
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      SCREAMING_SNAKE_CASE__  (			unittest.TestCase      ):





	'''simple docstring'''



	def 				A							(						self			:					Dict			):


				'''simple docstring'''


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



	@property
	def 				A							(						self			:					Optional[Any]			):


				'''simple docstring'''


				_snake_case						=    1
				_snake_case						=    3
				_snake_case						=    (32, 32)

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



	@property
	def 				A							(						self			:					Dict			):


				'''simple docstring'''


				torch.manual_seed(0			)
				_snake_case						=    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    ,						)
				return model



	@property
	def 				A							(						self			:					Optional[Any]			):


				'''simple docstring'''


				torch.manual_seed(0			)
				_snake_case						=    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    ,						)
				return model



	@property
	def 				A							(						self			:					List[str]			):


				'''simple docstring'''


				torch.manual_seed(0			)
				_snake_case						=    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    ,						)
				return CLIPTextModel(lowercase			)



	@property
	def 				A							(						self			:					Any			):


				'''simple docstring'''


				def extract(*lowercase			:					Optional[int]    ,						**lowercase			:					int			):




							class      SCREAMING_SNAKE_CASE__  :





								'''simple docstring'''



								def __init__(						self			:					Union[str, Any]			):


											'''simple docstring'''


											_snake_case						=    torch.ones([0]			)





								def 				A							(						self			:					str    ,						lowercase			:					Any			):


											'''simple docstring'''


											self.pixel_values.to(lowercase			)
											return self




							return Out()

				return extract



	def 				A							(						self			:					str			):


				'''simple docstring'''


				_snake_case						=    'cpu'  # ensure determinism for the device-dependent torch.Generator
				_snake_case						=    self.dummy_cond_unet
				_snake_case						=    DDIMScheduler(
				    beta_start=0.00085    ,						beta_end=0.012    ,						beta_schedule='scaled_linear'    ,						clip_sample=lowercase    ,						set_alpha_to_one=lowercase    ,						)

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

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

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

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

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

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

				assert image.shape == (1, 64, 64, 3)
				_snake_case						=    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 				A							(						self			:					List[Any]			):


				'''simple docstring'''


				_snake_case						=    'cpu'  # ensure determinism for the device-dependent torch.Generator
				_snake_case						=    self.dummy_cond_unet
				_snake_case						=    PNDMScheduler(skip_prk_steps=lowercase			)
				_snake_case						=    self.dummy_vae
				_snake_case						=    self.dummy_text_encoder
				_snake_case						=    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)

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

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

				_snake_case						=    output.images

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

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

				assert image.shape == (1, 64, 64, 3)
				_snake_case						=    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 				A							(						self			:					int			):


				'''simple docstring'''


				_snake_case						=    StableDiffusionPipeline.from_pretrained(
				    'hf-internal-testing/tiny-stable-diffusion-lms-pipe'    ,						safety_checker=lowercase			)
				assert isinstance(lowercase    ,						lowercase			)
				assert isinstance(pipe.scheduler    ,						lowercase			)
				assert pipe.safety_checker is None

				_snake_case						=    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(lowercase			)
							_snake_case						=    StableDiffusionPipeline.from_pretrained(lowercase			)

				# sanity check that the pipeline still works
				assert pipe.safety_checker is None
				_snake_case						=    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 				A							(						self			:					int			):


				'''simple docstring'''


				_snake_case						=    self.dummy_cond_unet
				_snake_case						=    PNDMScheduler(skip_prk_steps=lowercase			)
				_snake_case						=    self.dummy_vae
				_snake_case						=    self.dummy_text_encoder
				_snake_case						=    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)

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

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

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

				assert image.shape == (1, 64, 64, 3)




@nightly
@require_torch_gpu
class      SCREAMING_SNAKE_CASE__  (			unittest.TestCase      ):





	'''simple docstring'''



	def 				A							(						self			:					Any			):


				'''simple docstring'''


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



	def 				A							(						self			:					Dict			):


				'''simple docstring'''


