





"""simple docstring"""





import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ =       logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
SCREAMING_SNAKE_CASE__ =       []
for i in range(6):
 # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
 rename_keys.append(
     (f'transformer.encoder.layers.{i}.self_attn.out_proj.weight', f'encoder.layers.{i}.self_attn.out_proj.weight')
 )
 rename_keys.append(
     (f'transformer.encoder.layers.{i}.self_attn.out_proj.bias', f'encoder.layers.{i}.self_attn.out_proj.bias')
 )
 rename_keys.append((f'transformer.encoder.layers.{i}.linear1.weight', f'encoder.layers.{i}.fc1.weight'))
 rename_keys.append((f'transformer.encoder.layers.{i}.linear1.bias', f'encoder.layers.{i}.fc1.bias'))
 rename_keys.append((f'transformer.encoder.layers.{i}.linear2.weight', f'encoder.layers.{i}.fc2.weight'))
 rename_keys.append((f'transformer.encoder.layers.{i}.linear2.bias', f'encoder.layers.{i}.fc2.bias'))
 rename_keys.append(
     (f'transformer.encoder.layers.{i}.norm1.weight', f'encoder.layers.{i}.self_attn_layer_norm.weight')
 )
 rename_keys.append((f'transformer.encoder.layers.{i}.norm1.bias', f'encoder.layers.{i}.self_attn_layer_norm.bias'))
 rename_keys.append((f'transformer.encoder.layers.{i}.norm2.weight', f'encoder.layers.{i}.final_layer_norm.weight'))
 rename_keys.append((f'transformer.encoder.layers.{i}.norm2.bias', f'encoder.layers.{i}.final_layer_norm.bias'))
 # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
 rename_keys.append(
     (f'transformer.decoder.layers.{i}.self_attn.out_proj.weight', f'decoder.layers.{i}.self_attn.out_proj.weight')
 )
 rename_keys.append(
     (f'transformer.decoder.layers.{i}.self_attn.out_proj.bias', f'decoder.layers.{i}.self_attn.out_proj.bias')
 )
 rename_keys.append(
     (
         f'transformer.decoder.layers.{i}.multihead_attn.out_proj.weight',
         f'decoder.layers.{i}.encoder_attn.out_proj.weight',
     )
 )
 rename_keys.append(
     (
         f'transformer.decoder.layers.{i}.multihead_attn.out_proj.bias',
         f'decoder.layers.{i}.encoder_attn.out_proj.bias',
     )
 )
 rename_keys.append((f'transformer.decoder.layers.{i}.linear1.weight', f'decoder.layers.{i}.fc1.weight'))
 rename_keys.append((f'transformer.decoder.layers.{i}.linear1.bias', f'decoder.layers.{i}.fc1.bias'))
 rename_keys.append((f'transformer.decoder.layers.{i}.linear2.weight', f'decoder.layers.{i}.fc2.weight'))
 rename_keys.append((f'transformer.decoder.layers.{i}.linear2.bias', f'decoder.layers.{i}.fc2.bias'))
 rename_keys.append(
     (f'transformer.decoder.layers.{i}.norm1.weight', f'decoder.layers.{i}.self_attn_layer_norm.weight')
 )
 rename_keys.append((f'transformer.decoder.layers.{i}.norm1.bias', f'decoder.layers.{i}.self_attn_layer_norm.bias'))
 rename_keys.append(
     (f'transformer.decoder.layers.{i}.norm2.weight', f'decoder.layers.{i}.encoder_attn_layer_norm.weight')
 )
 rename_keys.append(
     (f'transformer.decoder.layers.{i}.norm2.bias', f'decoder.layers.{i}.encoder_attn_layer_norm.bias')
 )
 rename_keys.append((f'transformer.decoder.layers.{i}.norm3.weight', f'decoder.layers.{i}.final_layer_norm.weight'))
 rename_keys.append((f'transformer.decoder.layers.{i}.norm3.bias', f'decoder.layers.{i}.final_layer_norm.bias'))

