
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, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, 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


enable_full_determinism()







class 			A_  (     unittest.TestCase  ):




	'''simple docstring'''


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





	@property
	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       1
		lowercase	    =       3
		lowercase	    =       (32, 32)

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





	@property
	def       SCREAMING_SNAKE_CASE__		(			self			):
		torch.manual_seed(0			)
		lowercase	    =       UNetaDConditionModel(
		    block_out_channels=(32, 32, 64)						,       layers_per_block=2						,       sample_size=32						,       in_channels=7						,       out_channels=4						,       down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D', 'CrossAttnDownBlock2D')						,       up_block_types=('CrossAttnUpBlock2D', 'CrossAttnUpBlock2D', 'UpBlock2D')						,       cross_attention_dim=32						,       attention_head_dim=8						,       use_linear_projection=snake_case						,       only_cross_attention=(True, True, False)						,       num_class_embeds=100						,       )
		return model





	@property
	def       SCREAMING_SNAKE_CASE__		(			self			):
		torch.manual_seed(0			)
		lowercase	    =       AutoencoderKL(
		    block_out_channels=[32, 32, 64]						,       in_channels=3						,       out_channels=3						,       down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D', 'DownEncoderBlock2D']						,       up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D']						,       latent_channels=4						,       )
		return model





	@property
	def       SCREAMING_SNAKE_CASE__		(			self			):
		torch.manual_seed(0			)
		lowercase	    =       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						,       )
		return CLIPTextModel(snake_case			)





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       'cpu'  # ensure determinism for the device-dependent torch.Generator
		lowercase	    =       self.dummy_cond_unet_upscale
		lowercase	    =       DDPMScheduler()
		lowercase	    =       DDIMScheduler(prediction_type='v_prediction'			)
		lowercase	    =       self.dummy_vae
		lowercase	    =       self.dummy_text_encoder
		lowercase	    =       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)

		lowercase	    =       self.dummy_image.cpu().permute(0						,       2						,       3						,       1			)[0]
		lowercase	    =       Image.fromarray(np.uinta(snake_case			)			).convert('RGB'			).resize((64, 64)			)

		# make sure here that pndm scheduler skips prk
		lowercase	    =       StableDiffusionUpscalePipeline(
		    unet=snake_case						,       low_res_scheduler=snake_case						,       scheduler=snake_case						,       vae=snake_case						,       text_encoder=snake_case						,       tokenizer=snake_case						,       max_noise_level=350						,       )
		lowercase	    =       sd_pipe.to(snake_case			)
		sd_pipe.set_progress_bar_config(disable=snake_case			)

		lowercase	    =       'A painting of a squirrel eating a burger'
		lowercase	    =       torch.Generator(device=snake_case			).manual_seed(0			)
		lowercase	    =       sd_pipe(
		    [prompt]						,       image=snake_case						,       generator=snake_case						,       guidance_scale=6.0						,       noise_level=20						,       num_inference_steps=2						,       output_type='np'						,       )

		lowercase	    =       output.images

		lowercase	    =       torch.Generator(device=snake_case			).manual_seed(0			)
		lowercase	    =       sd_pipe(
		    [prompt]						,       image=snake_case						,       generator=snake_case						,       guidance_scale=6.0						,       noise_level=20						,       num_inference_steps=2						,       output_type='np'						,       return_dict=snake_case						,       )[0]

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

		lowercase	    =       low_res_image.size[0] * 4
		assert image.shape == (1, expected_height_width, expected_height_width, 3)
		lowercase	    =       np.array([0.3_113, 0.3_910, 0.4_272, 0.4_859, 0.5_061, 0.4_652, 0.5_362, 0.5_715, 0.5_661]			)

		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       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       'cpu'  # ensure determinism for the device-dependent torch.Generator
		lowercase	    =       self.dummy_cond_unet_upscale
		lowercase	    =       DDPMScheduler()
		lowercase	    =       DDIMScheduler(prediction_type='v_prediction'			)
		lowercase	    =       self.dummy_vae
		lowercase	    =       self.dummy_text_encoder
		lowercase	    =       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)

		lowercase	    =       self.dummy_image.cpu().permute(0						,       2						,       3						,       1			)[0]
		lowercase	    =       Image.fromarray(np.uinta(snake_case			)			).convert('RGB'			).resize((64, 64)			)

