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

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
A     :  Tuple					     =       logging.get_logger()


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase = True				):
      print(F'''Converting {name}...'''				)

      with torch.no_grad():
            if hidden_sizes == 1_2_8:
                  if name[-1] == "S":
                        SCREAMING_SNAKE_CASE_          =  timm.create_model("levit_128s"     ,      pretrained=__UpperCamelCase				)
                  else:
                        SCREAMING_SNAKE_CASE_          =  timm.create_model("levit_128"     ,      pretrained=__UpperCamelCase				)
            if hidden_sizes == 1_9_2:
                  SCREAMING_SNAKE_CASE_          =  timm.create_model("levit_192"     ,      pretrained=__UpperCamelCase				)
            if hidden_sizes == 2_5_6:
                  SCREAMING_SNAKE_CASE_          =  timm.create_model("levit_256"     ,      pretrained=__UpperCamelCase				)
            if hidden_sizes == 3_8_4:
                  SCREAMING_SNAKE_CASE_          =  timm.create_model("levit_384"     ,      pretrained=__UpperCamelCase				)

            from_model.eval()
            SCREAMING_SNAKE_CASE_          =  LevitForImageClassificationWithTeacher(__UpperCamelCase				).eval()
            SCREAMING_SNAKE_CASE_          =  OrderedDict()

            SCREAMING_SNAKE_CASE_          =  from_model.state_dict()
            SCREAMING_SNAKE_CASE_          =  list(from_model.state_dict().keys()				)
            SCREAMING_SNAKE_CASE_          =  list(our_model.state_dict().keys()				)
            print(len(__UpperCamelCase				)     ,      len(__UpperCamelCase				)				)
            for i in range(len(__UpperCamelCase				)				):
                  SCREAMING_SNAKE_CASE_          =  weights[og_keys[i]]
            our_model.load_state_dict(__UpperCamelCase				)

            SCREAMING_SNAKE_CASE_          =  torch.randn((2, 3, 2_2_4, 2_2_4)				)
            SCREAMING_SNAKE_CASE_          =  from_model(__UpperCamelCase				)
            SCREAMING_SNAKE_CASE_          =  our_model(__UpperCamelCase				).logits

      assert torch.allclose(__UpperCamelCase     ,      __UpperCamelCase				), "The model logits don't match the original one."

      SCREAMING_SNAKE_CASE_          =  name
      print(__UpperCamelCase				)

      if push_to_hub:
            our_model.save_pretrained(save_directory / checkpoint_name				)
            SCREAMING_SNAKE_CASE_          =  LevitImageProcessor()
            image_processor.save_pretrained(save_directory / checkpoint_name				)

            print(F'''Pushed {checkpoint_name}'''				)


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase = None     ,      __UpperCamelCase = True				):
      SCREAMING_SNAKE_CASE_          =  "imagenet-1k-id2label.json"
      SCREAMING_SNAKE_CASE_          =  1_0_0_0
      SCREAMING_SNAKE_CASE_          =  (1, num_labels)

      SCREAMING_SNAKE_CASE_          =  "huggingface/label-files"
      SCREAMING_SNAKE_CASE_          =  num_labels
      SCREAMING_SNAKE_CASE_          =  json.load(open(hf_hub_download(__UpperCamelCase     ,      __UpperCamelCase     ,      repo_type="dataset"				)     ,      "r"				)				)
      SCREAMING_SNAKE_CASE_          =  {int(__UpperCamelCase				): v for k, v in idalabel.items()}

      SCREAMING_SNAKE_CASE_          =  idalabel
      SCREAMING_SNAKE_CASE_          =  {v: k for k, v in idalabel.items()}

      SCREAMING_SNAKE_CASE_          =  partial(__UpperCamelCase     ,      num_labels=__UpperCamelCase     ,      idalabel=__UpperCamelCase     ,      labelaid=__UpperCamelCase				)

      SCREAMING_SNAKE_CASE_          =  {
          "levit-128S": 1_2_8,
          "levit-128": 1_2_8,
          "levit-192": 1_9_2,
          "levit-256": 2_5_6,
          "levit-384": 3_8_4,
      }

