
"""simple docstring"""

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		):
	lowercase_				=     StableDiffusionInpaintPipeline
	lowercase_				=     TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
	lowercase_				=     TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
	lowercase_				=     frozenset(
	    []		)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
	lowercase_				=     frozenset([]		)




	def       _UpperCamelCase   (			self )		->							int:
			torch.manual_seed(0 )
			lowerCamelCase					:      Optional[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=__lowerCamelCase		,			)
			lowerCamelCase					:      Optional[Any]          =   PNDMScheduler(skip_prk_steps=__lowerCamelCase )
			torch.manual_seed(0 )
			lowerCamelCase					:      List[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 )
			lowerCamelCase					:      Any          =   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=1000		,			hidden_act='gelu'		,			projection_dim=512		,			)
			lowerCamelCase					:      Dict          =   CLIPTextModel(__lowerCamelCase )
			lowerCamelCase					:      Optional[int]          =   CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )

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




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_=0 )		->							List[str]:
			# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
			lowerCamelCase					:      Tuple          =   floats_tensor((1, 3, 32, 32)		,			rng=random.Random(__lowerCamelCase ) ).to(__lowerCamelCase )
			lowerCamelCase					:      Optional[int]          =   image.cpu().permute(0		,			2		,			3		,			1 )[0]
			lowerCamelCase					:      Optional[int]          =   Image.fromarray(np.uinta(__lowerCamelCase ) ).convert('RGB' ).resize((64, 64) )
			lowerCamelCase					:      Tuple          =   Image.fromarray(np.uinta(image + 4 ) ).convert('RGB' ).resize((64, 64) )
			if str(__lowerCamelCase ).startswith('mps' ):
					lowerCamelCase					:      Dict          =   torch.manual_seed(__lowerCamelCase )
			else:
					lowerCamelCase					:      Dict          =   torch.Generator(device=__lowerCamelCase ).manual_seed(__lowerCamelCase )
			lowerCamelCase					:      Any          =   {
			    '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       _UpperCamelCase   (			self )		->							List[Any]:
			lowerCamelCase					:      Optional[Any]          =   'cpu'  # ensure determinism for the device-dependent torch.Generator
			lowerCamelCase					:      Dict          =   self.get_dummy_components()
			lowerCamelCase					:      List[str]          =   StableDiffusionInpaintPipeline(**__lowerCamelCase )
			lowerCamelCase					:      List[Any]          =   sd_pipe.to(__lowerCamelCase )
			sd_pipe.set_progress_bar_config(disable=__lowerCamelCase )

			lowerCamelCase					:      List[Any]          =   self.get_dummy_inputs(__lowerCamelCase )
			lowerCamelCase					:      List[Any]          =   sd_pipe(**__lowerCamelCase ).images
			lowerCamelCase					:      str          =   image[0, -3:, -3:, -1]

			assert image.shape == (1, 64, 64, 3)
			lowerCamelCase					:      Tuple          =   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       _UpperCamelCase   (			self )		->							Any:
			super().test_inference_batch_single_identical(expected_max_diff=3E-3 )






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




	def       _UpperCamelCase   (			self )		->							Tuple:
			# clean up the VRAM after each test
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()




	def       _UpperCamelCase   (			self )		->							List[str]:
			lowerCamelCase					:      Dict          =   load_image(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
			    '/sd2-inpaint/init_image.png' )
			lowerCamelCase					:      Optional[int]          =   load_image(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png' )
			lowerCamelCase					:      Optional[Any]          =   load_numpy(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
			    '/yellow_cat_sitting_on_a_park_bench.npy' )

			lowerCamelCase					:      Any          =   'stabilityai/stable-diffusion-2-inpainting'
			lowerCamelCase					:      str          =   StableDiffusionInpaintPipeline.from_pretrained(__lowerCamelCase		,			safety_checker=__lowerCamelCase )
			pipe.to(__lowerCamelCase )
			pipe.set_progress_bar_config(disable=__lowerCamelCase )
			pipe.enable_attention_slicing()

			lowerCamelCase					:      Tuple          =   'Face of a yellow cat, high resolution, sitting on a park bench'

			lowerCamelCase					:      List[Any]          =   torch.manual_seed(0 )
			lowerCamelCase					:      str          =   pipe(
			    prompt=__lowerCamelCase		,			image=__lowerCamelCase		,			mask_image=__lowerCamelCase		,			generator=__lowerCamelCase		,			output_type='np'		,			)
			lowerCamelCase					:      Dict          =   output.images[0]

