






"""simple docstring"""



# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
						from PIL import Image






class 							__SCREAMING_SNAKE_CASE  (			UpperCamelCase						):
							snake_case_       =							'Salesforce/blip-image-captioning-base'
							snake_case_       =							(
							    'This is a tool that generates a description of an image. It takes an input named `image` which should be the '
							    'image to caption, and returns a text that contains the description in English.'
							)
							snake_case_       =							'image_captioner'
							snake_case_       =							AutoModelForVisionaSeq

							snake_case_       =							['image']
							snake_case_       =							['text']


							def __init__(     self :  int     ,      *snake_case :  Optional[int]     ,      **snake_case :  Optional[int]							):
													'''simple docstring'''





													requires_backends(self     ,      ["""vision"""]							)
													super().__init__(*snake_case     ,      **snake_case							)


							def   _UpperCamelCase    (     self :  int     ,      snake_case :  "Image"							):
													'''simple docstring'''





													return self.pre_processor(images=snake_case     ,      return_tensors="""pt"""							)


							def   _UpperCamelCase    (     self :  int     ,      snake_case :  List[Any]							):
													'''simple docstring'''





													return self.model.generate(**snake_case							)


							def   _UpperCamelCase    (     self :  Optional[int]     ,      snake_case :  Any							):
													'''simple docstring'''





													return self.pre_processor.batch_decode(snake_case     ,      skip_special_tokens=snake_case							)[0].strip()














"""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        _lowerCAmelCase						(							UpperCAmelCase__    :   List[Any] )						->List[str]:
						A__  :					Union[str, Any]											=					DPTConfig()

						if "large" in checkpoint_url:
												A__  :					int											=					1_0_2_4
												A__  :					Union[str, Any]											=					4_0_9_6
												A__  :					Optional[int]											=					2_4
												A__  :					int											=					1_6
												A__  :					Union[str, Any]											=					[5, 1_1, 1_7, 2_3]
												A__  :					Tuple											=					[2_5_6, 5_1_2, 1_0_2_4, 1_0_2_4]
												A__  :					Tuple											=					(1, 3_8_4, 3_8_4)

						if "ade" in checkpoint_url:
												A__  :					Optional[int]											=					True

												A__  :					int											=					1_5_0
												A__  :					Union[str, Any]											=					"""huggingface/label-files"""
												A__  :					List[Any]											=					"""ade20k-id2label.json"""
												A__  :					Union[str, Any]											=					json.load(open(cached_download(hf_hub_url(UpperCAmelCase__,  UpperCAmelCase__,  repo_type="""dataset""" ) ),  """r""" ) )
												A__  :					List[Any]											=					{int(UpperCAmelCase__ ): v for k, v in idalabel.items()}
												A__  :					Dict											=					idalabel
												A__  :					List[Any]											=					{v: k for k, v in idalabel.items()}
												A__  :					Optional[Any]											=					[1, 1_5_0, 4_8_0, 4_8_0]

						return config, expected_shape




def        _lowerCAmelCase						(							UpperCAmelCase__    :   int )						->Any:
						A__  :					List[Any]											=					["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
						for k in ignore_keys:
												state_dict.pop(UpperCAmelCase__,  UpperCAmelCase__ )




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

						return name




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




def        _lowerCAmelCase						(							)						->List[str]:
						A__  :					int											=					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
						A__  :					int											=					Image.open(requests.get(UpperCAmelCase__,  stream=UpperCAmelCase__ ).raw )
						return im




@torch.no_grad()
def        _lowerCAmelCase						(							UpperCAmelCase__    :   int,  UpperCAmelCase__    :   Optional[int],  UpperCAmelCase__    :   str,  UpperCAmelCase__    :   int )						->str:
						A__					,					A__  :					Dict											=					get_dpt_config(UpperCAmelCase__ )
						# load original state_dict from URL
						A__  :					Any											=					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():
												A__  :					int											=					state_dict.pop(UpperCAmelCase__ )
												A__  :					str											=					val
						# read in qkv matrices
						read_in_q_k_v(UpperCAmelCase__,  UpperCAmelCase__ )

						# load HuggingFace model
						A__  :					Optional[Any]											=					DPTForSemanticSegmentation(UpperCAmelCase__ ) if """ade""" in checkpoint_url else DPTForDepthEstimation(UpperCAmelCase__ )
						model.load_state_dict(UpperCAmelCase__ )
						model.eval()

						# Check outputs on an image
						A__  :					Optional[Any]											=					4_8_0 if """ade""" in checkpoint_url else 3_8_4
						A__  :					Dict											=					DPTImageProcessor(size=UpperCAmelCase__ )

						A__  :					Optional[int]											=					prepare_img()
						A__  :					Any											=					image_processor(UpperCAmelCase__,  return_tensors="""pt""" )

						# forward pass
						A__  :					List[str]											=					model(**UpperCAmelCase__ ).logits if """ade""" in checkpoint_url else model(**UpperCAmelCase__ ).predicted_depth

						# Assert logits
						A__  :					Optional[Any]											=					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:
												A__  :					Optional[int]											=					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(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)






