
"""simple docstring"""

from collections import defaultdict
from typing import Optional

from ..image_utils import load_image
from ..utils import (
    add_end_docstrings,
    is_torch_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_torch_available():
					import torch

					from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING

_A						=			logging.get_logger(__name__)






@add_end_docstrings(__SCREAMING_SNAKE_CASE		)
class 	_lowercase       (    __SCREAMING_SNAKE_CASE		):




	def __init__(			self		,			**UpperCAmelCase_ )		->							Optional[Any]:
			super().__init__(**UpperCamelCase__ )
			requires_backends(self		,			'vision' )
			requires_backends(self		,			'torch' )

			if self.framework != "pt":
					raise ValueError(F"""The {self.__class__} is only available in PyTorch.""" )

			self.check_model_type(UpperCamelCase__ )




	def       _UpperCamelCase   (			self		,			**UpperCAmelCase_ )		->							Any:
			lowerCamelCase					:      List[Any]          =   {}
			lowerCamelCase					:      Optional[Any]          =   {}
			lowerCamelCase					:      str          =   {}
			# preprocess args
			if "points_per_batch" in kwargs:
					lowerCamelCase					:      str          =   kwargs['''points_per_batch''']
			if "points_per_crop" in kwargs:
					lowerCamelCase					:      Optional[Any]          =   kwargs['''points_per_crop''']
			if "crops_n_layers" in kwargs:
					lowerCamelCase					:      List[Any]          =   kwargs['''crops_n_layers''']
			if "crop_overlap_ratio" in kwargs:
					lowerCamelCase					:      Dict          =   kwargs['''crop_overlap_ratio''']
			if "crop_n_points_downscale_factor" in kwargs:
					lowerCamelCase					:      List[str]          =   kwargs['''crop_n_points_downscale_factor''']
			# postprocess args
			if "pred_iou_thresh" in kwargs:
					lowerCamelCase					:      Union[str, Any]          =   kwargs['''pred_iou_thresh''']
			if "stability_score_offset" in kwargs:
					lowerCamelCase					:      Tuple          =   kwargs['''stability_score_offset''']
			if "mask_threshold" in kwargs:
					lowerCamelCase					:      Optional[Any]          =   kwargs['''mask_threshold''']
			if "stability_score_thresh" in kwargs:
					lowerCamelCase					:      int          =   kwargs['''stability_score_thresh''']
			if "crops_nms_thresh" in kwargs:
					lowerCamelCase					:      Union[str, Any]          =   kwargs['''crops_nms_thresh''']
			if "output_rle_mask" in kwargs:
					lowerCamelCase					:      List[Any]          =   kwargs['''output_rle_mask''']
			if "output_bboxes_mask" in kwargs:
					lowerCamelCase					:      Any          =   kwargs['''output_bboxes_mask''']
			return preprocess_kwargs, forward_params, postprocess_kwargs




	def __call__(			self		,			UpperCAmelCase_		,			*UpperCAmelCase_		,			UpperCAmelCase_=None		,			UpperCAmelCase_=None		,			**UpperCAmelCase_ )		->							List[Any]:
			return super().__call__(UpperCamelCase__		,			*UpperCamelCase__		,			num_workers=UpperCamelCase__		,			batch_size=UpperCamelCase__		,			**UpperCamelCase__ )




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_=64		,			UpperCAmelCase_ = 0		,			UpperCAmelCase_ = 512 / 1500		,			UpperCAmelCase_ = 32		,			UpperCAmelCase_ = 1		,			)		->							int:
			lowerCamelCase					:      List[Any]          =   load_image(UpperCamelCase__ )
			lowerCamelCase					:      str          =   self.image_processor.size['''longest_edge''']
			lowerCamelCase					:      Any          =   self.image_processor.generate_crop_boxes(
			    UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__ )
			lowerCamelCase					:      int          =   self.image_processor(images=UpperCamelCase__		,			return_tensors='pt' )

			with self.device_placement():
					if self.framework == "pt":
							lowerCamelCase					:      Any          =   self.get_inference_context()
							with inference_context():
									lowerCamelCase					:      Tuple          =   self._ensure_tensor_on_device(UpperCamelCase__		,			device=self.device )
									lowerCamelCase					:      Union[str, Any]          =   self.model.get_image_embeddings(model_inputs.pop('pixel_values' ) )
									lowerCamelCase					:      str          =   image_embeddings

			lowerCamelCase					:      List[Any]          =   grid_points.shape[1]
			lowerCamelCase					:      int          =   points_per_batch if points_per_batch is not None else n_points

			if points_per_batch <= 0:
					raise ValueError(
					    'Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. '
					    'To return all points at once, set points_per_batch to None' )

			for i in range(0		,			UpperCamelCase__		,			UpperCamelCase__ ):
					lowerCamelCase					:      Tuple          =   grid_points[:, i : i + points_per_batch, :, :]
					lowerCamelCase					:      List[Any]          =   input_labels[:, i : i + points_per_batch]
					lowerCamelCase					:      Optional[Any]          =   i == n_points - points_per_batch
					yield {
					    "input_points": batched_points,
					    "input_labels": labels,
					    "input_boxes": crop_boxes,
					    "is_last": is_last,
					    **model_inputs,
					}




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_=0.88		,			UpperCAmelCase_=0.95		,			UpperCAmelCase_=0		,			UpperCAmelCase_=1		,			)		->							List[str]:
			lowerCamelCase					:      List[str]          =   model_inputs.pop('input_boxes' )
			lowerCamelCase					:      Dict          =   model_inputs.pop('is_last' )
			lowerCamelCase					:      List[str]          =   model_inputs.pop('original_sizes' ).tolist()
			lowerCamelCase					:      Union[str, Any]          =   model_inputs.pop('reshaped_input_sizes' ).tolist()

			lowerCamelCase					:      Any          =   self.model(**UpperCamelCase__ )