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




	def       _UpperCamelCase   (			self )		->							Dict:
			lowerCamelCase					:      Union[str, Any]          =   load_image(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
			    '/sd2-inpaint/init_image.png' )
			lowerCamelCase					:      str          =   load_image(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png' )
			lowerCamelCase					:      Tuple          =   load_numpy(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
			    '/yellow_cat_sitting_on_a_park_bench_fp16.npy' )

			lowerCamelCase					:      List[str]          =   'stabilityai/stable-diffusion-2-inpainting'
			lowerCamelCase					:      Optional[int]          =   StableDiffusionInpaintPipeline.from_pretrained(
			    __lowerCamelCase		,			torch_dtype=torch.floataa		,			safety_checker=__lowerCamelCase		,			)
			pipe.to(__lowerCamelCase )
			pipe.set_progress_bar_config(disable=__lowerCamelCase )
			pipe.enable_attention_slicing()

			lowerCamelCase					:      Optional[int]          =   'Face of a yellow cat, high resolution, sitting on a park bench'

			lowerCamelCase					:      int          =   torch.manual_seed(0 )
			lowerCamelCase					:      Tuple          =   pipe(
			    prompt=__lowerCamelCase		,			image=__lowerCamelCase		,			mask_image=__lowerCamelCase		,			generator=__lowerCamelCase		,			output_type='np'		,			)
			lowerCamelCase					:      List[str]          =   output.images[0]

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




	def       _UpperCamelCase   (			self )		->							Optional[Any]:
			torch.cuda.empty_cache()
			torch.cuda.reset_max_memory_allocated()
			torch.cuda.reset_peak_memory_stats()

			lowerCamelCase					:      Dict          =   load_image(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
			    '/sd2-inpaint/init_image.png' )
			lowerCamelCase					:      Union[str, Any]          =   load_image(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png' )

			lowerCamelCase					:      int          =   'stabilityai/stable-diffusion-2-inpainting'
			lowerCamelCase					:      List[Any]          =   PNDMScheduler.from_pretrained(__lowerCamelCase		,			subfolder='scheduler' )
			lowerCamelCase					:      Tuple          =   StableDiffusionInpaintPipeline.from_pretrained(
			    __lowerCamelCase		,			safety_checker=__lowerCamelCase		,			scheduler=__lowerCamelCase		,			torch_dtype=torch.floataa		,			)
			pipe.to(__lowerCamelCase )
			pipe.set_progress_bar_config(disable=__lowerCamelCase )
			pipe.enable_attention_slicing(1 )
			pipe.enable_sequential_cpu_offload()

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

			lowerCamelCase					:      Tuple          =   torch.manual_seed(0 )
			lowerCamelCase					:      Tuple          =   pipe(
			    prompt=__lowerCamelCase		,			image=__lowerCamelCase		,			mask_image=__lowerCamelCase		,			generator=__lowerCamelCase		,			num_inference_steps=2		,			output_type='np'		,			)

			lowerCamelCase					:      Optional[Any]          =   torch.cuda.max_memory_allocated()
			# make sure that less than 2.65 GB is allocated
			assert mem_bytes < 2.65 * 10**9








"""simple docstring"""

