





'''simple docstring'''

import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer

import datasets
from datasets import logging


__lowerCAmelCase							:     Union[str, Any]		     ="\\n\n"

__lowerCAmelCase							:     List[Any]		     ="\nPerplexity (PPL) is one of the most common metrics for evaluating language models.\nIt is defined as the exponentiated average negative log-likelihood of a sequence.\n\nFor more information, see https://huggingface.co/docs/transformers/perplexity\n"

__lowerCAmelCase							:     Any		     ="\nArgs:\n    model_id (str): model used for calculating Perplexity\n            NOTE: Perplexity can only be calculated for causal language models.\n                    This includes models such as gpt2, causal variations of bert,\n                    causal versions of t5, and more (the full list can be found\n                    in the AutoModelForCausalLM documentation here:\n                    https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )\n\n    input_texts (list of str): input text, each separate text snippet\n        is one list entry.\n    batch_size (int): the batch size to run texts through the model. Defaults to 16.\n    add_start_token (bool): whether to add the start token to the texts,\n        so the perplexity can include the probability of the first word. Defaults to True.\n    device (str): device to run on, defaults to 'cuda' when available\nReturns:\n    perplexity: dictionary containing the perplexity scores for the texts\n        in the input list, as well as the mean perplexity. If one of the input texts is\n        longer than the max input length of the model, then it is truncated to the\n        max length for the perplexity computation.\nExamples:\n    Example 1:\n        >>> perplexity = datasets.load_metric(\"perplexity\")\n        >>> input_texts = [\"lorem ipsum\", \"Happy Birthday!\", \"Bienvenue\"]\n        >>> results = perplexity.compute(model_id='gpt2',\n        ...                              add_start_token=False,\n        ...                              input_texts=input_texts) # doctest:+ELLIPSIS\n        >>> print(list(results.keys()))\n        ['perplexities', 'mean_perplexity']\n        >>> print(round(results[\"mean_perplexity\"], 2))\n        78.22\n        >>> print(round(results[\"perplexities\"][0], 2))\n        11.11\n\n    Example 2:\n        >>> perplexity = datasets.load_metric(\"perplexity\")\n        >>> input_texts = datasets.load_dataset(\"wikitext\",\n        ...                                     \"wikitext-2-raw-v1\",\n        ...                                     split=\"test\")[\"text\"][:50] # doctest:+ELLIPSIS\n        [...]\n        >>> input_texts = [s for s in input_texts if s!='']\n        >>> results = perplexity.compute(model_id='gpt2',\n        ...                              input_texts=input_texts) # doctest:+ELLIPSIS\n        >>> print(list(results.keys()))\n        ['perplexities', 'mean_perplexity']\n        >>> print(round(results[\"mean_perplexity\"], 2))\n        60.35\n        >>> print(round(results[\"perplexities\"][0], 2))\n        81.12\n"






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,	_KWARGS_DESCRIPTION    )
class        UpperCAmelCase     (	datasets.Metric    ):
 def 					UpperCAmelCase_							(    self				:List[Any]							)->							Tuple:
     return datasets.MetricInfo(
         description=_DESCRIPTION						,	citation=_CITATION						,	inputs_description=_KWARGS_DESCRIPTION						,	features=datasets.Features(
             {
                 "input_texts": datasets.Value("string"							),
             }							)						,	reference_urls=["https://huggingface.co/docs/transformers/perplexity"]						,	)



 def 					UpperCAmelCase_							(    self				:int						,	lowercase_				:Dict						,	lowercase_				:int						,	lowercase_				:int = 16						,	lowercase_				:bool = True						,	lowercase_				:str=None							)->							List[str]:
     if device is not None:
         assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
         if device == "gpu":
             A__    =      "cuda"
     else:
         A__    =      "cuda" if torch.cuda.is_available() else "cpu"

     A__    =      AutoModelForCausalLM.from_pretrained(lowercase_							)
     A__    =      model.to(lowercase_							)

     A__    =      AutoTokenizer.from_pretrained(lowercase_							)

     # if batch_size > 1 (which generally leads to padding being required), and
     # if there is not an already assigned pad_token, assign an existing
     # special token to also be the padding token
     if tokenizer.pad_token is None and batch_size > 1:
         A__    =      list(tokenizer.special_tokens_map_extended.values()							)
         # check that the model already has at least one special token defined
         assert (
             len(lowercase_							) > 0
         ), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
         # assign one of the special tokens to also be the pad token
         tokenizer.add_special_tokens({"pad_token": existing_special_tokens[0]}							)

     if add_start_token:
         # leave room for <BOS> token to be added:
         assert (
             tokenizer.bos_token is not None
         ), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
         A__    =      model.config.max_length - 1
     else:
         A__    =      model.config.max_length

     A__    =      tokenizer(
         lowercase_						,	add_special_tokens=lowercase_						,	padding=lowercase_						,	truncation=lowercase_						,	max_length=lowercase_						,	return_tensors="pt"						,	return_attention_mask=lowercase_						,	).to(lowercase_							)

     A__    =      encodings["input_ids"]
     A__    =      encodings["attention_mask"]

     # check that each input is long enough:
     if add_start_token:
         assert torch.all(torch.ge(attn_masks.sum(1							)						,	1							)							), "Each input text must be at least one token long."
     else:
         assert torch.all(
             torch.ge(attn_masks.sum(1							)						,	2							)							), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."

