




import gc
import random
import tempfile
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.semantic_stable_diffusion import SemanticStableDiffusionPipeline as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()






class 	A	(						unittest.TestCase						):
     """simple docstring"""

     def        __snake_case      (			self     :      int	) ->				Any:
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()




     @property
     def        __snake_case      (			self     :      List[Any]	) ->				str:
           lowerCamelCase__					     = 1
           lowerCamelCase__					     = 3
           lowerCamelCase__					     = (3_2, 3_2)

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




     @property
     def        __snake_case      (			self     :      Union[str, Any]	) ->				List[str]:
           torch.manual_seed(0	)
           lowerCamelCase__					     = UNetaDConditionModel(
               block_out_channels=(3_2, 6_4)		,     layers_per_block=2		,     sample_size=3_2		,     in_channels=4		,     out_channels=4		,     down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')		,     up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')		,     cross_attention_dim=3_2		,     )
           return model




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




     @property
     def        __snake_case      (			self     :      Tuple	) ->				Any:
           torch.manual_seed(0	)
           lowerCamelCase__					     = 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		,     )
           return CLIPTextModel(_UpperCamelCase	)




     @property
     def        __snake_case      (			self     :      int	) ->				List[Any]:
           def extract(*A__     :      Optional[int]		,     **A__     :      List[str]	):






                 class 	A	:
                      """simple docstring"""

                      def __init__(			self     :      int	) ->				Dict:
                            lowerCamelCase__					     = torch.ones([0]	)




                      def        __snake_case      (			self     :      List[Any]		,     A__     :      Union[str, Any]	) ->				str:
                            self.pixel_values.to(_UpperCamelCase	)
                            return self
                 return Out()

           return extract




     def        __snake_case      (			self     :      str	) ->				Tuple:
           lowerCamelCase__					     = 'cpu'  # ensure determinism for the device-dependent torch.Generator
           lowerCamelCase__					     = self.dummy_cond_unet
           lowerCamelCase__					     = DDIMScheduler(
               beta_start=0.00085		,     beta_end=0.012		,     beta_schedule='scaled_linear'		,     clip_sample=_UpperCamelCase		,     set_alpha_to_one=_UpperCamelCase		,     )

           lowerCamelCase__					     = self.dummy_vae
           lowerCamelCase__					     = self.dummy_text_encoder
           lowerCamelCase__					     = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)

           # make sure here that pndm scheduler skips prk
           lowerCamelCase__					     = StableDiffusionPipeline(
               unet=_UpperCamelCase		,     scheduler=_UpperCamelCase		,     vae=_UpperCamelCase		,     text_encoder=_UpperCamelCase		,     tokenizer=_UpperCamelCase		,     safety_checker=_UpperCamelCase		,     feature_extractor=self.dummy_extractor		,     )
           lowerCamelCase__					     = sd_pipe.to(_UpperCamelCase	)
           sd_pipe.set_progress_bar_config(disable=_UpperCamelCase	)

           lowerCamelCase__					     = 'A painting of a squirrel eating a burger'

           lowerCamelCase__					     = torch.Generator(device=_UpperCamelCase	).manual_seed(0	)
           lowerCamelCase__					     = sd_pipe([prompt]		,     generator=_UpperCamelCase		,     guidance_scale=6.0		,     num_inference_steps=2		,     output_type='np'	)
           lowerCamelCase__					     = output.images

           lowerCamelCase__					     = torch.Generator(device=_UpperCamelCase	).manual_seed(0	)
           lowerCamelCase__					     = sd_pipe(
               [prompt]		,     generator=_UpperCamelCase		,     guidance_scale=6.0		,     num_inference_steps=2		,     output_type='np'		,     return_dict=_UpperCamelCase		,     )[0]

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

           assert image.shape == (1, 6_4, 6_4, 3)
           lowerCamelCase__					     = np.array([0.5753, 0.6114, 0.5001, 0.5034, 0.5470, 0.4729, 0.4971, 0.4867, 0.4867]	)

