



'''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_      :				List[Any]   					=    logging.get_logger(__name__)







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Optional[int]						):
						lowercase        =							DPTConfig(embedding_type="""hybrid"""						)

						if "large" in checkpoint_url:
												lowercase        =							1024
												lowercase        =							4096
												lowercase        =							24
												lowercase        =							16
												lowercase        =							[5, 11, 17, 23]
												lowercase        =							[256, 512, 1024, 1024]
												lowercase        =							(1, 384, 384)

						if "nyu" or "midas" in checkpoint_url:
												lowercase        =							768
												lowercase        =							[1, 1, 1, 0.5]
												lowercase        =							[256, 512, 768, 768]
												lowercase        =							150
												lowercase        =							16
												lowercase        =							(1, 384, 384)
												lowercase        =							False
												lowercase        =							"""project"""

						if "ade" in checkpoint_url:
												lowercase        =							True
												lowercase        =							768
												lowercase        =							[1, 1, 1, 0.5]
												lowercase        =							150
												lowercase        =							16
												lowercase        =							"""huggingface/label-files"""
												lowercase        =							"""ade20k-id2label.json"""
												lowercase        =							json.load(open(cached_download(hf_hub_url(lowercase_ ,  lowercase_ ,  repo_type="""dataset"""						)						) ,  """r"""						)						)
												lowercase        =							{int(lowercase_						): v for k, v in idalabel.items()}
												lowercase        =							idalabel
												lowercase        =							{v: k for k, v in idalabel.items()}
												lowercase        =							[1, 150, 480, 480]

						return config, expected_shape







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      int						):
						lowercase        =							["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
						for k in ignore_keys:
												state_dict.pop(lowercase_ ,  lowercase_						)







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Dict						):
						if (
						    "pretrained.model" in name
						    and "cls_token" not in name
						    and "pos_embed" not in name
						    and "patch_embed" not in name
						):
												lowercase        =							name.replace("""pretrained.model""" ,  """dpt.encoder"""						)
						if "pretrained.model" in name:
												lowercase        =							name.replace("""pretrained.model""" ,  """dpt.embeddings"""						)
						if "patch_embed" in name:
												lowercase        =							name.replace("""patch_embed""" ,  """"""						)
						if "pos_embed" in name:
												lowercase        =							name.replace("""pos_embed""" ,  """position_embeddings"""						)
						if "attn.proj" in name:
												lowercase        =							name.replace("""attn.proj""" ,  """attention.output.dense"""						)
						if "proj" in name and "project" not in name:
												lowercase        =							name.replace("""proj""" ,  """projection"""						)
						if "blocks" in name:
												lowercase        =							name.replace("""blocks""" ,  """layer"""						)
						if "mlp.fc1" in name:
												lowercase        =							name.replace("""mlp.fc1""" ,  """intermediate.dense"""						)
						if "mlp.fc2" in name:
												lowercase        =							name.replace("""mlp.fc2""" ,  """output.dense"""						)
						if "norm1" in name and "backbone" not in name:
												lowercase        =							name.replace("""norm1""" ,  """layernorm_before"""						)
						if "norm2" in name and "backbone" not in name:
												lowercase        =							name.replace("""norm2""" ,  """layernorm_after"""						)
						if "scratch.output_conv" in name:
												lowercase        =							name.replace("""scratch.output_conv""" ,  """head"""						)
						if "scratch" in name:
												lowercase        =							name.replace("""scratch""" ,  """neck"""						)
						if "layer1_rn" in name:
												lowercase        =							name.replace("""layer1_rn""" ,  """convs.0"""						)
						if "layer2_rn" in name:
												lowercase        =							name.replace("""layer2_rn""" ,  """convs.1"""						)
						if "layer3_rn" in name:
												lowercase        =							name.replace("""layer3_rn""" ,  """convs.2"""						)
						if "layer4_rn" in name:
												lowercase        =							name.replace("""layer4_rn""" ,  """convs.3"""						)
						if "refinenet" in name:
												lowercase        =							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
												lowercase        =							name.replace(F"""refinenet{layer_idx}""" ,  F"""fusion_stage.layers.{abs(layer_idx-4						)}"""						)
						if "out_conv" in name:
												lowercase        =							name.replace("""out_conv""" ,  """projection"""						)
						if "resConfUnit1" in name:
												lowercase        =							name.replace("""resConfUnit1""" ,  """residual_layer1"""						)
						if "resConfUnit2" in name:
												lowercase        =							name.replace("""resConfUnit2""" ,  """residual_layer2"""						)
						if "conv1" in name:
												lowercase        =							name.replace("""conv1""" ,  """convolution1"""						)
						if "conv2" in name:
												lowercase        =							name.replace("""conv2""" ,  """convolution2"""						)
						# readout blocks
						if "pretrained.act_postprocess1.0.project.0" in name:
												lowercase        =							name.replace("""pretrained.act_postprocess1.0.project.0""" ,  """neck.reassemble_stage.readout_projects.0.0"""						)
						if "pretrained.act_postprocess2.0.project.0" in name:
												lowercase        =							name.replace("""pretrained.act_postprocess2.0.project.0""" ,  """neck.reassemble_stage.readout_projects.1.0"""						)
						if "pretrained.act_postprocess3.0.project.0" in name:
												lowercase        =							name.replace("""pretrained.act_postprocess3.0.project.0""" ,  """neck.reassemble_stage.readout_projects.2.0"""						)
						if "pretrained.act_postprocess4.0.project.0" in name:
												lowercase        =							name.replace("""pretrained.act_postprocess4.0.project.0""" ,  """neck.reassemble_stage.readout_projects.3.0"""						)

