str]						,      __snake_case			:  Union[str, Any]     ):
      try:
        a						: Union[str, Any]					       =					pl_module.model.model.num_parameters()
      except AttributeError:
        a						: Dict					       =					pl_module.model.num_parameters()

      a						: List[str]					       =					count_trainable_parameters(__snake_case     )
      # mp stands for million parameters
      trainer.logger.log_metrics({'n_params': npars, 'mp': npars / 1e6, 'grad_mp': n_trainable_pars / 1e6}     )







    @rank_zero_only
    def       lowercase_	(				self			:  Optional[Any]						,      __snake_case			:  pl.Trainer						,      __snake_case			:  pl.LightningModule     ):
      save_json(pl_module.metrics						,      pl_module.metrics_save_path     )
      return self._write_logs(__snake_case						,      __snake_case						,      'test'     )







    @rank_zero_only
    def       lowercase_	(				self			:  Optional[Any]						,      __snake_case			:  pl.Trainer						,      __snake_case			:  Optional[int]     ):
      save_json(pl_module.metrics						,      pl_module.metrics_save_path     )
      # Uncommenting this will save val generations
      # return self._write_logs(trainer, pl_module, "valid")




'''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 							a__( lowerCamelCase__		,			lowerCamelCase__		,			unittest.TestCase     ):
    lowercase__			=   CycleDiffusionPipeline
    lowercase__			=   TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
        """negative_prompt""",
        """height""",
        """width""",
        """negative_prompt_embeds""",
    }
    lowercase__			=   PipelineTesterMixin.required_optional_params - {"""latents"""}
    lowercase__			=   TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""source_prompt"""}     )
    lowercase__			=   IMAGE_TO_IMAGE_IMAGE_PARAMS
    lowercase__			=   IMAGE_TO_IMAGE_IMAGE_PARAMS







    def       lowercase_	(				self			:  Any     ):
      torch.manual_seed(0     )
      a						: Union[str, Any]					       =					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						,      )
      a						: str					       =					DDIMScheduler(
          beta_start=0.00085						,      beta_end=0.012						,      beta_schedule='scaled_linear'						,      num_train_timesteps=10_00						,      clip_sample=__snake_case						,      set_alpha_to_one=__snake_case						,      )
      torch.manual_seed(0     )
      a						: List[str]					       =					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     )
      a						: int					       =					CLIPTextConfig(
          bos_token_id=0						,      eos_token_id=2						,      hidden_size=32						,      intermediate_size=37						,      layer_norm_eps=1e-0_5						,      num_attention_heads=4						,      num_hidden_layers=5						,      pad_token_id=1						,      vocab_size=10_00						,      )
      a						: List[str]					       =					CLIPTextModel(__snake_case     )
      a						: int					       =					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     )

      a						: Tuple					       =					{
          'unet': unet,
          'scheduler': scheduler,
          'vae': vae,
          'text_encoder': text_encoder,
          'tokenizer': tokenizer,
          'safety_checker': None,
          'feature_extractor': None,
      }
      return components







    def       lowercase_	(				self			:  Optional[int]						,      __snake_case			:  Dict						,      __snake_case			:  Any=0     ):
      a						: str					       =					floats_tensor((1, 3, 32, 32)						,      rng=random.Random(__snake_case     )     ).to(__snake_case     )
      a						: Optional[Any]					       =					image / 2 + 0.5
      if str(__snake_case     ).startswith('mps'     ):
        a						: List[str]					       =					torch.manual_seed(__snake_case     )
      else:
        a						: Union[str, Any]					       =					torch.Generator(device=__snake_case     ).manual_seed(__snake_case     )
      a						: List[Any]					       =					{
          '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       lowercase_	(				self			:  Optional[int]     ):
      a						: Optional[Any]					       =					'cpu'  # ensure determinism for the device-dependent torch.Generator

      a						: int					       =					self.get_dummy_components()
      a						: str					       =					CycleDiffusionPipeline(**__snake_case     )
      a						: List[str]					       =					pipe.to(__snake_case     )
      pipe.set_progress_bar_config(disable=__snake_case     )

      a						: Dict					       =					self.get_dummy_inputs(__snake_case     )
      a						: Union[str, Any]					       =					pipe(**__snake_case     )
      a						: List[Any]					       =					output.images

      a						: Optional[Any]					       =					images[0, -3:, -3:, -1]

      assert images.shape == (1, 32, 32, 3)
      a						: Tuple					       =					np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179]     )

