





'''simple docstring'''

import sacrebleu as scb
from packaging import version
from sacrebleu import TER

import datasets


__lowerCAmelCase							:     Any		     ="\\n@inproceedings{snover-etal-2006-study,\n    title = \"A Study of Translation Edit Rate with Targeted Human Annotation\",\n    author = \"Snover, Matthew  and\n      Dorr, Bonnie  and\n      Schwartz, Rich  and\n      Micciulla, Linnea  and\n      Makhoul, John\",\n    booktitle = \"Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers\",\n    month = aug # \" 8-12\",\n    year = \"2006\",\n    address = \"Cambridge, Massachusetts, USA\",\n    publisher = \"Association for Machine Translation in the Americas\",\n    url = \"https://aclanthology.org/2006.amta-papers.25\",\n    pages = \"223--231\",\n}\n@inproceedings{post-2018-call,\n    title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n    author = \"Post, Matt\",\n    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n    month = oct,\n    year = \"2018\",\n    address = \"Belgium, Brussels\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W18-6319\",\n    pages = \"186--191\",\n}\n"

__lowerCAmelCase							:     Dict		     ="\\nTER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a\nhypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu\n(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found\nhere: https://github.com/jhclark/tercom.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.\n"

__lowerCAmelCase							:     str		     ="\nProduces TER scores alongside the number of edits and reference length.\n\nArgs:\n    predictions (list of str): The system stream (a sequence of segments).\n    references (list of list of str): A list of one or more reference streams (each a sequence of segments).\n    normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,\n                                    as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.\n                                    Only applies if `normalized = True`. Defaults to `False`.\n    case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.\n\nReturns:\n    'score' (float): TER score (num_edits / sum_ref_lengths * 100)\n    'num_edits' (int): The cumulative number of edits\n    'ref_length' (float): The cumulative average reference length\n\nExamples:\n    Example 1:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\",\n        ...                     \"What did the TER metric user say to the developer?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],\n        ...             [\"Your jokes are...\", \"...TERrible\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {'score': 150.0, 'num_edits': 15, 'ref_length': 10.0}\n\n    Example 2:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {'score': 62.5, 'num_edits': 5, 'ref_length': 8.0}\n\n    Example 3:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         normalized=True,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {'score': 57.14285714285714, 'num_edits': 6, 'ref_length': 10.5}\n\n    Example 4:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {'score': 0.0, 'num_edits': 0, 'ref_length': 8.0}\n\n    Example 5:\n        >>> predictions = [\"does this sentence match??\",\n        ...                    \"what about this sentence?\",\n        ...                    \"What did the TER metric user say to the developer?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],\n        ...             [\"Your jokes are...\", \"...TERrible\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {'score': 100.0, 'num_edits': 10, 'ref_length': 10.0}\n"






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,	_KWARGS_DESCRIPTION    )
class        UpperCAmelCase     (	datasets.Metric    ):
	def 					UpperCAmelCase_							(    self				:List[str]							)->							Any:
					if version.parse(scb.__version__							) < version.parse("1.4.12"							):
									raise ImportWarning(
									    "To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"
									    "You can install it with `pip install \"sacrebleu>=1.4.12\"`."							)
					return datasets.MetricInfo(
					    description=_DESCRIPTION						,	citation=_CITATION						,	homepage="http://www.cs.umd.edu/~snover/tercom/"						,	inputs_description=_KWARGS_DESCRIPTION						,	features=datasets.Features(
					        {
					            "predictions": datasets.Value("string"						,	id="sequence"							),
					            "references": datasets.Sequence(datasets.Value("string"						,	id="sequence"							)						,	id="references"							),
					        }							)						,	codebase_urls=["https://github.com/mjpost/sacreBLEU#ter"]						,	reference_urls=[
					        "https://github.com/jhclark/tercom",
					    ]						,	)



	def 					UpperCAmelCase_							(    self				:List[Any]						,	lowercase_				:Optional[Any]						,	lowercase_				:Tuple						,	lowercase_				:bool = False						,	lowercase_				:bool = False						,	lowercase_				:bool = False						,	lowercase_				:bool = False						,	)->							Optional[int]:
					A__    =      len(references[0]							)
					if any(len(lowercase_							) != references_per_prediction for refs in references							):
									raise ValueError("Sacrebleu requires the same number of references for each prediction"							)
					A__    =      [[refs[i] for refs in references] for i in range(lowercase_							)]

					A__    =      TER(
					    normalized=lowercase_						,	no_punct=lowercase_						,	asian_support=lowercase_						,	case_sensitive=lowercase_						,	)
					A__    =      sb_ter.corpus_score(lowercase_						,	lowercase_							)

					return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length}










'''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
  )