			# post processing happens here in order to avoid CPU GPU copies of ALL the masks
			lowerCamelCase					:      str          =   model_outputs['''pred_masks''']
			lowerCamelCase					:      List[Any]          =   self.image_processor.post_process_masks(
			    UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			binarize=UpperCamelCase__ )
			lowerCamelCase					:      str          =   model_outputs['''iou_scores''']
			lowerCamelCase					:      Dict          =   self.image_processor.filter_masks(
			    masks[0]		,			iou_scores[0]		,			original_sizes[0]		,			input_boxes[0]		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			)
			return {
			    "masks": masks,
			    "is_last": is_last,
			    "boxes": boxes,
			    "iou_scores": iou_scores,
			}




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_=False		,			UpperCAmelCase_=False		,			UpperCAmelCase_=0.7		,			)		->							List[Any]:
			lowerCamelCase					:      Union[str, Any]          =   []
			lowerCamelCase					:      Union[str, Any]          =   []
			lowerCamelCase					:      Dict          =   []
			for model_output in model_outputs:
					all_scores.append(model_output.pop('iou_scores' ) )
					all_masks.extend(model_output.pop('masks' ) )
					all_boxes.append(model_output.pop('boxes' ) )

			lowerCamelCase					:      Optional[Any]          =   torch.cat(UpperCamelCase__ )
			lowerCamelCase					:      str          =   torch.cat(UpperCamelCase__ )
			lowerCamelCase					:      Any          =   self.image_processor.post_process_for_mask_generation(
			    UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__ )

			lowerCamelCase					:      Union[str, Any]          =   defaultdict(UpperCamelCase__ )
			for output in model_outputs:
					for k, v in output.items():
							extra[k].append(UpperCamelCase__ )

			lowerCamelCase					:      str          =   {}
			if output_rle_mask:
					lowerCamelCase					:      int          =   rle_mask

			if output_bboxes_mask:
					lowerCamelCase					:      str          =   bounding_boxes

			return {"masks": output_masks, "scores": iou_scores, **optional, **extra}








"""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()
_A						=			logging.get_logger(__name__)
def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


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

		return new_state_dict
def 		UpperCAmelCase (		a_,						a_      ):





		'''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)
						lowerCamelCase					:      Any          =   state_dict.pop(F"""glpn.encoder.block.{i}.{j}.attention.self.kv.weight"""      )
						lowerCamelCase					:      Optional[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
						lowerCamelCase					:      Any          =   kv_weight[
						    : config.hidden_sizes[i], :
						]
						lowerCamelCase					:      List[Any]          =   kv_bias[: config.hidden_sizes[i]]
						lowerCamelCase					:      Dict          =   kv_weight[
						    config.hidden_sizes[i] :, :
						]
						lowerCamelCase					:      List[Any]          =   kv_bias[config.hidden_sizes[i] :]
def 		UpperCAmelCase (		):





		'''simple docstring'''


		lowerCamelCase					:      Dict          =   'http://images.cocodataset.org/val2017/000000039769.jpg'
		lowerCamelCase					:      List[Any]          =   Image.open(requests.get(a_,						stream=a_      ).raw      )

		return image
@torch.no_grad()
def 		UpperCAmelCase (		a_,						a_,						a_=False,						a_=None      ):





		'''simple docstring'''


		lowerCamelCase					:      int          =   GLPNConfig(hidden_sizes=[64, 128, 320, 512],						decoder_hidden_size=64,						depths=[3, 8, 27, 3]      )

		# load image processor (only resize + rescale)
		lowerCamelCase					:      Any          =   GLPNImageProcessor()

		# prepare image
		lowerCamelCase					:      int          =   prepare_img()
		lowerCamelCase					:      Tuple          =   image_processor(images=a_,						return_tensors='pt'      ).pixel_values

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

		# load original state dict
		lowerCamelCase					:      Optional[Any]          =   torch.load(a_,						map_location=torch.device('cpu'      )      )

		# rename keys
		lowerCamelCase					:      Any          =   rename_keys(a_      )

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

		# create HuggingFace model and load state dict
		lowerCamelCase					:      Optional[int]          =   GLPNForDepthEstimation(a_      )
		model.load_state_dict(a_      )
		model.eval()

		# forward pass
		lowerCamelCase					:      str          =   model(a_      )
		lowerCamelCase					:      Any          =   outputs.predicted_depth

		# verify output
		if model_name is not None:
				if "nyu" in model_name:
						lowerCamelCase					:      Any          =   torch.tensor(
						    [[4.4_1_4_7, 4.0_8_7_3, 4.0_6_7_3], [3.7_8_9_0, 3.2_8_8_1, 3.1_5_2_5], [3.7_6_7_4, 3.5_4_2_3, 3.4_9_1_3]]      )
				elif "kitti" in model_name:
						lowerCamelCase					:      str          =   torch.tensor(
						    [[3.4_2_9_1, 2.7_8_6_5, 2.5_1_5_1], [3.2_8_4_1, 2.7_0_2_1, 2.3_5_0_2], [3.1_1_4_7, 2.4_6_2_5, 2.2_4_8_1]]      )
				else:
						raise ValueError(F"""Unknown model name: {model_name}"""      )

				lowerCamelCase					:      int          =   torch.Size([1, 480, 640]      )

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


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






