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 			UpperCamelCase					(			unittest.TestCase			):

     '''simple docstring'''

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






           """simple docstring"""
           # clean up the VRAM after each test
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()



     @property
     def 		UpperCamelCase_    (       self    )			->					List[Any]:






           """simple docstring"""
           _lowerCamelCase           =					1
           _lowerCamelCase           =					3
           _lowerCamelCase           =					(32, 32)

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



     @property
     def 		UpperCamelCase_    (       self    )			->					Union[str, Any]:






           """simple docstring"""
           torch.manual_seed(0    )
           _lowerCamelCase           =					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 		UpperCamelCase_    (       self    )			->					Optional[Any]:






           """simple docstring"""
           torch.manual_seed(0    )
           _lowerCamelCase           =					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 		UpperCamelCase_    (       self    )			->					Tuple:






           """simple docstring"""
           torch.manual_seed(0    )
           _lowerCamelCase           =					CLIPTextConfig(
               bos_token_id=0					,					eos_token_id=2					,					hidden_size=32					,					intermediate_size=37					,					layer_norm_eps=1E-0_5					,					num_attention_heads=4					,					num_hidden_layers=5					,					pad_token_id=1					,					vocab_size=10_00					,					)
           return CLIPTextModel(A_    )



     @property
     def 		UpperCamelCase_    (       self    )			->					Any:






           """simple docstring"""
           def extract(*A_					,					**A_    ):



                 class 			UpperCamelCase					:

                      '''simple docstring'''

                      def __init__(       self    )			->					Any:






                            """simple docstring"""
                            _lowerCamelCase           =					torch.ones([0]    )





                      def 		UpperCamelCase_    (       self					,					A_    )			->					List[str]:






                            """simple docstring"""
                            self.pixel_values.to(A_    )
                            return self





                 return Out()

           return extract



     def 		UpperCamelCase_    (       self    )			->					Tuple:






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

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

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

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

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

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

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

           assert image.shape == (1, 64, 64, 3)
           _lowerCamelCase           =					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 		UpperCamelCase_    (       self    )			->					Optional[Any]:






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

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

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

           _lowerCamelCase           =					output.images

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

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

           assert image.shape == (1, 64, 64, 3)
           _lowerCamelCase           =					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 		UpperCamelCase_    (       self    )			->					str:






           """simple docstring"""
           _lowerCamelCase           =					StableDiffusionPipeline.from_pretrained(
               '''hf-internal-testing/tiny-stable-diffusion-lms-pipe'''					,					safety_checker=A_    )
           assert isinstance(A_					,					A_    )
           assert isinstance(pipe.scheduler					,					A_    )
           assert pipe.safety_checker is None

           _lowerCamelCase           =					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(A_    )
                 _lowerCamelCase           =					StableDiffusionPipeline.from_pretrained(A_    )

           # sanity check that the pipeline still works
           assert pipe.safety_checker is None
           _lowerCamelCase           =					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 		UpperCamelCase_    (       self    )			->					Optional[Any]:






           """simple docstring"""
           _lowerCamelCase           =					self.dummy_cond_unet
           _lowerCamelCase           =					PNDMScheduler(skip_prk_steps=A_    )
           _lowerCamelCase           =					self.dummy_vae
           _lowerCamelCase           =					self.dummy_text_encoder
           _lowerCamelCase           =					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''    )

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

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

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

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





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

     '''simple docstring'''

     def 		UpperCamelCase_    (       self    )			->					Dict:






           """simple docstring"""
           # clean up the VRAM after each test
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()



     def 		UpperCamelCase_    (       self    )			->					Any:






           """simple docstring"""
           _lowerCamelCase           =					'''cuda'''
           _lowerCamelCase           =					StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''    )
           _lowerCamelCase           =					pipe.to(A_    )
           pipe.set_progress_bar_config(disable=A_    )

           _lowerCamelCase           =					'''a photo of a cat'''
           _lowerCamelCase           =					{
               '''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,
           }

           _lowerCamelCase           =					3
           _lowerCamelCase           =					7

           # no sega enabled
           _lowerCamelCase           =					torch.Generator(A_    )
           generator.manual_seed(A_    )
           _lowerCamelCase           =					pipe(
               [prompt]					,					generator=A_					,					guidance_scale=A_					,					num_inference_steps=50					,					output_type='''np'''					,					width=5_12					,					height=5_12					,					)

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

           assert image.shape == (1, 5_12, 5_12, 3)

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

           # with sega enabled
           # generator = torch.manual_seed(seed)
           generator.manual_seed(A_    )
           _lowerCamelCase           =					pipe(
               [prompt]					,					generator=A_					,					guidance_scale=A_					,					num_inference_steps=50					,					output_type='''np'''					,					width=5_12					,					height=5_12					,					**A_					,					)

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

           assert image.shape == (1, 5_12, 5_12, 3)

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



     def 		UpperCamelCase_    (       self    )			->					int:






           """simple docstring"""
           _lowerCamelCase           =					'''cuda'''
           _lowerCamelCase           =					StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''    )
           _lowerCamelCase           =					pipe.to(A_    )
           pipe.set_progress_bar_config(disable=A_    )