     A__    =      []
     A__    =      CrossEntropyLoss(reduction="none"							)

     for start_index in logging.tqdm(range(0						,	len(lowercase_							)						,	lowercase_							)							):
         A__    =      min(start_index + batch_size						,	len(lowercase_							)							)
         A__    =      encoded_texts[start_index:end_index]
         A__    =      attn_masks[start_index:end_index]

         if add_start_token:
             A__    =      torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0							)							).to(lowercase_							)
             A__    =      torch.cat([bos_tokens_tensor, encoded_batch]						,	dim=1							)
             A__    =      torch.cat(
                 [torch.ones(bos_tokens_tensor.size()						,	dtype=torch.intaa							).to(lowercase_							), attn_mask]						,	dim=1							)

         A__    =      encoded_batch

         with torch.no_grad():
             A__    =      model(lowercase_						,	attention_mask=lowercase_							).logits

         A__    =      out_logits[..., :-1, :].contiguous()
         A__    =      labels[..., 1:].contiguous()
         A__    =      attn_mask[..., 1:].contiguous()

         A__    =      torch.expa(
             (loss_fct(shift_logits.transpose(1						,	2							)						,	lowercase_							) * shift_attention_mask_batch).sum(1							)
             / shift_attention_mask_batch.sum(1							)							)

         ppls += perplexity_batch.tolist()

     return {"perplexities": ppls, "mean_perplexity": np.mean(lowercase_							)}










'''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()
__lowerCAmelCase							:     Tuple		     =logging.get_logger(__name__)





def 						UpperCamelCase    (							_lowerCamelCase : Tuple	):
    A__    =      DPTConfig(embedding_type="hybrid"	)

    if "large" in checkpoint_url:
        A__    =      10_24
        A__    =      40_96
        A__    =      24
        A__    =      16
        A__    =      [5, 11, 17, 23]
        A__    =      [2_56, 5_12, 10_24, 10_24]
        A__    =      (1, 3_84, 3_84)

    if "nyu" or "midas" in checkpoint_url:
        A__    =      7_68
        A__    =      [1, 1, 1, 0.5]
        A__    =      [2_56, 5_12, 7_68, 7_68]
        A__    =      1_50
        A__    =      16
        A__    =      (1, 3_84, 3_84)
        A__    =      False
        A__    =      "project"

    if "ade" in checkpoint_url:
        A__    =      True
        A__    =      7_68
        A__    =      [1, 1, 1, 0.5]
        A__    =      1_50
        A__    =      16
        A__    =      "huggingface/label-files"
        A__    =      "ade20k-id2label.json"
        A__    =      json.load(open(cached_download(hf_hub_url(_lowerCamelCase						,	_lowerCamelCase						,	repo_type="dataset"	)	)						,	"r"	)	)
        A__    =      {int(_lowerCamelCase	): v for k, v in idalabel.items()}
        A__    =      idalabel
        A__    =      {v: k for k, v in idalabel.items()}
        A__    =      [1, 1_50, 4_80, 4_80]

    return config, expected_shape





def 						UpperCamelCase    (							_lowerCamelCase : Optional[Any]	):
    A__    =      ["pretrained.model.head.weight", "pretrained.model.head.bias"]
    for k in ignore_keys:
        state_dict.pop(_lowerCamelCase						,	_lowerCamelCase	)





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

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

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





def 						UpperCamelCase    (							_lowerCamelCase : Dict						,	_lowerCamelCase : int	):
    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__    =      state_dict.pop(F"dpt.encoder.layer.{i}.attn.qkv.weight"	)
        A__    =      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__    =      in_proj_weight[: config.hidden_size, :]
        A__    =      in_proj_bias[: config.hidden_size]
        A__    =      in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        A__    =      in_proj_bias[
            config.hidden_size : config.hidden_size * 2
        ]
        A__    =      in_proj_weight[
            -config.hidden_size :, :
        ]
        A__    =      in_proj_bias[-config.hidden_size :]





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





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

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

    # Check outputs on an image
    A__    =      4_80 if "ade" in checkpoint_url else 3_84
    A__    =      DPTImageProcessor(size=_lowerCamelCase	)

    A__    =      prepare_img()
    A__    =      image_processor(_lowerCamelCase						,	return_tensors="pt"	)

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

    if show_prediction:
        A__    =      (
            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()) * 2_55	).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							:     Tuple		     =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							:     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
  )



