



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	:   Optional[int]       			=       logging.get_logger(__name__)







def       lowerCAmelCase_     (lowerCAmelCase__:						Union[str, Any]   ):





			"""simple docstring"""


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

			return new_state_dict







def       lowerCAmelCase_     (lowerCAmelCase__:						Any						,		lowerCAmelCase__:						Union[str, Any]   ):





			"""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)
									UpperCAmelCase_:						Tuple					=			state_dict.pop(F'glpn.encoder.block.{i}.{j}.attention.self.kv.weight'   )
									UpperCAmelCase_:						List[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
									UpperCAmelCase_:						str					=			kv_weight[
									    : config.hidden_sizes[i], :
									]
									UpperCAmelCase_:						List[str]					=			kv_bias[: config.hidden_sizes[i]]
									UpperCAmelCase_:						Union[str, Any]					=			kv_weight[
									    config.hidden_sizes[i] :, :
									]
									UpperCAmelCase_:						List[str]					=			kv_bias[config.hidden_sizes[i] :]







def       lowerCAmelCase_     ():





			"""simple docstring"""


			UpperCAmelCase_:						int					=			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
			UpperCAmelCase_:						Dict					=			Image.open(requests.get(_lowerCAmelCase						,		stream=_lowerCAmelCase   ).raw   )

			return image







@torch.no_grad()
def       lowerCAmelCase_     (lowerCAmelCase__:						List[Any]						,		lowerCAmelCase__:						str						,		lowerCAmelCase__:						Optional[int]=False						,		lowerCAmelCase__:						Any=None   ):





			"""simple docstring"""


			UpperCAmelCase_:						str					=			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_:						Optional[Any]					=			GLPNImageProcessor()

			# prepare image
			UpperCAmelCase_:						Optional[int]					=			prepare_img()
			UpperCAmelCase_:						Optional[Any]					=			image_processor(images=_lowerCAmelCase						,		return_tensors="""pt"""   ).pixel_values

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

			# load original state dict
			UpperCAmelCase_:						List[Any]					=			torch.load(_lowerCAmelCase						,		map_location=torch.device("""cpu"""   )   )

			# rename keys
			UpperCAmelCase_:						str					=			rename_keys(_lowerCAmelCase   )

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

			# create HuggingFace model and load state dict
			UpperCAmelCase_:						int					=			GLPNForDepthEstimation(_lowerCAmelCase   )
			model.load_state_dict(_lowerCAmelCase   )
			model.eval()

			# forward pass
			UpperCAmelCase_:						int					=			model(_lowerCAmelCase   )
			UpperCAmelCase_:						List[str]					=			outputs.predicted_depth

			# verify output
			if model_name is not None:
						if "nyu" in model_name:
									UpperCAmelCase_:						Dict					=			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_:						Optional[int]					=			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_:						List[str]					=			torch.Size([1, 4_8_0, 6_4_0]   )

						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__":
	a	:   Dict       			=       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	:   Optional[Any]       			=       parser.parse_args()
	convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)











'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__a          =		{
    "configuration_bloom": ["BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP", "BloomConfig", "BloomOnnxConfig"],
}
try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __a          =		["BloomTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __a          =		[
           "BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST",
           "BloomForCausalLM",
           "BloomModel",
           "BloomPreTrainedModel",
           "BloomForSequenceClassification",
           "BloomForTokenClassification",
           "BloomForQuestionAnswering",
       ]

if TYPE_CHECKING:
       from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_bloom_fast import BloomTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_bloom import (
                  BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  BloomForCausalLM,
                  BloomForQuestionAnswering,
                  BloomForSequenceClassification,
                  BloomForTokenClassification,
                  BloomModel,
                  BloomPreTrainedModel,
              )

else:
       import sys

       __a          =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