           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        __snake_case      (			self     :      str	) ->				Union[str, Any]:
           lowerCamelCase__					     = 'cpu'  # ensure determinism for the device-dependent torch.Generator
           lowerCamelCase__					     = self.dummy_cond_unet
           lowerCamelCase__					     = PNDMScheduler(skip_prk_steps=_UpperCamelCase	)
           lowerCamelCase__					     = self.dummy_vae
           lowerCamelCase__					     = self.dummy_text_encoder
           lowerCamelCase__					     = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)

           # make sure here that pndm scheduler skips prk
           lowerCamelCase__					     = StableDiffusionPipeline(
               unet=_UpperCamelCase		,     scheduler=_UpperCamelCase		,     vae=_UpperCamelCase		,     text_encoder=_UpperCamelCase		,     tokenizer=_UpperCamelCase		,     safety_checker=_UpperCamelCase		,     feature_extractor=self.dummy_extractor		,     )
           lowerCamelCase__					     = sd_pipe.to(_UpperCamelCase	)
           sd_pipe.set_progress_bar_config(disable=_UpperCamelCase	)

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

           lowerCamelCase__					     = output.images

           lowerCamelCase__					     = torch.Generator(device=_UpperCamelCase	).manual_seed(0	)
           lowerCamelCase__					     = sd_pipe(
               [prompt]		,     generator=_UpperCamelCase		,     guidance_scale=6.0		,     num_inference_steps=2		,     output_type='np'		,     return_dict=_UpperCamelCase		,     )[0]

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

           assert image.shape == (1, 6_4, 6_4, 3)
           lowerCamelCase__					     = np.array([0.5122, 0.5712, 0.4825, 0.5053, 0.5646, 0.4769, 0.5179, 0.4894, 0.4994]	)

           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        __snake_case      (			self     :      Optional[int]	) ->				str:
           lowerCamelCase__					     = StableDiffusionPipeline.from_pretrained(
               'hf-internal-testing/tiny-stable-diffusion-lms-pipe'		,     safety_checker=_UpperCamelCase	)
           assert isinstance(_UpperCamelCase		,     _UpperCamelCase	)
           assert isinstance(pipe.scheduler		,     _UpperCamelCase	)
           assert pipe.safety_checker is None

           lowerCamelCase__					     = pipe('example prompt'		,     num_inference_steps=2	).images[0]
           assert image is not None

           # check that there's no error when saving a pipeline with one of the models being None
           with tempfile.TemporaryDirectory() as tmpdirname:
                 pipe.save_pretrained(_UpperCamelCase	)
                 lowerCamelCase__					     = StableDiffusionPipeline.from_pretrained(_UpperCamelCase	)

           # sanity check that the pipeline still works
           assert pipe.safety_checker is None
           lowerCamelCase__					     = pipe('example prompt'		,     num_inference_steps=2	).images[0]
           assert image is not None




     @unittest.skipIf(torch_device != 'cuda'		,     'This test requires a GPU'	)
     def        __snake_case      (			self     :      List[Any]	) ->				List[Any]:
           lowerCamelCase__					     = self.dummy_cond_unet
           lowerCamelCase__					     = PNDMScheduler(skip_prk_steps=_UpperCamelCase	)
           lowerCamelCase__					     = self.dummy_vae
           lowerCamelCase__					     = self.dummy_text_encoder
           lowerCamelCase__					     = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)

           # put models in fp16
           lowerCamelCase__					     = unet.half()
           lowerCamelCase__					     = vae.half()
           lowerCamelCase__					     = bert.half()

