
import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMParallelScheduler,
    DDPMParallelScheduler,
    StableDiffusionParadigmsPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    require_torch_gpu,
)

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class 					__lowercase		(					A							,       A							,       unittest.TestCase       ):
					__magic_name__						:							str     	=	StableDiffusionParadigmsPipeline
					__magic_name__						:							Any     	=	TEXT_TO_IMAGE_PARAMS
					__magic_name__						:							Union[str, Any]     	=	TEXT_TO_IMAGE_BATCH_PARAMS
					__magic_name__						:							Optional[int]     	=	TEXT_TO_IMAGE_IMAGE_PARAMS
					__magic_name__						:							List[Any]     	=	TEXT_TO_IMAGE_IMAGE_PARAMS


					def        lowerCAmelCase_    (	self						)		->							Optional[Any]:





									'''simple docstring'''
									torch.manual_seed(0						)
									A_       	=  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       ,       attention_head_dim=(2, 4)       ,       use_linear_projection=a__       ,       )
									A_       	=  DDIMParallelScheduler(
									    beta_start=0.0_00_85       ,       beta_end=0.0_12       ,       beta_schedule='''scaled_linear'''       ,       clip_sample=a__       ,       set_alpha_to_one=a__       ,       )
									torch.manual_seed(0						)
									A_       	=  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       ,       sample_size=1_2_8       ,       )
									torch.manual_seed(0						)
									A_       	=  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       ,       hidden_act='''gelu'''       ,       projection_dim=5_1_2       ,       )
									A_       	=  CLIPTextModel(a__						)
									A_       	=  CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						)

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


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__=0						)		->							Optional[int]:





									'''simple docstring'''
									if str(a__						).startswith('''mps'''						):
													A_       	=  torch.manual_seed(a__						)
									else:
													A_       	=  torch.Generator(device=a__						).manual_seed(a__						)
									A_       	=  {
									    '''prompt''': '''a photograph of an astronaut riding a horse''',
									    '''generator''': generator,
									    '''num_inference_steps''': 1_0,
									    '''guidance_scale''': 6.0,
									    '''output_type''': '''numpy''',
									    '''parallel''': 3,
									    '''debug''': True,
									}
									return inputs


					def        lowerCAmelCase_    (	self						)		->							Dict:





									'''simple docstring'''
									A_       	=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
									A_       	=  self.get_dummy_components()
									A_       	=  StableDiffusionParadigmsPipeline(**a__						)
									A_       	=  sd_pipe.to(a__						)
									sd_pipe.set_progress_bar_config(disable=a__						)

									A_       	=  self.get_dummy_inputs(a__						)
									A_       	=  sd_pipe(**a__						).images
									A_       	=  image[0, -3:, -3:, -1]
									assert image.shape == (1, 6_4, 6_4, 3)

									A_       	=  np.array([0.47_73, 0.54_17, 0.47_23, 0.49_25, 0.56_31, 0.47_52, 0.52_40, 0.49_35, 0.50_23]						)

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


					def        lowerCAmelCase_    (	self						)		->							Tuple:





									'''simple docstring'''
									A_       	=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
									A_       	=  self.get_dummy_components()
									torch.manual_seed(0						)
									A_       	=  DDPMParallelScheduler()
									torch.manual_seed(0						)
									A_       	=  StableDiffusionParadigmsPipeline(**a__						)
									A_       	=  sd_pipe.to(a__						)
									sd_pipe.set_progress_bar_config(disable=a__						)

									A_       	=  self.get_dummy_inputs(a__						)
									A_       	=  sd_pipe(**a__						).images
									A_       	=  image[0, -3:, -3:, -1]
									assert image.shape == (1, 6_4, 6_4, 3)

									A_       	=  np.array([0.35_73, 0.44_20, 0.49_60, 0.47_99, 0.37_96, 0.38_79, 0.48_19, 0.43_65, 0.44_68]						)

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


					def        lowerCAmelCase_    (	self						)		->							Any:





									'''simple docstring'''
									super().test_inference_batch_consistent(batch_sizes=[1, 2]						)


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





									'''simple docstring'''
									super().test_inference_batch_single_identical(batch_size=2       ,       expected_max_diff=3E-3						)


					def        lowerCAmelCase_    (	self						)		->							Any:





									'''simple docstring'''
									A_       	=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
									A_       	=  self.get_dummy_components()
									A_       	=  StableDiffusionParadigmsPipeline(**a__						)
									A_       	=  sd_pipe.to(a__						)
									sd_pipe.set_progress_bar_config(disable=a__						)

