
"""simple docstring"""
import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification





def    _lowerCAmelCase    (     lowerCamelCase__							:							List[Any]				)  ->    Optional[int]:
  _SCREAMING_SNAKE_CASE   :				Optional[int]							    = []
  embed.append(
      (
          f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight''',
          f'''stage{idx}.patch_embed.proj.weight''',
      )				)
  embed.append(
      (
          f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias''',
          f'''stage{idx}.patch_embed.proj.bias''',
      )				)
  embed.append(
      (
          f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight''',
          f'''stage{idx}.patch_embed.norm.weight''',
      )				)
  embed.append(
      (
          f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias''',
          f'''stage{idx}.patch_embed.norm.bias''',
      )				)
  return embed





def    _lowerCAmelCase    (     lowerCamelCase__							:							Tuple,						lowerCamelCase__							:							List[str]				)  ->    Dict:
  _SCREAMING_SNAKE_CASE   :				int							    = []
  attention_weights.append(
      (
          f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight''',
          f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight''',
      )				)
  attention_weights.append(
      (
          f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight''',
          f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight''',
      )				)
  attention_weights.append(
      (
          f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias''',
          f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias''',
      )				)
  attention_weights.append(
      (
          f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean''',
          f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean''',
      )				)
  attention_weights.append(
      (
          f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var''',
          f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var''',
      )				)
  attention_weights.append(
      (
          f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked''',
          f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked''',
      )				)
  attention_weights.append(
      (
          f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight''',
          f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight''',
      )				)
  attention_weights.append(
      (
          f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight''',
          f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight''',
      )				)
  attention_weights.append(
      (
          f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias''',
          f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias''',
      )				)
  attention_weights.append(
      (
          f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean''',
          f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean''',
      )				)
  attention_weights.append(
      (
          f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var''',
          f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var''',
      )				)
  attention_weights.append(
      (
          f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked''',
          f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked''',
      )				)
  attention_weights.append(
      (
          f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight''',
          f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight''',
      )				)
  attention_weights.append(
      (
          f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight''',
          f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight''',
      )				)
  attention_weights.append(
      (
          f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias''',
          f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias''',
      )				)
  attention_weights.append(
      (
          f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean''',
          f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean''',
      )				)
  attention_weights.append(
      (
          f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var''',
          f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var''',
      )				)
  attention_weights.append(
      (
          f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked''',
          f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked''',
      )				)
  attention_weights.append(
      (
          f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight''',
          f'''stage{idx}.blocks.{cnt}.attn.proj_q.weight''',
      )				)
  attention_weights.append(
      (
          f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias''',
          f'''stage{idx}.blocks.{cnt}.attn.proj_q.bias''',
      )				)
  attention_weights.append(
      (
          f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight''',
          f'''stage{idx}.blocks.{cnt}.attn.proj_k.weight''',
      )				)
  attention_weights.append(
      (
          f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias''',
          f'''stage{idx}.blocks.{cnt}.attn.proj_k.bias''',
      )				)
  attention_weights.append(
      (
          f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight''',
          f'''stage{idx}.blocks.{cnt}.attn.proj_v.weight''',
      )				)
  attention_weights.append(
      (
          f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias''',
          f'''stage{idx}.blocks.{cnt}.attn.proj_v.bias''',
      )				)
  attention_weights.append(
      (
          f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight''',
          f'''stage{idx}.blocks.{cnt}.attn.proj.weight''',
      )				)
  attention_weights.append(
      (
          f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias''',
          f'''stage{idx}.blocks.{cnt}.attn.proj.bias''',
      )				)
  attention_weights.append(
      (f'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight''', f'''stage{idx}.blocks.{cnt}.mlp.fc1.weight''')				)
  attention_weights.append(
      (f'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias''', f'''stage{idx}.blocks.{cnt}.mlp.fc1.bias''')				)
  attention_weights.append(
      (f'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight''', f'''stage{idx}.blocks.{cnt}.mlp.fc2.weight''')				)
  attention_weights.append(
      (f'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias''', f'''stage{idx}.blocks.{cnt}.mlp.fc2.bias''')				)
  attention_weights.append(
      (f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight''', f'''stage{idx}.blocks.{cnt}.norm1.weight''')				)
  attention_weights.append(
      (f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias''', f'''stage{idx}.blocks.{cnt}.norm1.bias''')				)
  attention_weights.append(
      (f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight''', f'''stage{idx}.blocks.{cnt}.norm2.weight''')				)
  attention_weights.append(
      (f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias''', f'''stage{idx}.blocks.{cnt}.norm2.bias''')				)
  return attention_weights