import argparse
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_A						=			logging.get_logger(__name__)
def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		lowerCamelCase					:      int          =   OrderedDict()
		for key, value in state_dict.items():
				if key.startswith('module.encoder'      ):
						lowerCamelCase					:      Tuple          =   key.replace('module.encoder',						'glpn.encoder'      )
				if key.startswith('module.decoder'      ):
						lowerCamelCase					:      str          =   key.replace('module.decoder',						'decoder.stages'      )
				if "patch_embed" in key:
						# replace for example patch_embed1 by patch_embeddings.0
						lowerCamelCase					:      Any          =   key[key.find('patch_embed'      ) + len('patch_embed'      )]
						lowerCamelCase					:      Dict          =   key.replace(F"""patch_embed{idx}""",						F"""patch_embeddings.{int(a_      )-1}"""      )
				if "norm" in key:
						lowerCamelCase					:      Optional[int]          =   key.replace('norm',						'layer_norm'      )
				if "glpn.encoder.layer_norm" in key:
						# replace for example layer_norm1 by layer_norm.0
						lowerCamelCase					:      List[str]          =   key[key.find('glpn.encoder.layer_norm'      ) + len('glpn.encoder.layer_norm'      )]
						lowerCamelCase					:      List[str]          =   key.replace(F"""layer_norm{idx}""",						F"""layer_norm.{int(a_      )-1}"""      )
				if "layer_norm1" in key:
						lowerCamelCase					:      List[Any]          =   key.replace('layer_norm1',						'layer_norm_1'      )
				if "layer_norm2" in key:
						lowerCamelCase					:      str          =   key.replace('layer_norm2',						'layer_norm_2'      )
				if "block" in key:
						# replace for example block1 by block.0
						lowerCamelCase					:      Union[str, Any]          =   key[key.find('block'      ) + len('block'      )]
						lowerCamelCase					:      List[str]          =   key.replace(F"""block{idx}""",						F"""block.{int(a_      )-1}"""      )
				if "attn.q" in key:
						lowerCamelCase					:      Union[str, Any]          =   key.replace('attn.q',						'attention.self.query'      )
				if "attn.proj" in key:
						lowerCamelCase					:      Dict          =   key.replace('attn.proj',						'attention.output.dense'      )
				if "attn" in key:
						lowerCamelCase					:      int          =   key.replace('attn',						'attention.self'      )
				if "fc1" in key:
						lowerCamelCase					:      Any          =   key.replace('fc1',						'dense1'      )
				if "fc2" in key:
						lowerCamelCase					:      List[Any]          =   key.replace('fc2',						'dense2'      )
				if "linear_pred" in key:
						lowerCamelCase					:      Optional[Any]          =   key.replace('linear_pred',						'classifier'      )
				if "linear_fuse" in key:
						lowerCamelCase					:      Union[str, Any]          =   key.replace('linear_fuse.conv',						'linear_fuse'      )
						lowerCamelCase					:      Optional[int]          =   key.replace('linear_fuse.bn',						'batch_norm'      )
				if "linear_c" in key:
						# replace for example linear_c4 by linear_c.3
						lowerCamelCase					:      str          =   key[key.find('linear_c'      ) + len('linear_c'      )]
						lowerCamelCase					:      List[Any]          =   key.replace(F"""linear_c{idx}""",						F"""linear_c.{int(a_      )-1}"""      )
				if "bot_conv" in key:
						lowerCamelCase					:      int          =   key.replace('bot_conv',						'0.convolution'      )
				if "skip_conv1" in key:
						lowerCamelCase					:      Any          =   key.replace('skip_conv1',						'1.convolution'      )
				if "skip_conv2" in key:
						lowerCamelCase					:      Optional[Any]          =   key.replace('skip_conv2',						'2.convolution'      )
				if "fusion1" in key:
						lowerCamelCase					:      str          =   key.replace('fusion1',						'1.fusion'      )
				if "fusion2" in key:
						lowerCamelCase					:      Optional[Any]          =   key.replace('fusion2',						'2.fusion'      )
				if "fusion3" in key:
						lowerCamelCase					:      List[str]          =   key.replace('fusion3',						'3.fusion'      )
				if "fusion" in key and "conv" in key:
						lowerCamelCase					:      Optional[int]          =   key.replace('conv',						'convolutional_layer'      )
				if key.startswith('module.last_layer_depth'      ):
						lowerCamelCase					:      Tuple          =   key.replace('module.last_layer_depth',						'head.head'      )
				lowerCamelCase					:      List[Any]          =   value

		return new_state_dict
def 		UpperCAmelCase (		a_,						a_      ):





		'''simple docstring'''


		for i in range(config.num_encoder_blocks      ):
				for j in range(config.depths[i]      ):
						# read in weights + bias of keys and values (which is a single matrix in the original implementation)
						lowerCamelCase					:      Any          =   state_dict.pop(F"""glpn.encoder.block.{i}.{j}.attention.self.kv.weight"""      )
						lowerCamelCase					:      Optional[Any]          =   state_dict.pop(F"""glpn.encoder.block.{i}.{j}.attention.self.kv.bias"""      )
						# next, add keys and values (in that order) to the state dict
						lowerCamelCase					:      Any          =   kv_weight[
						    : config.hidden_sizes[i], :
						]
						lowerCamelCase					:      List[Any]          =   kv_bias[: config.hidden_sizes[i]]
						lowerCamelCase					:      Dict          =   kv_weight[
						    config.hidden_sizes[i] :, :
						]
						lowerCamelCase					:      List[Any]          =   kv_bias[config.hidden_sizes[i] :]
def 		UpperCAmelCase (		):





