






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()
__UpperCAmelCase							:   int							=  logging.get_logger(__name__)





def 					lowercase_ (    __snake_case     :							Optional[Any]      )     -> List[str]:





				'''simple docstring'''


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

				return new_state_dict





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





def 					lowercase_ (    )     -> Tuple:





				'''simple docstring'''


				snake_case__      :Optional[int]         =   "http://images.cocodataset.org/val2017/000000039769.jpg"
				snake_case__      :Any         =   Image.open(requests.get(__snake_case	,	stream=__snake_case      ).raw      )

				return image





@torch.no_grad()
def 					lowercase_ (    __snake_case     :							List[Any]	,	__snake_case     :							List[str]	,	__snake_case     :							Dict=False	,	__snake_case     :							Tuple=None      )     -> Optional[Any]:





				'''simple docstring'''


				snake_case__      :Union[str, Any]         =   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__      :List[Any]         =   GLPNImageProcessor()

				# prepare image
				snake_case__      :Dict         =   prepare_img()
				snake_case__      :Dict         =   image_processor(images=__snake_case	,	return_tensors="pt"      ).pixel_values

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

				# load original state dict
				snake_case__      :Optional[Any]         =   torch.load(__snake_case	,	map_location=torch.device("cpu"      )      )

				# rename keys
				snake_case__      :List[str]         =   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
				snake_case__      :List[str]         =   GLPNForDepthEstimation(__snake_case      )
				model.load_state_dict(__snake_case      )
				model.eval()

				# forward pass
				snake_case__      :List[str]         =   model(__snake_case      )
				snake_case__      :List[str]         =   outputs.predicted_depth

				# verify output
				if model_name is not None:
								if "nyu" in model_name:
												snake_case__      :Union[str, 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:
												snake_case__      :Optional[int]         =   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}'      )

								snake_case__      :str         =   torch.Size([1, 4_80, 6_40]      )

								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__":
	__UpperCAmelCase							:   Optional[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.",
	)
	__UpperCAmelCase							:   str							=  parser.parse_args()
	convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)







import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError

from transformers import (
    AlbertTokenizer,
    AutoTokenizer,
    BertTokenizer,
    BertTokenizerFast,
    GPTaTokenizerFast,
    is_tokenizers_available,
)
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_tokenizers
from transformers.tokenization_utils import Trie


sys.path.append(str(Path(__file__).parent.parent / "utils"))

from test_module.custom_tokenization import CustomTokenizer  # noqa E402


if is_tokenizers_available():
	from test_module.custom_tokenization_fast import CustomTokenizerFast


class 					_snake_case						(			unittest.TestCase ):






	def    lowerCAmelCase_							(		self       )						->    List[Any]:
					# A mock response for an HTTP head request to emulate server down
					snake_case__      :Tuple         =   mock.Mock()
					snake_case__      :List[str]         =   500
					snake_case__      :Any         =   {}
					snake_case__      :Union[str, Any]         =   HTTPError
					snake_case__      :Tuple         =   {}

					# Download this model to make sure it's in the cache.
					snake_case__      :Any         =   BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert"       )

					# Under the mock environment we get a 500 error when trying to reach the tokenizer.
					with mock.patch("requests.Session.request"					,return_value=UpperCamelCase       ) as mock_head:
									snake_case__      :Dict         =   BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert"       )
									# This check we did call the fake head request
									mock_head.assert_called()






	@require_tokenizers
	def    lowerCAmelCase_							(		self       )						->    Dict:
					# A mock response for an HTTP head request to emulate server down
					snake_case__      :Union[str, Any]         =   mock.Mock()
					snake_case__      :int         =   500
					snake_case__      :Any         =   {}
					snake_case__      :Dict         =   HTTPError
					snake_case__      :List[Any]         =   {}

					# Download this model to make sure it's in the cache.
					snake_case__      :Optional[int]         =   GPTaTokenizerFast.from_pretrained("gpt2"       )

					# Under the mock environment we get a 500 error when trying to reach the tokenizer.
					with mock.patch("requests.Session.request"					,return_value=UpperCamelCase       ) as mock_head:
									snake_case__      :Any         =   GPTaTokenizerFast.from_pretrained("gpt2"       )
									# This check we did call the fake head request
									mock_head.assert_called()






	def    lowerCAmelCase_							(		self       )						->    int:
					# This test is for deprecated behavior and can be removed in v5
					try:
									snake_case__      :Union[str, Any]         =   tempfile.mktemp()
									with open(UpperCamelCase					,"wb"       ) as f:
													http_get("https://huggingface.co/albert-base-v1/resolve/main/spiece.model"					,UpperCamelCase       )

									snake_case__      :Tuple         =   AlbertTokenizer.from_pretrained(UpperCamelCase       )
					finally:
									os.remove(UpperCamelCase       )

