




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()
UpperCamelCase__    			=			logging.get_logger(__name__)







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

					return new_state_dict







def 	_a      (						SCREAMING_SNAKE_CASE_							:				int      ,      SCREAMING_SNAKE_CASE_							:				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)
															__lowerCAmelCase				   =       state_dict.pop(F"""glpn.encoder.block.{i}.{j}.attention.self.kv.weight"""     )
															__lowerCAmelCase				   =       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				   =       kv_weight[
															    : config.hidden_sizes[i], :
															]
															__lowerCAmelCase				   =       kv_bias[: config.hidden_sizes[i]]
															__lowerCAmelCase				   =       kv_weight[
															    config.hidden_sizes[i] :, :
															]
															__lowerCAmelCase				   =       kv_bias[config.hidden_sizes[i] :]







def 	_a      (						):
					__lowerCAmelCase				   =       """http://images.cocodataset.org/val2017/000000039769.jpg"""
					__lowerCAmelCase				   =       Image.open(requests.get(UpperCAmelCase__      ,      stream=UpperCAmelCase__     ).raw     )

					return image







@torch.no_grad()
def 	_a      (						SCREAMING_SNAKE_CASE_							:				List[str]      ,      SCREAMING_SNAKE_CASE_							:				Union[str, Any]      ,      SCREAMING_SNAKE_CASE_							:				Optional[int]=False      ,      SCREAMING_SNAKE_CASE_							:				str=None     ):
					__lowerCAmelCase				   =       GLPNConfig(hidden_sizes=[64, 1_28, 3_20, 5_12]      ,      decoder_hidden_size=64      ,      depths=[3, 8, 27, 3]     )

					# load image processor (only resize + rescale)
					__lowerCAmelCase				   =       GLPNImageProcessor()

					# prepare image
					__lowerCAmelCase				   =       prepare_img()
					__lowerCAmelCase				   =       image_processor(images=UpperCAmelCase__      ,      return_tensors="pt"     ).pixel_values

					logger.info("Converting model..."     )

					# load original state dict
					__lowerCAmelCase				   =       torch.load(UpperCAmelCase__      ,      map_location=torch.device("cpu"     )     )

					# rename keys
					__lowerCAmelCase				   =       rename_keys(UpperCAmelCase__     )

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

					# create HuggingFace model and load state dict
					__lowerCAmelCase				   =       GLPNForDepthEstimation(UpperCAmelCase__     )
					model.load_state_dict(UpperCAmelCase__     )
					model.eval()

					# forward pass
					__lowerCAmelCase				   =       model(UpperCAmelCase__     )
					__lowerCAmelCase				   =       outputs.predicted_depth

					# verify output
					if model_name is not None:
										if "nyu" in model_name:
															__lowerCAmelCase				   =       torch.tensor(
															    [[4.41_47, 4.08_73, 4.06_73], [3.78_90, 3.28_81, 3.15_25], [3.76_74, 3.54_23, 3.49_13]]     )
										elif "kitti" in model_name:
															__lowerCAmelCase				   =       torch.tensor(
															    [[3.42_91, 2.78_65, 2.51_51], [3.28_41, 2.70_21, 2.35_02], [3.11_47, 2.46_25, 2.24_81]]     )
										else:
															raise ValueError(F"""Unknown model name: {model_name}"""     )

										__lowerCAmelCase				   =       torch.Size([1, 4_80, 6_40]     )

										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__":
					UpperCamelCase__    			=			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.""",
					)
					UpperCamelCase__    			=			parser.parse_args()
					convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)





'''simple docstring'''




import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()





class     __magic_name__						(    _UpperCAmelCase,     unittest.TestCase):
			UpperCamelCase__				=       PriorTransformer
			UpperCamelCase__				=       '''hidden_states'''







			@property
			def 	SCREAMING_SNAKE_CASE_     (	self				:  int    ):
										lowercase_					:			Optional[Any]										=       4
										lowercase_					:			int										=       8
										lowercase_					:			Union[str, Any]										=       7

										lowercase_					:			List[str]										=       floats_tensor((batch_size, embedding_dim)    ).to(lowercase_    )

										lowercase_					:			Tuple										=       floats_tensor((batch_size, embedding_dim)    ).to(lowercase_    )
										lowercase_					:			List[str]										=       floats_tensor((batch_size, num_embeddings, embedding_dim)    ).to(lowercase_    )

										return {
										    "hidden_states": hidden_states,
										    "timestep": 2,
										    "proj_embedding": proj_embedding,
										    "encoder_hidden_states": encoder_hidden_states,
										}







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]				,			lowercase_				:  Optional[Any]=0    ):
										torch.manual_seed(lowercase_    )
										lowercase_					:			int										=       4
										lowercase_					:			Any										=       8
										lowercase_					:			Tuple										=       7

										lowercase_					:			Union[str, Any]										=       torch.randn((batch_size, embedding_dim)    ).to(lowercase_    )

										lowercase_					:			Tuple										=       torch.randn((batch_size, embedding_dim)    ).to(lowercase_    )
										lowercase_					:			Union[str, Any]										=       torch.randn((batch_size, num_embeddings, embedding_dim)    ).to(lowercase_    )

										return {
										    "hidden_states": hidden_states,
										    "timestep": 2,
										    "proj_embedding": proj_embedding,
										    "encoder_hidden_states": encoder_hidden_states,
										}