									A_       	=  self.get_dummy_inputs(a__						)
									A_       	=  '''french fries'''
									A_       	=  sd_pipe(**a__       ,       negative_prompt=a__						)
									A_       	=  output.images
									A_       	=  image[0, -3:, -3:, -1]

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

									A_       	=  np.array([0.47_71, 0.54_20, 0.46_83, 0.49_18, 0.56_36, 0.47_25, 0.52_30, 0.49_23, 0.50_15]						)

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






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


					def        lowerCAmelCase_    (	self						)		->							Optional[Any]:





									'''simple docstring'''
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()


					def        lowerCAmelCase_    (	self       ,       a__=0						)		->							Any:





									'''simple docstring'''
									A_       	=  torch.Generator(device=a__						).manual_seed(a__						)
									A_       	=  {
									    '''prompt''': '''a photograph of an astronaut riding a horse''',
									    '''generator''': generator,
									    '''num_inference_steps''': 1_0,
									    '''guidance_scale''': 7.5,
									    '''output_type''': '''numpy''',
									    '''parallel''': 3,
									    '''debug''': True,
									}
									return inputs


					def        lowerCAmelCase_    (	self						)		->							Any:





									'''simple docstring'''
									A_       	=  '''stabilityai/stable-diffusion-2-base'''
									A_       	=  DDIMParallelScheduler.from_pretrained(a__       ,       subfolder='''scheduler'''						)
									A_       	=  StableDiffusionParadigmsPipeline.from_pretrained(a__       ,       scheduler=a__       ,       safety_checker=a__						)
									pipe.to(a__						)
									pipe.set_progress_bar_config(disable=a__						)
									pipe.enable_attention_slicing()

									A_       	=  self.get_inputs()
									A_       	=  pipe(**a__						).images
									A_       	=  image[0, -3:, -3:, -1].flatten()

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

									A_       	=  np.array([0.96_22, 0.96_02, 0.97_48, 0.95_91, 0.96_30, 0.96_91, 0.96_61, 0.96_31, 0.97_41]						)

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

import argparse
import json
import os

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextImageProcessor, ConvNextVaConfig, ConvNextVaForImageClassification
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE   :		List[str]					 =       logging.get_logger(__name__)



def        lowerCamelCase_  (   __UpperCamelCase     ):
				A_       	=  ConvNextVaConfig()

				if "atto" in checkpoint_url:
								A_       	=  [2, 2, 6, 2]
								A_       	=  [40, 80, 1_60, 3_20]
				if "femto" in checkpoint_url:
								A_       	=  [2, 2, 6, 2]
								A_       	=  [48, 96, 1_92, 3_84]
				if "pico" in checkpoint_url:
								A_       	=  [2, 2, 6, 2]
								A_       	=  [64, 1_28, 2_56, 5_12]
				if "nano" in checkpoint_url:
								A_       	=  [2, 2, 8, 2]
								A_       	=  [80, 1_60, 3_20, 6_40]
				if "tiny" in checkpoint_url:
								A_       	=  [3, 3, 9, 3]
								A_       	=  [96, 1_92, 3_84, 7_68]
				if "base" in checkpoint_url:
								A_       	=  [3, 3, 27, 3]
								A_       	=  [1_28, 2_56, 5_12, 10_24]
				if "large" in checkpoint_url:
								A_       	=  [3, 3, 27, 3]
								A_       	=  [1_92, 3_84, 7_68, 15_36]
				if "huge" in checkpoint_url:
								A_       	=  [3, 3, 27, 3]
								A_       	=  [3_52, 7_04, 14_08, 28_16]

				A_       	=  10_00
				A_       	=  '''imagenet-1k-id2label.json'''
				A_       	=  (1, 10_00)

				A_       	=  '''huggingface/label-files'''
				A_       	=  num_labels
				A_       	=  json.load(open(hf_hub_download(__UpperCamelCase ,						__UpperCamelCase ,						repo_type='''dataset'''     ) ,						'''r'''     )     )
				A_       	=  {int(__UpperCamelCase     ): v for k, v in idalabel.items()}