						# resize blocks
						if "pretrained.act_postprocess1.3" in name:
												lowercase        =							name.replace("""pretrained.act_postprocess1.3""" ,  """neck.reassemble_stage.layers.0.projection"""						)
						if "pretrained.act_postprocess1.4" in name:
												lowercase        =							name.replace("""pretrained.act_postprocess1.4""" ,  """neck.reassemble_stage.layers.0.resize"""						)
						if "pretrained.act_postprocess2.3" in name:
												lowercase        =							name.replace("""pretrained.act_postprocess2.3""" ,  """neck.reassemble_stage.layers.1.projection"""						)
						if "pretrained.act_postprocess2.4" in name:
												lowercase        =							name.replace("""pretrained.act_postprocess2.4""" ,  """neck.reassemble_stage.layers.1.resize"""						)
						if "pretrained.act_postprocess3.3" in name:
												lowercase        =							name.replace("""pretrained.act_postprocess3.3""" ,  """neck.reassemble_stage.layers.2.projection"""						)
						if "pretrained.act_postprocess4.3" in name:
												lowercase        =							name.replace("""pretrained.act_postprocess4.3""" ,  """neck.reassemble_stage.layers.3.projection"""						)
						if "pretrained.act_postprocess4.4" in name:
												lowercase        =							name.replace("""pretrained.act_postprocess4.4""" ,  """neck.reassemble_stage.layers.3.resize"""						)
						if "pretrained" in name:
												lowercase        =							name.replace("""pretrained""" ,  """dpt"""						)
						if "bn" in name:
												lowercase        =							name.replace("""bn""" ,  """batch_norm"""						)
						if "head" in name:
												lowercase        =							name.replace("""head""" ,  """head.head"""						)
						if "encoder.norm" in name:
												lowercase        =							name.replace("""encoder.norm""" ,  """layernorm"""						)
						if "auxlayer" in name:
												lowercase        =							name.replace("""auxlayer""" ,  """auxiliary_head.head"""						)
						if "backbone" in name:
												lowercase        =							name.replace("""backbone""" ,  """backbone.bit.encoder"""						)

						if ".." in name:
												lowercase        =							name.replace("""..""" ,  """."""						)

