






'''simple docstring'''





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
a_							=    logging.get_logger(__name__)


def        _a  (     UpperCamelCase_					:				Any   ) ->   Tuple:
						"""simple docstring"""







						lowerCAmelCase__       =						DPTConfig()

						if "large" in checkpoint_url:
												lowerCAmelCase__       =						1_024
												lowerCAmelCase__       =						4_096
												lowerCAmelCase__       =						24
												lowerCAmelCase__       =						16
												lowerCAmelCase__       =						[5, 11, 17, 23]
												lowerCAmelCase__       =						[256, 512, 1_024, 1_024]
												lowerCAmelCase__       =						(1, 384, 384)

						if "ade" in checkpoint_url:
												lowerCAmelCase__       =						True

												lowerCAmelCase__       =						150
												lowerCAmelCase__       =						"huggingface/label-files"
												lowerCAmelCase__       =						"ade20k-id2label.json"
												lowerCAmelCase__       =						json.load(open(cached_download(hf_hub_url(UpperCamelCase_    ,   UpperCamelCase_    ,   repo_type="dataset"   )   )    ,   "r"   )   )
												lowerCAmelCase__       =						{int(UpperCamelCase_   ): v for k, v in idalabel.items()}
												lowerCAmelCase__       =						idalabel
												lowerCAmelCase__       =						{v: k for k, v in idalabel.items()}
												lowerCAmelCase__       =						[1, 150, 480, 480]

						return config, expected_shape


def        _a  (     UpperCamelCase_					:				List[Any]   ) ->   Optional[int]:
						"""simple docstring"""







						lowerCAmelCase__       =						["pretrained.model.head.weight", "pretrained.model.head.bias"]
						for k in ignore_keys:
												state_dict.pop(UpperCamelCase_    ,   UpperCamelCase_   )


def        _a  (     UpperCamelCase_					:				Optional[Any]   ) ->   str:
						"""simple docstring"""