				A_       	=  idalabel
				A_       	=  {v: k for k, v in idalabel.items()}
				A_       	=  hidden_sizes
				A_       	=  depths

				return config, expected_shape



def        lowerCamelCase_  (   __UpperCamelCase     ):
				if "downsample_layers.0.0" in name:
								A_       	=  name.replace('''downsample_layers.0.0''' ,						'''embeddings.patch_embeddings'''     )
				if "downsample_layers.0.1" in name:
								A_       	=  name.replace('''downsample_layers.0.1''' ,						'''embeddings.norm'''     )  # we rename to layernorm later on
				if "downsample_layers.1.0" in name:
								A_       	=  name.replace('''downsample_layers.1.0''' ,						'''stages.1.downsampling_layer.0'''     )
				if "downsample_layers.1.1" in name:
								A_       	=  name.replace('''downsample_layers.1.1''' ,						'''stages.1.downsampling_layer.1'''     )
				if "downsample_layers.2.0" in name:
								A_       	=  name.replace('''downsample_layers.2.0''' ,						'''stages.2.downsampling_layer.0'''     )
				if "downsample_layers.2.1" in name:
								A_       	=  name.replace('''downsample_layers.2.1''' ,						'''stages.2.downsampling_layer.1'''     )
				if "downsample_layers.3.0" in name:
								A_       	=  name.replace('''downsample_layers.3.0''' ,						'''stages.3.downsampling_layer.0'''     )
				if "downsample_layers.3.1" in name:
								A_       	=  name.replace('''downsample_layers.3.1''' ,						'''stages.3.downsampling_layer.1'''     )
				if "stages" in name and "downsampling_layer" not in name:
								# stages.0.0. for instance should be renamed to stages.0.layers.0.
								A_       	=  name[: len('''stages.0'''     )] + '''.layers''' + name[len('''stages.0'''     ) :]
				if "gamma" in name:
								A_       	=  name.replace('''gamma''' ,						'''weight'''     )
				if "beta" in name:
								A_       	=  name.replace('''beta''' ,						'''bias'''     )
				if "stages" in name:
								A_       	=  name.replace('''stages''' ,						'''encoder.stages'''     )
				if "norm" in name:
								A_       	=  name.replace('''norm''' ,						'''layernorm'''     )
				if "head" in name:
								A_       	=  name.replace('''head''' ,						'''classifier'''     )

				return name



def        lowerCamelCase_  (   ):
				A_       	=  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
				A_       	=  Image.open(requests.get(__UpperCamelCase ,						stream=__UpperCamelCase     ).raw     )
				return im



def        lowerCamelCase_  (   __UpperCamelCase     ):
				if "224" in checkpoint_url:
								A_       	=  2_24
								A_       	=  2_24 / 2_56
				elif "384" in checkpoint_url:
								A_       	=  3_84
								A_       	=  None
				else:
								A_       	=  5_12
								A_       	=  None

				return ConvNextImageProcessor(
				    size=__UpperCamelCase ,						crop_pct=__UpperCamelCase ,						image_mean=[0.485, 0.456, 0.406] ,						image_std=[0.229, 0.224, 0.225] ,						resample=PILImageResampling.BICUBIC ,						)



@torch.no_grad()
def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase     ):
				print('''Downloading original model from checkpoint...'''     )
				# define ConvNeXTV2 configuration based on URL
				A_	,					A_       	=  get_convnextva_config(__UpperCamelCase     )
				# load original state_dict from URL
				A_       	=  torch.hub.load_state_dict_from_url(__UpperCamelCase     )['''model''']

				print('''Converting model parameters...'''     )
				# rename keys
				for key in state_dict.copy().keys():
								A_       	=  state_dict.pop(__UpperCamelCase     )
								A_       	=  val
				# add prefix to all keys expect classifier head
				for key in state_dict.copy().keys():
								A_       	=  state_dict.pop(__UpperCamelCase     )
								if not key.startswith('''classifier'''     ):
												A_       	=  '''convnextv2.''' + key
								A_       	=  val

				# load HuggingFace model
				A_       	=  ConvNextVaForImageClassification(__UpperCamelCase     )
				model.load_state_dict(__UpperCamelCase     )
				model.eval()

				# Check outputs on an image, prepared by ConvNextImageProcessor
				A_       	=  convert_preprocessor(__UpperCamelCase     )
				A_       	=  preprocessor(images=prepare_img() ,						return_tensors='''pt'''     )
				A_       	=  model(**__UpperCamelCase     ).logits

