"""simple docstring"""







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import ChineseCLIPImageProcessor

class 	UpperCamelCase    (					unittest.TestCase		):



				def __init__(self       :   Optional[int]			,						_A       :   Tuple			,						_A       :   int=7			,						_A       :   Dict=3			,						_A       :   List[str]=18			,						_A       :   List[Any]=30			,						_A       :   str=4_00			,						_A       :   Any=True			,						_A       :   Optional[Any]=None			,						_A       :   Optional[int]=True			,						_A       :   List[Any]=None			,						_A       :   List[str]=True			,						_A       :   Optional[int]=[0.48_145_466, 0.4_578_275, 0.40_821_073]			,						_A       :   List[Any]=[0.26_862_954, 0.26_130_258, 0.27_577_711]			,						_A       :   List[str]=True			,						)					->			List[str]:
					__snake_case    :							str   	=				size if size is not None else {'height': 2_24, 'width': 2_24}
					__snake_case    :							Optional[Any]   	=				crop_size if crop_size is not None else {'height': 18, 'width': 18}
					__snake_case    :							Optional[int]   	=				parent
					__snake_case    :							Optional[Any]   	=				batch_size
					__snake_case    :							Any   	=				num_channels
					__snake_case    :							Union[str, Any]   	=				image_size
					__snake_case    :							str   	=				min_resolution
					__snake_case    :							Optional[Any]   	=				max_resolution
					__snake_case    :							Tuple   	=				do_resize
					__snake_case    :							Optional[int]   	=				size
					__snake_case    :							int   	=				do_center_crop
					__snake_case    :							List[str]   	=				crop_size
					__snake_case    :							Tuple   	=				do_normalize
					__snake_case    :							Optional[int]   	=				image_mean
					__snake_case    :							Tuple   	=				image_std
					__snake_case    :							List[Any]   	=				do_convert_rgb



				def       _lowercase  (self       :   Optional[Any])					->			Optional[int]:
					return {
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "do_center_crop": self.do_center_crop,
					    "crop_size": self.crop_size,
					    "do_normalize": self.do_normalize,
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					    "do_convert_rgb": self.do_convert_rgb,
					}






				def       _lowercase  (self       :   Optional[int]			,						_A       :   Tuple=False			,						_A       :   str=False			,						_A       :   Tuple=False)					->			str:
					assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

					if equal_resolution:
						__snake_case    :							List[str]   	=				[]
						for i in range(self.batch_size):
							image_inputs.append(
							    np.random.randint(
							        2_55			,						size=(self.num_channels, self.max_resolution, self.max_resolution)			,						dtype=np.uinta))
					else:
						__snake_case    :							List[str]   	=				[]
						for i in range(self.batch_size):
							__snake_case	,       __snake_case    :							str   	=				np.random.choice(np.arange(self.min_resolution			,						self.max_resolution)			,						2)
							image_inputs.append(np.random.randint(2_55			,						size=(self.num_channels, width, height)			,						dtype=np.uinta))

					if not numpify and not torchify:
						# PIL expects the channel dimension as last dimension
						__snake_case    :							str   	=				[Image.fromarray(np.moveaxis(_A			,						0			,						-1)) for x in image_inputs]

					if torchify:
						__snake_case    :							Optional[Any]   	=				[torch.from_numpy(_A) for x in image_inputs]

					return image_inputs







@require_torch
@require_vision
class 	UpperCamelCase    (					lowercase   ,			unittest.TestCase		):
				UpperCAmelCase       :   Dict					=	ChineseCLIPImageProcessor if is_vision_available() else None



				def       _lowercase  (self       :   Any)					->			List[str]:
					__snake_case    :							List[str]   	=				ChineseCLIPImageProcessingTester(self			,						do_center_crop=_A)



				@property
				def       _lowercase  (self       :   Union[str, Any])					->			Tuple:
					return self.image_processor_tester.prepare_image_processor_dict()



				def       _lowercase  (self       :   List[Any])					->			Optional[Any]:
					__snake_case    :							Union[str, Any]   	=				self.image_processing_class(**self.image_processor_dict)
					self.assertTrue(hasattr(_A			,						'do_resize'))
					self.assertTrue(hasattr(_A			,						'size'))
					self.assertTrue(hasattr(_A			,						'do_center_crop'))
					self.assertTrue(hasattr(_A			,						'center_crop'))
					self.assertTrue(hasattr(_A			,						'do_normalize'))
					self.assertTrue(hasattr(_A			,						'image_mean'))
					self.assertTrue(hasattr(_A			,						'image_std'))
					self.assertTrue(hasattr(_A			,						'do_convert_rgb'))



				def       _lowercase  (self       :   List[str])					->			Optional[int]:
					__snake_case    :							int   	=				self.image_processing_class.from_dict(self.image_processor_dict)
					self.assertEqual(image_processor.size			,						{'height': 2_24, 'width': 2_24})
					self.assertEqual(image_processor.crop_size			,						{'height': 18, 'width': 18})

