


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()
lowerCamelCase__							:						Dict							     =   logging.get_logger(__name__)







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				Optional[Any]:
  snake_case__						= DPTConfig(embedding_type='''hybrid'''				)

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

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

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

  return config, expected_shape







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				Optional[Any]:
  snake_case__						= ['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
  for k in ignore_keys:
    state_dict.pop(_lowerCAmelCase							,   _lowerCAmelCase				)







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				Any:
  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__						= name.replace('''pretrained.model'''							,   '''dpt.encoder'''				)
  if "pretrained.model" in name:
    snake_case__						= name.replace('''pretrained.model'''							,   '''dpt.embeddings'''				)
  if "patch_embed" in name:
    snake_case__						= name.replace('''patch_embed'''							,   ''''''				)
  if "pos_embed" in name:
    snake_case__						= name.replace('''pos_embed'''							,   '''position_embeddings'''				)
  if "attn.proj" in name:
    snake_case__						= name.replace('''attn.proj'''							,   '''attention.output.dense'''				)
  if "proj" in name and "project" not in name:
    snake_case__						= name.replace('''proj'''							,   '''projection'''				)
  if "blocks" in name:
    snake_case__						= name.replace('''blocks'''							,   '''layer'''				)
  if "mlp.fc1" in name:
    snake_case__						= name.replace('''mlp.fc1'''							,   '''intermediate.dense'''				)
  if "mlp.fc2" in name:
    snake_case__						= name.replace('''mlp.fc2'''							,   '''output.dense'''				)
  if "norm1" in name and "backbone" not in name:
    snake_case__						= name.replace('''norm1'''							,   '''layernorm_before'''				)
  if "norm2" in name and "backbone" not in name:
    snake_case__						= name.replace('''norm2'''							,   '''layernorm_after'''				)
  if "scratch.output_conv" in name:
    snake_case__						= name.replace('''scratch.output_conv'''							,   '''head'''				)
  if "scratch" in name:
    snake_case__						= name.replace('''scratch'''							,   '''neck'''				)
  if "layer1_rn" in name:
    snake_case__						= name.replace('''layer1_rn'''							,   '''convs.0'''				)
  if "layer2_rn" in name:
    snake_case__						= name.replace('''layer2_rn'''							,   '''convs.1'''				)
  if "layer3_rn" in name:
    snake_case__						= name.replace('''layer3_rn'''							,   '''convs.2'''				)
  if "layer4_rn" in name:
    snake_case__						= name.replace('''layer4_rn'''							,   '''convs.3'''				)
  if "refinenet" in name:
    snake_case__						= 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__						= name.replace(F"""refinenet{layer_idx}"""							,   F"""fusion_stage.layers.{abs(layer_idx-4				)}"""				)
  if "out_conv" in name:
    snake_case__						= name.replace('''out_conv'''							,   '''projection'''				)
  if "resConfUnit1" in name:
    snake_case__						= name.replace('''resConfUnit1'''							,   '''residual_layer1'''				)
  if "resConfUnit2" in name:
    snake_case__						= name.replace('''resConfUnit2'''							,   '''residual_layer2'''				)
  if "conv1" in name:
    snake_case__						= name.replace('''conv1'''							,   '''convolution1'''				)
  if "conv2" in name:
    snake_case__						= name.replace('''conv2'''							,   '''convolution2'''				)
  # readout blocks
  if "pretrained.act_postprocess1.0.project.0" in name:
    snake_case__						= 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__						= 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__						= 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__						= 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__						= name.replace('''pretrained.act_postprocess1.3'''							,   '''neck.reassemble_stage.layers.0.projection'''				)
  if "pretrained.act_postprocess1.4" in name:
    snake_case__						= name.replace('''pretrained.act_postprocess1.4'''							,   '''neck.reassemble_stage.layers.0.resize'''				)
  if "pretrained.act_postprocess2.3" in name:
    snake_case__						= name.replace('''pretrained.act_postprocess2.3'''							,   '''neck.reassemble_stage.layers.1.projection'''				)
  if "pretrained.act_postprocess2.4" in name:
    snake_case__						= name.replace('''pretrained.act_postprocess2.4'''							,   '''neck.reassemble_stage.layers.1.resize'''				)
  if "pretrained.act_postprocess3.3" in name:
    snake_case__						= name.replace('''pretrained.act_postprocess3.3'''							,   '''neck.reassemble_stage.layers.2.projection'''				)
  if "pretrained.act_postprocess4.3" in name:
    snake_case__						= name.replace('''pretrained.act_postprocess4.3'''							,   '''neck.reassemble_stage.layers.3.projection'''				)
  if "pretrained.act_postprocess4.4" in name:
    snake_case__						= name.replace('''pretrained.act_postprocess4.4'''							,   '''neck.reassemble_stage.layers.3.resize'''				)
  if "pretrained" in name:
    snake_case__						= name.replace('''pretrained'''							,   '''dpt'''				)
  if "bn" in name:
    snake_case__						= name.replace('''bn'''							,   '''batch_norm'''				)
  if "head" in name:
    snake_case__						= name.replace('''head'''							,   '''head.head'''				)
  if "encoder.norm" in name:
    snake_case__						= name.replace('''encoder.norm'''							,   '''layernorm'''				)
  if "auxlayer" in name:
    snake_case__						= name.replace('''auxlayer'''							,   '''auxiliary_head.head'''				)
  if "backbone" in name:
    snake_case__						= name.replace('''backbone'''							,   '''backbone.bit.encoder'''				)