		# make sure here that pndm scheduler skips prk
		lowercase	    =       StableDiffusionUpscalePipeline(
		    unet=snake_case						,       low_res_scheduler=snake_case						,       scheduler=snake_case						,       vae=snake_case						,       text_encoder=snake_case						,       tokenizer=snake_case						,       max_noise_level=350						,       )
		lowercase	    =       sd_pipe.to(snake_case			)
		sd_pipe.set_progress_bar_config(disable=snake_case			)

		lowercase	    =       'A painting of a squirrel eating a burger'
		lowercase	    =       sd_pipe(
		    2 * [prompt]						,       image=2 * [low_res_image]						,       guidance_scale=6.0						,       noise_level=20						,       num_inference_steps=2						,       output_type='np'						,       )
		lowercase	    =       output.images
		assert image.shape[0] == 2

		lowercase	    =       torch.Generator(device=snake_case			).manual_seed(0			)
		lowercase	    =       sd_pipe(
		    [prompt]						,       image=snake_case						,       generator=snake_case						,       num_images_per_prompt=2						,       guidance_scale=6.0						,       noise_level=20						,       num_inference_steps=2						,       output_type='np'						,       )
		lowercase	    =       output.images
		assert image.shape[0] == 2





	@unittest.skipIf(torch_device != 'cuda'						,       'This test requires a GPU'			)
	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       self.dummy_cond_unet_upscale
		lowercase	    =       DDPMScheduler()
		lowercase	    =       DDIMScheduler(prediction_type='v_prediction'			)
		lowercase	    =       self.dummy_vae
		lowercase	    =       self.dummy_text_encoder
		lowercase	    =       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)

		lowercase	    =       self.dummy_image.cpu().permute(0						,       2						,       3						,       1			)[0]
		lowercase	    =       Image.fromarray(np.uinta(snake_case			)			).convert('RGB'			).resize((64, 64)			)

		# put models in fp16, except vae as it overflows in fp16
		lowercase	    =       unet.half()
		lowercase	    =       text_encoder.half()

		# make sure here that pndm scheduler skips prk
		lowercase	    =       StableDiffusionUpscalePipeline(
		    unet=snake_case						,       low_res_scheduler=snake_case						,       scheduler=snake_case						,       vae=snake_case						,       text_encoder=snake_case						,       tokenizer=snake_case						,       max_noise_level=350						,       )
		lowercase	    =       sd_pipe.to(snake_case			)
		sd_pipe.set_progress_bar_config(disable=snake_case			)

		lowercase	    =       'A painting of a squirrel eating a burger'
		lowercase	    =       torch.manual_seed(0			)
		lowercase	    =       sd_pipe(
		    [prompt]						,       image=snake_case						,       generator=snake_case						,       num_inference_steps=2						,       output_type='np'						,       ).images

		lowercase	    =       low_res_image.size[0] * 4
		assert image.shape == (1, expected_height_width, expected_height_width, 3)







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




	'''simple docstring'''


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





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       load_image(
		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
		    '/sd2-upscale/low_res_cat.png'			)
		lowercase	    =       load_numpy(
		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
		    '/upsampled_cat.npy'			)

		lowercase	    =       'stabilityai/stable-diffusion-x4-upscaler'
		lowercase	    =       StableDiffusionUpscalePipeline.from_pretrained(snake_case			)
		pipe.to(snake_case			)
		pipe.set_progress_bar_config(disable=snake_case			)
		pipe.enable_attention_slicing()

		lowercase	    =       'a cat sitting on a park bench'

		lowercase	    =       torch.manual_seed(0			)
		lowercase	    =       pipe(
		    prompt=snake_case						,       image=snake_case						,       generator=snake_case						,       output_type='np'						,       )
		lowercase	    =       output.images[0]

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





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       load_image(
		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
		    '/sd2-upscale/low_res_cat.png'			)
		lowercase	    =       load_numpy(
		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
		    '/upsampled_cat_fp16.npy'			)

		lowercase	    =       'stabilityai/stable-diffusion-x4-upscaler'
		lowercase	    =       StableDiffusionUpscalePipeline.from_pretrained(
		    snake_case						,       torch_dtype=torch.floataa						,       )
		pipe.to(snake_case			)
		pipe.set_progress_bar_config(disable=snake_case			)
		pipe.enable_attention_slicing()

		lowercase	    =       'a cat sitting on a park bench'

		lowercase	    =       torch.manual_seed(0			)
		lowercase	    =       pipe(
		    prompt=snake_case						,       image=snake_case						,       generator=snake_case						,       output_type='np'						,       )
		lowercase	    =       output.images[0]