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
rename_keys.extend(
    [
        ("input_proj.weight", "input_projection.weight"),
        ("input_proj.bias", "input_projection.bias"),
        ("query_embed.weight", "query_position_embeddings.weight"),
        ("transformer.decoder.norm.weight", "decoder.layernorm.weight"),
        ("transformer.decoder.norm.bias", "decoder.layernorm.bias"),
        ("class_embed.weight", "class_labels_classifier.weight"),
        ("class_embed.bias", "class_labels_classifier.bias"),
        ("bbox_embed.layers.0.weight", "bbox_predictor.layers.0.weight"),
        ("bbox_embed.layers.0.bias", "bbox_predictor.layers.0.bias"),
        ("bbox_embed.layers.1.weight", "bbox_predictor.layers.1.weight"),
        ("bbox_embed.layers.1.bias", "bbox_predictor.layers.1.bias"),
        ("bbox_embed.layers.2.weight", "bbox_predictor.layers.2.weight"),
        ("bbox_embed.layers.2.bias", "bbox_predictor.layers.2.bias"),
    ]
)


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						Optional[int]							,						SCREAMING_SNAKE_CASE					:						Dict							,						SCREAMING_SNAKE_CASE					:						List[str]      ):







      '''simple docstring'''




      lowerCAmelCase  						=	state_dict.pop(SCREAMING_SNAKE_CASE      )
      lowerCAmelCase  						=	val


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						Tuple      ):







      '''simple docstring'''




      lowerCAmelCase  						=	OrderedDict()
      for key, value in state_dict.items():
            if "backbone.0.body" in key:
                  lowerCAmelCase  						=	key.replace("""backbone.0.body"""							,						"""backbone.conv_encoder.model"""      )
                  lowerCAmelCase  						=	value
            else:
                  lowerCAmelCase  						=	value

      return new_state_dict


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						str							,						SCREAMING_SNAKE_CASE					:						int=False      ):







      '''simple docstring'''




      lowerCAmelCase  						=	""""""
      if is_panoptic:
            lowerCAmelCase  						=	"""detr."""

      # first: transformer encoder
      for i in range(6      ):
            # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
            lowerCAmelCase  						=	state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'      )
            lowerCAmelCase  						=	state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'      )
            # next, add query, keys and values (in that order) to the state dict
            lowerCAmelCase  						=	in_proj_weight[:2_56, :]
            lowerCAmelCase  						=	in_proj_bias[:2_56]
            lowerCAmelCase  						=	in_proj_weight[2_56:5_12, :]
            lowerCAmelCase  						=	in_proj_bias[2_56:5_12]
            lowerCAmelCase  						=	in_proj_weight[-2_56:, :]
            lowerCAmelCase  						=	in_proj_bias[-2_56:]
      # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
      for i in range(6      ):
            # read in weights + bias of input projection layer of self-attention
            lowerCAmelCase  						=	state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'      )
            lowerCAmelCase  						=	state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias'      )
            # next, add query, keys and values (in that order) to the state dict
            lowerCAmelCase  						=	in_proj_weight[:2_56, :]
            lowerCAmelCase  						=	in_proj_bias[:2_56]
            lowerCAmelCase  						=	in_proj_weight[2_56:5_12, :]
            lowerCAmelCase  						=	in_proj_bias[2_56:5_12]
            lowerCAmelCase  						=	in_proj_weight[-2_56:, :]
            lowerCAmelCase  						=	in_proj_bias[-2_56:]
            # read in weights + bias of input projection layer of cross-attention
            lowerCAmelCase  						=	state_dict.pop(
                F'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'      )
            lowerCAmelCase  						=	state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias'      )
            # next, add query, keys and values (in that order) of cross-attention to the state dict
            lowerCAmelCase  						=	in_proj_weight_cross_attn[:2_56, :]
            lowerCAmelCase  						=	in_proj_bias_cross_attn[:2_56]
            lowerCAmelCase  						=	in_proj_weight_cross_attn[2_56:5_12, :]
            lowerCAmelCase  						=	in_proj_bias_cross_attn[2_56:5_12]
            lowerCAmelCase  						=	in_proj_weight_cross_attn[-2_56:, :]
            lowerCAmelCase  						=	in_proj_bias_cross_attn[-2_56:]


def       UpperCAmelCase__							(  ):







