

import gc
import random
import unittest

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

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class   __lowercase				( UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_		,       unittest.TestCase	):




			"""simple docstring"""







			_UpperCAmelCase   :							Any										=     StableDiffusionInpaintPipeline
			_UpperCAmelCase   :							Dict										=     TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			_UpperCAmelCase   :							int										=     TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			_UpperCAmelCase   :							Optional[int]										=     frozenset(
			    []	)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
			_UpperCAmelCase   :							Tuple										=     frozenset([]	)



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									torch.manual_seed(0)
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       UNetaDConditionModel(
									    block_out_channels=(32, 64)   ,						layers_per_block=2   ,						sample_size=32   ,						in_channels=9   ,						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=A__   ,						)
									SCREAMING_SNAKE_CASE_: Any  		=       PNDMScheduler(skip_prk_steps=A__)
									torch.manual_seed(0)
									SCREAMING_SNAKE_CASE_: Any  		=       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)
									SCREAMING_SNAKE_CASE_: Tuple  		=       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   ,						)
									SCREAMING_SNAKE_CASE_: Dict  		=       CLIPTextModel(A__)
									SCREAMING_SNAKE_CASE_: List[Any]  		=       CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       {
									    """unet""": unet,
									    """scheduler""": scheduler,
									    """vae""": vae,
									    """text_encoder""": text_encoder,
									    """tokenizer""": tokenizer,
									    """safety_checker""": None,
									    """feature_extractor""": None,
									}
									return components



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]   ,						lowerCAmelCase__  :     Tuple   ,						lowerCAmelCase__  :     Dict=0):
									# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
									SCREAMING_SNAKE_CASE_: List[str]  		=       floats_tensor((1, 3, 32, 32)   ,						rng=random.Random(A__)).to(A__)
									SCREAMING_SNAKE_CASE_: Dict  		=       image.cpu().permute(0   ,						2   ,						3   ,						1)[0]
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       Image.fromarray(np.uinta(A__)).convert("RGB").resize((64, 64))
									SCREAMING_SNAKE_CASE_: Any  		=       Image.fromarray(np.uinta(image + 4)).convert("RGB").resize((64, 64))
									if str(A__).startswith("mps"):
															SCREAMING_SNAKE_CASE_: List[str]  		=       torch.manual_seed(A__)
									else:
															SCREAMING_SNAKE_CASE_: int  		=       torch.Generator(device=A__).manual_seed(A__)
									SCREAMING_SNAKE_CASE_: Tuple  		=       {
									    """prompt""": """A painting of a squirrel eating a burger""",
									    """image""": init_image,
									    """mask_image""": mask_image,
									    """generator""": generator,
									    """num_inference_steps""": 2,
									    """guidance_scale""": 6.0,
									    """output_type""": """numpy""",
									}
									return inputs



			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]):
									SCREAMING_SNAKE_CASE_: str  		=       """cpu"""  # ensure determinism for the device-dependent torch.Generator
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       self.get_dummy_components()
									SCREAMING_SNAKE_CASE_: List[Any]  		=       StableDiffusionInpaintPipeline(**A__)
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       sd_pipe.to(A__)
									sd_pipe.set_progress_bar_config(disable=A__)

									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       self.get_dummy_inputs(A__)
									SCREAMING_SNAKE_CASE_: List[Any]  		=       sd_pipe(**A__).images
									SCREAMING_SNAKE_CASE_: List[Any]  		=       image[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476])

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




			def        _SCREAMING_SNAKE_CASE			( self  :     Tuple):
									super().test_inference_batch_single_identical(expected_max_diff=3E-3)







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




			"""simple docstring"""







			def        _SCREAMING_SNAKE_CASE			( self  :     Any):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									SCREAMING_SNAKE_CASE_: Tuple  		=       load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
									    "/sd2-inpaint/init_image.png")
									SCREAMING_SNAKE_CASE_: Any  		=       load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png")
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       load_numpy(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
									    "/yellow_cat_sitting_on_a_park_bench.npy")

									SCREAMING_SNAKE_CASE_: int  		=       """stabilityai/stable-diffusion-2-inpainting"""
									SCREAMING_SNAKE_CASE_: List[str]  		=       StableDiffusionInpaintPipeline.from_pretrained(A__   ,						safety_checker=A__)
									pipe.to(A__)
									pipe.set_progress_bar_config(disable=A__)
									pipe.enable_attention_slicing()

									SCREAMING_SNAKE_CASE_: int  		=       """Face of a yellow cat, high resolution, sitting on a park bench"""