def    _lowerCAmelCase    (     lowerCamelCase__							:							str				)  ->    List[str]:
  _SCREAMING_SNAKE_CASE   :				int							    = []
  token.append((f'''cvt.encoder.stages.{idx}.cls_token''', "stage2.cls_token")				)
  return token





def    _lowerCAmelCase    (     )  ->    List[str]:
  _SCREAMING_SNAKE_CASE   :				int							    = []
  head.append(("layernorm.weight", "norm.weight")				)
  head.append(("layernorm.bias", "norm.bias")				)
  head.append(("classifier.weight", "head.weight")				)
  head.append(("classifier.bias", "head.bias")				)
  return head





def    _lowerCAmelCase    (     lowerCamelCase__							:							List[str],						lowerCamelCase__							:							Union[str, Any],						lowerCamelCase__							:							Tuple,						lowerCamelCase__							:							Union[str, Any]				)  ->    List[Any]:
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = "imagenet-1k-id2label.json"
  _SCREAMING_SNAKE_CASE   :				int							    = 1_0_0_0

  _SCREAMING_SNAKE_CASE   :				Any							    = "huggingface/label-files"
  _SCREAMING_SNAKE_CASE   :				int							    = num_labels
  _SCREAMING_SNAKE_CASE   :				str							    = json.load(open(cached_download(hf_hub_url(lowerCamelCase__,						lowerCamelCase__,						repo_type="dataset"				)				),						"r"				)				)
  _SCREAMING_SNAKE_CASE   :				Tuple							    = {int(lowerCamelCase__				): v for k, v in idalabel.items()}

  _SCREAMING_SNAKE_CASE   :				List[str]							    = idalabel
  _SCREAMING_SNAKE_CASE   :				Tuple							    = {v: k for k, v in idalabel.items()}

  _SCREAMING_SNAKE_CASE   :				str							    = CvtConfig(num_labels=lowerCamelCase__,						idalabel=lowerCamelCase__,						labelaid=lowerCamelCase__				)

  # For depth size 13 (13 = 1+2+10)
  if cvt_model.rsplit("/",						1				)[-1][4:6] == "13":
    _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = [1, 2, 1_0]

  # For depth size 21 (21 = 1+4+16)
  elif cvt_model.rsplit("/",						1				)[-1][4:6] == "21":
    _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = [1, 4, 1_6]

  # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
  else:
    _SCREAMING_SNAKE_CASE   :				Any							    = [2, 2, 2_0]
    _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = [3, 1_2, 1_6]
    _SCREAMING_SNAKE_CASE   :				int							    = [1_9_2, 7_6_8, 1_0_2_4]

  _SCREAMING_SNAKE_CASE   :				Tuple							    = CvtForImageClassification(lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				Dict							    = AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"				)
  _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = image_size
  _SCREAMING_SNAKE_CASE   :				Tuple							    = torch.load(lowerCamelCase__,						map_location=torch.device("cpu"				)				)

  _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = OrderedDict()
  _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = []

  for idx in range(len(config.depth				)				):
    if config.cls_token[idx]:
      _SCREAMING_SNAKE_CASE   :				int							    = list_of_state_dict + cls_token(lowerCamelCase__				)
    _SCREAMING_SNAKE_CASE   :				Optional[int]							    = list_of_state_dict + embeddings(lowerCamelCase__				)
    for cnt in range(config.depth[idx]				):
      _SCREAMING_SNAKE_CASE   :				List[Any]							    = list_of_state_dict + attention(lowerCamelCase__,						lowerCamelCase__				)

