




import gc
import random
import unittest

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

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class       a_       (     SCREAMING_SNAKE_CASE__  ,    SCREAMING_SNAKE_CASE__  ,    unittest.TestCase     ):
	A    							= CycleDiffusionPipeline
	A    							= TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
	    '''negative_prompt''',
	    '''height''',
	    '''width''',
	    '''negative_prompt_embeds''',
	}
	A    							= PipelineTesterMixin.required_optional_params - {'''latents'''}
	A    							= TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'''source_prompt'''}     )
	A    							= IMAGE_TO_IMAGE_IMAGE_PARAMS
	A    							= IMAGE_TO_IMAGE_IMAGE_PARAMS





	def 						A_(							self      )  ->					Dict:




				"""simple docstring"""



				torch.manual_seed(0      )
				SCREAMING_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      ,     )
				SCREAMING_SNAKE_CASE_								=						DDIMScheduler(
				    beta_start=0.0_0_0_8_5      ,     beta_end=0.0_1_2      ,     beta_schedule='scaled_linear'      ,     num_train_timesteps=1000      ,     clip_sample=SCREAMING_SNAKE_CASE      ,     set_alpha_to_one=SCREAMING_SNAKE_CASE      ,     )
				torch.manual_seed(0      )
				SCREAMING_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      ,     )
				torch.manual_seed(0      )
				SCREAMING_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=1000      ,     )
				SCREAMING_SNAKE_CASE_								=						CLIPTextModel(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

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





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE=0      )  ->					List[str]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						floats_tensor((1, 3, 32, 32)      ,     rng=random.Random(SCREAMING_SNAKE_CASE      )      ).to(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						image / 2 + 0.5
				if str(SCREAMING_SNAKE_CASE      ).startswith('mps'      ):
							SCREAMING_SNAKE_CASE_								=						torch.manual_seed(SCREAMING_SNAKE_CASE      )
				else:
							SCREAMING_SNAKE_CASE_								=						torch.Generator(device=SCREAMING_SNAKE_CASE      ).manual_seed(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						{
				    'prompt': 'An astronaut riding an elephant',
				    'source_prompt': 'An astronaut riding a horse',
				    'image': image,
				    'generator': generator,
				    'num_inference_steps': 2,
				    'eta': 0.1,
				    'strength': 0.8,
				    'guidance_scale': 3,
				    'source_guidance_scale': 1,
				    'output_type': 'numpy',
				}
				return inputs





	def 						A_(							self      )  ->					Tuple:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						'cpu'  # ensure determinism for the device-dependent torch.Generator

				SCREAMING_SNAKE_CASE_								=						self.get_dummy_components()
				SCREAMING_SNAKE_CASE_								=						CycleDiffusionPipeline(**SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						pipe.to(SCREAMING_SNAKE_CASE      )
				pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE      )

				SCREAMING_SNAKE_CASE_								=						self.get_dummy_inputs(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						pipe(**SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						output.images

				SCREAMING_SNAKE_CASE_								=						images[0, -3:, -3:, -1]

				assert images.shape == (1, 32, 32, 3)
				SCREAMING_SNAKE_CASE_								=						np.array([0.4_4_5_9, 0.4_9_4_3, 0.4_5_4_4, 0.6_6_4_3, 0.5_4_7_4, 0.4_3_2_7, 0.5_7_0_1, 0.5_9_5_9, 0.5_1_7_9]      )

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





	@unittest.skipIf(torch_device != 'cuda'      ,     'This test requires a GPU'      )
	def 						A_(							self      )  ->					Dict:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.get_dummy_components()
				for name, module in components.items():
							if hasattr(SCREAMING_SNAKE_CASE      ,     'half'      ):
										SCREAMING_SNAKE_CASE_								=						module.half()
				SCREAMING_SNAKE_CASE_								=						CycleDiffusionPipeline(**SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						pipe.to(SCREAMING_SNAKE_CASE      )
				pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE      )

				SCREAMING_SNAKE_CASE_								=						self.get_dummy_inputs(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						pipe(**SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						output.images

				SCREAMING_SNAKE_CASE_								=						images[0, -3:, -3:, -1]

				assert images.shape == (1, 32, 32, 3)
				SCREAMING_SNAKE_CASE_								=						np.array([0.3_5_0_6, 0.4_5_4_3, 0.4_4_6, 0.4_5_7_5, 0.5_1_9_5, 0.4_1_5_5, 0.5_2_7_3, 0.5_1_8, 0.4_1_1_6]      )

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





	@skip_mps
	def 						A_(							self      )  ->					List[Any]:




				"""simple docstring"""



				return super().test_save_load_local()





	@unittest.skip('non-deterministic pipeline'      )
	def 						A_(							self      )  ->					Optional[Any]:




				"""simple docstring"""



				return super().test_inference_batch_single_identical()





	@skip_mps
	def 						A_(							self      )  ->					Dict:




				"""simple docstring"""



				return super().test_dict_tuple_outputs_equivalent()





	@skip_mps
	def 						A_(							self      )  ->					List[Any]:




				"""simple docstring"""



				return super().test_save_load_optional_components()