      '''simple docstring'''




      lowerCAmelCase  						=	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
      lowerCAmelCase  						=	Image.open(requests.get(SCREAMING_SNAKE_CASE							,						stream=SCREAMING_SNAKE_CASE      ).raw      )

      return im


@torch.no_grad()
def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						Dict							,						SCREAMING_SNAKE_CASE					:						Optional[Any]      ):







      '''simple docstring'''




      lowerCAmelCase  						=	DetrConfig()
      # set backbone and dilation attributes
      if "resnet101" in model_name:
            lowerCAmelCase  						=	"""resnet101"""
      if "dc5" in model_name:
            lowerCAmelCase  						=	True
      lowerCAmelCase  						=	"""panoptic""" in model_name
      if is_panoptic:
            lowerCAmelCase  						=	2_50
      else:
            lowerCAmelCase  						=	91
            lowerCAmelCase  						=	"""huggingface/label-files"""
            lowerCAmelCase  						=	"""coco-detection-id2label.json"""
            lowerCAmelCase  						=	json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						repo_type="""dataset"""      )							,						"""r"""      )      )
            lowerCAmelCase  						=	{int(SCREAMING_SNAKE_CASE      ): v for k, v in idalabel.items()}
            lowerCAmelCase  						=	idalabel
            lowerCAmelCase  						=	{v: k for k, v in idalabel.items()}

      # load image processor
      lowerCAmelCase  						=	"""coco_panoptic""" if is_panoptic else """coco_detection"""
      lowerCAmelCase  						=	DetrImageProcessor(format=SCREAMING_SNAKE_CASE      )

      # prepare image
      lowerCAmelCase  						=	prepare_img()
      lowerCAmelCase  						=	image_processor(images=SCREAMING_SNAKE_CASE							,						return_tensors="""pt"""      )
      lowerCAmelCase  						=	encoding["""pixel_values"""]

      logger.info(F'Converting model {model_name}...'      )

      # load original model from torch hub
      lowerCAmelCase  						=	torch.hub.load("""facebookresearch/detr"""							,						SCREAMING_SNAKE_CASE							,						pretrained=SCREAMING_SNAKE_CASE      ).eval()
      lowerCAmelCase  						=	detr.state_dict()
      # rename keys
      for src, dest in rename_keys:
            if is_panoptic:
                  lowerCAmelCase  						=	"""detr.""" + src
            rename_key(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE      )
      lowerCAmelCase  						=	rename_backbone_keys(SCREAMING_SNAKE_CASE      )
      # query, key and value matrices need special treatment
      read_in_q_k_v(SCREAMING_SNAKE_CASE							,						is_panoptic=SCREAMING_SNAKE_CASE      )
      # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
      lowerCAmelCase  						=	"""detr.model.""" if is_panoptic else """model."""
      for key in state_dict.copy().keys():
            if is_panoptic:
                  if (
                      key.startswith("""detr"""      )
                      and not key.startswith("""class_labels_classifier"""      )
                      and not key.startswith("""bbox_predictor"""      )
                  ):
                        lowerCAmelCase  						=	state_dict.pop(SCREAMING_SNAKE_CASE      )
                        lowerCAmelCase  						=	val
                  elif "class_labels_classifier" in key or "bbox_predictor" in key:
                        lowerCAmelCase  						=	state_dict.pop(SCREAMING_SNAKE_CASE      )
                        lowerCAmelCase  						=	val
                  elif key.startswith("""bbox_attention"""      ) or key.startswith("""mask_head"""      ):
                        continue
                  else:
                        lowerCAmelCase  						=	state_dict.pop(SCREAMING_SNAKE_CASE      )
                        lowerCAmelCase  						=	val
            else:
                  if not key.startswith("""class_labels_classifier"""      ) and not key.startswith("""bbox_predictor"""      ):
                        lowerCAmelCase  						=	state_dict.pop(SCREAMING_SNAKE_CASE      )
                        lowerCAmelCase  						=	val
    # finally, create HuggingFace model and load state dict
      lowerCAmelCase  						=	DetrForSegmentation(SCREAMING_SNAKE_CASE      ) if is_panoptic else DetrForObjectDetection(SCREAMING_SNAKE_CASE      )
      model.load_state_dict(SCREAMING_SNAKE_CASE      )
      model.eval()
      # verify our conversion
      lowerCAmelCase  						=	detr(SCREAMING_SNAKE_CASE      )
      lowerCAmelCase  						=	model(SCREAMING_SNAKE_CASE      )
      assert torch.allclose(outputs.logits							,						original_outputs["""pred_logits"""]							,						atol=1e-4      )
      assert torch.allclose(outputs.pred_boxes							,						original_outputs["""pred_boxes"""]							,						atol=1e-4      )
      if is_panoptic:
            assert torch.allclose(outputs.pred_masks							,						original_outputs["""pred_masks"""]							,						atol=1e-4      )

