
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					:			List[Any]			      =		logging.get_logger(__name__)


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

    return new_state_dict


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase	) ->						Optional[int]:
    # 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)
            __lowercase		:			Any	    =       state_dict.pop(F'glpn.encoder.block.{i}.{j}.attention.self.kv.weight'	)
            __lowercase		:			Dict	    =       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
            __lowercase		:			Any	    =       kv_weight[
                : config.hidden_sizes[i], :
            ]
            __lowercase		:			int	    =       kv_bias[: config.hidden_sizes[i]]
            __lowercase		:			Any	    =       kv_weight[
                config.hidden_sizes[i] :, :
            ]
            __lowercase		:			Dict	    =       kv_bias[config.hidden_sizes[i] :]


def     UpperCAmelCase_  (						) ->						Any:
    __lowercase		:			int	    =       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    __lowercase		:			Union[str, Any]	    =       Image.open(requests.get(__lowerCAmelCase	,					stream=__lowerCAmelCase	).raw	)

    return image


@torch.no_grad()
def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase=False	,					__lowerCAmelCase=None	) ->						List[Any]:
    __lowercase		:			int	    =       GLPNConfig(hidden_sizes=[64, 128, 320, 512]	,					decoder_hidden_size=64	,					depths=[3, 8, 27, 3]	)

    # load image processor (only resize + rescale)
    __lowercase		:			List[str]	    =       GLPNImageProcessor()

    # prepare image
    __lowercase		:			List[Any]	    =       prepare_img()
    __lowercase		:			Tuple	    =       image_processor(images=__lowerCAmelCase	,					return_tensors='''pt'''	).pixel_values

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

    # load original state dict
    __lowercase		:			Union[str, Any]	    =       torch.load(__lowerCAmelCase	,					map_location=torch.device('''cpu'''	)	)

    # rename keys
    __lowercase		:			Dict	    =       rename_keys(__lowerCAmelCase	)

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

    # create HuggingFace model and load state dict
    __lowercase		:			List[Any]	    =       GLPNForDepthEstimation(__lowerCAmelCase	)
    model.load_state_dict(__lowerCAmelCase	)
    model.eval()

    # forward pass
    __lowercase		:			Union[str, Any]	    =       model(__lowerCAmelCase	)
    __lowercase		:			List[Any]	    =       outputs.predicted_depth

    # verify output
    if model_name is not None:
        if "nyu" in model_name:
            __lowercase		:			Optional[int]	    =       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:
            __lowercase		:			str	    =       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}'	)

        __lowercase		:			Union[str, Any]	    =       torch.Size([1, 480, 640]	)

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


if __name__ == "__main__":
 __lowerCAmelCase					:			int			      =		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					:			List[Any]			      =		parser.parse_args()
 convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)




import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    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 						__lowerCAmelCase	(	lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase      ):



      """simple docstring"""







      A__	:			int        =    StableDiffusionSAGPipeline
      A__	:			Union[str, Any]        =    TEXT_TO_IMAGE_PARAMS
      A__	:			Dict        =    TEXT_TO_IMAGE_BATCH_PARAMS
      A__	:			List[str]        =    TEXT_TO_IMAGE_IMAGE_PARAMS
      A__	:			int        =    TEXT_TO_IMAGE_IMAGE_PARAMS
      A__	:			List[str]        =    False




      def 		snake_case_			(      self		:       Optional[Any]				):
          torch.manual_seed(0				)
          __lowercase		:			Optional[Any]	    =       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				,       )
          __lowercase		:			int	    =       DDIMScheduler(
              beta_start=0.0_00_85				,       beta_end=0.0_12				,       beta_schedule='''scaled_linear'''				,       clip_sample=_snake_case				,       set_alpha_to_one=_snake_case				,       )
          torch.manual_seed(0				)
          __lowercase		:			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				,       )
          torch.manual_seed(0				)
          __lowercase		:			Tuple	    =       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				,       )
          __lowercase		:			List[Any]	    =       CLIPTextModel(_snake_case				)
          __lowercase		:			List[Any]	    =       CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