  if ".." in name:
    snake_case__						= name.replace('''..'''							,   '''.'''				)

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







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase				)    ->				Dict:
  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__						= state_dict.pop(F"""dpt.encoder.layer.{i}.attn.qkv.weight"""				)
    snake_case__						= 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__						= in_proj_weight[: config.hidden_size, :]
    snake_case__						= in_proj_bias[: config.hidden_size]
    snake_case__						= in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    snake_case__						= in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    snake_case__						= in_proj_weight[
        -config.hidden_size :, :
    ]
    snake_case__						= in_proj_bias[-config.hidden_size :]







def SCREAMING_SNAKE_CASE	(			)    ->				Any:
  snake_case__						= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
  snake_case__						= Image.open(requests.get(_lowerCAmelCase							,   stream=_lowerCAmelCase				).raw				)
  return im







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

  # load HuggingFace model
  snake_case__						= DPTForSemanticSegmentation(_lowerCAmelCase				) if '''ade''' in checkpoint_url else DPTForDepthEstimation(_lowerCAmelCase				)
  model.load_state_dict(_lowerCAmelCase				)
  model.eval()

  # Check outputs on an image
  snake_case__						= 480 if '''ade''' in checkpoint_url else 384
  snake_case__						= DPTImageProcessor(size=_lowerCAmelCase				)

  snake_case__						= prepare_img()
  snake_case__						= image_processor(_lowerCAmelCase							,   return_tensors='''pt'''				)

  # forward pass
  snake_case__						= model(**_lowerCAmelCase				).logits if '''ade''' in checkpoint_url else model(**_lowerCAmelCase				).predicted_depth

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

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

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

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


if __name__ == "__main__":
   lowerCamelCase__							:						Optional[Any]							     =   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""",
   )

   lowerCamelCase__							:						Union[str, 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"""
import enum
import warnings

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
			import tensorflow as tf

			from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

if is_torch_available():
			from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

SCREAMING_SNAKE_CASE_					     =	logging.get_logger(__name__)
class       lowerCAmelCase_		(				enum.Enum ):



							'''simple docstring'''





							_snake_case						      =		0
							_snake_case						      =		1
@add_end_docstrings(A__ )
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							_snake_case						      =		'''generated'''




							def __init__( self    ,				*snake_case_    ,				**snake_case_				)       ->  Optional[int]:
													super().__init__(*snake_case_    ,				**snake_case_				)

													self.check_model_type(
													    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
													    if self.framework == """tf"""
													    else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING				)




							def    A__						( self    ,				snake_case_=None    ,				snake_case_=None    ,				snake_case_=None    ,				snake_case_=None    ,				snake_case_=None    ,				snake_case_=None    ,				**snake_case_    ,				)       ->  Union[str, Any]:
													__lowerCAmelCase     =  {}
													if truncation is not None:
																			__lowerCAmelCase     =  truncation