      # Save model and image processor
      logger.info(F'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'      )
      Path(SCREAMING_SNAKE_CASE      ).mkdir(exist_ok=SCREAMING_SNAKE_CASE      )
      model.save_pretrained(SCREAMING_SNAKE_CASE      )
      image_processor.save_pretrained(SCREAMING_SNAKE_CASE      )


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE__ =       argparse.ArgumentParser()

 parser.add_argument(
     "--model_name", default="detr_resnet50", type=str, help="Name of the DETR model you'd like to convert."
 )
 parser.add_argument(
     "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
 )
 SCREAMING_SNAKE_CASE__ =       parser.parse_args()
 convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)













"""simple docstring"""





import argparse
import os
import sys
import urllib.request
import zipfile


SCREAMING_SNAKE_CASE__ =       ["CoLA", "SST", "MRPC", "QQP", "STS", "MNLI", "SNLI", "QNLI", "RTE", "WNLI", "diagnostic"]
SCREAMING_SNAKE_CASE__ =       {
    "CoLA": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FCoLA.zip?alt=media&token=46d5e637-3411-4188-bc44-5809b5bfb5f4",
    "SST": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSST-2.zip?alt=media&token=aabc5f6b-e466-44a2-b9b4-cf6337f84ac8",
    "MRPC": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2Fmrpc_dev_ids.tsv?alt=media&token=ec5c0836-31d5-48f4-b431-7480817f1adc",
    "QQP": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FQQP.zip?alt=media&token=700c6acf-160d-4d89-81d1-de4191d02cb5",
    "STS": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSTS-B.zip?alt=media&token=bddb94a7-8706-4e0d-a694-1109e12273b5",
    "MNLI": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FMNLI.zip?alt=media&token=50329ea1-e339-40e2-809c-10c40afff3ce",
    "SNLI": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSNLI.zip?alt=media&token=4afcfbb2-ff0c-4b2d-a09a-dbf07926f4df",
    "QNLI": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FQNLIv2.zip?alt=media&token=6fdcf570-0fc5-4631-8456-9505272d1601",
    "RTE": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb",
    "WNLI": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FWNLI.zip?alt=media&token=068ad0a0-ded7-4bd7-99a5-5e00222e0faf",
    "diagnostic": "https://storage.googleapis.com/mtl-sentence-representations.appspot.com/tsvsWithoutLabels%2FAX.tsv?GoogleAccessId=firebase-adminsdk-0khhl@mtl-sentence-representations.iam.gserviceaccount.com&Expires=2498860800&Signature=DuQ2CSPt2Yfre0C%2BiISrVYrIFaZH1Lc7hBVZDD4ZyR7fZYOMNOUGpi8QxBmTNOrNPjR3z1cggo7WXFfrgECP6FBJSsURv8Ybrue8Ypt%2FTPxbuJ0Xc2FhDi%2BarnecCBFO77RSbfuz%2Bs95hRrYhTnByqu3U%2FYZPaj3tZt5QdfpH2IUROY8LiBXoXS46LE%2FgOQc%2FKN%2BA9SoscRDYsnxHfG0IjXGwHN%2Bf88q6hOmAxeNPx6moDulUF6XMUAaXCSFU%2BnRO2RDL9CapWxj%2BDl7syNyHhB7987hZ80B%2FwFkQ3MEs8auvt5XW1%2Bd4aCU7ytgM69r8JDCwibfhZxpaa4gd50QXQ%3D%3D",
}

