


"""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()
lowercase_							=							logging.get_logger(__name__)
def      UpperCAmelCase     (       _lowercase :   List[Any]	)						-> Optional[int]:


	"""simple docstring"""




	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      UpperCAmelCase     (       _lowercase :   Any			,   _lowercase :   List[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)
			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      UpperCAmelCase     (       )						-> int:


	"""simple docstring"""




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

	return image
@torch.no_grad()
def      UpperCAmelCase     (       _lowercase :   Optional[Any]			,   _lowercase :   Optional[int]			,   _lowercase :   Optional[Any]=False			,   _lowercase :   List[Any]=None	)						-> Union[str, Any]:


	"""simple docstring"""




	lowerCAmelCase_      							=			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)
	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(
			    [[4.4_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_      							=			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_      							=			torch.Size([1, 4_8_0, 6_4_0]	)

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




'''simple docstring'''

import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 						a_		(     snake_case    ,   unittest.TestCase    ):
							UpperCAmelCase			:		int        =				MgpstrTokenizer
							UpperCAmelCase			:		List[Any]        =				False
							UpperCAmelCase			:		Optional[int]        =				{}
							UpperCAmelCase			:		Any        =				False


							def    UpperCamelCase     (				self   :						Any			)			->					Dict:
										super().setUp()

										# fmt: off
										snake_case:    List[Any]      							=['[GO]', '[s]', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']
										# fmt: on
										snake_case:    Union[str, Any]      							=dict(zip(a_      ,						range(len(a_			)			)			)			)

										snake_case:    List[Any]      							=os.path.join(self.tmpdirname      ,						VOCAB_FILES_NAMES['vocab_file']			)
										with open(self.vocab_file      ,						'w'      ,						encoding='utf-8'			) as fp:
													fp.write(json.dumps(a_			) + '\n'			)


							def    UpperCamelCase     (				self   :						Optional[Any]      ,						**a_   :						List[Any]			)			->					str:
										return MgpstrTokenizer.from_pretrained(self.tmpdirname      ,						**a_			)


							def    UpperCamelCase     (				self   :						int      ,						a_   :						Dict			)			->					int:
										snake_case:    Any      							='tester'
										snake_case:    List[str]      							='tester'
										return input_text, output_text


							@unittest.skip('MGP-STR always lower cases letters.'			)
							def    UpperCamelCase     (				self   :						Any			)			->					Dict:
										pass


							def    UpperCamelCase     (				self   :						Optional[int]			)			->					int:
										snake_case:    int      							=self.get_tokenizers(do_lower_case=a_			)
										for tokenizer in tokenizers:
													with self.subTest(F'''{tokenizer.__class__.__name__}'''			):
																snake_case:    Optional[Any]      							='[SPECIAL_TOKEN]'

																tokenizer.add_special_tokens({'cls_token': special_token}			)
																snake_case:    List[str]      							=tokenizer.encode([special_token]      ,						add_special_tokens=a_			)
																self.assertEqual(len(a_			)      ,						1			)

																snake_case:    Union[str, Any]      							=tokenizer.decode(a_      ,						skip_special_tokens=a_			)
																self.assertTrue(special_token not in decoded			)


							def    UpperCamelCase     (				self   :						int			)			->					str:
										snake_case:    int      							=self.get_tokenizers()
										for tokenizer in tokenizers:
													with self.subTest(F'''{tokenizer.__class__.__name__}'''			):
																snake_case							,  snake_case:    Dict      							=self.get_input_output_texts(a_			)

																snake_case:    Dict      							=tokenizer.tokenize(a_			)
																snake_case:    str      							=tokenizer.convert_tokens_to_ids(a_			)
																snake_case:    List[Any]      							=tokenizer.encode(a_      ,						add_special_tokens=a_			)
																self.assertListEqual(a_      ,						a_			)

																snake_case:    str      							=tokenizer.convert_ids_to_tokens(a_			)
																self.assertNotEqual(len(a_			)      ,						0			)
																snake_case:    Tuple      							=tokenizer.decode(a_			)
																self.assertIsInstance(a_      ,						a_			)

																self.assertEqual(text_a.replace(' '      ,						''			)      ,						a_			)


							@unittest.skip('MGP-STR tokenizer only handles one sequence.'			)
							def    UpperCamelCase     (				self   :						Dict			)			->					str:
										pass




							@unittest.skip('inputs cannot be pretokenized in MgpstrTokenizer'			)
							def    UpperCamelCase     (				self   :						Optional[Any]			)			->					Optional[int]:
										pass