           # make sure here that pndm scheduler skips prk
           lowerCamelCase__					     = StableDiffusionPipeline(
               unet=_UpperCamelCase		,     scheduler=_UpperCamelCase		,     vae=_UpperCamelCase		,     text_encoder=_UpperCamelCase		,     tokenizer=_UpperCamelCase		,     safety_checker=_UpperCamelCase		,     feature_extractor=self.dummy_extractor		,     )
           lowerCamelCase__					     = sd_pipe.to(_UpperCamelCase	)
           sd_pipe.set_progress_bar_config(disable=_UpperCamelCase	)

           lowerCamelCase__					     = 'A painting of a squirrel eating a burger'
           lowerCamelCase__					     = sd_pipe([prompt]		,     num_inference_steps=2		,     output_type='np'	).images

           assert image.shape == (1, 6_4, 6_4, 3)






@nightly
@require_torch_gpu
class 	A	(						unittest.TestCase						):
     """simple docstring"""

     def        __snake_case      (			self     :      List[Any]	) ->				Any:
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()




     def        __snake_case      (			self     :      Union[str, Any]	) ->				int:
           lowerCamelCase__					     = 'cuda'
           lowerCamelCase__					     = StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'	)
           lowerCamelCase__					     = pipe.to(_UpperCamelCase	)
           pipe.set_progress_bar_config(disable=_UpperCamelCase	)

           lowerCamelCase__					     = 'a photo of a cat'
           lowerCamelCase__					     = {
               'editing_prompt': ['sunglasses'],
               'reverse_editing_direction': [False],
               'edit_warmup_steps': 1_0,
               'edit_guidance_scale': 6,
               'edit_threshold': 0.95,
               'edit_momentum_scale': 0.5,
               'edit_mom_beta': 0.6,
           }

           lowerCamelCase__					     = 3
           lowerCamelCase__					     = 7

           # no sega enabled
           lowerCamelCase__					     = torch.Generator(_UpperCamelCase	)
           generator.manual_seed(_UpperCamelCase	)
           lowerCamelCase__					     = pipe(
               [prompt]		,     generator=_UpperCamelCase		,     guidance_scale=_UpperCamelCase		,     num_inference_steps=5_0		,     output_type='np'		,     width=5_1_2		,     height=5_1_2		,     )

           lowerCamelCase__					     = output.images
           lowerCamelCase__					     = image[0, -3:, -3:, -1]
           lowerCamelCase__					     = [
               0.34673113,
               0.38492733,
               0.37597352,
               0.34086335,
               0.35650748,
               0.35579205,
               0.3384763,
               0.34340236,
               0.3573271,
           ]

           assert image.shape == (1, 5_1_2, 5_1_2, 3)

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

           # with sega enabled
           # generator = torch.manual_seed(seed)
           generator.manual_seed(_UpperCamelCase	)
           lowerCamelCase__					     = pipe(
               [prompt]		,     generator=_UpperCamelCase		,     guidance_scale=_UpperCamelCase		,     num_inference_steps=5_0		,     output_type='np'		,     width=5_1_2		,     height=5_1_2		,     **_UpperCamelCase		,     )

           lowerCamelCase__					     = output.images
           lowerCamelCase__					     = image[0, -3:, -3:, -1]
           lowerCamelCase__					     = [
               0.41887826,
               0.37728766,
               0.30138272,
               0.41416335,
               0.41664985,
               0.36283392,
               0.36191246,
               0.43364465,
               0.43001732,
           ]

           assert image.shape == (1, 5_1_2, 5_1_2, 3)

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




     def        __snake_case      (			self     :      str	) ->				Dict:
           lowerCamelCase__					     = 'cuda'
           lowerCamelCase__					     = StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'	)
           lowerCamelCase__					     = pipe.to(_UpperCamelCase	)
           pipe.set_progress_bar_config(disable=_UpperCamelCase	)

           lowerCamelCase__					     = 'an image of a crowded boulevard, realistic, 4k'
           lowerCamelCase__					     = {
               'editing_prompt': 'crowd, crowded, people',
               'reverse_editing_direction': True,
               'edit_warmup_steps': 1_0,
               'edit_guidance_scale': 8.3,
               'edit_threshold': 0.9,
               'edit_momentum_scale': 0.5,
               'edit_mom_beta': 0.6,
           }

