'''simple docstring'''





import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , unittest.TestCase	):







				'''simple docstring'''
				UpperCAmelCase__							=							StableDiffusionDiffEditPipeline
				UpperCAmelCase__							=							TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''height''', '''width''', '''image'''} | {'''image_latents'''}
				UpperCAmelCase__							=							TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'''image'''} | {'''image_latents'''}
				UpperCAmelCase__							=							frozenset(
				    []	)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
				UpperCAmelCase__							=							frozenset([]	)





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


										'''simple docstring'''




										torch.manual_seed(0					)
										_UpperCamelCase			:			Tuple   			=						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       ,		attention_head_dim=(2, 4)       ,		use_linear_projection=lowercase__       ,		)
										_UpperCamelCase			:			str   			=						DDIMScheduler(
										    beta_start=0.0_0_0_8_5       ,		beta_end=0.0_1_2       ,		beta_schedule="scaled_linear"       ,		clip_sample=lowercase__       ,		set_alpha_to_one=lowercase__       ,		)
										_UpperCamelCase			:			Union[str, Any]   			=						DDIMInverseScheduler(
										    beta_start=0.0_0_0_8_5       ,		beta_end=0.0_1_2       ,		beta_schedule="scaled_linear"       ,		clip_sample=lowercase__       ,		set_alpha_to_zero=lowercase__       ,		)
										torch.manual_seed(0					)
										_UpperCamelCase			:			Union[str, Any]   			=						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       ,		sample_size=128       ,		)
										torch.manual_seed(0					)
										_UpperCamelCase			:			Any   			=						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=1_000       ,		hidden_act="gelu"       ,		projection_dim=512       ,		)
										_UpperCamelCase			:			Tuple   			=						CLIPTextModel(lowercase__					)
										_UpperCamelCase			:			List[Any]   			=						CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"					)

										_UpperCamelCase			:			Optional[int]   			=						{
										    "unet": unet,
										    "scheduler": scheduler,
										    "inverse_scheduler": inverse_scheduler,
										    "vae": vae,
										    "text_encoder": text_encoder,
										    "tokenizer": tokenizer,
										    "safety_checker": None,
										    "feature_extractor": None,
										}

										return components





				def  snake_case__				(						self					:						List[Any]       ,		lowercase__					:						Dict       ,		lowercase__					:						Dict=0					)							->Union[str, Any]:


										'''simple docstring'''




										_UpperCamelCase			:			List[Any]   			=						floats_tensor((1, 16, 16)       ,		rng=random.Random(lowercase__					)					).to(lowercase__					)
										_UpperCamelCase			:			Any   			=						floats_tensor((1, 2, 4, 16, 16)       ,		rng=random.Random(lowercase__					)					).to(lowercase__					)
										if str(lowercase__					).startswith("mps"					):
																_UpperCamelCase			:			List[Any]   			=						torch.manual_seed(lowercase__					)
										else:
																_UpperCamelCase			:			Union[str, Any]   			=						torch.Generator(device=lowercase__					).manual_seed(lowercase__					)
										_UpperCamelCase			:			Union[str, Any]   			=						{
										    "prompt": "a dog and a newt",
										    "mask_image": mask,
										    "image_latents": latents,
										    "generator": generator,
										    "num_inference_steps": 2,
										    "inpaint_strength": 1.0,
										    "guidance_scale": 6.0,
										    "output_type": "numpy",
										}

										return inputs





				def  snake_case__				(						self					:						int       ,		lowercase__					:						Optional[int]       ,		lowercase__					:						List[str]=0					)							->Dict:


										'''simple docstring'''




										_UpperCamelCase			:			str   			=						floats_tensor((1, 3, 32, 32)       ,		rng=random.Random(lowercase__					)					).to(lowercase__					)
										_UpperCamelCase			:			Optional[Any]   			=						image.cpu().permute(0       ,		2       ,		3       ,		1					)[0]
										_UpperCamelCase			:			List[Any]   			=						Image.fromarray(np.uinta(lowercase__					)					).convert("RGB"					)
										if str(lowercase__					).startswith("mps"					):
																_UpperCamelCase			:			str   			=						torch.manual_seed(lowercase__					)
										else:
																_UpperCamelCase			:			Optional[Any]   			=						torch.Generator(device=lowercase__					).manual_seed(lowercase__					)
										_UpperCamelCase			:			str   			=						{
										    "image": image,
										    "source_prompt": "a cat and a frog",
										    "target_prompt": "a dog and a newt",
										    "generator": generator,
										    "num_inference_steps": 2,
										    "num_maps_per_mask": 2,
										    "mask_encode_strength": 1.0,
										    "guidance_scale": 6.0,
										    "output_type": "numpy",
										}

										return inputs





				def  snake_case__				(						self					:						List[str]       ,		lowercase__					:						Optional[int]       ,		lowercase__					:						int=0					)							->List[str]:


										'''simple docstring'''




										_UpperCamelCase			:			str   			=						floats_tensor((1, 3, 32, 32)       ,		rng=random.Random(lowercase__					)					).to(lowercase__					)
										_UpperCamelCase			:			List[Any]   			=						image.cpu().permute(0       ,		2       ,		3       ,		1					)[0]
										_UpperCamelCase			:			List[str]   			=						Image.fromarray(np.uinta(lowercase__					)					).convert("RGB"					)
										if str(lowercase__					).startswith("mps"					):
																_UpperCamelCase			:			List[Any]   			=						torch.manual_seed(lowercase__					)
										else:
																_UpperCamelCase			:			List[Any]   			=						torch.Generator(device=lowercase__					).manual_seed(lowercase__					)
										_UpperCamelCase			:			int   			=						{
										    "image": image,
										    "prompt": "a cat and a frog",
										    "generator": generator,
										    "num_inference_steps": 2,
										    "inpaint_strength": 1.0,
										    "guidance_scale": 6.0,
										    "decode_latents": True,
										    "output_type": "numpy",
										}
										return inputs





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


										'''simple docstring'''




										if not hasattr(self.pipeline_class       ,		"_optional_components"					):
																return

										_UpperCamelCase			:			List[str]   			=						self.get_dummy_components()
										_UpperCamelCase			:			Dict   			=						self.pipeline_class(**lowercase__					)
										pipe.to(lowercase__					)
										pipe.set_progress_bar_config(disable=lowercase__					)

										# set all optional components to None and update pipeline config accordingly
										for optional_component in pipe._optional_components:
																setattr(lowercase__       ,		lowercase__       ,		lowercase__					)
										pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}					)

										_UpperCamelCase			:			Dict   			=						self.get_dummy_inputs(lowercase__					)
										_UpperCamelCase			:			Union[str, Any]   			=						pipe(**lowercase__					)[0]

										with tempfile.TemporaryDirectory() as tmpdir:
																pipe.save_pretrained(lowercase__					)
																_UpperCamelCase			:			Tuple   			=						self.pipeline_class.from_pretrained(lowercase__					)
																pipe_loaded.to(lowercase__					)
																pipe_loaded.set_progress_bar_config(disable=lowercase__					)

										for optional_component in pipe._optional_components:
																self.assertTrue(
																    getattr(lowercase__       ,		lowercase__					) is None       ,		f'''`{optional_component}` did not stay set to None after loading.'''       ,		)

										_UpperCamelCase			:			Any   			=						self.get_dummy_inputs(lowercase__					)
										_UpperCamelCase			:			Dict   			=						pipe_loaded(**lowercase__					)[0]

										_UpperCamelCase			:			Tuple   			=						np.abs(output - output_loaded					).max()
										self.assertLess(lowercase__       ,		1e-4					)





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


										'''simple docstring'''




										_UpperCamelCase			:			str   			=						"cpu"

										_UpperCamelCase			:			str   			=						self.get_dummy_components()
										_UpperCamelCase			:			Dict   			=						self.pipeline_class(**lowercase__					)
										pipe.to(lowercase__					)
										pipe.set_progress_bar_config(disable=lowercase__					)

										_UpperCamelCase			:			str   			=						self.get_dummy_mask_inputs(lowercase__					)
										_UpperCamelCase			:			Optional[Any]   			=						pipe.generate_mask(**lowercase__					)
										_UpperCamelCase			:			Dict   			=						mask[0, -3:, -3:]

										self.assertEqual(mask.shape       ,		(1, 16, 16)					)
										_UpperCamelCase			:			int   			=						np.array([0] * 9					)
										_UpperCamelCase			:			Dict   			=						np.abs(mask_slice.flatten() - expected_slice					).max()
										self.assertLessEqual(lowercase__       ,		1e-3					)
										self.assertEqual(mask[0, -3, -4]       ,		0					)





				def  snake_case__				(						self					:						Dict					)							->Dict:


										'''simple docstring'''




