

import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
					from PIL import Image
else:

					class 			a					:


											@staticmethod
											def     _UpperCAmelCase			(    *A_  ,   **A_		):







															'''simple docstring'''


															pass

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Image	):
				_UpperCAmelCase    :						List[str]							=      hashlib.mda(image.tobytes()	)
				return m.hexdigest()[:10]

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Image	):
				_UpperCAmelCase    :						Optional[Any]							=      np.array(lowerCAmelCase	)
				_UpperCAmelCase    :						Dict							=      npimg.shape
				return {"hash": hashimage(lowerCAmelCase	), "shape": shape}





@is_pipeline_test
@require_vision
@require_torch
class 			a					(		unittest.TestCase			):
						_lowercase        =     dict(
						    (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()			) if MODEL_FOR_MASK_GENERATION_MAPPING else [])			)
						_lowercase        =     dict(
						    (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()			) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])			)


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Tuple							=      MaskGenerationPipeline(model=A_  ,   image_processor=A_		)
										return image_segmenter, [
										    "./tests/fixtures/tests_samples/COCO/000000039769.png",
										    "./tests/fixtures/tests_samples/COCO/000000039769.png",
										]


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_		):







										'''simple docstring'''


										pass


						@require_tf
						@unittest.skip("Image segmentation not implemented in TF"		)
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										pass


						@slow
						@require_torch
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[Any]							=      pipeline("mask-generation"  ,   model="facebook/sam-vit-huge"		)

										_UpperCAmelCase    :						Any							=      image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg"  ,   points_per_batch=256		)

										# Shortening by hashing
										_UpperCAmelCase    :						Optional[int]							=      []
										for i, o in enumerate(outputs["masks"]		):
														new_outupt += [{"mask": mask_to_test_readable(A_		), "scores": outputs["scores"][i]}]

										# fmt: off
										self.assertEqual(
										    nested_simplify(A_  ,   decimals=4		)  ,   [
										        {"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.04_44},
										        {"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.0_21},
										        {"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.01_67},
										        {"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.01_32},
										        {"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.00_53},
										        {"mask": {"hash": "e2d0b7a0b7", "shape": (480, 640)}, "scores": 0.99_67},
										        {"mask": {"hash": "453c7844bd", "shape": (480, 640)}, "scores": 0.9_93},
										        {"mask": {"hash": "3d44f2926d", "shape": (480, 640)}, "scores": 0.99_09},
										        {"mask": {"hash": "64033ddc3f", "shape": (480, 640)}, "scores": 0.98_79},
										        {"mask": {"hash": "801064ff79", "shape": (480, 640)}, "scores": 0.98_34},
										        {"mask": {"hash": "6172f276ef", "shape": (480, 640)}, "scores": 0.97_16},
										        {"mask": {"hash": "b49e60e084", "shape": (480, 640)}, "scores": 0.96_12},
										        {"mask": {"hash": "a811e775fd", "shape": (480, 640)}, "scores": 0.95_99},
										        {"mask": {"hash": "a6a8ebcf4b", "shape": (480, 640)}, "scores": 0.95_52},
										        {"mask": {"hash": "9d8257e080", "shape": (480, 640)}, "scores": 0.95_32},
										        {"mask": {"hash": "32de6454a8", "shape": (480, 640)}, "scores": 0.95_16},
										        {"mask": {"hash": "af3d4af2c8", "shape": (480, 640)}, "scores": 0.94_99},
										        {"mask": {"hash": "3c6db475fb", "shape": (480, 640)}, "scores": 0.94_83},
										        {"mask": {"hash": "c290813fb9", "shape": (480, 640)}, "scores": 0.94_64},
										        {"mask": {"hash": "b6f0b8f606", "shape": (480, 640)}, "scores": 0.9_43},
										        {"mask": {"hash": "92ce16bfdf", "shape": (480, 640)}, "scores": 0.9_43},
										        {"mask": {"hash": "c749b25868", "shape": (480, 640)}, "scores": 0.94_08},
										        {"mask": {"hash": "efb6cab859", "shape": (480, 640)}, "scores": 0.93_35},
										        {"mask": {"hash": "1ff2eafb30", "shape": (480, 640)}, "scores": 0.93_26},
										        {"mask": {"hash": "788b798e24", "shape": (480, 640)}, "scores": 0.92_62},
										        {"mask": {"hash": "abea804f0e", "shape": (480, 640)}, "scores": 0.89_99},
										        {"mask": {"hash": "7b9e8ddb73", "shape": (480, 640)}, "scores": 0.89_86},
										        {"mask": {"hash": "cd24047c8a", "shape": (480, 640)}, "scores": 0.89_84},
										        {"mask": {"hash": "6943e6bcbd", "shape": (480, 640)}, "scores": 0.88_73},
										        {"mask": {"hash": "b5f47c9191", "shape": (480, 640)}, "scores": 0.88_71}
										    ]  ,   )
										# fmt: on





						@require_torch
						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						str							=      "facebook/sam-vit-huge"
										_UpperCAmelCase    :						Any							=      pipeline("mask-generation"  ,   model=A_		)