           lowerCamelCase__					     = 9
           lowerCamelCase__					     = 7

           # no sega enabled
           lowerCamelCase__					     = torch.Generator(_UpperCamelCase	)
           generator.manual_seed(_UpperCamelCase	)
           lowerCamelCase__					     = pipe(
               [prompt]		,     generator=_UpperCamelCase		,     guidance_scale=_UpperCamelCase		,     num_inference_steps=5_0		,     output_type='np'		,     width=5_1_2		,     height=5_1_2		,     )

           lowerCamelCase__					     = output.images
           lowerCamelCase__					     = image[0, -3:, -3:, -1]
           lowerCamelCase__					     = [
               0.43497998,
               0.91814065,
               0.7540739,
               0.55580205,
               0.8467265,
               0.5389691,
               0.62574506,
               0.58897763,
               0.50926757,
           ]

           assert image.shape == (1, 5_1_2, 5_1_2, 3)

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

           # with sega enabled
           # generator = torch.manual_seed(seed)
           generator.manual_seed(_UpperCamelCase	)
           lowerCamelCase__					     = pipe(
               [prompt]		,     generator=_UpperCamelCase		,     guidance_scale=_UpperCamelCase		,     num_inference_steps=5_0		,     output_type='np'		,     width=5_1_2		,     height=5_1_2		,     **_UpperCamelCase		,     )

           lowerCamelCase__					     = output.images
           lowerCamelCase__					     = image[0, -3:, -3:, -1]
           lowerCamelCase__					     = [
               0.3089719,
               0.30500144,
               0.29016042,
               0.30630964,
               0.325687,
               0.29419225,
               0.2908091,
               0.28723598,
               0.27696294,
           ]

           assert image.shape == (1, 5_1_2, 5_1_2, 3)

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




     def        __snake_case      (			self     :      Tuple	) ->				Any:
           lowerCamelCase__					     = 'cuda'
           lowerCamelCase__					     = StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'	)
           lowerCamelCase__					     = pipe.to(_UpperCamelCase	)
           pipe.set_progress_bar_config(disable=_UpperCamelCase	)

           lowerCamelCase__					     = 'a castle next to a river'
           lowerCamelCase__					     = {
               'editing_prompt': ['boat on a river, boat', 'monet, impression, sunrise'],
               'reverse_editing_direction': False,
               'edit_warmup_steps': [1_5, 1_8],
               'edit_guidance_scale': 6,
               'edit_threshold': [0.9, 0.8],
               'edit_momentum_scale': 0.5,
               'edit_mom_beta': 0.6,
           }

           lowerCamelCase__					     = 4_8
           lowerCamelCase__					     = 7

           # no sega enabled
           lowerCamelCase__					     = torch.Generator(_UpperCamelCase	)
           generator.manual_seed(_UpperCamelCase	)
           lowerCamelCase__					     = pipe(
               [prompt]		,     generator=_UpperCamelCase		,     guidance_scale=_UpperCamelCase		,     num_inference_steps=5_0		,     output_type='np'		,     width=5_1_2		,     height=5_1_2		,     )

           lowerCamelCase__					     = output.images
           lowerCamelCase__					     = image[0, -3:, -3:, -1]
           lowerCamelCase__					     = [
               0.75163555,
               0.76037145,
               0.61785,
               0.9189673,
               0.8627701,
               0.85189694,
               0.8512813,
               0.87012076,
               0.8312857,
           ]

           assert image.shape == (1, 5_1_2, 5_1_2, 3)