										_UpperCamelCase			:			Tuple   			=						"cpu"

										_UpperCamelCase			:			Dict   			=						self.get_dummy_components()
										_UpperCamelCase			:			str   			=						self.pipeline_class(**lowercase__					)
										pipe.to(lowercase__					)
										pipe.set_progress_bar_config(disable=lowercase__					)

										_UpperCamelCase			:			Tuple   			=						self.get_dummy_inversion_inputs(lowercase__					)
										_UpperCamelCase			:			Dict   			=						pipe.invert(**lowercase__					).images
										_UpperCamelCase			:			int   			=						image[0, -1, -3:, -3:]

										self.assertEqual(image.shape       ,		(2, 32, 32, 3)					)
										_UpperCamelCase			:			Dict   			=						np.array(
										    [0.5_1_5_0, 0.5_1_3_4, 0.5_0_4_3, 0.5_3_7_6, 0.4_6_9_4, 0.5_1_0_5_0, 0.5_0_1_5, 0.4_4_0_7, 0.4_7_9_9]       ,		)
										_UpperCamelCase			:			str   			=						np.abs(image_slice.flatten() - expected_slice					).max()
										self.assertLessEqual(lowercase__       ,		1e-3					)





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


										'''simple docstring'''




										super().test_inference_batch_single_identical(expected_max_diff=5e-3					)







				def  snake_case__				(						self					:						Optional[Any]					)							->int:


										'''simple docstring'''




										_UpperCamelCase			:			Dict   			=						"cpu"

										_UpperCamelCase			:			Optional[Any]   			=						self.get_dummy_components()

										_UpperCamelCase			:			str   			=						{"beta_start": 0.0_0_0_8_5, "beta_end": 0.0_1_2, "beta_schedule": "scaled_linear"}
										_UpperCamelCase			:			List[Any]   			=						DPMSolverMultistepScheduler(**lowercase__					)
										_UpperCamelCase			:			Optional[int]   			=						DPMSolverMultistepInverseScheduler(**lowercase__					)

										_UpperCamelCase			:			Dict   			=						self.pipeline_class(**lowercase__					)
										pipe.to(lowercase__					)
										pipe.set_progress_bar_config(disable=lowercase__					)

										_UpperCamelCase			:			List[Any]   			=						self.get_dummy_inversion_inputs(lowercase__					)
										_UpperCamelCase			:			int   			=						pipe.invert(**lowercase__					).images
										_UpperCamelCase			:			Dict   			=						image[0, -1, -3:, -3:]

										self.assertEqual(image.shape       ,		(2, 32, 32, 3)					)
										_UpperCamelCase			:			Optional[Any]   			=						np.array(
										    [0.5_1_5_0, 0.5_1_3_4, 0.5_0_4_3, 0.5_3_7_6, 0.4_6_9_4, 0.5_1_0_5_0, 0.5_0_1_5, 0.4_4_0_7, 0.4_7_9_9]       ,		)
										_UpperCamelCase			:			Any   			=						np.abs(image_slice.flatten() - expected_slice					).max()
										self.assertLessEqual(lowercase__       ,		1e-3					)





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







				'''simple docstring'''





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


										'''simple docstring'''




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





				@classmethod
				def  snake_case__				(						cls					:						Dict					)							->Tuple:


										'''simple docstring'''




										_UpperCamelCase			:			int   			=						load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png"					)

										_UpperCamelCase			:			Tuple   			=						raw_image.convert("RGB"					).resize((768, 768)					)

										_UpperCamelCase			:			Any   			=						raw_image





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


										'''simple docstring'''




										_UpperCamelCase			:			Any   			=						torch.manual_seed(0					)

										_UpperCamelCase			:			Dict   			=						StableDiffusionDiffEditPipeline.from_pretrained(
										    "stabilityai/stable-diffusion-2-1"       ,		safety_checker=lowercase__       ,		torch_dtype=torch.floataa					)
										_UpperCamelCase			:			List[str]   			=						DDIMScheduler.from_config(pipe.scheduler.config					)
										_UpperCamelCase			:			Dict   			=						DDIMInverseScheduler.from_config(pipe.scheduler.config					)
										pipe.enable_model_cpu_offload()
										pipe.set_progress_bar_config(disable=lowercase__					)

										_UpperCamelCase			:			Union[str, Any]   			=						"a bowl of fruit"
										_UpperCamelCase			:			str   			=						"a bowl of pears"