				_snake_case						=    'cuda'
				_snake_case						=    StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'			)
				_snake_case						=    pipe.to(lowercase			)
				pipe.set_progress_bar_config(disable=lowercase			)

				_snake_case						=    'a photo of a cat'
				_snake_case						=    {
				    'editing_prompt': ['sunglasses'],
				    'reverse_editing_direction': [False],
				    'edit_warmup_steps': 10,
				    'edit_guidance_scale': 6,
				    'edit_threshold': 0.95,
				    'edit_momentum_scale': 0.5,
				    'edit_mom_beta': 0.6,
				}

				_snake_case						=    3
				_snake_case						=    7

				# no sega enabled
				_snake_case						=    torch.Generator(lowercase			)
				generator.manual_seed(lowercase			)
				_snake_case						=    pipe(
				    [prompt]    ,						generator=lowercase    ,						guidance_scale=lowercase    ,						num_inference_steps=50    ,						output_type='np'    ,						width=512    ,						height=512    ,						)

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

				assert image.shape == (1, 512, 512, 3)

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

				# with sega enabled
				# generator = torch.manual_seed(seed)
				generator.manual_seed(lowercase			)
				_snake_case						=    pipe(
				    [prompt]    ,						generator=lowercase    ,						guidance_scale=lowercase    ,						num_inference_steps=50    ,						output_type='np'    ,						width=512    ,						height=512    ,						**lowercase    ,						)

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

				assert image.shape == (1, 512, 512, 3)

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



	def 				A							(						self			:					Any			):


				'''simple docstring'''


				_snake_case						=    'cuda'
				_snake_case						=    StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'			)
				_snake_case						=    pipe.to(lowercase			)
				pipe.set_progress_bar_config(disable=lowercase			)

				_snake_case						=    'an image of a crowded boulevard, realistic, 4k'
				_snake_case						=    {
				    'editing_prompt': 'crowd, crowded, people',
				    'reverse_editing_direction': True,
				    'edit_warmup_steps': 10,
				    'edit_guidance_scale': 8.3,
				    'edit_threshold': 0.9,
				    'edit_momentum_scale': 0.5,
				    'edit_mom_beta': 0.6,
				}

				_snake_case						=    9
				_snake_case						=    7

				# no sega enabled
				_snake_case						=    torch.Generator(lowercase			)
				generator.manual_seed(lowercase			)
				_snake_case						=    pipe(
				    [prompt]    ,						generator=lowercase    ,						guidance_scale=lowercase    ,						num_inference_steps=50    ,						output_type='np'    ,						width=512    ,						height=512    ,						)

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

				assert image.shape == (1, 512, 512, 3)

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

				# with sega enabled
				# generator = torch.manual_seed(seed)
				generator.manual_seed(lowercase			)
				_snake_case						=    pipe(
				    [prompt]    ,						generator=lowercase    ,						guidance_scale=lowercase    ,						num_inference_steps=50    ,						output_type='np'    ,						width=512    ,						height=512    ,						**lowercase    ,						)

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

				assert image.shape == (1, 512, 512, 3)

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



	def 				A							(						self			:					Any			):


				'''simple docstring'''


				_snake_case						=    'cuda'
				_snake_case						=    StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'			)
				_snake_case						=    pipe.to(lowercase			)
				pipe.set_progress_bar_config(disable=lowercase			)

				_snake_case						=    'a castle next to a river'
				_snake_case						=    {
				    'editing_prompt': ['boat on a river, boat', 'monet, impression, sunrise'],
				    'reverse_editing_direction': False,
				    'edit_warmup_steps': [15, 18],
				    'edit_guidance_scale': 6,
				    'edit_threshold': [0.9, 0.8],
				    'edit_momentum_scale': 0.5,
				    'edit_mom_beta': 0.6,
				}

				_snake_case						=    48
				_snake_case						=    7

				# no sega enabled
				_snake_case						=    torch.Generator(lowercase			)
				generator.manual_seed(lowercase			)
				_snake_case						=    pipe(
				    [prompt]    ,						generator=lowercase    ,						guidance_scale=lowercase    ,						num_inference_steps=50    ,						output_type='np'    ,						width=512    ,						height=512    ,						)