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





	def       SCREAMING_SNAKE_CASE__		(			self			):
		torch.cuda.empty_cache()
		torch.cuda.reset_max_memory_allocated()
		torch.cuda.reset_peak_memory_stats()

		lowercase	    =       load_image(
		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
		    '/sd2-upscale/low_res_cat.png'			)

		lowercase	    =       'stabilityai/stable-diffusion-x4-upscaler'
		lowercase	    =       StableDiffusionUpscalePipeline.from_pretrained(
		    snake_case						,       torch_dtype=torch.floataa						,       )
		pipe.to(snake_case			)
		pipe.set_progress_bar_config(disable=snake_case			)
		pipe.enable_attention_slicing(1			)
		pipe.enable_sequential_cpu_offload()

		lowercase	    =       'a cat sitting on a park bench'

		lowercase	    =       torch.manual_seed(0			)
		lowercase	    =       pipe(
		    prompt=snake_case						,       image=snake_case						,       generator=snake_case						,       num_inference_steps=5						,       output_type='np'						,       )

		lowercase	    =       torch.cuda.max_memory_allocated()
		# make sure that less than 2.9 GB is allocated
		assert mem_bytes < 2.9 * 10**9



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()
lowerCamelCase  =logging.get_logger(__name__)


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

      return new_state_dict


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__		):
      # for each of the encoder blocks:
      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)
                  UpperCamelCase__     : Optional[Any]											=					state_dict.pop(f'''glpn.encoder.block.{i}.{j}.attention.self.kv.weight'''		)
                  UpperCamelCase__     : int											=					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
                  UpperCamelCase__     : Union[str, Any]											=					kv_weight[
                      : config.hidden_sizes[i], :
                  ]
                  UpperCamelCase__     : Tuple											=					kv_bias[: config.hidden_sizes[i]]
                  UpperCamelCase__     : Tuple											=					kv_weight[
                      config.hidden_sizes[i] :, :
                  ]
                  UpperCamelCase__     : Optional[Any]											=					kv_bias[config.hidden_sizes[i] :]


def 				SCREAMING_SNAKE_CASE_   (      ):
      UpperCamelCase__     : int											=					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
      UpperCamelCase__     : Any											=					Image.open(requests.get(UpperCamelCase__			,		stream=UpperCamelCase__		).raw		)

      return image


@torch.no_grad()
def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__=False			,		UpperCamelCase__=None		):
      UpperCamelCase__     : Any											=					GLPNConfig(hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]			,		decoder_hidden_size=6_4			,		depths=[3, 8, 2_7, 3]		)

      # load image processor (only resize + rescale)
      UpperCamelCase__     : str											=					GLPNImageProcessor()

      # prepare image
      UpperCamelCase__     : List[str]											=					prepare_img()
      UpperCamelCase__     : List[str]											=					image_processor(images=UpperCamelCase__			,		return_tensors='''pt'''		).pixel_values

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

      # load original state dict
      UpperCamelCase__     : Tuple											=					torch.load(UpperCamelCase__			,		map_location=torch.device('''cpu'''		)		)

      # rename keys
      UpperCamelCase__     : Optional[Any]											=					rename_keys(UpperCamelCase__		)

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

      # create HuggingFace model and load state dict
      UpperCamelCase__     : str											=					GLPNForDepthEstimation(UpperCamelCase__		)
      model.load_state_dict(UpperCamelCase__		)
      model.eval()

      # forward pass
      UpperCamelCase__     : Union[str, Any]											=					model(UpperCamelCase__		)
      UpperCamelCase__     : Optional[int]											=					outputs.predicted_depth

      # verify output
      if model_name is not None:
            if "nyu" in model_name:
                  UpperCamelCase__     : Tuple											=					torch.tensor(
                      [[4.4147, 4.0873, 4.0673], [3.7890, 3.2881, 3.1525], [3.7674, 3.5423, 3.4913]]		)
            elif "kitti" in model_name:
                  UpperCamelCase__     : Union[str, Any]											=					torch.tensor(
                      [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]]		)
            else:
                  raise ValueError(f'''Unknown model name: {model_name}'''		)

            UpperCamelCase__     : Optional[Any]											=					torch.Size([1, 4_8_0, 6_4_0]		)

            assert predicted_depth.shape == expected_shape
            assert torch.allclose(predicted_depth[0, :3, :3]			,		UpperCamelCase__			,		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(UpperCamelCase__			,		UpperCamelCase__		)			,		organization='''nielsr'''			,		commit_message='''Add model'''			,		use_temp_dir=UpperCamelCase__			,		)
            image_processor.push_to_hub(
                repo_path_or_name=Path(UpperCamelCase__			,		UpperCamelCase__		)			,		organization='''nielsr'''			,		commit_message='''Add image processor'''			,		use_temp_dir=UpperCamelCase__			,		)


if __name__ == "__main__":
    lowerCamelCase  =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.",
    )
    lowerCamelCase  =parser.parse_args()
    convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)