										_UpperCamelCase			:			List[str]   			=						pipe.generate_mask(
										    image=self.raw_image       ,		source_prompt=lowercase__       ,		target_prompt=lowercase__       ,		generator=lowercase__       ,		)

										_UpperCamelCase			:			Tuple   			=						pipe.invert(
										    prompt=lowercase__       ,		image=self.raw_image       ,		inpaint_strength=0.7       ,		generator=lowercase__					).latents

										_UpperCamelCase			:			Tuple   			=						pipe(
										    prompt=lowercase__       ,		mask_image=lowercase__       ,		image_latents=lowercase__       ,		generator=lowercase__       ,		negative_prompt=lowercase__       ,		inpaint_strength=0.7       ,		output_type="numpy"       ,		).images[0]

										_UpperCamelCase			:			Dict   			=						(
										    np.array(
										        load_image(
										            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										            "/diffedit/pears.png"					).resize((768, 768)					)					)
										    / 255
										)
										assert np.abs((expected_image - image).max()					) < 5e-1







				def  snake_case__				(						self					:						Any					)							->Dict:


										'''simple docstring'''




										_UpperCamelCase			:			List[str]   			=						torch.manual_seed(0					)

										_UpperCamelCase			:			Optional[int]   			=						StableDiffusionDiffEditPipeline.from_pretrained(
										    "stabilityai/stable-diffusion-2-1"       ,		safety_checker=lowercase__       ,		torch_dtype=torch.floataa					)
										_UpperCamelCase			:			Optional[int]   			=						DPMSolverMultistepScheduler.from_config(pipe.scheduler.config					)
										_UpperCamelCase			:			Union[str, Any]   			=						DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config					)
										pipe.enable_model_cpu_offload()
										pipe.set_progress_bar_config(disable=lowercase__					)

										_UpperCamelCase			:			Optional[Any]   			=						"a bowl of fruit"
										_UpperCamelCase			:			int   			=						"a bowl of pears"

										_UpperCamelCase			:			Optional[Any]   			=						pipe.generate_mask(
										    image=self.raw_image       ,		source_prompt=lowercase__       ,		target_prompt=lowercase__       ,		generator=lowercase__       ,		)

										_UpperCamelCase			:			Union[str, Any]   			=						pipe.invert(
										    prompt=lowercase__       ,		image=self.raw_image       ,		inpaint_strength=0.7       ,		generator=lowercase__       ,		num_inference_steps=25       ,		).latents

										_UpperCamelCase			:			Dict   			=						pipe(
										    prompt=lowercase__       ,		mask_image=lowercase__       ,		image_latents=lowercase__       ,		generator=lowercase__       ,		negative_prompt=lowercase__       ,		inpaint_strength=0.7       ,		num_inference_steps=25       ,		output_type="numpy"       ,		).images[0]

										_UpperCamelCase			:			List[Any]   			=						(
										    np.array(
										        load_image(
										            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										            "/diffedit/pears.png"					).resize((768, 768)					)					)
										    / 255
										)
										assert np.abs((expected_image - image).max()					) < 5e-1



'''simple docstring'''





import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
lowerCAmelCase_	:		Optional[int]			=     logging.getLogger(__name__)

if __name__ == "__main__":
			lowerCAmelCase_	:		Optional[Any]			=     argparse.ArgumentParser(
			    description="""Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)"""
			)
			parser.add_argument(
			    """--data_file""", type=str, default="""data/dump.bert-base-uncased.pickle""", help="""The binarized dataset."""
			)
			parser.add_argument(
			    """--token_counts_dump""", type=str, default="""data/token_counts.bert-base-uncased.pickle""", help="""The dump file."""
			)
			parser.add_argument("""--vocab_size""", default=3_0522, type=int)
			lowerCAmelCase_	:		str			=     parser.parse_args()

			logger.info(f"""Loading data from {args.data_file}""")
			with open(args.data_file, """rb""") as fp:
						lowerCAmelCase_	:		Tuple			=     pickle.load(fp)

			logger.info("""Counting occurrences for MLM.""")
			lowerCAmelCase_	:		str			=     Counter()
			for tk_ids in data:
						counter.update(tk_ids)
			lowerCAmelCase_	:		Any			=     [0] * args.vocab_size
			for k, v in counter.items():
						lowerCAmelCase_	:		Optional[Any]			=     v

			logger.info(f"""Dump to {args.token_counts_dump}""")
			with open(args.token_counts_dump, """wb""") as handle:
						pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)