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




      def 		snake_case_			(      self		:       Tuple				,       _snake_case		:       List[str]				,       _snake_case		:       List[str]=0				):
          if str(_snake_case				).startswith('''mps'''				):
              __lowercase		:			Optional[int]	    =       torch.manual_seed(_snake_case				)
          else:
              __lowercase		:			Tuple	    =       torch.Generator(device=_snake_case				).manual_seed(_snake_case				)
          __lowercase		:			Tuple	    =       {
              '''prompt''': '''.''',
              '''generator''': generator,
              '''num_inference_steps''': 2,
              '''guidance_scale''': 1.0,
              '''sag_scale''': 1.0,
              '''output_type''': '''numpy''',
          }
          return inputs




      def 		snake_case_			(      self		:       Any				):
          super().test_inference_batch_single_identical(expected_max_diff=3E-3				)






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



      """simple docstring"""







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




      def 		snake_case_			(      self		:       Dict				):
          __lowercase		:			Optional[int]	    =       StableDiffusionSAGPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''				)
          __lowercase		:			Optional[int]	    =       sag_pipe.to(_snake_case				)
          sag_pipe.set_progress_bar_config(disable=_snake_case				)

          __lowercase		:			List[str]	    =       '''.'''
          __lowercase		:			List[str]	    =       torch.manual_seed(0				)
          __lowercase		:			List[str]	    =       sag_pipe(
              [prompt]				,       generator=_snake_case				,       guidance_scale=7.5				,       sag_scale=1.0				,       num_inference_steps=20				,       output_type='''np'''				)

          __lowercase		:			Optional[Any]	    =       output.images

          __lowercase		:			Dict	    =       image[0, -3:, -3:, -1]

          assert image.shape == (1, 512, 512, 3)
          __lowercase		:			int	    =       np.array([0.15_68, 0.17_38, 0.16_95, 0.16_93, 0.15_07, 0.17_05, 0.15_47, 0.17_51, 0.19_49]				)

          assert np.abs(image_slice.flatten() - expected_slice				).max() < 5E-2




      def 		snake_case_			(      self		:       Optional[int]				):
          __lowercase		:			Dict	    =       StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''				)
          __lowercase		:			Any	    =       sag_pipe.to(_snake_case				)
          sag_pipe.set_progress_bar_config(disable=_snake_case				)

          __lowercase		:			List[str]	    =       '''.'''
          __lowercase		:			List[str]	    =       torch.manual_seed(0				)
          __lowercase		:			int	    =       sag_pipe(
              [prompt]				,       generator=_snake_case				,       guidance_scale=7.5				,       sag_scale=1.0				,       num_inference_steps=20				,       output_type='''np'''				)

          __lowercase		:			str	    =       output.images

          __lowercase		:			Tuple	    =       image[0, -3:, -3:, -1]

          assert image.shape == (1, 512, 512, 3)
          __lowercase		:			Any	    =       np.array([0.34_59, 0.28_76, 0.25_37, 0.30_02, 0.26_71, 0.21_60, 0.30_26, 0.22_62, 0.23_71]				)

          assert np.abs(image_slice.flatten() - expected_slice				).max() < 5E-2




      def 		snake_case_			(      self		:       int				):
          __lowercase		:			List[str]	    =       StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''				)
          __lowercase		:			Tuple	    =       sag_pipe.to(_snake_case				)
          sag_pipe.set_progress_bar_config(disable=_snake_case				)

          __lowercase		:			Union[str, Any]	    =       '''.'''
          __lowercase		:			Tuple	    =       torch.manual_seed(0				)
          __lowercase		:			Optional[int]	    =       sag_pipe(
              [prompt]				,       width=768				,       height=512				,       generator=_snake_case				,       guidance_scale=7.5				,       sag_scale=1.0				,       num_inference_steps=20				,       output_type='''np'''				,       )

          __lowercase		:			Dict	    =       output.images

          assert image.shape == (1, 512, 768, 3)