						if (
						    "pretrained.model" in name
						    and "cls_token" not in name
						    and "pos_embed" not in name
						    and "patch_embed" not in name
						):
												lowerCAmelCase__       =						name.replace("pretrained.model"    ,   "dpt.encoder"   )
						if "pretrained.model" in name:
												lowerCAmelCase__       =						name.replace("pretrained.model"    ,   "dpt.embeddings"   )
						if "patch_embed" in name:
												lowerCAmelCase__       =						name.replace("patch_embed"    ,   "patch_embeddings"   )
						if "pos_embed" in name:
												lowerCAmelCase__       =						name.replace("pos_embed"    ,   "position_embeddings"   )
						if "attn.proj" in name:
												lowerCAmelCase__       =						name.replace("attn.proj"    ,   "attention.output.dense"   )
						if "proj" in name and "project" not in name:
												lowerCAmelCase__       =						name.replace("proj"    ,   "projection"   )
						if "blocks" in name:
												lowerCAmelCase__       =						name.replace("blocks"    ,   "layer"   )
						if "mlp.fc1" in name:
												lowerCAmelCase__       =						name.replace("mlp.fc1"    ,   "intermediate.dense"   )
						if "mlp.fc2" in name:
												lowerCAmelCase__       =						name.replace("mlp.fc2"    ,   "output.dense"   )
						if "norm1" in name:
												lowerCAmelCase__       =						name.replace("norm1"    ,   "layernorm_before"   )
						if "norm2" in name:
												lowerCAmelCase__       =						name.replace("norm2"    ,   "layernorm_after"   )
						if "scratch.output_conv" in name:
												lowerCAmelCase__       =						name.replace("scratch.output_conv"    ,   "head"   )
						if "scratch" in name:
												lowerCAmelCase__       =						name.replace("scratch"    ,   "neck"   )
						if "layer1_rn" in name:
												lowerCAmelCase__       =						name.replace("layer1_rn"    ,   "convs.0"   )
						if "layer2_rn" in name:
												lowerCAmelCase__       =						name.replace("layer2_rn"    ,   "convs.1"   )
						if "layer3_rn" in name:
												lowerCAmelCase__       =						name.replace("layer3_rn"    ,   "convs.2"   )
						if "layer4_rn" in name:
												lowerCAmelCase__       =						name.replace("layer4_rn"    ,   "convs.3"   )
						if "refinenet" in name:
												lowerCAmelCase__       =						int(name[len("neck.refinenet"   ) : len("neck.refinenet"   ) + 1]   )
												# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
												lowerCAmelCase__       =						name.replace(F"refinenet{layer_idx}"    ,   F"fusion_stage.layers.{abs(layer_idx-4   )}"   )
						if "out_conv" in name:
												lowerCAmelCase__       =						name.replace("out_conv"    ,   "projection"   )
						if "resConfUnit1" in name:
												lowerCAmelCase__       =						name.replace("resConfUnit1"    ,   "residual_layer1"   )
						if "resConfUnit2" in name:
												lowerCAmelCase__       =						name.replace("resConfUnit2"    ,   "residual_layer2"   )
						if "conv1" in name:
												lowerCAmelCase__       =						name.replace("conv1"    ,   "convolution1"   )
						if "conv2" in name:
												lowerCAmelCase__       =						name.replace("conv2"    ,   "convolution2"   )
						# readout blocks
						if "pretrained.act_postprocess1.0.project.0" in name:
												lowerCAmelCase__       =						name.replace("pretrained.act_postprocess1.0.project.0"    ,   "neck.reassemble_stage.readout_projects.0.0"   )
						if "pretrained.act_postprocess2.0.project.0" in name:
												lowerCAmelCase__       =						name.replace("pretrained.act_postprocess2.0.project.0"    ,   "neck.reassemble_stage.readout_projects.1.0"   )
						if "pretrained.act_postprocess3.0.project.0" in name:
												lowerCAmelCase__       =						name.replace("pretrained.act_postprocess3.0.project.0"    ,   "neck.reassemble_stage.readout_projects.2.0"   )
						if "pretrained.act_postprocess4.0.project.0" in name:
												lowerCAmelCase__       =						name.replace("pretrained.act_postprocess4.0.project.0"    ,   "neck.reassemble_stage.readout_projects.3.0"   )
						# resize blocks
						if "pretrained.act_postprocess1.3" in name:
												lowerCAmelCase__       =						name.replace("pretrained.act_postprocess1.3"    ,   "neck.reassemble_stage.layers.0.projection"   )
						if "pretrained.act_postprocess1.4" in name:
												lowerCAmelCase__       =						name.replace("pretrained.act_postprocess1.4"    ,   "neck.reassemble_stage.layers.0.resize"   )
						if "pretrained.act_postprocess2.3" in name:
												lowerCAmelCase__       =						name.replace("pretrained.act_postprocess2.3"    ,   "neck.reassemble_stage.layers.1.projection"   )
						if "pretrained.act_postprocess2.4" in name:
												lowerCAmelCase__       =						name.replace("pretrained.act_postprocess2.4"    ,   "neck.reassemble_stage.layers.1.resize"   )
						if "pretrained.act_postprocess3.3" in name:
												lowerCAmelCase__       =						name.replace("pretrained.act_postprocess3.3"    ,   "neck.reassemble_stage.layers.2.projection"   )
						if "pretrained.act_postprocess4.3" in name:
												lowerCAmelCase__       =						name.replace("pretrained.act_postprocess4.3"    ,   "neck.reassemble_stage.layers.3.projection"   )
						if "pretrained.act_postprocess4.4" in name:
												lowerCAmelCase__       =						name.replace("pretrained.act_postprocess4.4"    ,   "neck.reassemble_stage.layers.3.resize"   )
						if "pretrained" in name:
												lowerCAmelCase__       =						name.replace("pretrained"    ,   "dpt"   )
						if "bn" in name:
												lowerCAmelCase__       =						name.replace("bn"    ,   "batch_norm"   )
						if "head" in name:
												lowerCAmelCase__       =						name.replace("head"    ,   "head.head"   )
						if "encoder.norm" in name:
												lowerCAmelCase__       =						name.replace("encoder.norm"    ,   "layernorm"   )
						if "auxlayer" in name:
												lowerCAmelCase__       =						name.replace("auxlayer"    ,   "auxiliary_head.head"   )

						return name


def        _a  (     UpperCamelCase_					:				str    ,   UpperCamelCase_					:				List[Any]   ) ->   Tuple:
						"""simple docstring"""







						for i in range(config.num_hidden_layers   ):
												# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
												lowerCAmelCase__       =						state_dict.pop(F"dpt.encoder.layer.{i}.attn.qkv.weight"   )
												lowerCAmelCase__       =						state_dict.pop(F"dpt.encoder.layer.{i}.attn.qkv.bias"   )
												# next, add query, keys and values (in that order) to the state dict
												lowerCAmelCase__       =						in_proj_weight[: config.hidden_size, :]
												lowerCAmelCase__       =						in_proj_bias[: config.hidden_size]
												lowerCAmelCase__       =						in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												lowerCAmelCase__       =						in_proj_bias[
												    config.hidden_size : config.hidden_size * 2
												]
												lowerCAmelCase__       =						in_proj_weight[
												    -config.hidden_size :, :
												]
												lowerCAmelCase__       =						in_proj_bias[-config.hidden_size :]


def        _a  (     ) ->   Dict:
						"""simple docstring"""







						lowerCAmelCase__       =						"http://images.cocodataset.org/val2017/000000039769.jpg"
						lowerCAmelCase__       =						Image.open(requests.get(UpperCamelCase_    ,   stream=UpperCamelCase_   ).raw   )
						return im