													__lowerCAmelCase     =  generate_kwargs

													__lowerCAmelCase     =  {}
													if return_tensors is not None and return_type is None:
																			__lowerCAmelCase     =  ReturnType.TENSORS if return_tensors else ReturnType.TEXT
													if return_type is not None:
																			__lowerCAmelCase     =  return_type

													if clean_up_tokenization_spaces is not None:
																			__lowerCAmelCase     =  clean_up_tokenization_spaces

													if stop_sequence is not None:
																			__lowerCAmelCase     =  self.tokenizer.encode(snake_case_    ,				add_special_tokens=snake_case_				)
																			if len(snake_case_				) > 1:
																									warnings.warn(
																									    """Stopping on a multiple token sequence is not yet supported on transformers. The first token of"""
																									    """ the stop sequence will be used as the stop sequence string in the interim."""				)
																			__lowerCAmelCase     =  stop_sequence_ids[0]

													return preprocess_params, forward_params, postprocess_params




							def    A__						( self    ,				snake_case_    ,				snake_case_    ,				snake_case_				)       ->  Any:
													return True




							def    A__						( self    ,				*snake_case_    ,				snake_case_				)       ->  Dict:
													__lowerCAmelCase     =  self.model.config.prefix if self.model.config.prefix is not None else """"""
													if isinstance(args[0]    ,				snake_case_				):
																			if self.tokenizer.pad_token_id is None:
																									raise ValueError("""Please make sure that the tokenizer has a pad_token_id when using a batch input"""				)
																			__lowerCAmelCase     =  ([prefix + arg for arg in args[0]],)
																			__lowerCAmelCase     =  True

													elif isinstance(args[0]    ,				snake_case_				):
																			__lowerCAmelCase     =  (prefix + args[0],)
																			__lowerCAmelCase     =  False
													else:
																			raise ValueError(
																			    f""" `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`"""				)
													__lowerCAmelCase     =  self.tokenizer(*snake_case_    ,				padding=snake_case_    ,				truncation=snake_case_    ,				return_tensors=self.framework				)
													# This is produced by tokenizers but is an invalid generate kwargs
													if "token_type_ids" in inputs:
																			del inputs["token_type_ids"]
													return inputs




							def __call__( self    ,				*snake_case_    ,				**snake_case_				)       ->  Dict:
													__lowerCAmelCase     =  super().__call__(*snake_case_    ,				**snake_case_				)
													if (
													    isinstance(args[0]    ,				snake_case_				)
													    and all(isinstance(snake_case_    ,				snake_case_				) for el in args[0]				)
													    and all(len(snake_case_				) == 1 for res in result				)
													):
																			return [res[0] for res in result]
													return result




							def    A__						( self    ,				snake_case_    ,				snake_case_=TruncationStrategy.DO_NOT_TRUNCATE    ,				**snake_case_				)       ->  Tuple:
													__lowerCAmelCase     =  self._parse_and_tokenize(snake_case_    ,				truncation=snake_case_    ,				**snake_case_				)
													return inputs




							def    A__						( self    ,				snake_case_    ,				**snake_case_				)       ->  Union[str, Any]:
													if self.framework == "pt":
																			__lowerCAmelCase   ,     __lowerCAmelCase     =  model_inputs["""input_ids"""].shape
													elif self.framework == "tf":
																			__lowerCAmelCase   ,     __lowerCAmelCase     =  tf.shape(model_inputs["""input_ids"""]				).numpy()