	@skip_mps
	def 						A_(							self      )  ->					Any:




				"""simple docstring"""



				return super().test_attention_slicing_forward_pass()




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





	def 						A_(							self      )  ->					Optional[int]:




				"""simple docstring"""



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





	def 						A_(							self      )  ->					int:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						load_image(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
				    '/cycle-diffusion/black_colored_car.png'      )
				SCREAMING_SNAKE_CASE_								=						load_numpy(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'      )
				SCREAMING_SNAKE_CASE_								=						init_image.resize((512, 512)      )

				SCREAMING_SNAKE_CASE_								=						'CompVis/stable-diffusion-v1-4'
				SCREAMING_SNAKE_CASE_								=						DDIMScheduler.from_pretrained(SCREAMING_SNAKE_CASE      ,     subfolder='scheduler'      )
				SCREAMING_SNAKE_CASE_								=						CycleDiffusionPipeline.from_pretrained(
				    SCREAMING_SNAKE_CASE      ,     scheduler=SCREAMING_SNAKE_CASE      ,     safety_checker=SCREAMING_SNAKE_CASE      ,     torch_dtype=torch.floataa      ,     revision='fp16'      )

				pipe.to(SCREAMING_SNAKE_CASE      )
				pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE      )
				pipe.enable_attention_slicing()

				SCREAMING_SNAKE_CASE_								=						'A black colored car'
				SCREAMING_SNAKE_CASE_								=						'A blue colored car'

				SCREAMING_SNAKE_CASE_								=						torch.manual_seed(0      )
				SCREAMING_SNAKE_CASE_								=						pipe(
				    prompt=SCREAMING_SNAKE_CASE      ,     source_prompt=SCREAMING_SNAKE_CASE      ,     image=SCREAMING_SNAKE_CASE      ,     num_inference_steps=100      ,     eta=0.1      ,     strength=0.8_5      ,     guidance_scale=3      ,     source_guidance_scale=1      ,     generator=SCREAMING_SNAKE_CASE      ,     output_type='np'      ,     )
				SCREAMING_SNAKE_CASE_								=						output.images

				# the values aren't exactly equal, but the images look the same visually
				assert np.abs(image - expected_image      ).max() < 5e-1





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




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						load_image(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
				    '/cycle-diffusion/black_colored_car.png'      )
				SCREAMING_SNAKE_CASE_								=						load_numpy(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'      )
				SCREAMING_SNAKE_CASE_								=						init_image.resize((512, 512)      )

				SCREAMING_SNAKE_CASE_								=						'CompVis/stable-diffusion-v1-4'
				SCREAMING_SNAKE_CASE_								=						DDIMScheduler.from_pretrained(SCREAMING_SNAKE_CASE      ,     subfolder='scheduler'      )
				SCREAMING_SNAKE_CASE_								=						CycleDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE      ,     scheduler=SCREAMING_SNAKE_CASE      ,     safety_checker=SCREAMING_SNAKE_CASE      )

				pipe.to(SCREAMING_SNAKE_CASE      )
				pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE      )
				pipe.enable_attention_slicing()

				SCREAMING_SNAKE_CASE_								=						'A black colored car'
				SCREAMING_SNAKE_CASE_								=						'A blue colored car'

				SCREAMING_SNAKE_CASE_								=						torch.manual_seed(0      )
				SCREAMING_SNAKE_CASE_								=						pipe(
				    prompt=SCREAMING_SNAKE_CASE      ,     source_prompt=SCREAMING_SNAKE_CASE      ,     image=SCREAMING_SNAKE_CASE      ,     num_inference_steps=100      ,     eta=0.1      ,     strength=0.8_5      ,     guidance_scale=3      ,     source_guidance_scale=1      ,     generator=SCREAMING_SNAKE_CASE      ,     output_type='np'      ,     )
				SCREAMING_SNAKE_CASE_								=						output.images

				assert np.abs(image - expected_image      ).max() < 2e-2






import argparse

import torch

from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging


logging.set_verbosity_info()

def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							Union[str, Any]:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						FunnelConfig.from_json_file(SCREAMING_SNAKE_CASE )
			print(F'Building PyTorch model from configuration: {config}' )
			SCREAMING_SNAKE_CASE_								=						FunnelBaseModel(SCREAMING_SNAKE_CASE ) if base_model else FunnelModel(SCREAMING_SNAKE_CASE )

			# Load weights from tf checkpoint
			load_tf_weights_in_funnel(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )

			# Save pytorch-model
			print(F'Save PyTorch model to {pytorch_dump_path}' )
			torch.save(model.state_dict()       ,    SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
				SCREAMING_SNAKE_CASE__     :     Union[str, Any]		    =       argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
				)
				parser.add_argument(
				    "--config_file",
				    default=None,
				    type=str,
				    required=True,
				    help="The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.",
				)
				parser.add_argument(
				    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
				)
				parser.add_argument(
				    "--base_model", action="store_true", help="Whether you want just the base model (no decoder) or not."
				)
				SCREAMING_SNAKE_CASE__     :     List[Any]		    =       parser.parse_args()
				convert_tf_checkpoint_to_pytorch(
				    args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
				)