      SCREAMING_SNAKE_CASE_          =  {
          "levit-128S": ImageNetPreTrainedConfig(
              hidden_sizes=[1_2_8, 2_5_6, 3_8_4]     ,      num_attention_heads=[4, 6, 8]     ,      depths=[2, 3, 4]     ,      key_dim=[1_6, 1_6, 1_6]     ,      drop_path_rate=0     ,      ),
          "levit-128": ImageNetPreTrainedConfig(
              hidden_sizes=[1_2_8, 2_5_6, 3_8_4]     ,      num_attention_heads=[4, 8, 1_2]     ,      depths=[4, 4, 4]     ,      key_dim=[1_6, 1_6, 1_6]     ,      drop_path_rate=0     ,      ),
          "levit-192": ImageNetPreTrainedConfig(
              hidden_sizes=[1_9_2, 2_8_8, 3_8_4]     ,      num_attention_heads=[3, 5, 6]     ,      depths=[4, 4, 4]     ,      key_dim=[3_2, 3_2, 3_2]     ,      drop_path_rate=0     ,      ),
          "levit-256": ImageNetPreTrainedConfig(
              hidden_sizes=[2_5_6, 3_8_4, 5_1_2]     ,      num_attention_heads=[4, 6, 8]     ,      depths=[4, 4, 4]     ,      key_dim=[3_2, 3_2, 3_2]     ,      drop_path_rate=0     ,      ),
          "levit-384": ImageNetPreTrainedConfig(
              hidden_sizes=[3_8_4, 5_1_2, 7_6_8]     ,      num_attention_heads=[6, 9, 1_2]     ,      depths=[4, 4, 4]     ,      key_dim=[3_2, 3_2, 3_2]     ,      drop_path_rate=0.1     ,      ),
      }

      if model_name:
            convert_weight_and_push(
                names_to_hidden_sizes[model_name]     ,      __UpperCamelCase     ,      names_to_config[model_name]     ,      __UpperCamelCase     ,      __UpperCamelCase				)
      else:
            for model_name, config in names_to_config.items():
                  convert_weight_and_push(names_to_hidden_sizes[model_name]     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				)
      return config, expected_shape


if __name__ == "__main__":
      A     :  Optional[Any]					     =       argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--model_name",
          default=None,
          type=str,
          help="The name of the model you wish to convert, it must be one of the supported Levit* architecture,",
      )
      parser.add_argument(
          "--pytorch_dump_folder_path",
          default="levit-dump-folder/",
          type=Path,
          required=False,
          help="Path to the output PyTorch model directory.",
      )
      parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")
      parser.add_argument(
          "--no-push_to_hub",
          dest="push_to_hub",
          action="store_false",
          help="Do not push model and image processor to the hub",
      )

      A     :  List[str]					     =       parser.parse_args()
      A     :  Path					     =       args.pytorch_dump_folder_path
      pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
      convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)



import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


A     :  Any					     =       logging.getLogger(__name__)


class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  def __init__(							self :       List[Any]  ,     __magic_name__ :       Optional[Any]=-1   )  ->	Optional[Any]:
        # in NER datasets, the last column is usually reserved for NER label
        SCREAMING_SNAKE_CASE_          =  label_idx







  def    __A     (							self :       Optional[Any]  ,     __magic_name__ :       List[Any]  ,     __magic_name__ :       Union[Split, str]   )  ->	List[InputExample]:
        if isinstance(__magic_name__  ,     __magic_name__   ):
              SCREAMING_SNAKE_CASE_          =  mode.value
        SCREAMING_SNAKE_CASE_          =  os.path.join(__magic_name__  ,     F'''{mode}.txt'''   )
        SCREAMING_SNAKE_CASE_          =  1
        SCREAMING_SNAKE_CASE_          =  []
        with open(__magic_name__  ,     encoding="utf-8"   ) as f:
              SCREAMING_SNAKE_CASE_          =  []
              SCREAMING_SNAKE_CASE_          =  []
              for line in f:
                    if line.startswith("-DOCSTART-"   ) or line == "" or line == "\n":
                          if words:
                                examples.append(InputExample(guid=F'''{mode}-{guid_index}'''  ,     words=__magic_name__  ,     labels=__magic_name__   )   )
                                guid_index += 1
                                SCREAMING_SNAKE_CASE_          =  []
                                SCREAMING_SNAKE_CASE_          =  []
                    else:
                          SCREAMING_SNAKE_CASE_          =  line.split(" "   )
                          words.append(splits[0]   )
                          if len(__magic_name__   ) > 1:
                                labels.append(splits[self.label_idx].replace("\n"  ,     ""   )   )
                          else:
                                # Examples could have no label for mode = "test"
                                labels.append("O"   )
              if words:
                    examples.append(InputExample(guid=F'''{mode}-{guid_index}'''  ,     words=__magic_name__  ,     labels=__magic_name__   )   )
        return examples