           _lowerCamelCase           =					'''an image of a crowded boulevard, realistic, 4k'''
           _lowerCamelCase           =					{
               '''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,
           }

           _lowerCamelCase           =					9
           _lowerCamelCase           =					7

           # no sega enabled
           _lowerCamelCase           =					torch.Generator(A_    )
           generator.manual_seed(A_    )
           _lowerCamelCase           =					pipe(
               [prompt]					,					generator=A_					,					guidance_scale=A_					,					num_inference_steps=50					,					output_type='''np'''					,					width=5_12					,					height=5_12					,					)

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

           assert image.shape == (1, 5_12, 5_12, 3)

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

           # with sega enabled
           # generator = torch.manual_seed(seed)
           generator.manual_seed(A_    )
           _lowerCamelCase           =					pipe(
               [prompt]					,					generator=A_					,					guidance_scale=A_					,					num_inference_steps=50					,					output_type='''np'''					,					width=5_12					,					height=5_12					,					**A_					,					)

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

           assert image.shape == (1, 5_12, 5_12, 3)

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



     def 		UpperCamelCase_    (       self    )			->					Any:






           """simple docstring"""
           _lowerCamelCase           =					'''cuda'''
           _lowerCamelCase           =					StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''    )
           _lowerCamelCase           =					pipe.to(A_    )
           pipe.set_progress_bar_config(disable=A_    )

           _lowerCamelCase           =					'''a castle next to a river'''
           _lowerCamelCase           =					{
               '''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,
           }

           _lowerCamelCase           =					48
           _lowerCamelCase           =					7

           # no sega enabled
           _lowerCamelCase           =					torch.Generator(A_    )
           generator.manual_seed(A_    )
           _lowerCamelCase           =					pipe(
               [prompt]					,					generator=A_					,					guidance_scale=A_					,					num_inference_steps=50					,					output_type='''np'''					,					width=5_12					,					height=5_12					,					)

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

           assert image.shape == (1, 5_12, 5_12, 3)

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

           # with sega enabled
           # generator = torch.manual_seed(seed)
           generator.manual_seed(A_    )
           _lowerCamelCase           =					pipe(
               [prompt]					,					generator=A_					,					guidance_scale=A_					,					num_inference_steps=50					,					output_type='''np'''					,					width=5_12					,					height=5_12					,					**A_					,					)

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

           assert image.shape == (1, 5_12, 5_12, 3)

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





     def 		UpperCamelCase_    (       self    )			->					Any:






           """simple docstring"""
           _lowerCamelCase           =					'''cuda'''
           _lowerCamelCase           =					StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''					,					torch_dtype=torch.floataa    )
           _lowerCamelCase           =					pipe.to(A_    )
           pipe.set_progress_bar_config(disable=A_    )

           _lowerCamelCase           =					'''a photo of a cat'''
           _lowerCamelCase           =					{
               '''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,
           }

           _lowerCamelCase           =					3
           _lowerCamelCase           =					7

           # no sega enabled
           _lowerCamelCase           =					torch.Generator(A_    )
           generator.manual_seed(A_    )
           _lowerCamelCase           =					pipe(
               [prompt]					,					generator=A_					,					guidance_scale=A_					,					num_inference_steps=50					,					output_type='''np'''					,					width=5_12					,					height=5_12					,					)

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

           assert image.shape == (1, 5_12, 5_12, 3)

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

           # with sega enabled
           # generator = torch.manual_seed(seed)
           generator.manual_seed(A_    )
           _lowerCamelCase           =					pipe(
               [prompt]					,					generator=A_					,					guidance_scale=A_					,					num_inference_steps=50					,					output_type='''np'''					,					width=5_12					,					height=5_12					,					**A_					,					)

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

           assert image.shape == (1, 5_12, 5_12, 3)

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

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()
def    __magic_name__(	__UpperCAmelCase			,    __UpperCAmelCase		)    ->     Tuple:



      '''simple docstring'''

      _lowerCamelCase           =					CanineConfig()
      _lowerCamelCase           =					CanineModel(__UpperCAmelCase		)
      model.eval()

      print(F'Building PyTorch model from configuration: {config}'		)

      # Load weights from tf checkpoint
      load_tf_weights_in_canine(__UpperCAmelCase			,    __UpperCAmelCase			,    __UpperCAmelCase		)

      # Save pytorch-model (weights and configuration)
      print(F'Save PyTorch model to {pytorch_dump_path}'		)
      model.save_pretrained(__UpperCAmelCase		)

      # Save tokenizer files
      _lowerCamelCase           =					CanineTokenizer()
      print(F'Save tokenizer files to {pytorch_dump_path}'		)
      tokenizer.save_pretrained(__UpperCAmelCase		)


if __name__ == "__main__":
     snake_case__						      =			argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--tf_checkpoint_path',
         default=None,
         type=str,
         required=True,
         help='Path to the TensorFlow checkpoint. Should end with model.ckpt',
     )
     parser.add_argument(
         '--pytorch_dump_path',
         default=None,
         type=str,
         required=True,
         help='Path to a folder where the PyTorch model will be placed.',
     )
     snake_case__						      =			parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)