SCREAMING_SNAKE_CASE__ =       "https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt"
SCREAMING_SNAKE_CASE__ =       "https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt"


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						Any							,						SCREAMING_SNAKE_CASE					:						List[Any]      ):







      '''simple docstring'''




      print(F'Downloading and extracting {task}...'      )
      lowerCAmelCase  						=	F'{task}.zip'
      urllib.request.urlretrieve(TASKaPATH[task]							,						SCREAMING_SNAKE_CASE      )
      with zipfile.ZipFile(SCREAMING_SNAKE_CASE      ) as zip_ref:
            zip_ref.extractall(SCREAMING_SNAKE_CASE      )
      os.remove(SCREAMING_SNAKE_CASE      )
      print("""\tCompleted!"""      )


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						Optional[Any]							,						SCREAMING_SNAKE_CASE					:						Union[str, Any]      ):







      '''simple docstring'''




      print("""Processing MRPC..."""      )
      lowerCAmelCase  						=	os.path.join(SCREAMING_SNAKE_CASE							,						"""MRPC"""      )
      if not os.path.isdir(SCREAMING_SNAKE_CASE      ):
            os.mkdir(SCREAMING_SNAKE_CASE      )
      if path_to_data:
            lowerCAmelCase  						=	os.path.join(SCREAMING_SNAKE_CASE							,						"""msr_paraphrase_train.txt"""      )
            lowerCAmelCase  						=	os.path.join(SCREAMING_SNAKE_CASE							,						"""msr_paraphrase_test.txt"""      )
      else:
            print("""Local MRPC data not specified, downloading data from %s""" % MRPC_TRAIN      )
            lowerCAmelCase  						=	os.path.join(SCREAMING_SNAKE_CASE							,						"""msr_paraphrase_train.txt"""      )
            lowerCAmelCase  						=	os.path.join(SCREAMING_SNAKE_CASE							,						"""msr_paraphrase_test.txt"""      )
            urllib.request.urlretrieve(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE      )
            urllib.request.urlretrieve(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE      )
      if not os.path.isfile(SCREAMING_SNAKE_CASE      ):
            raise ValueError(F'Train data not found at {mrpc_train_file}'      )
      if not os.path.isfile(SCREAMING_SNAKE_CASE      ):
            raise ValueError(F'Test data not found at {mrpc_test_file}'      )
      urllib.request.urlretrieve(TASKaPATH["""MRPC"""]							,						os.path.join(SCREAMING_SNAKE_CASE							,						"""dev_ids.tsv"""      )      )

      lowerCAmelCase  						=	[]
      with open(os.path.join(SCREAMING_SNAKE_CASE							,						"""dev_ids.tsv"""      )							,						encoding="""utf8"""      ) as ids_fh:
            for row in ids_fh:
                  dev_ids.append(row.strip().split("""\t"""      )      )