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







    @unittest.skipIf(torch_device != 'cuda'						,      'This test requires a GPU'     )
    def       lowercase_	(				self			:  int     ):
      a						: List[Any]					       =					self.get_dummy_components()
      for name, module in components.items():
        if hasattr(__snake_case						,      'half'     ):
          a						: Any					       =					module.half()
      a						: Tuple					       =					CycleDiffusionPipeline(**__snake_case     )
      a						: Any					       =					pipe.to(__snake_case     )
      pipe.set_progress_bar_config(disable=__snake_case     )

      a						: str					       =					self.get_dummy_inputs(__snake_case     )
      a						: int					       =					pipe(**__snake_case     )
      a						: Optional[int]					       =					output.images

      a						: Tuple					       =					images[0, -3:, -3:, -1]

      assert images.shape == (1, 32, 32, 3)
      a						: int					       =					np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116]     )

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







    @skip_mps
    def       lowercase_	(				self			:  List[Any]     ):
      return super().test_save_load_local()







    @unittest.skip('non-deterministic pipeline'     )
    def       lowercase_	(				self			:  Dict     ):
      return super().test_inference_batch_single_identical()







    @skip_mps
    def       lowercase_	(				self			:  int     ):
      return super().test_dict_tuple_outputs_equivalent()







    @skip_mps
    def       lowercase_	(				self			:  Dict     ):
      return super().test_save_load_optional_components()







    @skip_mps
    def       lowercase_	(				self			:  List[Any]     ):
      return super().test_attention_slicing_forward_pass()

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







    def       lowercase_	(				self			:  Tuple     ):
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()







    def       lowercase_	(				self			:  Optional[int]     ):
      a						: Any					       =					load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/cycle-diffusion/black_colored_car.png'     )
      a						: Optional[int]					       =					load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'     )
      a						: List[str]					       =					init_image.resize((5_12, 5_12)     )

      a						: Dict					       =					'CompVis/stable-diffusion-v1-4'
      a						: List[str]					       =					DDIMScheduler.from_pretrained(__snake_case						,      subfolder='scheduler'     )
      a						: Any					       =					CycleDiffusionPipeline.from_pretrained(
          __snake_case						,      scheduler=__snake_case						,      safety_checker=__snake_case						,      torch_dtype=torch.floataa						,      revision='fp16'     )

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

      a						: Union[str, Any]					       =					'A black colored car'
      a						: Optional[Any]					       =					'A blue colored car'

      a						: int					       =					torch.manual_seed(0     )
      a						: Optional[Any]					       =					pipe(
          prompt=__snake_case						,      source_prompt=__snake_case						,      image=__snake_case						,      num_inference_steps=1_00						,      eta=0.1						,      strength=0.85						,      guidance_scale=3						,      source_guidance_scale=1						,      generator=__snake_case						,      output_type='np'						,      )
      a						: Dict					       =					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       lowercase_	(				self			:  int     ):
      a						: Any					       =					load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/cycle-diffusion/black_colored_car.png'     )
      a						: List[str]					       =					load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'     )
      a						: str					       =					init_image.resize((5_12, 5_12)     )

      a						: Optional[int]					       =					'CompVis/stable-diffusion-v1-4'
      a						: Union[str, Any]					       =					DDIMScheduler.from_pretrained(__snake_case						,      subfolder='scheduler'     )
      a						: str					       =					CycleDiffusionPipeline.from_pretrained(__snake_case						,      scheduler=__snake_case						,      safety_checker=__snake_case     )

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

      a						: Tuple					       =					'A black colored car'
      a						: Tuple					       =					'A blue colored car'

      a						: List[str]					       =					torch.manual_seed(0     )
      a						: str					       =					pipe(
          prompt=__snake_case						,      source_prompt=__snake_case						,      image=__snake_case						,      num_inference_steps=1_00						,      eta=0.1						,      strength=0.85						,      guidance_scale=3						,      source_guidance_scale=1						,      generator=__snake_case						,      output_type='np'						,      )
      a						: Tuple					       =					output.images

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