						if "stem.conv" in name:
												lowercase        =							name.replace("""stem.conv""" ,  """bit.embedder.convolution"""						)
						if "blocks" in name:
												lowercase        =							name.replace("""blocks""" ,  """layers"""						)
						if "convolution" in name and "backbone" in name:
												lowercase        =							name.replace("""convolution""" ,  """conv"""						)
						if "layer" in name and "backbone" in name:
												lowercase        =							name.replace("""layer""" ,  """layers"""						)
						if "backbone.bit.encoder.bit" in name:
												lowercase        =							name.replace("""backbone.bit.encoder.bit""" ,  """backbone.bit"""						)
						if "embedder.conv" in name:
												lowercase        =							name.replace("""embedder.conv""" ,  """embedder.convolution"""						)
						if "backbone.bit.encoder.stem.norm" in name:
												lowercase        =							name.replace("""backbone.bit.encoder.stem.norm""" ,  """backbone.bit.embedder.norm"""						)
						return name







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







def      SCREAMING_SNAKE_CASE				(				):
						lowercase        =							"""http://images.cocodataset.org/val2017/000000039769.jpg"""
						lowercase        =							Image.open(requests.get(lowercase_ ,  stream=lowercase_						).raw						)
						return im







@torch.no_grad()
def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Any ,  lowercase_    :      Optional[Any] ,  lowercase_    :      Optional[Any] ,  lowercase_    :      str ,  lowercase_    :      int						):
						lowercase     ,   lowercase        =							get_dpt_config(lowercase_						)
						# load original state_dict from URL
						# state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
						lowercase        =							torch.load(lowercase_ ,  map_location="""cpu"""						)
						# remove certain keys
						remove_ignore_keys_(lowercase_						)
						# rename keys
						for key in state_dict.copy().keys():
												lowercase        =							state_dict.pop(lowercase_						)
												lowercase        =							val
						# read in qkv matrices
						read_in_q_k_v(lowercase_ ,  lowercase_						)

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

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

						lowercase        =							prepare_img()
						lowercase        =							image_processor(lowercase_ ,  return_tensors="""pt"""						)

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

						if show_prediction:
												lowercase        =							(
												    torch.nn.functional.interpolate(
												        outputs.unsqueeze(1						) ,  size=(image.size[1], image.size[0]) ,  mode="""bicubic""" ,  align_corners=lowercase_ ,  )
												    .squeeze()
												    .cpu()
												    .numpy()
												)

												Image.fromarray((prediction / prediction.max()) * 255						).show()

						if pytorch_dump_folder_path is not None:
												Path(lowercase_						).mkdir(exist_ok=lowercase_						)
												print(F"""Saving model to {pytorch_dump_folder_path}"""						)
												model.save_pretrained(lowercase_						)
												print(F"""Saving image processor to {pytorch_dump_folder_path}"""						)
												image_processor.save_pretrained(lowercase_						)

						if push_to_hub:
												model.push_to_hub("""ybelkada/dpt-hybrid-midas"""						)
												image_processor.push_to_hub("""ybelkada/dpt-hybrid-midas"""						)


if __name__ == "__main__":
						lowercase_      :				Dict   					=    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=False,
						    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.''',
						)
						parser.add_argument(
						    '''--show_prediction''',
						    action='''store_true''',
						)

						lowercase_      :				List[Any]   					=    parser.parse_args()
						convert_dpt_checkpoint(
						    args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
						)







'''simple docstring'''


from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
						from PIL import Image

						from ..image_utils import load_image

if is_tf_available():
						from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
						import torch

						from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

lowercase_      :				Union[str, Any]   					=    logging.get_logger(__name__)