@torch.no_grad()
def        _a  (     UpperCamelCase_					:				Optional[Any]    ,   UpperCamelCase_					:				Optional[int]    ,   UpperCamelCase_					:				Union[str, Any]    ,   UpperCamelCase_					:				Optional[int]   ) ->   int:
						"""simple docstring"""







						lowerCAmelCase__      ,							lowerCAmelCase__       =						get_dpt_config(UpperCamelCase_   )
						# load original state_dict from URL
						lowerCAmelCase__       =						torch.hub.load_state_dict_from_url(UpperCamelCase_    ,   map_location="cpu"   )
						# remove certain keys
						remove_ignore_keys_(UpperCamelCase_   )
						# rename keys
						for key in state_dict.copy().keys():
												lowerCAmelCase__       =						state_dict.pop(UpperCamelCase_   )
												lowerCAmelCase__       =						val
						# read in qkv matrices
						read_in_q_k_v(UpperCamelCase_    ,   UpperCamelCase_   )

						# load HuggingFace model
						lowerCAmelCase__       =						DPTForSemanticSegmentation(UpperCamelCase_   ) if "ade" in checkpoint_url else DPTForDepthEstimation(UpperCamelCase_   )
						model.load_state_dict(UpperCamelCase_   )
						model.eval()

						# Check outputs on an image
						lowerCAmelCase__       =						480 if "ade" in checkpoint_url else 384
						lowerCAmelCase__       =						DPTImageProcessor(size=UpperCamelCase_   )

						lowerCAmelCase__       =						prepare_img()
						lowerCAmelCase__       =						image_processor(UpperCamelCase_    ,   return_tensors="pt"   )

						# forward pass
						lowerCAmelCase__       =						model(**UpperCamelCase_   ).logits if "ade" in checkpoint_url else model(**UpperCamelCase_   ).predicted_depth

						# Assert logits
						lowerCAmelCase__       =						torch.tensor([[6.3_199, 6.3_629, 6.4_148], [6.3_850, 6.3_615, 6.4_166], [6.3_519, 6.3_176, 6.3_575]]   )
						if "ade" in checkpoint_url:
												lowerCAmelCase__       =						torch.tensor([[4.0_480, 4.2_420, 4.4_360], [4.3_124, 4.5_693, 4.8_261], [4.5_768, 4.8_965, 5.2_163]]   )
						assert outputs.shape == torch.Size(UpperCamelCase_   )
						assert (
						    torch.allclose(outputs[0, 0, :3, :3]    ,   UpperCamelCase_    ,   atol=1e-4   )
						    if "ade" in checkpoint_url
						    else torch.allclose(outputs[0, :3, :3]    ,   UpperCamelCase_   )
						)

						Path(UpperCamelCase_   ).mkdir(exist_ok=UpperCamelCase_   )
						print(F"Saving model to {pytorch_dump_folder_path}"   )
						model.save_pretrained(UpperCamelCase_   )
						print(F"Saving image processor to {pytorch_dump_folder_path}"   )
						image_processor.save_pretrained(UpperCamelCase_   )

						if push_to_hub:
												print("Pushing model to 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_							=    argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--checkpoint_url''',
	    default='''https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt''',
	    type=str,
	    help='''URL of the original DPT checkpoint you\'d like to convert.''',
	)
	parser.add_argument(
	    '''--pytorch_dump_folder_path''',
	    default=None,
	    type=str,
	    required=True,
	    help='''Path to the output PyTorch model directory.''',
	)
	parser.add_argument(
	    '''--push_to_hub''',
	    action='''store_true''',
	)
	parser.add_argument(
	    '''--model_name''',
	    default='''dpt-large''',
	    type=str,
	    help='''Name of the model, in case you\'re pushing to the hub.''',
	)

	a_							=    parser.parse_args()
	convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)














import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class      lowercase__     (			_UpperCAmelCase,					unittest.TestCase	):
	a_         =None
	a_         =BloomTokenizerFast
	a_         =BloomTokenizerFast
	a_         =True
	a_         =False
	a_         ="""tokenizer_file"""
	a_         ={"""bos_token""": """<s>""", """eos_token""": """</s>""", """unk_token""": """<unk>""", """pad_token""": """<pad>"""}

	def 		UpperCAmelCase  (							self       )->					int:







							'''simple docstring'''





							super().setUp()
							lowerCAmelCase__       =						BloomTokenizerFast.from_pretrained("bigscience/tokenizer"       )
							tokenizer.save_pretrained(self.tmpdirname       )

	def 		UpperCAmelCase  (							self		,    **__UpperCAmelCase       )->					int:







							'''simple docstring'''





							kwargs.update(self.special_tokens_map       )
							return BloomTokenizerFast.from_pretrained(self.tmpdirname		,    **__UpperCAmelCase       )

	def 		UpperCAmelCase  (							self       )->					Any:







							'''simple docstring'''





							lowerCAmelCase__       =						self.get_rust_tokenizer()

							lowerCAmelCase__       =						["The quick brown fox</s>", "jumps over the lazy dog</s>"]
							lowerCAmelCase__       =						[[2175, 23714, 73173, 144252, 2], [77, 132619, 3478, 368, 109586, 35433, 2]]

							lowerCAmelCase__       =						tokenizer.batch_encode_plus(__UpperCAmelCase       )["input_ids"]
							self.assertListEqual(__UpperCAmelCase		,    __UpperCAmelCase       )

							lowerCAmelCase__       =						tokenizer.batch_decode(__UpperCAmelCase       )
							self.assertListEqual(__UpperCAmelCase		,    __UpperCAmelCase       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase=6       )->					str:







							'''simple docstring'''





							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"       ):
																			lowerCAmelCase__       =						self.rust_tokenizer_class.from_pretrained(__UpperCAmelCase		,    **__UpperCAmelCase       )
																			# tokenizer_r.pad_token = None # Hotfixing padding = None
																			# Simple input
																			lowerCAmelCase__       =						"This is a simple input"
																			lowerCAmelCase__       =						["This is a simple input 1", "This is a simple input 2"]
																			lowerCAmelCase__       =						("This is a simple input", "This is a pair")
																			lowerCAmelCase__       =						[
																			    ("This is a simple input 1", "This is a simple input 2"),
																			    ("This is a simple pair 1", "This is a simple pair 2"),
																			]

																			# Simple input tests
																			try:
																									tokenizer_r.encode(__UpperCAmelCase		,    max_length=__UpperCAmelCase       )
																									tokenizer_r.encode_plus(__UpperCAmelCase		,    max_length=__UpperCAmelCase       )

																									tokenizer_r.batch_encode_plus(__UpperCAmelCase		,    max_length=__UpperCAmelCase       )
																									tokenizer_r.encode(__UpperCAmelCase		,    max_length=__UpperCAmelCase       )
																									tokenizer_r.batch_encode_plus(__UpperCAmelCase		,    max_length=__UpperCAmelCase       )
																			except ValueError:
																									self.fail("Bloom Tokenizer should be able to deal with padding"       )

																			lowerCAmelCase__       =						None  # Hotfixing padding = None
																			self.assertRaises(__UpperCAmelCase		,    tokenizer_r.encode		,    __UpperCAmelCase		,    max_length=__UpperCAmelCase		,    padding="max_length"       )

																			# Simple input
																			self.assertRaises(__UpperCAmelCase		,    tokenizer_r.encode_plus		,    __UpperCAmelCase		,    max_length=__UpperCAmelCase		,    padding="max_length"       )

																			# Simple input
																			self.assertRaises(
																			    __UpperCAmelCase		,    tokenizer_r.batch_encode_plus		,    __UpperCAmelCase		,    max_length=__UpperCAmelCase		,    padding="max_length"		,    )

																			# Pair input
																			self.assertRaises(__UpperCAmelCase		,    tokenizer_r.encode		,    __UpperCAmelCase		,    max_length=__UpperCAmelCase		,    padding="max_length"       )

																			# Pair input
																			self.assertRaises(__UpperCAmelCase		,    tokenizer_r.encode_plus		,    __UpperCAmelCase		,    max_length=__UpperCAmelCase		,    padding="max_length"       )

																			# Pair input
																			self.assertRaises(
																			    __UpperCAmelCase		,    tokenizer_r.batch_encode_plus		,    __UpperCAmelCase		,    max_length=__UpperCAmelCase		,    padding="max_length"		,    )

	def 		UpperCAmelCase  (							self       )->					int:







							'''simple docstring'''





							lowerCAmelCase__       =						self.get_rust_tokenizer()
							lowerCAmelCase__       =						load_dataset("xnli"		,    "all_languages"		,    split="test"		,    streaming=__UpperCAmelCase       )

							lowerCAmelCase__       =						next(iter(__UpperCAmelCase       )       )["premise"]  # pick up one data
							lowerCAmelCase__       =						list(sample_data.values()       )

							lowerCAmelCase__       =						list(map(tokenizer.encode		,    __UpperCAmelCase       )       )
							lowerCAmelCase__       =						[tokenizer.decode(__UpperCAmelCase		,    clean_up_tokenization_spaces=__UpperCAmelCase       ) for x in output_tokens]
							self.assertListEqual(__UpperCAmelCase		,    __UpperCAmelCase       )


	def 		UpperCAmelCase  (							self       )->					List[str]:







							'''simple docstring'''





							self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map       )		,    1       )
							self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()       )[0]       )		,    1       )