			@property
			def 	SCREAMING_SNAKE_CASE_     (	self				:  str    ):
										return (4, 8)







			@property
			def 	SCREAMING_SNAKE_CASE_     (	self				:  Union[str, Any]    ):
										return (4, 8)







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple    ):
										lowercase_					:			Optional[Any]										=       {
										    """num_attention_heads""": 2,
										    """attention_head_dim""": 4,
										    """num_layers""": 2,
										    """embedding_dim""": 8,
										    """num_embeddings""": 7,
										    """additional_embeddings""": 4,
										}
										lowercase_					:			List[Any]										=       self.dummy_input
										return init_dict, inputs_dict







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]    ):
										lowercase_						,						lowercase_					:			Tuple										=       PriorTransformer.from_pretrained(
										    """hf-internal-testing/prior-dummy"""				,			output_loading_info=lowercase_    )
										self.assertIsNotNone(lowercase_    )
										self.assertEqual(len(loading_info["""missing_keys"""]    )				,			0    )

										model.to(lowercase_    )
										lowercase_					:			Optional[int]										=       model(**self.dummy_input    )[0]

										assert hidden_states is not None, "Make sure output is not None"







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Any    ):
										lowercase_						,						lowercase_					:			str										=       self.prepare_init_args_and_inputs_for_common()

										lowercase_					:			List[Any]										=       self.model_class(**lowercase_    )
										lowercase_					:			Any										=       inspect.signature(model.forward    )
										# signature.parameters is an OrderedDict => so arg_names order is deterministic
										lowercase_					:			Any										=       [*signature.parameters.keys()]

										lowercase_					:			Optional[Any]										=       ["""hidden_states""", """timestep"""]
										self.assertListEqual(arg_names[:2]				,			lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]    ):
										lowercase_					:			Optional[Any]										=       PriorTransformer.from_pretrained("""hf-internal-testing/prior-dummy"""    )
										lowercase_					:			Any										=       model.to(lowercase_    )

										if hasattr(lowercase_				,			"""set_default_attn_processor"""    ):
																	model.set_default_attn_processor()

										lowercase_					:			Any										=       self.get_dummy_seed_input()

										with torch.no_grad():
																	lowercase_					:			List[str]										=       model(**lowercase_    )[0]

										lowercase_					:			Tuple										=       output[0, :5].flatten().cpu()
										print(lowercase_    )

										# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
										# the expected output slices are not the same for CPU and GPU.
										lowercase_					:			Dict										=       torch.tensor([-1.34_36, -0.28_70, 0.75_38, 0.43_68, -0.02_39]    )
										self.assertTrue(torch_all_close(lowercase_				,			lowercase_				,			rtol=1E-2    )    )





@slow
class     __magic_name__						(    unittest.TestCase):







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]				,			lowercase_				:  Union[str, Any]=1				,			lowercase_				:  Optional[Any]=768				,			lowercase_				:  Tuple=77				,			lowercase_				:  Optional[int]=0    ):
										torch.manual_seed(lowercase_    )
										lowercase_					:			Optional[Any]										=       batch_size
										lowercase_					:			int										=       embedding_dim
										lowercase_					:			int										=       num_embeddings

										lowercase_					:			Dict										=       torch.randn((batch_size, embedding_dim)    ).to(lowercase_    )

										lowercase_					:			Any										=       torch.randn((batch_size, embedding_dim)    ).to(lowercase_    )
										lowercase_					:			List[Any]										=       torch.randn((batch_size, num_embeddings, embedding_dim)    ).to(lowercase_    )

										return {
										    "hidden_states": hidden_states,
										    "timestep": 2,
										    "proj_embedding": proj_embedding,
										    "encoder_hidden_states": encoder_hidden_states,
										}







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







			@parameterized.expand(
			    [
			        # fmt: off
			        [13, [-0.58_61,  0.12_83, -0.09_31,  0.08_82,  0.44_76,  0.13_29, -0.04_98,  0.06_40]],
			        [37, [-0.49_13,  0.01_10, -0.04_83,  0.05_41,  0.49_54, -0.01_70,  0.03_54,  0.16_51]],
			        # fmt: on
			    ]    )
			def 	SCREAMING_SNAKE_CASE_     (	self				:  int				,			lowercase_				:  Any				,			lowercase_				:  List[str]    ):
										lowercase_					:			List[Any]										=       PriorTransformer.from_pretrained("""kandinsky-community/kandinsky-2-1-prior"""				,			subfolder="""prior"""    )
										model.to(lowercase_    )
										lowercase_					:			Optional[Any]										=       self.get_dummy_seed_input(seed=lowercase_    )

										with torch.no_grad():
																	lowercase_					:			Tuple										=       model(**lowercase_    )[0]

										assert list(sample.shape    ) == [1, 768]

										lowercase_					:			Union[str, Any]										=       sample[0, :8].flatten().cpu()
										print(lowercase_    )
										lowercase_					:			Optional[Any]										=       torch.tensor(lowercase_    )

										assert torch_all_close(lowercase_				,			lowercase_				,			atol=1E-3    )