@add_end_docstrings(_UpperCAmelCase			)
class      __UpperCamelCase       (_UpperCAmelCase			):







	def __init__(    self ,				*_lowerCAmelCase ,				**_lowerCAmelCase						)    ->    Dict:
							'''simple docstring'''






							super().__init__(*_lowerCAmelCase ,				**_lowerCAmelCase						)
							requires_backends(self ,				"""vision"""						)
							self.check_model_type(
							    TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == """tf""" else MODEL_FOR_VISION_2_SEQ_MAPPING						)







	def   _a   (    self ,				_lowerCAmelCase=None ,				_lowerCAmelCase=None ,				_lowerCAmelCase=None						)    ->    str:
							'''simple docstring'''






							lowercase        =							{}
							lowercase        =							{}

							if prompt is not None:
													lowercase        =							prompt

							if generate_kwargs is not None:
													lowercase        =							generate_kwargs
							if max_new_tokens is not None:
													if "generate_kwargs" not in forward_kwargs:
																			lowercase        =							{}
													if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
																			raise ValueError(
																			    """'max_new_tokens' is defined twice, once in 'generate_kwargs' and once as a direct parameter,"""
																			    """ please use only one"""						)
													lowercase        =							max_new_tokens
							return preprocess_params, forward_kwargs, {}







	def __call__(    self ,				_lowerCAmelCase ,				**_lowerCAmelCase						)    ->    Any:
							'''simple docstring'''






							return super().__call__(_lowerCAmelCase ,				**_lowerCAmelCase						)







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase=None						)    ->    List[str]:
							'''simple docstring'''






							lowercase        =							load_image(_lowerCAmelCase						)

							if prompt is not None:
													if not isinstance(_lowerCAmelCase ,				_lowerCAmelCase						):
																			raise ValueError(
																			    F"""Received an invalid text input, got - {type(_lowerCAmelCase						)} - but expected a single string. """
																			    """Note also that one single text can be provided for conditional image to text generation."""						)

													lowercase        =							self.model.config.model_type

													if model_type == "git":
																			lowercase        =							self.image_processor(images=_lowerCAmelCase ,				return_tensors=self.framework						)
																			lowercase        =							self.tokenizer(text=_lowerCAmelCase ,				add_special_tokens=_lowerCAmelCase						).input_ids
																			lowercase        =							[self.tokenizer.cls_token_id] + input_ids
																			lowercase        =							torch.tensor(_lowerCAmelCase						).unsqueeze(0						)
																			model_inputs.update({"""input_ids""": input_ids}						)

													elif model_type == "pix2struct":
																			lowercase        =							self.image_processor(images=_lowerCAmelCase ,				header_text=_lowerCAmelCase ,				return_tensors=self.framework						)

													elif model_type != "vision-encoder-decoder":
																			# vision-encoder-decoder does not support conditional generation
																			lowercase        =							self.image_processor(images=_lowerCAmelCase ,				return_tensors=self.framework						)
																			lowercase        =							self.tokenizer(_lowerCAmelCase ,				return_tensors=self.framework						)
																			model_inputs.update(_lowerCAmelCase						)

													else:
																			raise ValueError(F"""Model type {model_type} does not support conditional text generation"""						)

							else:
													lowercase        =							self.image_processor(images=_lowerCAmelCase ,				return_tensors=self.framework						)

							if self.model.config.model_type == "git" and prompt is None:
													lowercase        =							None

							return model_inputs







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase=None						)    ->    Union[str, Any]:
							'''simple docstring'''






							if (
							    "input_ids" in model_inputs
							    and isinstance(model_inputs["""input_ids"""] ,				_lowerCAmelCase						)
							    and all(x is None for x in model_inputs["""input_ids"""]						)
							):
													lowercase        =							None

							if generate_kwargs is None:
													lowercase        =							{}
							# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
							#  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
							#  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
							#  in the `_prepare_model_inputs` method.
							lowercase        =							model_inputs.pop(self.model.main_input_name						)
							lowercase        =							self.model.generate(_lowerCAmelCase ,				**_lowerCAmelCase ,				**_lowerCAmelCase						)
							return model_outputs







	def   _a   (    self ,				_lowerCAmelCase						)    ->    List[str]:
							'''simple docstring'''






							lowercase        =							[]
							for output_ids in model_outputs:
													lowercase        =							{
													    """generated_text""": self.tokenizer.decode(
													        _lowerCAmelCase ,				skip_special_tokens=_lowerCAmelCase ,				)
													}
													records.append(_lowerCAmelCase						)
							return records


