
"""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 lowercase_  (   _snake_case						):
							SCREAMING_SNAKE_CASE__						:       Any           =   filter(lambda _snake_case						: p.requires_grad   ,model.parameters()						)
							SCREAMING_SNAKE_CASE__						:       Any           =   sum([np.prod(p.size()						) for p in model_parameters]						)
							return params


UpperCAmelCase__      :    int    							= logging.getLogger(__name__)

def lowercase_  (   _snake_case   ,_snake_case						):
							if metric == "rouge2":
														SCREAMING_SNAKE_CASE__						:       Any           =   """{val_avg_rouge2:.4f}-{step_count}"""
							elif metric == "bleu":
														SCREAMING_SNAKE_CASE__						:       Tuple           =   """{val_avg_bleu:.4f}-{step_count}"""
							elif metric == "em":
														SCREAMING_SNAKE_CASE__						:       Tuple           =   """{val_avg_em:.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."""						)

							SCREAMING_SNAKE_CASE__						:       List[Any]           =   ModelCheckpoint(
							    dirpath=_snake_case   ,filename=_snake_case   ,monitor=f'''val_{metric}'''   ,mode="""max"""   ,save_top_k=3   ,every_n_epochs=1   ,)
							return checkpoint_callback

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



class 	lowerCAmelCase_						(pl.Callback  ):


				"""simple docstring"""



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		Any:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   {F'''lr_group_{i}''': param["""lr"""] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups    )}
											pl_module.logger.log_metrics(SCREAMING_SNAKE_CASE__    )



				@rank_zero_only
				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__=True    )					->		None:



											"""simple docstring"""




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

											if not save_generations:
																		return

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



				@rank_zero_only
				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		Optional[Any]:



											"""simple docstring"""




											try:
																		SCREAMING_SNAKE_CASE__						:       Tuple           =   pl_module.model.model.num_parameters()
											except AttributeError:
																		SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   pl_module.model.num_parameters()

											SCREAMING_SNAKE_CASE__						:       int           =   count_trainable_parameters(SCREAMING_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 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		Tuple:



											"""simple docstring"""




											save_json(pl_module.metrics   ,       pl_module.metrics_save_path    )
											return self._write_logs(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       """test"""    )



				@rank_zero_only
				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		str:



											"""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")






"""simple docstring"""




import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin



@skip_mps
class 	lowerCAmelCase_						(a__ ,					a__ ,					unittest.TestCase  ):


				"""simple docstring"""



				__UpperCamelCase      :						Optional[Any]					       =   IFPipeline
				__UpperCamelCase      :						Dict					       =   TEXT_TO_IMAGE_PARAMS - {'''width''', '''height''', '''latents'''}
				__UpperCamelCase      :						Any					       =   TEXT_TO_IMAGE_BATCH_PARAMS
				__UpperCamelCase      :						Union[str, Any]					       =   PipelineTesterMixin.required_optional_params - {'''latents'''}



				def 	__magic_name__    (self    )					->		Union[str, Any]:



											"""simple docstring"""




											return self._get_dummy_components()



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__=0    )					->		List[Any]:



											"""simple docstring"""




											if str(SCREAMING_SNAKE_CASE__    ).startswith("""mps"""    ):
																		SCREAMING_SNAKE_CASE__						:       Dict           =   torch.manual_seed(SCREAMING_SNAKE_CASE__    )
											else:
																		SCREAMING_SNAKE_CASE__						:       Any           =   torch.Generator(device=SCREAMING_SNAKE_CASE__    ).manual_seed(SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       int           =   {
											    """prompt""": """A painting of a squirrel eating a burger""",
											    """generator""": generator,
											    """num_inference_steps""": 2,
											    """output_type""": """numpy""",
											}

											return inputs



				def 	__magic_name__    (self    )					->		Tuple:



											"""simple docstring"""




											self._test_save_load_optional_components()



				@unittest.skipIf(torch_device != """cuda"""   ,       reason="""float16 requires CUDA"""    )
				def 	__magic_name__    (self    )					->		List[str]:



											"""simple docstring"""




											super().test_save_load_floataa(expected_max_diff=1E-1    )



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



											"""simple docstring"""




											self._test_attention_slicing_forward_pass(expected_max_diff=1E-2    )



				def 	__magic_name__    (self    )					->		Tuple:



											"""simple docstring"""




											self._test_save_load_local()



				def 	__magic_name__    (self    )					->		Dict:



											"""simple docstring"""




											self._test_inference_batch_single_identical(
											    expected_max_diff=1E-2   ,       )



				@unittest.skipIf(
				    torch_device != """cuda""" or not is_xformers_available()   ,       reason="""XFormers attention is only available with CUDA and `xformers` installed"""   ,       )
				def 	__magic_name__    (self    )					->		Optional[int]:



											"""simple docstring"""




											self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3    )






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


				"""simple docstring"""



				def 	__magic_name__    (self    )					->		Dict:



											"""simple docstring"""




											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   IFPipeline.from_pretrained("""DeepFloyd/IF-I-XL-v1.0"""   ,       variant="""fp16"""   ,       torch_dtype=torch.floataa    )

											SCREAMING_SNAKE_CASE__						:       Dict           =   IFSuperResolutionPipeline.from_pretrained(
											    """DeepFloyd/IF-II-L-v1.0"""   ,       variant="""fp16"""   ,       torch_dtype=torch.floataa   ,       text_encoder=SCREAMING_SNAKE_CASE__   ,       tokenizer=SCREAMING_SNAKE_CASE__    )

											# pre compute text embeddings and remove T5 to save memory

											pipe_a.text_encoder.to("""cuda"""    )

											SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   pipe_a.encode_prompt("""anime turtle"""   ,       device="""cuda"""    )

											del pipe_a.tokenizer
											del pipe_a.text_encoder
											gc.collect()

											SCREAMING_SNAKE_CASE__						:       List[str]           =   None
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   None

											pipe_a.enable_model_cpu_offload()
											pipe_a.enable_model_cpu_offload()

											pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()    )
											pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()    )

											self._test_if(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )

											pipe_a.remove_all_hooks()
											pipe_a.remove_all_hooks()

											# img2img

											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   IFImgaImgPipeline(**pipe_a.components    )
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   IFImgaImgSuperResolutionPipeline(**pipe_a.components    )

											pipe_a.enable_model_cpu_offload()
											pipe_a.enable_model_cpu_offload()

											pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()    )
											pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()    )

											self._test_if_imgaimg(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )

											pipe_a.remove_all_hooks()
											pipe_a.remove_all_hooks()

											# inpainting

											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   IFInpaintingPipeline(**pipe_a.components    )
											SCREAMING_SNAKE_CASE__						:       int           =   IFInpaintingSuperResolutionPipeline(**pipe_a.components    )

											pipe_a.enable_model_cpu_offload()
											pipe_a.enable_model_cpu_offload()

											pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()    )
											pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()    )

											self._test_if_inpainting(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		Optional[int]:



											"""simple docstring"""




											_start_torch_memory_measurement()

											SCREAMING_SNAKE_CASE__						:       Tuple           =   torch.Generator(device="""cpu"""    ).manual_seed(0    )
											SCREAMING_SNAKE_CASE__						:       Dict           =   pipe_a(
											    prompt_embeds=SCREAMING_SNAKE_CASE__   ,       negative_prompt_embeds=SCREAMING_SNAKE_CASE__   ,       num_inference_steps=2   ,       generator=SCREAMING_SNAKE_CASE__   ,       output_type="""np"""   ,       )

											SCREAMING_SNAKE_CASE__						:       int           =   output.images[0]

											assert image.shape == (64, 64, 3)

											SCREAMING_SNAKE_CASE__						:       Any           =   torch.cuda.max_memory_allocated()
											assert mem_bytes < 13 * 10**9

											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   load_numpy(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy"""    )
											assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )

											# pipeline 2

											_start_torch_memory_measurement()

											SCREAMING_SNAKE_CASE__						:       int           =   torch.Generator(device="""cpu"""    ).manual_seed(0    )

											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   floats_tensor((1, 3, 64, 64)   ,       rng=random.Random(0    )    ).to(SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   pipe_a(
											    prompt_embeds=SCREAMING_SNAKE_CASE__   ,       negative_prompt_embeds=SCREAMING_SNAKE_CASE__   ,       image=SCREAMING_SNAKE_CASE__   ,       generator=SCREAMING_SNAKE_CASE__   ,       num_inference_steps=2   ,       output_type="""np"""   ,       )

											SCREAMING_SNAKE_CASE__						:       List[str]           =   output.images[0]

											assert image.shape == (2_56, 2_56, 3)

											SCREAMING_SNAKE_CASE__						:       List[Any]           =   torch.cuda.max_memory_allocated()
											assert mem_bytes < 4 * 10**9

											SCREAMING_SNAKE_CASE__						:       int           =   load_numpy(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy"""    )
											assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		str:



											"""simple docstring"""




											_start_torch_memory_measurement()

											SCREAMING_SNAKE_CASE__						:       int           =   floats_tensor((1, 3, 64, 64)   ,       rng=random.Random(0    )    ).to(SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Tuple           =   torch.Generator(device="""cpu"""    ).manual_seed(0    )

