






"""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()
_lowerCAmelCase       =				logging.get_logger(__name__)






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

	return new_state_dict






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	# 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)
			A_		:    Dict						     = state_dict.pop(F"""glpn.encoder.block.{i}.{j}.attention.self.kv.weight"""       )
			A_		:    str						     = 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
			A_		:    Optional[int]						     = kv_weight[
			    : config.hidden_sizes[i], :
			]
			A_		:    Any						     = kv_bias[: config.hidden_sizes[i]]
			A_		:    Optional[int]						     = kv_weight[
			    config.hidden_sizes[i] :, :
			]
			A_		:    Dict						     = kv_bias[config.hidden_sizes[i] :]






def       __UpperCamelCase						(		):
	A_		:    List[Any]						     = """http://images.cocodataset.org/val2017/000000039769.jpg"""
	A_		:    str						     = Image.open(requests.get(snake_case__ , stream=snake_case__       ).raw       )

	return image






@torch.no_grad()
def       __UpperCamelCase						(		snake_case__ , snake_case__ , snake_case__=False , snake_case__=None       ):
	A_		:    int						     = GLPNConfig(hidden_sizes=[64, 128, 320, 512] , decoder_hidden_size=64 , depths=[3, 8, 27, 3]       )

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

	# prepare image
	A_		:    Optional[int]						     = prepare_img()
	A_		:    List[str]						     = image_processor(images=snake_case__ , return_tensors="""pt"""       ).pixel_values

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

	# load original state dict
	A_		:    Tuple						     = torch.load(snake_case__ , map_location=torch.device("""cpu"""       )       )

	# rename keys
	A_		:    Union[str, Any]						     = rename_keys(snake_case__       )

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

	# create HuggingFace model and load state dict
	A_		:    Dict						     = GLPNForDepthEstimation(snake_case__       )
	model.load_state_dict(snake_case__       )
	model.eval()

	# forward pass
	A_		:    Dict						     = model(snake_case__       )
	A_		:    Union[str, Any]						     = outputs.predicted_depth

	# verify output
	if model_name is not None:
		if "nyu" in model_name:
			A_		:    int						     = torch.tensor(
			    [[4.4_147, 4.0_873, 4.0_673], [3.7_890, 3.2_881, 3.1_525], [3.7_674, 3.5_423, 3.4_913]]       )
		elif "kitti" in model_name:
			A_		:    List[str]						     = torch.tensor(
			    [[3.4_291, 2.7_865, 2.5_151], [3.2_841, 2.7_021, 2.3_502], [3.1_147, 2.4_625, 2.2_481]]       )
		else:
			raise ValueError(F"""Unknown model name: {model_name}"""       )

		A_		:    Optional[Any]						     = torch.Size([1, 480, 640]       )

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


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)









"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCAmelCase       =				{
    "configuration_timesformer": ["TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "TimesformerConfig"],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowerCAmelCase       =				[
							    "TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "TimesformerModel",
							    "TimesformerForVideoClassification",
							    "TimesformerPreTrainedModel",
							]

if TYPE_CHECKING:
							from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_timesformer import (
														    TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TimesformerForVideoClassification,
														    TimesformerModel,
														    TimesformerPreTrainedModel,
														)

else:
							import sys

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

