






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        a__      (	lowerCAmelCase__	,       lowerCAmelCase__	,       unittest.TestCase		):
		UpperCAmelCase_							:       Optional[int] 					= StableDiffusionParadigmsPipeline
		UpperCAmelCase_							:       Tuple 					= TEXT_TO_IMAGE_PARAMS
		UpperCAmelCase_							:       Dict 					= TEXT_TO_IMAGE_BATCH_PARAMS
		UpperCAmelCase_							:       List[Any] 					= TEXT_TO_IMAGE_IMAGE_PARAMS
		UpperCAmelCase_							:       Tuple 					= TEXT_TO_IMAGE_IMAGE_PARAMS






		def 					_lowerCamelCase					(      self						)				->				Optional[int]:
				torch.manual_seed(0						)
				__A    =							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							, attention_head_dim=(2, 4)							, use_linear_projection=lowercase__							, )
				__A    =							DDIMParallelScheduler(
				    beta_start=0.0_0085							, beta_end=0.012							, beta_schedule="scaled_linear"							, clip_sample=lowercase__							, set_alpha_to_one=lowercase__							, )
				torch.manual_seed(0						)
				__A    =							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							, sample_size=128							, )
				torch.manual_seed(0						)
				__A    =							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							, hidden_act="gelu"							, projection_dim=512							, )
				__A    =							CLIPTextModel(lowercase__						)
				__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							, lowercase__							, lowercase__=0						)				->				Union[str, Any]:
				if str(lowercase__						).startswith("mps"						):
						__A    =							torch.manual_seed(lowercase__						)
				else:
						__A    =							torch.Generator(device=lowercase__						).manual_seed(lowercase__						)
				__A    =							{
				    "prompt": "a photograph of an astronaut riding a horse",
				    "generator": generator,
				    "num_inference_steps": 10,
				    "guidance_scale": 6.0,
				    "output_type": "numpy",
				    "parallel": 3,
				    "debug": True,
				}
				return inputs






		def 					_lowerCamelCase					(      self						)				->				Tuple:
				__A    =							"cpu"  # ensure determinism for the device-dependent torch.Generator
				__A    =							self.get_dummy_components()
				__A    =							StableDiffusionParadigmsPipeline(**lowercase__						)
				__A    =							sd_pipe.to(lowercase__						)
				sd_pipe.set_progress_bar_config(disable=lowercase__						)

				__A    =							self.get_dummy_inputs(lowercase__						)
				__A    =							sd_pipe(**lowercase__						).images
				__A    =							image[0, -3:, -3:, -1]
				assert image.shape == (1, 64, 64, 3)

				__A    =							np.array([0.4773, 0.5417, 0.4723, 0.4925, 0.5631, 0.4752, 0.5240, 0.4935, 0.5023]						)

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






		def 					_lowerCamelCase					(      self						)				->				Optional[Any]:
				__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(**lowercase__						)
				__A    =							sd_pipe.to(lowercase__						)
				sd_pipe.set_progress_bar_config(disable=lowercase__						)

				__A    =							self.get_dummy_inputs(lowercase__						)
				__A    =							sd_pipe(**lowercase__						).images
				__A    =							image[0, -3:, -3:, -1]
				assert image.shape == (1, 64, 64, 3)

				__A    =							np.array([0.3573, 0.4420, 0.4960, 0.4799, 0.3796, 0.3879, 0.4819, 0.4365, 0.4468]						)

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






		def 					_lowerCamelCase					(      self						)				->				Union[str, Any]:
				super().test_inference_batch_consistent(batch_sizes=[1, 2]						)






		def 					_lowerCamelCase					(      self						)				->				List[str]:
				super().test_inference_batch_single_identical(batch_size=2							, expected_max_diff=3e-3						)






		def 					_lowerCamelCase					(      self						)				->				Optional[Any]:
				__A    =							"cpu"  # ensure determinism for the device-dependent torch.Generator
				__A    =							self.get_dummy_components()
				__A    =							StableDiffusionParadigmsPipeline(**lowercase__						)
				__A    =							sd_pipe.to(lowercase__						)
				sd_pipe.set_progress_bar_config(disable=lowercase__						)

				__A    =							self.get_dummy_inputs(lowercase__						)
				__A    =							"french fries"
				__A    =							sd_pipe(**lowercase__							, negative_prompt=lowercase__						)
				__A    =							output.images
				__A    =							image[0, -3:, -3:, -1]

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

				__A    =							np.array([0.4771, 0.5420, 0.4683, 0.4918, 0.5636, 0.4725, 0.5230, 0.4923, 0.5015]						)

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





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






		def 					_lowerCamelCase					(      self						)				->				Optional[int]:
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()






		def 					_lowerCamelCase					(      self							, lowercase__=0						)				->				Optional[Any]:
				__A    =							torch.Generator(device=lowercase__						).manual_seed(lowercase__						)
				__A    =							{
				    "prompt": "a photograph of an astronaut riding a horse",
				    "generator": generator,
				    "num_inference_steps": 10,
				    "guidance_scale": 7.5,
				    "output_type": "numpy",
				    "parallel": 3,
				    "debug": True,
				}
				return inputs






		def 					_lowerCamelCase					(      self						)				->				Dict:
				__A    =							"stabilityai/stable-diffusion-2-base"
				__A    =							DDIMParallelScheduler.from_pretrained(lowercase__							, subfolder="scheduler"						)
				__A    =							StableDiffusionParadigmsPipeline.from_pretrained(lowercase__							, scheduler=lowercase__							, safety_checker=lowercase__						)
				pipe.to(lowercase__						)
				pipe.set_progress_bar_config(disable=lowercase__						)
				pipe.enable_attention_slicing()

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

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

				__A    =							np.array([0.9622, 0.9602, 0.9748, 0.9591, 0.9630, 0.9691, 0.9661, 0.9631, 0.9741]						)

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




"""simple docstring"""
import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()







def       UpperCamelCase_					(     lowerCamelCase							:					List[str]     , lowerCamelCase							:					Optional[int]     , lowerCamelCase							:					List[str]   )						->     Optional[int]:



       """simple docstring"""







       __magic_name__			:		int        =     TaConfig.from_json_file(lowerCamelCase   )
       print(f"""Building PyTorch model from configuration: {config}"""   )
       __magic_name__			:		Union[str, Any]        =     TaForConditionalGeneration(lowerCamelCase   )

       # Load weights from tf checkpoint
       load_tf_weights_in_ta(lowerCamelCase     , lowerCamelCase     , lowerCamelCase   )

       # Save pytorch-model
       print(f"""Save PyTorch model to {pytorch_dump_path}"""   )
       model.save_pretrained(lowerCamelCase   )


if __name__ == "__main__":
 A									=						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 T5 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."""
 )
 A									=						parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