		'''simple docstring'''


		lowerCamelCase					:      Dict          =   'http://images.cocodataset.org/val2017/000000039769.jpg'
		lowerCamelCase					:      List[Any]          =   Image.open(requests.get(a_,						stream=a_      ).raw      )

		return image
@torch.no_grad()
def 		UpperCAmelCase (		a_,						a_,						a_=False,						a_=None      ):





		'''simple docstring'''


		lowerCamelCase					:      int          =   GLPNConfig(hidden_sizes=[64, 128, 320, 512],						decoder_hidden_size=64,						depths=[3, 8, 27, 3]      )

		# load image processor (only resize + rescale)
		lowerCamelCase					:      Any          =   GLPNImageProcessor()

		# prepare image
		lowerCamelCase					:      int          =   prepare_img()
		lowerCamelCase					:      Tuple          =   image_processor(images=a_,						return_tensors='pt'      ).pixel_values

		logger.info('Converting model...'      )

		# load original state dict
		lowerCamelCase					:      Optional[Any]          =   torch.load(a_,						map_location=torch.device('cpu'      )      )

		# rename keys
		lowerCamelCase					:      Any          =   rename_keys(a_      )

		# key and value matrices need special treatment
		read_in_k_v(a_,						a_      )

		# create HuggingFace model and load state dict
		lowerCamelCase					:      Optional[int]          =   GLPNForDepthEstimation(a_      )
		model.load_state_dict(a_      )
		model.eval()

		# forward pass
		lowerCamelCase					:      str          =   model(a_      )
		lowerCamelCase					:      Any          =   outputs.predicted_depth

		# verify output
		if model_name is not None:
				if "nyu" in model_name:
						lowerCamelCase					:      Any          =   torch.tensor(
						    [[4.4_1_4_7, 4.0_8_7_3, 4.0_6_7_3], [3.7_8_9_0, 3.2_8_8_1, 3.1_5_2_5], [3.7_6_7_4, 3.5_4_2_3, 3.4_9_1_3]]      )
				elif "kitti" in model_name:
						lowerCamelCase					:      str          =   torch.tensor(
						    [[3.4_2_9_1, 2.7_8_6_5, 2.5_1_5_1], [3.2_8_4_1, 2.7_0_2_1, 2.3_5_0_2], [3.1_1_4_7, 2.4_6_2_5, 2.2_4_8_1]]      )
				else:
						raise ValueError(F"""Unknown model name: {model_name}"""      )

				lowerCamelCase					:      int          =   torch.Size([1, 480, 640]      )

				assert predicted_depth.shape == expected_shape
				assert torch.allclose(predicted_depth[0, :3, :3],						a_,						atol=1E-4      )
				print('Looks ok!'      )

		# finally, push to hub if required
		if push_to_hub:
				logger.info('Pushing model and image processor to the hub...'      )
				model.push_to_hub(
				    repo_path_or_name=Path(a_,						a_      ),						organization='nielsr',						commit_message='Add model',						use_temp_dir=a_,						)
				image_processor.push_to_hub(
				    repo_path_or_name=Path(a_,						a_      ),						organization='nielsr',						commit_message='Add image processor',						use_temp_dir=a_,						)


if __name__ == "__main__":
					_A						=			argparse.ArgumentParser()

					parser.add_argument(
					    '--checkpoint_path',
					    default=None,
					    type=str,
					    help='Path to the original PyTorch checkpoint (.pth file).',
					)
					parser.add_argument(
					    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
					)
					parser.add_argument(
					    '--push_to_hub', action='store_true', help='Whether to upload the model to the HuggingFace hub.'
					)
					parser.add_argument(
					    '--model_name',
					    default='glpn-kitti',
					    type=str,
					    help='Name of the model in case you\'re pushing to the hub.',
					)
					_A						=			parser.parse_args()
					convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)