										_UpperCAmelCase    :						str							=      image_segmenter(
										    "http://images.cocodataset.org/val2017/000000039769.jpg"  ,   pred_iou_thresh=1  ,   points_per_batch=256		)

										# Shortening by hashing
										_UpperCAmelCase    :						Union[str, Any]							=      []
										for i, o in enumerate(outputs["masks"]		):
														new_outupt += [{"mask": mask_to_test_readable(A_		), "scores": outputs["scores"][i]}]

										self.assertEqual(
										    nested_simplify(A_  ,   decimals=4		)  ,   [
										        {"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.04_44},
										        {"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.02_10},
										        {"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.01_67},
										        {"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.01_32},
										        {"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.00_53},
										    ]  ,   )





import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_            = argparse.ArgumentParser()

					parser.add_argument(
					    '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
					)
					# !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
					parser.add_argument(
					    '--original_config_file',
					    default=None,
					    type=str,
					    help='The YAML config file corresponding to the original architecture.',
					)
					parser.add_argument(
					    '--num_in_channels',
					    default=None,
					    type=int,
					    help='The number of input channels. If `None` number of input channels will be automatically inferred.',
					)
					parser.add_argument(
					    '--scheduler_type',
					    default='pndm',
					    type=str,
					    help='Type of scheduler to use. Should be one of [\'pndm\', \'lms\', \'ddim\', \'euler\', \'euler-ancestral\', \'dpm\']',
					)
					parser.add_argument(
					    '--pipeline_type',
					    default=None,
					    type=str,
					    help=(
					        'The pipeline type. One of \'FrozenOpenCLIPEmbedder\', \'FrozenCLIPEmbedder\', \'PaintByExample\''
					        '. If `None` pipeline will be automatically inferred.'
					    ),
					)
					parser.add_argument(
					    '--image_size',
					    default=None,
					    type=int,
					    help=(
					        'The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'
					        ' Base. Use 768 for Stable Diffusion v2.'
					    ),
					)
					parser.add_argument(
					    '--prediction_type',
					    default=None,
					    type=str,
					    help=(
					        'The prediction type that the model was trained on. Use \'epsilon\' for Stable Diffusion v1.X and Stable'
					        ' Diffusion v2 Base. Use \'v_prediction\' for Stable Diffusion v2.'
					    ),
					)
					parser.add_argument(
					    '--extract_ema',
					    action='store_true',
					    help=(
					        'Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'
					        ' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'
					        ' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'
					    ),
					)
					parser.add_argument(
					    '--upcast_attention',
					    action='store_true',
					    help=(
					        'Whether the attention computation should always be upcasted. This is necessary when running stable'
					        ' diffusion 2.1.'
					    ),
					)
					parser.add_argument(
					    '--from_safetensors',
					    action='store_true',
					    help='If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.',
					)
					parser.add_argument(
					    '--to_safetensors',
					    action='store_true',
					    help='Whether to store pipeline in safetensors format or not.',
					)
					parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
					parser.add_argument('--device', type=str, help='Device to use (e.g. cpu, cuda:0, cuda:1, etc.)')
					parser.add_argument(
					    '--stable_unclip',
					    type=str,
					    default=None,
					    required=False,
					    help='Set if this is a stable unCLIP model. One of \'txt2img\' or \'img2img\'.',
					)
					parser.add_argument(
					    '--stable_unclip_prior',
					    type=str,
					    default=None,
					    required=False,
					    help='Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.',
					)
					parser.add_argument(
					    '--clip_stats_path',
					    type=str,
					    help='Path to the clip stats file. Only required if the stable unclip model\'s config specifies `model.params.noise_aug_config.params.clip_stats_path`.',
					    required=False,
					)
					parser.add_argument(
					    '--controlnet', action='store_true', default=None, help='Set flag if this is a controlnet checkpoint.'
					)
					parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
					parser.add_argument(
					    '--vae_path',
					    type=str,
					    default=None,
					    required=False,
					    help='Set to a path, hub id to an already converted vae to not convert it again.',
					)
					SCREAMING_SNAKE_CASE_            = parser.parse_args()

					SCREAMING_SNAKE_CASE_            = download_from_original_stable_diffusion_ckpt(
					    checkpoint_path=args.checkpoint_path,
					    original_config_file=args.original_config_file,
					    image_size=args.image_size,
					    prediction_type=args.prediction_type,
					    model_type=args.pipeline_type,
					    extract_ema=args.extract_ema,
					    scheduler_type=args.scheduler_type,
					    num_in_channels=args.num_in_channels,
					    upcast_attention=args.upcast_attention,
					    from_safetensors=args.from_safetensors,
					    device=args.device,
					    stable_unclip=args.stable_unclip,
					    stable_unclip_prior=args.stable_unclip_prior,
					    clip_stats_path=args.clip_stats_path,
					    controlnet=args.controlnet,
					    vae_path=args.vae_path,
					)

					if args.half:
										pipe.to(torch_dtype=torch.floataa)

					if args.controlnet:
										# only save the controlnet model
										pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
					else:
										pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)