  def    __A     (							self :       Tuple  ,     __magic_name__ :       TextIO  ,     __magic_name__ :       TextIO  ,     __magic_name__ :       List   )  ->	List[Any]:
        SCREAMING_SNAKE_CASE_          =  0
        for line in test_input_reader:
              if line.startswith("-DOCSTART-"   ) or line == "" or line == "\n":
                    writer.write(__magic_name__   )
                    if not preds_list[example_id]:
                          example_id += 1
              elif preds_list[example_id]:
                    SCREAMING_SNAKE_CASE_          =  line.split()[0] + " " + preds_list[example_id].pop(0   ) + "\n"
                    writer.write(__magic_name__   )
              else:
                    logger.warning("Maximum sequence length exceeded: No prediction for '%s'."  ,     line.split()[0]   )







  def    __A     (							self :       Optional[int]  ,     __magic_name__ :       str   )  ->	List[str]:
        if path:
              with open(__magic_name__  ,     "r"   ) as f:
                    SCREAMING_SNAKE_CASE_          =  f.read().splitlines()
              if "O" not in labels:
                    SCREAMING_SNAKE_CASE_          =  ["O"] + labels
              return labels
        else:
              return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]



class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  def __init__(							self :       Optional[int]   )  ->	str:
        # in CONLL2003 dataset chunk column is second-to-last
        super().__init__(label_idx=-2   )







  def    __A     (							self :       Any  ,     __magic_name__ :       str   )  ->	List[str]:
        if path:
              with open(__magic_name__  ,     "r"   ) as f:
                    SCREAMING_SNAKE_CASE_          =  f.read().splitlines()
              if "O" not in labels:
                    SCREAMING_SNAKE_CASE_          =  ["O"] + labels
              return labels
        else:
              return [
                  "O",
                  "B-ADVP",
                  "B-INTJ",
                  "B-LST",
                  "B-PRT",
                  "B-NP",
                  "B-SBAR",
                  "B-VP",
                  "B-ADJP",
                  "B-CONJP",
                  "B-PP",
                  "I-ADVP",
                  "I-INTJ",
                  "I-LST",
                  "I-PRT",
                  "I-NP",
                  "I-SBAR",
                  "I-VP",
                  "I-ADJP",
                  "I-CONJP",
                  "I-PP",
              ]



class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  def    __A     (							self :       str  ,     __magic_name__ :       Optional[Any]  ,     __magic_name__ :       Union[Split, str]   )  ->	List[InputExample]:
        if isinstance(__magic_name__  ,     __magic_name__   ):
              SCREAMING_SNAKE_CASE_          =  mode.value
        SCREAMING_SNAKE_CASE_          =  os.path.join(__magic_name__  ,     F'''{mode}.txt'''   )
        SCREAMING_SNAKE_CASE_          =  1
        SCREAMING_SNAKE_CASE_          =  []

        with open(__magic_name__  ,     encoding="utf-8"   ) as f:
              for sentence in parse_incr(__magic_name__   ):
                    SCREAMING_SNAKE_CASE_          =  []
                    SCREAMING_SNAKE_CASE_          =  []
                    for token in sentence:
                          words.append(token["form"]   )
                          labels.append(token["upos"]   )
                    assert len(__magic_name__   ) == len(__magic_name__   )
                    if words:
                          examples.append(InputExample(guid=F'''{mode}-{guid_index}'''  ,     words=__magic_name__  ,     labels=__magic_name__   )   )
                          guid_index += 1
        return examples







  def    __A     (							self :       Optional[int]  ,     __magic_name__ :       TextIO  ,     __magic_name__ :       TextIO  ,     __magic_name__ :       List   )  ->	Union[str, Any]:
        SCREAMING_SNAKE_CASE_          =  0
        for sentence in parse_incr(__magic_name__   ):
              SCREAMING_SNAKE_CASE_          =  preds_list[example_id]
              SCREAMING_SNAKE_CASE_          =  ""
              for token in sentence:
                    out += F'''{token["form"]} ({token["upos"]}|{s_p.pop(0   )}) '''
              out += "\n"
              writer.write(__magic_name__   )
              example_id += 1







  def    __A     (							self :       Optional[int]  ,     __magic_name__ :       str   )  ->	List[str]:
        if path:
              with open(__magic_name__  ,     "r"   ) as f:
                    return f.read().splitlines()
        else:
              return [
                  "ADJ",
                  "ADP",
                  "ADV",
                  "AUX",
                  "CCONJ",
                  "DET",
                  "INTJ",
                  "NOUN",
                  "NUM",
                  "PART",
                  "PRON",
                  "PROPN",
                  "PUNCT",
                  "SCONJ",
                  "SYM",
                  "VERB",
                  "X",
              ]