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

           # with sega enabled
           # generator = torch.manual_seed(seed)
           generator.manual_seed(_UpperCamelCase	)
           lowerCamelCase__					     = pipe(
               [prompt]		,     generator=_UpperCamelCase		,     guidance_scale=_UpperCamelCase		,     num_inference_steps=5_0		,     output_type='np'		,     width=5_1_2		,     height=5_1_2		,     **_UpperCamelCase		,     )

           lowerCamelCase__					     = output.images
           lowerCamelCase__					     = image[0, -3:, -3:, -1]
           lowerCamelCase__					     = [
               0.73553365,
               0.7537271,
               0.74341905,
               0.66480356,
               0.6472925,
               0.63039416,
               0.64812905,
               0.6749717,
               0.6517102,
           ]

           assert image.shape == (1, 5_1_2, 5_1_2, 3)

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




     def        __snake_case      (			self     :      Union[str, Any]	) ->				Optional[int]:
           lowerCamelCase__					     = 'cuda'
           lowerCamelCase__					     = StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'		,     torch_dtype=torch.floataa	)
           lowerCamelCase__					     = pipe.to(_UpperCamelCase	)
           pipe.set_progress_bar_config(disable=_UpperCamelCase	)

           lowerCamelCase__					     = 'a photo of a cat'
           lowerCamelCase__					     = {
               'editing_prompt': ['sunglasses'],
               'reverse_editing_direction': [False],
               'edit_warmup_steps': 1_0,
               'edit_guidance_scale': 6,
               'edit_threshold': 0.95,
               'edit_momentum_scale': 0.5,
               'edit_mom_beta': 0.6,
           }

           lowerCamelCase__					     = 3
           lowerCamelCase__					     = 7

           # no sega enabled
           lowerCamelCase__					     = torch.Generator(_UpperCamelCase	)
           generator.manual_seed(_UpperCamelCase	)
           lowerCamelCase__					     = pipe(
               [prompt]		,     generator=_UpperCamelCase		,     guidance_scale=_UpperCamelCase		,     num_inference_steps=5_0		,     output_type='np'		,     width=5_1_2		,     height=5_1_2		,     )

           lowerCamelCase__					     = output.images
           lowerCamelCase__					     = image[0, -3:, -3:, -1]
           lowerCamelCase__					     = [
               0.34887695,
               0.3876953,
               0.375,
               0.34423828,
               0.3581543,
               0.35717773,
               0.3383789,
               0.34570312,
               0.359375,
           ]

           assert image.shape == (1, 5_1_2, 5_1_2, 3)

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

           # with sega enabled
           # generator = torch.manual_seed(seed)
           generator.manual_seed(_UpperCamelCase	)
           lowerCamelCase__					     = pipe(
               [prompt]		,     generator=_UpperCamelCase		,     guidance_scale=_UpperCamelCase		,     num_inference_steps=5_0		,     output_type='np'		,     width=5_1_2		,     height=5_1_2		,     **_UpperCamelCase		,     )

           lowerCamelCase__					     = output.images
           lowerCamelCase__					     = image[0, -3:, -3:, -1]
           lowerCamelCase__					     = [
               0.42285156,
               0.36914062,
               0.29077148,
               0.42041016,
               0.41918945,
               0.35498047,
               0.3618164,
               0.4423828,
               0.43115234,
           ]

           assert image.shape == (1, 5_1_2, 5_1_2, 3)

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







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 import save_json







def 		_UpperCamelCase      (			UpperCamelCase_				:    Tuple						)     ->      str:

						"""simple docstring"""



						lowerCAmelCase__			  =  filter(lambda UpperCamelCase_						: p.requires_grad			,				model.parameters()						)
						lowerCAmelCase__			  =  sum([np.prod(p.size()						) for p in model_parameters]						)
						return params


__snake_case			:			Dict			   =   logging.getLogger(__name__)


class 			__SCREAMING_SNAKE_CASE    (      pl.Callback):






						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase      ):






												"""simple docstring"""
												lowerCAmelCase__			  =  {F"lr_group_{i}": param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups      )}
												pl_module.logger.log_metrics(_UpperCamelCase      )






						@rank_zero_only
						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase=True      ):