      with open(SCREAMING_SNAKE_CASE							,						encoding="""utf8"""      ) as data_fh, open(
          os.path.join(SCREAMING_SNAKE_CASE							,						"""train.tsv"""      )							,						"""w"""							,						encoding="""utf8"""      ) as train_fh, open(os.path.join(SCREAMING_SNAKE_CASE							,						"""dev.tsv"""      )							,						"""w"""							,						encoding="""utf8"""      ) as dev_fh:
            lowerCAmelCase  						=	data_fh.readline()
            train_fh.write(SCREAMING_SNAKE_CASE      )
            dev_fh.write(SCREAMING_SNAKE_CASE      )
            for row in data_fh:
                  lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  						=	row.strip().split("""\t"""      )
                  if [ida, ida] in dev_ids:
                        dev_fh.write("""%s\t%s\t%s\t%s\t%s\n""" % (label, ida, ida, sa, sa)      )
                  else:
                        train_fh.write("""%s\t%s\t%s\t%s\t%s\n""" % (label, ida, ida, sa, sa)      )

      with open(SCREAMING_SNAKE_CASE							,						encoding="""utf8"""      ) as data_fh, open(
          os.path.join(SCREAMING_SNAKE_CASE							,						"""test.tsv"""      )							,						"""w"""							,						encoding="""utf8"""      ) as test_fh:
            lowerCAmelCase  						=	data_fh.readline()
            test_fh.write("""index\t#1 ID\t#2 ID\t#1 String\t#2 String\n"""      )
            for idx, row in enumerate(SCREAMING_SNAKE_CASE      ):
                  lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  						=	row.strip().split("""\t"""      )
                  test_fh.write("""%d\t%s\t%s\t%s\t%s\n""" % (idx, ida, ida, sa, sa)      )
      print("""\tCompleted!"""      )


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						List[str]      ):







      '''simple docstring'''




      print("""Downloading and extracting diagnostic..."""      )
      if not os.path.isdir(os.path.join(SCREAMING_SNAKE_CASE							,						"""diagnostic"""      )      ):
            os.mkdir(os.path.join(SCREAMING_SNAKE_CASE							,						"""diagnostic"""      )      )
      lowerCAmelCase  						=	os.path.join(SCREAMING_SNAKE_CASE							,						"""diagnostic"""							,						"""diagnostic.tsv"""      )
      urllib.request.urlretrieve(TASKaPATH["""diagnostic"""]							,						SCREAMING_SNAKE_CASE      )
      print("""\tCompleted!"""      )
      return


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						str      ):







      '''simple docstring'''




      lowerCAmelCase  						=	task_names.split(""","""      )
      if "all" in task_names:
            lowerCAmelCase  						=	TASKS
      else:
            lowerCAmelCase  						=	[]
            for task_name in task_names:
                  if task_name not in TASKS:
                        raise ValueError(F'Task {task_name} not found!'      )
                  tasks.append(SCREAMING_SNAKE_CASE      )
      return tasks


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						int      ):







      '''simple docstring'''




      lowerCAmelCase  						=	argparse.ArgumentParser()
      parser.add_argument("""--data_dir"""							,						help="""directory to save data to"""							,						type=SCREAMING_SNAKE_CASE							,						default="""glue_data"""      )
      parser.add_argument(
          """--tasks"""							,						help="""tasks to download data for as a comma separated string"""							,						type=SCREAMING_SNAKE_CASE							,						default="""all"""      )
      parser.add_argument(
          """--path_to_mrpc"""							,						help="""path to directory containing extracted MRPC data, msr_paraphrase_train.txt and msr_paraphrase_text.txt"""							,						type=SCREAMING_SNAKE_CASE							,						default=""""""							,						)
      lowerCAmelCase  						=	parser.parse_args(SCREAMING_SNAKE_CASE      )

      if not os.path.isdir(args.data_dir      ):
            os.mkdir(args.data_dir      )
      lowerCAmelCase  						=	get_tasks(args.tasks      )

      for task in tasks:
            if task == "MRPC":
                  format_mrpc(args.data_dir							,						args.path_to_mrpc      )
            elif task == "diagnostic":
                  download_diagnostic(args.data_dir      )
            else:
                  download_and_extract(SCREAMING_SNAKE_CASE							,						args.data_dir      )


if __name__ == "__main__":
       sys.exit(main(sys.argv[1:]))






