



"""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()
lowercase__    :  Tuple							    =	logging.get_logger(__name__)




def __lowercase			(							_a    ):
    snake_case_ :     Dict				=	DPTConfig()

    if "large" in checkpoint_url:
        snake_case_ :     Optional[int]				=	1_024
        snake_case_ :     str				=	4_096
        snake_case_ :     Any				=	24
        snake_case_ :     Any				=	16
        snake_case_ :     Tuple				=	[5, 11, 17, 23]
        snake_case_ :     Any				=	[256, 512, 1_024, 1_024]
        snake_case_ :     Optional[Any]				=	(1, 384, 384)

    if "ade" in checkpoint_url:
        snake_case_ :     Union[str, Any]				=	True

        snake_case_ :     int				=	150
        snake_case_ :     Dict				=	'''huggingface/label-files'''
        snake_case_ :     Any				=	'''ade20k-id2label.json'''
        snake_case_ :     int				=	json.load(open(cached_download(hf_hub_url(_a		,			_a		,			repo_type='''dataset'''    )    )		,			'''r'''    )    )
        snake_case_ :     Dict				=	{int(_a    ): v for k, v in idalabel.items()}
        snake_case_ :     int				=	idalabel
        snake_case_ :     Any				=	{v: k for k, v in idalabel.items()}
        snake_case_ :     Union[str, Any]				=	[1, 150, 480, 480]

    return config, expected_shape




def __lowercase			(							_a    ):
    snake_case_ :     List[Any]				=	['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
    for k in ignore_keys:
        state_dict.pop(_a		,			_a    )




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

    return name




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




def __lowercase			(							):
    snake_case_ :     int				=	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    snake_case_ :     str				=	Image.open(requests.get(_a		,			stream=_a    ).raw    )
    return im




@torch.no_grad()
def __lowercase			(							_a		,			_a		,			_a		,			_a    ):
    snake_case_,	snake_case_ :     Dict				=	get_dpt_config(_a    )
    # load original state_dict from URL
    snake_case_ :     Dict				=	torch.hub.load_state_dict_from_url(_a		,			map_location='''cpu'''    )
    # remove certain keys
    remove_ignore_keys_(_a    )
    # rename keys
    for key in state_dict.copy().keys():
        snake_case_ :     str				=	state_dict.pop(_a    )
        snake_case_ :     Tuple				=	val
    # read in qkv matrices
    read_in_q_k_v(_a		,			_a    )

    # load HuggingFace model
    snake_case_ :     Tuple				=	DPTForSemanticSegmentation(_a    ) if '''ade''' in checkpoint_url else DPTForDepthEstimation(_a    )
    model.load_state_dict(_a    )
    model.eval()

    # Check outputs on an image
    snake_case_ :     Optional[int]				=	480 if '''ade''' in checkpoint_url else 384
    snake_case_ :     Optional[int]				=	DPTImageProcessor(size=_a    )

    snake_case_ :     Dict				=	prepare_img()
    snake_case_ :     int				=	image_processor(_a		,			return_tensors='''pt'''    )

    # forward pass
    snake_case_ :     Union[str, Any]				=	model(**_a    ).logits if '''ade''' in checkpoint_url else model(**_a    ).predicted_depth

    # Assert logits
    snake_case_ :     str				=	torch.tensor([[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]    )
    if "ade" in checkpoint_url:
        snake_case_ :     Optional[Any]				=	torch.tensor([[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]    )
    assert outputs.shape == torch.Size(_a    )
    assert (
        torch.allclose(outputs[0, 0, :3, :3]		,			_a		,			atol=1E-4    )
        if "ade" in checkpoint_url
        else torch.allclose(outputs[0, :3, :3]		,			_a    )
    )

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

    if push_to_hub:
        print('''Pushing model to 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__":
     lowercase__    :  Optional[int]							    =	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.''',
     )

     lowercase__    :  str							    =	parser.parse_args()
     convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)










"""simple docstring"""

import os
import tempfile
import unittest
import uuid
from pathlib import Path

from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_torch_available, is_vision_available


if is_torch_available():
     import torch

if is_soundfile_availble():
     import soundfile as sf

if is_vision_available():
     from PIL import Image




def __lowercase			(							_a=""    ):
    snake_case_ :     List[str]				=	tempfile.mkdtemp()
    return os.path.join(_a		,			str(uuid.uuida()    ) + suffix    )