											SCREAMING_SNAKE_CASE__						:       Any           =   pipe_a(
											    prompt_embeds=SCREAMING_SNAKE_CASE__   ,       negative_prompt_embeds=SCREAMING_SNAKE_CASE__   ,       image=SCREAMING_SNAKE_CASE__   ,       num_inference_steps=2   ,       generator=SCREAMING_SNAKE_CASE__   ,       output_type="""np"""   ,       )

											SCREAMING_SNAKE_CASE__						:       Dict           =   output.images[0]

											assert image.shape == (64, 64, 3)

											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   torch.cuda.max_memory_allocated()
											assert mem_bytes < 10 * 10**9

											SCREAMING_SNAKE_CASE__						:       str           =   load_numpy(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy"""    )
											assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )

											# pipeline 2

											_start_torch_memory_measurement()

											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   torch.Generator(device="""cpu"""    ).manual_seed(0    )

											SCREAMING_SNAKE_CASE__						:       List[Any]           =   floats_tensor((1, 3, 2_56, 2_56)   ,       rng=random.Random(0    )    ).to(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   floats_tensor((1, 3, 64, 64)   ,       rng=random.Random(0    )    ).to(SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   pipe_a(
											    prompt_embeds=SCREAMING_SNAKE_CASE__   ,       negative_prompt_embeds=SCREAMING_SNAKE_CASE__   ,       image=SCREAMING_SNAKE_CASE__   ,       original_image=SCREAMING_SNAKE_CASE__   ,       generator=SCREAMING_SNAKE_CASE__   ,       num_inference_steps=2   ,       output_type="""np"""   ,       )

											SCREAMING_SNAKE_CASE__						:       List[Any]           =   output.images[0]

											assert image.shape == (2_56, 2_56, 3)

											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   torch.cuda.max_memory_allocated()
											assert mem_bytes < 4 * 10**9

											SCREAMING_SNAKE_CASE__						:       str           =   load_numpy(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy"""    )
											assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		Union[str, Any]:



											"""simple docstring"""




											_start_torch_memory_measurement()

											SCREAMING_SNAKE_CASE__						:       Dict           =   floats_tensor((1, 3, 64, 64)   ,       rng=random.Random(0    )    ).to(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Dict           =   floats_tensor((1, 3, 64, 64)   ,       rng=random.Random(1    )    ).to(SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Dict           =   torch.Generator(device="""cpu"""    ).manual_seed(0    )
											SCREAMING_SNAKE_CASE__						:       List[str]           =   pipe_a(
											    prompt_embeds=SCREAMING_SNAKE_CASE__   ,       negative_prompt_embeds=SCREAMING_SNAKE_CASE__   ,       image=SCREAMING_SNAKE_CASE__   ,       mask_image=SCREAMING_SNAKE_CASE__   ,       num_inference_steps=2   ,       generator=SCREAMING_SNAKE_CASE__   ,       output_type="""np"""   ,       )

											SCREAMING_SNAKE_CASE__						:       List[Any]           =   output.images[0]

											assert image.shape == (64, 64, 3)

											SCREAMING_SNAKE_CASE__						:       Tuple           =   torch.cuda.max_memory_allocated()
											assert mem_bytes < 10 * 10**9

											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   load_numpy(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy"""    )
											assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )

											# pipeline 2

											_start_torch_memory_measurement()

											SCREAMING_SNAKE_CASE__						:       int           =   torch.Generator(device="""cpu"""    ).manual_seed(0    )

											SCREAMING_SNAKE_CASE__						:       Dict           =   floats_tensor((1, 3, 64, 64)   ,       rng=random.Random(0    )    ).to(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       int           =   floats_tensor((1, 3, 2_56, 2_56)   ,       rng=random.Random(0    )    ).to(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       int           =   floats_tensor((1, 3, 2_56, 2_56)   ,       rng=random.Random(1    )    ).to(SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       int           =   pipe_a(
											    prompt_embeds=SCREAMING_SNAKE_CASE__   ,       negative_prompt_embeds=SCREAMING_SNAKE_CASE__   ,       image=SCREAMING_SNAKE_CASE__   ,       mask_image=SCREAMING_SNAKE_CASE__   ,       original_image=SCREAMING_SNAKE_CASE__   ,       generator=SCREAMING_SNAKE_CASE__   ,       num_inference_steps=2   ,       output_type="""np"""   ,       )

											SCREAMING_SNAKE_CASE__						:       Dict           =   output.images[0]

											assert image.shape == (2_56, 2_56, 3)

											SCREAMING_SNAKE_CASE__						:       List[str]           =   torch.cuda.max_memory_allocated()
											assert mem_bytes < 4 * 10**9

											SCREAMING_SNAKE_CASE__						:       Any           =   load_numpy(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy"""    )
											assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )






def lowercase_  (   ):
							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()
							torch.cuda.reset_peak_memory_stats()