					__snake_case    :							List[str]   	=				self.image_processing_class.from_dict(self.image_processor_dict			,						size=42			,						crop_size=84)
					self.assertEqual(image_processor.size			,						{'shortest_edge': 42})
					self.assertEqual(image_processor.crop_size			,						{'height': 84, 'width': 84})



				def       _lowercase  (self       :   int)					->			Any:
					pass



				def       _lowercase  (self       :   Optional[Any])					->			Optional[Any]:
					# Initialize image_processing
					__snake_case    :							int   	=				self.image_processing_class(**self.image_processor_dict)
					# create random PIL images
					__snake_case    :							List[Any]   	=				self.image_processor_tester.prepare_inputs(equal_resolution=_A)
					for image in image_inputs:
						self.assertIsInstance(_A			,						Image.Image)

					# Test not batched input
					__snake_case    :							Any   	=				image_processing(image_inputs[0]			,						return_tensors='pt').pixel_values
					self.assertEqual(
					    encoded_images.shape			,						(
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['height'],
					        self.image_processor_tester.crop_size['width'],
					    )			,						)

					# Test batched
					__snake_case    :							str   	=				image_processing(_A			,						return_tensors='pt').pixel_values
					self.assertEqual(
					    encoded_images.shape			,						(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['height'],
					        self.image_processor_tester.crop_size['width'],
					    )			,						)



				def       _lowercase  (self       :   Optional[int])					->			Dict:
					# Initialize image_processing
					__snake_case    :							Tuple   	=				self.image_processing_class(**self.image_processor_dict)
					# create random numpy tensors
					__snake_case    :							int   	=				self.image_processor_tester.prepare_inputs(equal_resolution=_A			,						numpify=_A)
					for image in image_inputs:
						self.assertIsInstance(_A			,						np.ndarray)

					# Test not batched input
					__snake_case    :							Dict   	=				image_processing(image_inputs[0]			,						return_tensors='pt').pixel_values
					self.assertEqual(
					    encoded_images.shape			,						(
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['height'],
					        self.image_processor_tester.crop_size['width'],
					    )			,						)

					# Test batched
					__snake_case    :							Union[str, Any]   	=				image_processing(_A			,						return_tensors='pt').pixel_values
					self.assertEqual(
					    encoded_images.shape			,						(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['height'],
					        self.image_processor_tester.crop_size['width'],
					    )			,						)






				def       _lowercase  (self       :   Tuple)					->			Dict:
					# Initialize image_processing
					__snake_case    :							List[Any]   	=				self.image_processing_class(**self.image_processor_dict)
					# create random PyTorch tensors
					__snake_case    :							Dict   	=				self.image_processor_tester.prepare_inputs(equal_resolution=_A			,						torchify=_A)
					for image in image_inputs:
						self.assertIsInstance(_A			,						torch.Tensor)

					# Test not batched input
					__snake_case    :							Any   	=				image_processing(image_inputs[0]			,						return_tensors='pt').pixel_values
					self.assertEqual(
					    encoded_images.shape			,						(
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['height'],
					        self.image_processor_tester.crop_size['width'],
					    )			,						)

					# Test batched
					__snake_case    :							List[str]   	=				image_processing(_A			,						return_tensors='pt').pixel_values
					self.assertEqual(
					    encoded_images.shape			,						(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['height'],
					        self.image_processor_tester.crop_size['width'],
					    )			,						)







@require_torch
@require_vision
class 	UpperCamelCase    (					lowercase   ,			unittest.TestCase		):
				UpperCAmelCase       :   Optional[int]					=	ChineseCLIPImageProcessor if is_vision_available() else None



				def       _lowercase  (self       :   Dict)					->			Union[str, Any]:
					__snake_case    :							List[Any]   	=				ChineseCLIPImageProcessingTester(self			,						num_channels=4			,						do_center_crop=_A)
					__snake_case    :							Optional[int]   	=				3