@require_soundfile
@require_torch
class        _UpperCAmelCase       (			unittest.TestCase):

 def 						_snake_case					(     self		:       str				):
     snake_case_ :     int				=	torch.rand(12  ,     dtype=torch.floataa				) - 0.5
     snake_case_ :     Optional[int]				=	AgentAudio(lowercase_				)
     snake_case_ :     List[str]				=	str(agent_type.to_string()				)

     # Ensure that the tensor and the agent_type's tensor are the same
     self.assertTrue(torch.allclose(lowercase_  ,     agent_type.to_raw()  ,     atol=1E-4				)				)

     del agent_type

     # Ensure the path remains even after the object deletion
     self.assertTrue(os.path.exists(lowercase_				)				)

     # Ensure that the file contains the same value as the original tensor
     snake_case_,	snake_case_ :     int				=	sf.read(lowercase_				)
     self.assertTrue(torch.allclose(lowercase_  ,     torch.tensor(lowercase_				)  ,     atol=1E-4				)				)






 def 						_snake_case					(     self		:       Optional[int]				):
     snake_case_ :     Any				=	torch.rand(12  ,     dtype=torch.floataa				) - 0.5
     snake_case_ :     List[str]				=	get_new_path(suffix='''.wav'''				)
     sf.write(lowercase_  ,     lowercase_  ,     16000				)

     snake_case_ :     Tuple				=	AgentAudio(lowercase_				)

     self.assertTrue(torch.allclose(lowercase_  ,     agent_type.to_raw()  ,     atol=1E-4				)				)
     self.assertEqual(agent_type.to_string()  ,     lowercase_				)





@require_vision
@require_torch
class        _UpperCAmelCase       (			unittest.TestCase):

 def 						_snake_case					(     self		:       Tuple				):
     snake_case_ :     List[Any]				=	torch.randint(0  ,     256  ,     (64, 64, 3)				)
     snake_case_ :     str				=	AgentImage(lowercase_				)
     snake_case_ :     Union[str, Any]				=	str(agent_type.to_string()				)

     # Ensure that the tensor and the agent_type's tensor are the same
     self.assertTrue(torch.allclose(lowercase_  ,     agent_type._tensor  ,     atol=1E-4				)				)

     self.assertIsInstance(agent_type.to_raw()  ,     Image.Image				)

     # Ensure the path remains even after the object deletion
     del agent_type
     self.assertTrue(os.path.exists(lowercase_				)				)

 def 						_snake_case					(     self		:       str				):
     snake_case_ :     Any				=	Path(get_tests_dir('''fixtures/tests_samples/COCO'''				)				) / '''000000039769.png'''
     snake_case_ :     Optional[int]				=	Image.open(lowercase_				)
     snake_case_ :     Tuple				=	AgentImage(lowercase_				)

     self.assertTrue(path.samefile(agent_type.to_string()				)				)
     self.assertTrue(image == agent_type.to_raw()				)

     # Ensure the path remains even after the object deletion
     del agent_type
     self.assertTrue(os.path.exists(lowercase_				)				)






 def 						_snake_case					(     self		:       str				):
     snake_case_ :     int				=	Path(get_tests_dir('''fixtures/tests_samples/COCO'''				)				) / '''000000039769.png'''
     snake_case_ :     Dict				=	Image.open(lowercase_				)
     snake_case_ :     List[str]				=	AgentImage(lowercase_				)

     self.assertFalse(path.samefile(agent_type.to_string()				)				)
     self.assertTrue(image == agent_type.to_raw()				)

     # Ensure the path remains even after the object deletion
     del agent_type
     self.assertTrue(os.path.exists(lowercase_				)				)





class        _UpperCAmelCase       (			unittest.TestCase):

 def 						_snake_case					(     self		:       Any				):
     snake_case_ :     Tuple				=	'''Hey!'''
     snake_case_ :     Optional[Any]				=	AgentText(lowercase_				)

     self.assertEqual(lowercase_  ,     agent_type.to_string()				)
     self.assertEqual(lowercase_  ,     agent_type.to_raw()				)
     self.assertEqual(lowercase_  ,     lowercase_				)