					# Supporting this legacy load introduced a weird bug where the tokenizer would load local files if they are in
					# the current folder and have the right name.
					if os.path.isfile("tokenizer.json"       ):
									# We skip the test if the user has a `tokenizer.json` in this folder to avoid deleting it.
									return
					try:
									with open("tokenizer.json"					,"wb"       ) as f:
													http_get("https://huggingface.co/hf-internal-testing/tiny-random-bert/blob/main/tokenizer.json"					,UpperCamelCase       )
									snake_case__      :Dict         =   AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"       )
									# The tiny random BERT has a vocab size of 1024, tiny gpt2 as a vocab size of 1000
									self.assertEqual(tokenizer.vocab_size					,1_000       )
									# Tokenizer should depend on the remote checkpoint, not the local tokenizer.json file.

					finally:
									os.remove("tokenizer.json"       )






	def    lowerCAmelCase_							(		self       )						->    Union[str, Any]:
					# This test is for deprecated behavior and can be removed in v5
					snake_case__      :Union[str, Any]         =   AlbertTokenizer.from_pretrained("https://huggingface.co/albert-base-v1/resolve/main/spiece.model"       )




@is_staging_test
class 					_snake_case						(			unittest.TestCase ):
	_A          =   ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'bla', 'blou']






	@classmethod
	def    lowerCAmelCase_							(		cls       )						->    Optional[int]:
					snake_case__      :List[str]         =   TOKEN
					HfFolder.save_token(UpperCamelCase       )






	@classmethod
	def    lowerCAmelCase_							(		cls       )						->    Union[str, Any]:
					try:
									delete_repo(token=cls._token					,repo_id="test-tokenizer"       )
					except HTTPError:
									pass

					try:
									delete_repo(token=cls._token					,repo_id="valid_org/test-tokenizer-org"       )
					except HTTPError:
									pass

					try:
									delete_repo(token=cls._token					,repo_id="test-dynamic-tokenizer"       )
					except HTTPError:
									pass






	def    lowerCAmelCase_							(		self       )						->    Optional[Any]:
					with tempfile.TemporaryDirectory() as tmp_dir:
									snake_case__      :List[str]         =   os.path.join(UpperCamelCase					,"vocab.txt"       )
									with open(UpperCamelCase					,"w"					,encoding="utf-8"       ) as vocab_writer:
													vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]       )       )
									snake_case__      :str         =   BertTokenizer(UpperCamelCase       )

					tokenizer.push_to_hub("test-tokenizer"					,use_auth_token=self._token       )
					snake_case__      :Dict         =   BertTokenizer.from_pretrained(f'{USER}/test-tokenizer'       )
					self.assertDictEqual(new_tokenizer.vocab					,tokenizer.vocab       )

					# Reset repo
					delete_repo(token=self._token					,repo_id="test-tokenizer"       )

					# Push to hub via save_pretrained
					with tempfile.TemporaryDirectory() as tmp_dir:
									tokenizer.save_pretrained(UpperCamelCase					,repo_id="test-tokenizer"					,push_to_hub=UpperCamelCase					,use_auth_token=self._token       )

					snake_case__      :List[str]         =   BertTokenizer.from_pretrained(f'{USER}/test-tokenizer'       )
					self.assertDictEqual(new_tokenizer.vocab					,tokenizer.vocab       )






	def    lowerCAmelCase_							(		self       )						->    Optional[int]:
					with tempfile.TemporaryDirectory() as tmp_dir:
									snake_case__      :List[Any]         =   os.path.join(UpperCamelCase					,"vocab.txt"       )
									with open(UpperCamelCase					,"w"					,encoding="utf-8"       ) as vocab_writer:
													vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]       )       )
									snake_case__      :Any         =   BertTokenizer(UpperCamelCase       )

					tokenizer.push_to_hub("valid_org/test-tokenizer-org"					,use_auth_token=self._token       )
					snake_case__      :Any         =   BertTokenizer.from_pretrained("valid_org/test-tokenizer-org"       )
					self.assertDictEqual(new_tokenizer.vocab					,tokenizer.vocab       )

					# Reset repo
					delete_repo(token=self._token					,repo_id="valid_org/test-tokenizer-org"       )

					# Push to hub via save_pretrained
					with tempfile.TemporaryDirectory() as tmp_dir:
									tokenizer.save_pretrained(
									    UpperCamelCase					,repo_id="valid_org/test-tokenizer-org"					,push_to_hub=UpperCamelCase					,use_auth_token=self._token       )

					snake_case__      :Union[str, Any]         =   BertTokenizer.from_pretrained("valid_org/test-tokenizer-org"       )
					self.assertDictEqual(new_tokenizer.vocab					,tokenizer.vocab       )






	@require_tokenizers
	def    lowerCAmelCase_							(		self       )						->    Any:
					CustomTokenizer.register_for_auto_class()
					with tempfile.TemporaryDirectory() as tmp_dir:
									snake_case__      :str         =   os.path.join(UpperCamelCase					,"vocab.txt"       )
									with open(UpperCamelCase					,"w"					,encoding="utf-8"       ) as vocab_writer:
													vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]       )       )
									snake_case__      :Optional[int]         =   CustomTokenizer(UpperCamelCase       )