									SCREAMING_SNAKE_CASE_: Tuple  		=       torch.manual_seed(0)
									SCREAMING_SNAKE_CASE_: Tuple  		=       pipe(
									    prompt=A__   ,						image=A__   ,						mask_image=A__   ,						generator=A__   ,						output_type="np"   ,						)
									SCREAMING_SNAKE_CASE_: int  		=       output.images[0]

									assert image.shape == (512, 512, 3)
									assert np.abs(expected_image - image).max() < 9E-3



			def        _SCREAMING_SNAKE_CASE			( self  :     str):
									SCREAMING_SNAKE_CASE_: str  		=       load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
									    "/sd2-inpaint/init_image.png")
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png")
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       load_numpy(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
									    "/yellow_cat_sitting_on_a_park_bench_fp16.npy")

									SCREAMING_SNAKE_CASE_: Optional[int]  		=       """stabilityai/stable-diffusion-2-inpainting"""
									SCREAMING_SNAKE_CASE_: Any  		=       StableDiffusionInpaintPipeline.from_pretrained(
									    A__   ,						torch_dtype=torch.floataa   ,						safety_checker=A__   ,						)
									pipe.to(A__)
									pipe.set_progress_bar_config(disable=A__)
									pipe.enable_attention_slicing()

									SCREAMING_SNAKE_CASE_: List[str]  		=       """Face of a yellow cat, high resolution, sitting on a park bench"""

									SCREAMING_SNAKE_CASE_: List[str]  		=       torch.manual_seed(0)
									SCREAMING_SNAKE_CASE_: List[Any]  		=       pipe(
									    prompt=A__   ,						image=A__   ,						mask_image=A__   ,						generator=A__   ,						output_type="np"   ,						)
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       output.images[0]

									assert image.shape == (512, 512, 3)
									assert np.abs(expected_image - image).max() < 5E-1




			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]):
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

									SCREAMING_SNAKE_CASE_: Any  		=       load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
									    "/sd2-inpaint/init_image.png")
									SCREAMING_SNAKE_CASE_: List[str]  		=       load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png")

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       """stabilityai/stable-diffusion-2-inpainting"""
									SCREAMING_SNAKE_CASE_: List[str]  		=       PNDMScheduler.from_pretrained(A__   ,						subfolder="scheduler")
									SCREAMING_SNAKE_CASE_: List[Any]  		=       StableDiffusionInpaintPipeline.from_pretrained(
									    A__   ,						safety_checker=A__   ,						scheduler=A__   ,						torch_dtype=torch.floataa   ,						)
									pipe.to(A__)
									pipe.set_progress_bar_config(disable=A__)
									pipe.enable_attention_slicing(1)
									pipe.enable_sequential_cpu_offload()

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       """Face of a yellow cat, high resolution, sitting on a park bench"""

									SCREAMING_SNAKE_CASE_: Any  		=       torch.manual_seed(0)
									SCREAMING_SNAKE_CASE_: str  		=       pipe(
									    prompt=A__   ,						image=A__   ,						mask_image=A__   ,						generator=A__   ,						num_inference_steps=2   ,						output_type="np"   ,						)

									SCREAMING_SNAKE_CASE_: int  		=       torch.cuda.max_memory_allocated()
									# make sure that less than 2.65 GB is allocated
									assert mem_bytes < 2.65 * 10**9









import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()

def 						UpperCamelCase					(lowercase_: Optional[int]    ,      lowercase_: Union[str, Any]    ,      lowercase_: Optional[Any]					)     ->      Tuple:
	# Initialise PyTorch model
	A__							:    str        =		AlbertConfig.from_json_file(lowercase_					)
	print(f"""Building PyTorch model from configuration: {config}"""					)
	A__							:    List[Any]        =		AlbertForPreTraining(lowercase_					)

	# Load weights from tf checkpoint
	load_tf_weights_in_albert(lowercase_    ,      lowercase_    ,      lowercase_					)

	# Save pytorch-model
	print(f"""Save PyTorch model to {pytorch_dump_path}"""					)
	torch.save(model.state_dict()    ,      lowercase_					)


if __name__ == "__main__":
	A_       : int         =			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(
	    '--albert_config_file',
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        'The config json file corresponding to the pre-trained ALBERT model. \n'
	        'This specifies the model architecture.'
	    ),
	)
	parser.add_argument(
	    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
	)
	A_       : int         =			parser.parse_args()
	convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)