													__lowerCAmelCase     =  generate_kwargs.get("""min_length"""    ,				self.model.config.min_length				)
													__lowerCAmelCase     =  generate_kwargs.get("""max_length"""    ,				self.model.config.max_length				)
													self.check_inputs(snake_case_    ,				generate_kwargs["""min_length"""]    ,				generate_kwargs["""max_length"""]				)
													__lowerCAmelCase     =  self.model.generate(**snake_case_    ,				**snake_case_				)
													__lowerCAmelCase     =  output_ids.shape[0]
													if self.framework == "pt":
																			__lowerCAmelCase     =  output_ids.reshape(snake_case_    ,				out_b // in_b    ,				*output_ids.shape[1:]				)
													elif self.framework == "tf":
																			__lowerCAmelCase     =  tf.reshape(snake_case_    ,				(in_b, out_b // in_b, *output_ids.shape[1:])				)
													return {"output_ids": output_ids}




							def    A__						( self    ,				snake_case_    ,				snake_case_=ReturnType.TEXT    ,				snake_case_=False				)       ->  Dict:
													__lowerCAmelCase     =  []
													for output_ids in model_outputs["output_ids"][0]:
																			if return_type == ReturnType.TENSORS:
																									__lowerCAmelCase     =  {f"""{self.return_name}_token_ids""": output_ids}
																			elif return_type == ReturnType.TEXT:
																									__lowerCAmelCase     =  {
																									    f"""{self.return_name}_text""": self.tokenizer.decode(
																									        snake_case_    ,				skip_special_tokens=snake_case_    ,				clean_up_tokenization_spaces=snake_case_    ,				)
																									}
																			records.append(snake_case_				)
													return records
@add_end_docstrings(A__ )
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							_snake_case						      =		'''summary'''




							def __call__( self    ,				*snake_case_    ,				**snake_case_				)       ->  Tuple:
													return super().__call__(*snake_case_    ,				**snake_case_				)




							def    A__						( self    ,				snake_case_    ,				snake_case_    ,				snake_case_				)       ->  bool:
													if max_length < min_length:
																			logger.warning(f"""Your min_length={min_length} must be inferior than your max_length={max_length}."""				)

													if input_length < max_length:
																			logger.warning(
																			    f"""Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is """
																			    """a summarization task, where outputs shorter than the input are typically wanted, you might """
																			    f"""consider decreasing max_length manually, e.g. summarizer('...', max_length={input_length//2})"""				)
@add_end_docstrings(A__ )
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							_snake_case						      =		'''translation'''




							def    A__						( self    ,				snake_case_    ,				snake_case_    ,				snake_case_				)       ->  Optional[int]:
													if input_length > 0.9 * max_length:
																			logger.warning(
																			    f"""Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider """
																			    """increasing your max_length manually, e.g. translator('...', max_length=400)"""				)
													return True




							def    A__						( self    ,				*snake_case_    ,				snake_case_=TruncationStrategy.DO_NOT_TRUNCATE    ,				snake_case_=None    ,				snake_case_=None				)       ->  List[Any]:
													if getattr(self.tokenizer    ,				"""_build_translation_inputs"""    ,				snake_case_				):
																			return self.tokenizer._build_translation_inputs(
																			    *snake_case_    ,				return_tensors=self.framework    ,				truncation=snake_case_    ,				src_lang=snake_case_    ,				tgt_lang=snake_case_				)
													else:
																			return super()._parse_and_tokenize(*snake_case_    ,				truncation=snake_case_				)




							def    A__						( self    ,				snake_case_=None    ,				snake_case_=None    ,				**snake_case_				)       ->  List[Any]:
													__lowerCAmelCase   ,     __lowerCAmelCase   ,     __lowerCAmelCase     =  super()._sanitize_parameters(**snake_case_				)
													if src_lang is not None:
																			__lowerCAmelCase     =  src_lang
													if tgt_lang is not None:
																			__lowerCAmelCase     =  tgt_lang
													if src_lang is None and tgt_lang is None:
																			# Backward compatibility, direct arguments use is preferred.
																			__lowerCAmelCase     =  kwargs.get("""task"""    ,				self.task				)
																			__lowerCAmelCase     =  task.split("""_"""				)
																			if task and len(snake_case_				) == 4:
																									# translation, XX, to YY
																									__lowerCAmelCase     =  items[1]
																									__lowerCAmelCase     =  items[3]
													return preprocess_params, forward_params, postprocess_params




							def __call__( self    ,				*snake_case_    ,				**snake_case_				)       ->  List[Any]:
													return super().__call__(*snake_case_    ,				**snake_case_				)