					# No fast custom tokenizer
					tokenizer.push_to_hub("test-dynamic-tokenizer"					,use_auth_token=self._token       )

					snake_case__      :Union[str, Any]         =   AutoTokenizer.from_pretrained(f'{USER}/test-dynamic-tokenizer'					,trust_remote_code=UpperCamelCase       )
					# Can't make an isinstance check because the new_model.config is from the CustomTokenizer class of a dynamic module
					self.assertEqual(tokenizer.__class__.__name__					,"CustomTokenizer"       )

					# Fast and slow custom tokenizer
					CustomTokenizerFast.register_for_auto_class()
					with tempfile.TemporaryDirectory() as tmp_dir:
									snake_case__      :int         =   os.path.join(UpperCamelCase					,"vocab.txt"       )
									with open(UpperCamelCase					,"w"					,encoding="utf-8"       ) as vocab_writer:
													vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]       )       )

									snake_case__      :Tuple         =   BertTokenizerFast.from_pretrained(UpperCamelCase       )
									bert_tokenizer.save_pretrained(UpperCamelCase       )
									snake_case__      :List[Any]         =   CustomTokenizerFast.from_pretrained(UpperCamelCase       )

					tokenizer.push_to_hub("test-dynamic-tokenizer"					,use_auth_token=self._token       )

					snake_case__      :List[Any]         =   AutoTokenizer.from_pretrained(f'{USER}/test-dynamic-tokenizer'					,trust_remote_code=UpperCamelCase       )
					# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
					self.assertEqual(tokenizer.__class__.__name__					,"CustomTokenizerFast"       )
					snake_case__      :List[str]         =   AutoTokenizer.from_pretrained(
					    f'{USER}/test-dynamic-tokenizer'					,use_fast=UpperCamelCase					,trust_remote_code=UpperCamelCase       )
					# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
					self.assertEqual(tokenizer.__class__.__name__					,"CustomTokenizer"       )




class 					_snake_case						(			unittest.TestCase ):






	def    lowerCAmelCase_							(		self       )						->    List[Any]:
					snake_case__      :int         =   Trie()
					trie.add("Hello 友達"       )
					self.assertEqual(trie.data					,{"H": {"e": {"l": {"l": {"o": {" ": {"友": {"達": {"": 1}}}}}}}}}       )
					trie.add("Hello"       )
					trie.data
					self.assertEqual(trie.data					,{"H": {"e": {"l": {"l": {"o": {"": 1, " ": {"友": {"達": {"": 1}}}}}}}}}       )






	def    lowerCAmelCase_							(		self       )						->    int:
					snake_case__      :List[str]         =   Trie()
					self.assertEqual(trie.split("[CLS] This is a extra_id_100"       )					,["[CLS] This is a extra_id_100"]       )
					trie.add("[CLS]"       )
					trie.add("extra_id_1"       )
					trie.add("extra_id_100"       )
					self.assertEqual(trie.split("[CLS] This is a extra_id_100"       )					,["[CLS]", " This is a ", "extra_id_100"]       )






	def    lowerCAmelCase_							(		self       )						->    str:
					snake_case__      :Optional[Any]         =   Trie()
					trie.add("A"       )
					self.assertEqual(trie.split("ABC"       )					,["A", "BC"]       )
					self.assertEqual(trie.split("BCA"       )					,["BC", "A"]       )






	def    lowerCAmelCase_							(		self       )						->    Dict:
					snake_case__      :Any         =   Trie()
					trie.add("TOKEN]"       )
					trie.add("[SPECIAL_TOKEN]"       )
					self.assertEqual(trie.split("This is something [SPECIAL_TOKEN]"       )					,["This is something ", "[SPECIAL_TOKEN]"]       )






	def    lowerCAmelCase_							(		self       )						->    Tuple:
					snake_case__      :List[Any]         =   Trie()
					trie.add("A"       )
					trie.add("P"       )
					trie.add("[SPECIAL_TOKEN]"       )
					self.assertEqual(trie.split("This is something [SPECIAL_TOKEN]"       )					,["This is something ", "[SPECIAL_TOKEN]"]       )






	def    lowerCAmelCase_							(		self       )						->    Tuple:
					snake_case__      :str         =   Trie()
					trie.add("AB"       )
					trie.add("B"       )
					trie.add("C"       )
					self.assertEqual(trie.split("ABC"       )					,["AB", "C"]       )






	def    lowerCAmelCase_							(		self       )						->    Union[str, Any]:
					snake_case__      :Dict         =   Trie()
					trie.add("ABC"       )
					trie.add("B"       )
					trie.add("CD"       )
					self.assertEqual(trie.split("ABCD"       )					,["ABC", "D"]       )






	def    lowerCAmelCase_							(		self       )						->    int:
					# Even if the offsets are wrong, we necessarily output correct string
					# parts.
					snake_case__      :Optional[int]         =   Trie()
					snake_case__      :Union[str, Any]         =   trie.cut_text("ABC"					,[0, 0, 2, 1, 2, 3]       )
					self.assertEqual(UpperCamelCase					,["AB", "C"]       )