				@property
				def       _lowercase  (self       :   str)					->			str:
					return self.image_processor_tester.prepare_image_processor_dict()



				def       _lowercase  (self       :   Optional[Any])					->			int:
					__snake_case    :							Optional[int]   	=				self.image_processing_class(**self.image_processor_dict)
					self.assertTrue(hasattr(_A			,						'do_resize'))
					self.assertTrue(hasattr(_A			,						'size'))
					self.assertTrue(hasattr(_A			,						'do_center_crop'))
					self.assertTrue(hasattr(_A			,						'center_crop'))
					self.assertTrue(hasattr(_A			,						'do_normalize'))
					self.assertTrue(hasattr(_A			,						'image_mean'))
					self.assertTrue(hasattr(_A			,						'image_std'))
					self.assertTrue(hasattr(_A			,						'do_convert_rgb'))



				def       _lowercase  (self       :   Dict)					->			Any:
					pass






				def       _lowercase  (self       :   Optional[int])					->			Any:
					# Initialize image_processing
					__snake_case    :							Union[str, Any]   	=				self.image_processing_class(**self.image_processor_dict)
					# create random PIL images
					__snake_case    :							int   	=				self.image_processor_tester.prepare_inputs(equal_resolution=_A)
					for image in image_inputs:
						self.assertIsInstance(_A			,						Image.Image)

					# Test not batched input
					__snake_case    :							str   	=				image_processing(image_inputs[0]			,						return_tensors='pt').pixel_values
					self.assertEqual(
					    encoded_images.shape			,						(
					        1,
					        self.expected_encoded_image_num_channels,
					        self.image_processor_tester.crop_size['height'],
					        self.image_processor_tester.crop_size['width'],
					    )			,						)

					# Test batched
					__snake_case    :							str   	=				image_processing(_A			,						return_tensors='pt').pixel_values
					self.assertEqual(
					    encoded_images.shape			,						(
					        self.image_processor_tester.batch_size,
					        self.expected_encoded_image_num_channels,
					        self.image_processor_tester.crop_size['height'],
					        self.image_processor_tester.crop_size['width'],
					    )			,						)






"""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  :  Union[str, Any]=				logging.get_logger(__name__)
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Union[str, Any]						)							->    List[Any]:

	'''simple docstring'''





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

	return new_state_dict
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Union[str, Any]    ,							UpperCAmelCase_					:       List[Any]						)							->    str:

	'''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)
			__snake_case    :							Union[str, Any]   	=				state_dict.pop(F"glpn.encoder.block.{i}.{j}.attention.self.kv.weight"						)
			__snake_case    :							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
			__snake_case    :							int   	=				kv_weight[
			    : config.hidden_sizes[i], :
			]
			__snake_case    :							List[str]   	=				kv_bias[: config.hidden_sizes[i]]
			__snake_case    :							Dict   	=				kv_weight[
			    config.hidden_sizes[i] :, :
			]
			__snake_case    :							Any   	=				kv_bias[config.hidden_sizes[i] :]
def 						__UpperCAmelCase       (					)							->    Any:

	'''simple docstring'''





	__snake_case    :							Tuple   	=				'http://images.cocodataset.org/val2017/000000039769.jpg'
	__snake_case    :							Tuple   	=				Image.open(requests.get(UpperCAmelCase_    ,							stream=UpperCAmelCase_						).raw						)

	return image
@torch.no_grad()
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Optional[int]    ,							UpperCAmelCase_					:       List[str]    ,							UpperCAmelCase_					:       Dict=False    ,							UpperCAmelCase_					:       Optional[int]=None						)							->    Dict:

	'''simple docstring'''





	__snake_case    :							Dict   	=				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)
	__snake_case    :							Tuple   	=				GLPNImageProcessor()

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

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

	# load original state dict
	__snake_case    :							Dict   	=				torch.load(UpperCAmelCase_    ,							map_location=torch.device('cpu'						)						)

	# rename keys
	__snake_case    :							Union[str, Any]   	=				rename_keys(UpperCAmelCase_						)

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

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

	# forward pass
	__snake_case    :							List[Any]   	=				model(UpperCAmelCase_						)
	__snake_case    :							Optional[int]   	=				outputs.predicted_depth

	# verify output
	if model_name is not None:
		if "nyu" in model_name:
			__snake_case    :							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:
			__snake_case    :							Union[str, Any]   	=				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}"						)

		__snake_case    :							Tuple   	=				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__":
	_a  :  List[Any]=				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  :  int=				parser.parse_args()
	convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)