				# note: the logits below were obtained without center cropping
				if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt":
								A_       	=  torch.tensor([-0.3930, 0.1747, -0.5246, 0.4177, 0.4295]     )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
								A_       	=  torch.tensor([-0.1727, -0.5341, -0.7818, -0.4745, -0.6566]     )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
								A_       	=  torch.tensor([-0.0333, 0.1563, -0.9137, 0.1054, 0.0381]     )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
								A_       	=  torch.tensor([-0.1744, -0.1555, -0.0713, 0.0950, -0.1431]     )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
								A_       	=  torch.tensor([0.9996, 0.1966, -0.4386, -0.3472, 0.6661]     )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
								A_       	=  torch.tensor([-0.2553, -0.6708, -0.1359, 0.2518, -0.2488]     )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
								A_       	=  torch.tensor([-0.0673, -0.5627, -0.3753, -0.2722, 0.0178]     )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
								A_       	=  torch.tensor([-0.6377, -0.7458, -0.2150, 0.1184, -0.0597]     )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
								A_       	=  torch.tensor([1.0799, 0.2322, -0.8860, 1.0219, 0.6231]     )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
								A_       	=  torch.tensor([0.3766, 0.4917, -1.1426, 0.9942, 0.6024]     )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
								A_       	=  torch.tensor([0.4220, -0.6919, -0.4317, -0.2881, -0.6609]     )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
								A_       	=  torch.tensor([0.1082, -0.8286, -0.5095, 0.4681, -0.8085]     )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
								A_       	=  torch.tensor([-0.2419, -0.6221, 0.2176, -0.0980, -0.7527]     )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
								A_       	=  torch.tensor([0.0391, -0.4371, 0.3786, 0.1251, -0.2784]     )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
								A_       	=  torch.tensor([-0.0504, 0.5636, -0.1729, -0.6507, -0.3949]     )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
								A_       	=  torch.tensor([0.3560, 0.9486, 0.3149, -0.2667, -0.5138]     )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
								A_       	=  torch.tensor([-0.2469, -0.4550, -0.5853, -0.0810, 0.0309]     )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
								A_       	=  torch.tensor([-0.3090, 0.0802, -0.0682, -0.1979, -0.2826]     )
				else:
								raise ValueError(F"Unknown URL: {checkpoint_url}"     )

				assert torch.allclose(logits[0, :5] ,						__UpperCamelCase ,						atol=1e-3     )
				assert logits.shape == expected_shape
				print('''Model outputs match the original results!'''     )

				if save_model:
								print('''Saving model to local...'''     )
								# Create folder to save model
								if not os.path.isdir(__UpperCamelCase     ):
												os.mkdir(__UpperCamelCase     )

								model.save_pretrained(__UpperCamelCase     )
								preprocessor.save_pretrained(__UpperCamelCase     )

				A_       	=  '''convnextv2'''
				if "atto" in checkpoint_url:
								model_name += "-atto"
				if "femto" in checkpoint_url:
								model_name += "-femto"
				if "pico" in checkpoint_url:
								model_name += "-pico"
				if "nano" in checkpoint_url:
								model_name += "-nano"
				elif "tiny" in checkpoint_url:
								model_name += "-tiny"
				elif "base" in checkpoint_url:
								model_name += "-base"
				elif "large" in checkpoint_url:
								model_name += "-large"
				elif "huge" in checkpoint_url:
								model_name += "-huge"
				if "22k" in checkpoint_url and "1k" not in checkpoint_url:
								model_name += "-22k"
				elif "22k" in checkpoint_url and "1k" in checkpoint_url:
								model_name += "-22k-1k"
				elif "1k" in checkpoint_url:
								model_name += "-1k"
				if "224" in checkpoint_url:
								model_name += "-224"
				elif "384" in checkpoint_url:
								model_name += "-384"
				elif "512" in checkpoint_url:
								model_name += "-512"

				if push_to_hub:
								print(F"Pushing {model_name} to the hub..."     )
								model.push_to_hub(__UpperCamelCase     )
								preprocessor.push_to_hub(__UpperCamelCase     )


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE   :		Tuple					 =       argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--checkpoint_url",
						    default="https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt",
						    type=str,
						    help="URL of the original ConvNeXTV2 checkpoint you'd like to convert.",
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path",
						    default="model",
						    type=str,
						    help="Path to the output PyTorch model directory.",
						)
						parser.add_argument("--save_model", action="store_true", help="Save model to local")
						parser.add_argument("--push_to_hub", action="store_true", help="Push model and image preprocessor to the hub")

						SCREAMING_SNAKE_CASE   :		Dict					 =       parser.parse_args()
						convert_convnextva_checkpoint(
						    args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
						)