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

				assert image.shape == (1, 512, 512, 3)

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

				# with sega enabled
				# generator = torch.manual_seed(seed)
				generator.manual_seed(lowercase			)
				_snake_case						=    pipe(
				    [prompt]    ,						generator=lowercase    ,						guidance_scale=lowercase    ,						num_inference_steps=50    ,						output_type='np'    ,						width=512    ,						height=512    ,						**lowercase    ,						)

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

				assert image.shape == (1, 512, 512, 3)

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





	def 				A							(						self			:					str			):


				'''simple docstring'''


				_snake_case						=    'cuda'
				_snake_case						=    StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'    ,						torch_dtype=torch.floataa			)
				_snake_case						=    pipe.to(lowercase			)
				pipe.set_progress_bar_config(disable=lowercase			)

				_snake_case						=    'a photo of a cat'
				_snake_case						=    {
				    'editing_prompt': ['sunglasses'],
				    'reverse_editing_direction': [False],
				    'edit_warmup_steps': 10,
				    'edit_guidance_scale': 6,
				    'edit_threshold': 0.95,
				    'edit_momentum_scale': 0.5,
				    'edit_mom_beta': 0.6,
				}

				_snake_case						=    3
				_snake_case						=    7

				# no sega enabled
				_snake_case						=    torch.Generator(lowercase			)
				generator.manual_seed(lowercase			)
				_snake_case						=    pipe(
				    [prompt]    ,						generator=lowercase    ,						guidance_scale=lowercase    ,						num_inference_steps=50    ,						output_type='np'    ,						width=512    ,						height=512    ,						)

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

				assert image.shape == (1, 512, 512, 3)

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

				# with sega enabled
				# generator = torch.manual_seed(seed)
				generator.manual_seed(lowercase			)
				_snake_case						=    pipe(
				    [prompt]    ,						generator=lowercase    ,						guidance_scale=lowercase    ,						num_inference_steps=50    ,						output_type='np'    ,						width=512    ,						height=512    ,						**lowercase    ,						)

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

				assert image.shape == (1, 512, 512, 3)

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

import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()






def  a_	( __lowercase     :			Optional[Any] ,      __lowercase     :			Union[str, Any] ,      __lowercase     :			str				)				->						Any:
			# Construct configuration depending on size
			_snake_case						=    {'small': (512, 8, 24), 'medium': (1_024, 8, 36), 'large': (1_536, 16, 48)}
			_snake_case			,      _snake_case			,      _snake_case						=    MODELS[model_size]  # set model hyperparameters
			_snake_case						=    ImageGPTConfig(n_embd=__lowercase ,      n_layer=__lowercase ,      n_head=__lowercase				)
			_snake_case						=    ImageGPTForCausalLM(__lowercase				)

			# Load weights from numpy
			load_tf_weights_in_imagegpt(__lowercase ,      __lowercase ,      __lowercase				)

			# Save pytorch-model
			_snake_case						=    pytorch_dump_folder_path + '/' + WEIGHTS_NAME
			_snake_case						=    pytorch_dump_folder_path + '/' + CONFIG_NAME
			print(f'''Save PyTorch model to {pytorch_weights_dump_path}'''				)
			torch.save(model.state_dict() ,      __lowercase				)
			print(f'''Save configuration file to {pytorch_config_dump_path}'''				)
			with open(__lowercase ,      'w' ,      encoding='utf-8'				) as f:
						f.write(config.to_json_string()				)


if __name__ == "__main__":
			_lowerCamelCase :       List[Any]     					=    argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--imagegpt_checkpoint_path''',
			    default=None,
			    type=str,
			    required=True,
			    help='''Path to the TensorFlow checkpoint path.''',
			)
			parser.add_argument(
			    '''--model_size''',
			    default=None,
			    type=str,
			    required=True,
			    help='''Size of the model (can be either \'small\', \'medium\' or \'large\').''',
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
			)
			_lowerCamelCase :       Optional[Any]     					=    parser.parse_args()
			convert_imagegpt_checkpoint_to_pytorch(
			    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
			)