												"""simple docstring"""
												logger.info(F"***** {type_path} results at step {trainer.global_step:05d} *****"      )
												lowerCAmelCase__			  =  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
												lowerCAmelCase__			  =  Path(pl_module.hparams.output_dir      )
												if type_path == "test":
																		lowerCAmelCase__			  =  od / 'test_results.txt'
																		lowerCAmelCase__			  =  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.
																		lowerCAmelCase__			  =  od / F"{type_path}_results/{trainer.global_step:05d}.txt"
																		lowerCAmelCase__			  =  od / F"{type_path}_generations/{trainer.global_step:05d}.txt"
																		results_file.parent.mkdir(exist_ok=_UpperCamelCase      )
																		generations_file.parent.mkdir(exist_ok=_UpperCamelCase      )
												with open(_UpperCamelCase   ,  'a+'      ) as writer:
																		for key in sorted(_UpperCamelCase      ):
																								if key in ["log", "progress_bar", "preds"]:
																														continue
																								lowerCAmelCase__			  =  metrics[key]
																								if isinstance(_UpperCamelCase   ,  torch.Tensor      ):
																														lowerCAmelCase__			  =  val.item()
																								lowerCAmelCase__			  =  F"{key}: {val:.6f}\n"
																								writer.write(_UpperCamelCase      )

												if not save_generations:
																		return

												if "preds" in metrics:
																		lowerCAmelCase__			  =  '\n'.join(metrics['preds']      )
																		generations_file.open('w+'      ).write(_UpperCamelCase      )






						@rank_zero_only
						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase      ):






												"""simple docstring"""
												try:
																		lowerCAmelCase__			  =  pl_module.model.model.num_parameters()
												except AttributeError:
																		lowerCAmelCase__			  =  pl_module.model.num_parameters()

												lowerCAmelCase__			  =  count_trainable_parameters(_UpperCamelCase      )
												# 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 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase      ):






												"""simple docstring"""
												save_json(pl_module.metrics   ,  pl_module.metrics_save_path      )
												return self._write_logs(_UpperCamelCase   ,  _UpperCamelCase   ,  'test'      )







						@rank_zero_only
						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase      ):






												"""simple docstring"""
												save_json(pl_module.metrics   ,  pl_module.metrics_save_path      )
												# Uncommenting this will save val generations
												# return self._write_logs(trainer, pl_module, "valid")







def 		_UpperCamelCase      (			UpperCamelCase_				:    Union[str, Any]			,				UpperCamelCase_				:    Union[str, Any]			,				UpperCamelCase_				:    List[Any]=1			,				UpperCamelCase_				:    List[str]=False						)     ->      Optional[Any]:

						"""simple docstring"""



						if metric == "rouge2":
												lowerCAmelCase__			  =  '{val_avg_rouge2:.4f}-{step_count}'
						elif metric == "bleu":
												lowerCAmelCase__			  =  '{val_avg_bleu:.4f}-{step_count}'
						elif metric == "loss":
												lowerCAmelCase__			  =  '{val_avg_loss:.4f}-{step_count}'
						else:
												raise NotImplementedError(
												    F"seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to"
												    ' this function.'						)

						lowerCAmelCase__			  =  ModelCheckpoint(
						    dirpath=UpperCamelCase_			,				filename=UpperCamelCase_			,				monitor=F"val_{metric}"			,				mode='min' if 'loss' in metric else 'max'			,				save_top_k=UpperCamelCase_			,				)
						return checkpoint_callback







def 		_UpperCamelCase      (			UpperCamelCase_				:    Union[str, Any]			,				UpperCamelCase_				:    int						)     ->      Dict:

						"""simple docstring"""



						return EarlyStopping(
						    monitor=F"val_{metric}"			,				mode='min' if 'loss' in metric else 'max'			,				patience=UpperCamelCase_			,				verbose=UpperCamelCase_			,				)