  _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = list_of_state_dict + final()
  for gg in list_of_state_dict:
    print(lowerCamelCase__				)
  for i in range(len(lowerCamelCase__				)				):
    _SCREAMING_SNAKE_CASE   :				Optional[int]							    = original_weights[list_of_state_dict[i][1]]

  model.load_state_dict(lowerCamelCase__				)
  model.save_pretrained(lowerCamelCase__				)
  image_processor.save_pretrained(lowerCamelCase__				)


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
      lowercase_       :						Union[str, Any]     					=	argparse.ArgumentParser()
      parser.add_argument(
          '''--cvt_model''',
          default='''cvt-w24''',
          type=str,
          help='''Name of the cvt model you\'d like to convert.''',
      )
      parser.add_argument(
          '''--image_size''',
          default=3_84,
          type=int,
          help='''Input Image Size''',
      )
      parser.add_argument(
          '''--cvt_file_name''',
          default=R'''cvtmodels\CvT-w24-384x384-IN-22k.pth''',
          type=str,
          help='''Input Image Size''',
      )
      parser.add_argument(
          '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
      )

      lowercase_       :						int     					=	parser.parse_args()
      convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)





"""simple docstring"""
import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


lowercase_       :						List[str]     					=	datasets.utils.logging.get_logger(__name__)
@dataclass
class 			UpperCamelCase      (							datasets.BuilderConfig  ):
     A__      =			10000
     A__      =			None
     A__      =			None
class 			UpperCamelCase      (							datasets.ArrowBasedBuilder  ):
     A__      =			ParquetConfig
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       return datasets.DatasetInfo(features=self.config.features				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       if not self.config.data_files:
         raise ValueError(F'''At least one data file must be specified, but got data_files={self.config.data_files}'''				)
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = dl_manager.download_and_extract(self.config.data_files				)
       if isinstance(snake_case__  ,     (str, list, tuple)				):
         _SCREAMING_SNAKE_CASE   :				int							    = data_files
         if isinstance(snake_case__  ,     snake_case__				):
           _SCREAMING_SNAKE_CASE   :				List[Any]							    = [files]
         # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
         _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = [dl_manager.iter_files(snake_case__				) for file in files]
         return [datasets.SplitGenerator(name=datasets.Split.TRAIN  ,     gen_kwargs={"files": files}				)]
       _SCREAMING_SNAKE_CASE   :				Tuple							    = []
       for split_name, files in data_files.items():
         if isinstance(snake_case__  ,     snake_case__				):
           _SCREAMING_SNAKE_CASE   :				Optional[int]							    = [files]
         # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
         _SCREAMING_SNAKE_CASE   :				Tuple							    = [dl_manager.iter_files(snake_case__				) for file in files]
         # Infer features is they are stoed in the arrow schema
         if self.info.features is None:
           for file in itertools.chain.from_iterable(snake_case__				):
             with open(snake_case__  ,     "rb"				) as f:
               _SCREAMING_SNAKE_CASE   :				Optional[int]							    = datasets.Features.from_arrow_schema(pq.read_schema(snake_case__				)				)
             break
         splits.append(datasets.SplitGenerator(name=snake_case__  ,     gen_kwargs={"files": files}				)				)
       return splits
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       if self.info.features is not None:
         # more expensive cast to support nested features with keys in a different order
         # allows str <-> int/float or str to Audio for example
         _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = table_cast(snake_case__  ,     self.info.features.arrow_schema				)
       return pa_table




     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Any							    = self.info.features.arrow_schema if self.info.features is not None else None
       if self.info.features is not None and self.config.columns is not None:
         if sorted(field.name for field in schema				) != sorted(self.config.columns				):
           raise ValueError(
               F'''Tried to load parquet data with columns \'{self.config.columns}\' with mismatching features \'{self.info.features}\''''				)
       for file_idx, file in enumerate(itertools.chain.from_iterable(snake_case__				)				):
         with open(snake_case__  ,     "rb"				) as f:
           _SCREAMING_SNAKE_CASE   :				List[Any]							    = pq.ParquetFile(snake_case__				)
           try:
             for batch_idx, record_batch in enumerate(
                 parquet_file.iter_batches(batch_size=self.config.batch_size  ,     columns=self.config.columns				)				):
               _SCREAMING_SNAKE_CASE   :				int							    = pa.Table.from_batches([record_batch]				)
               # Uncomment for debugging (will print the Arrow table size and elements)
               # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
               # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
               yield F'''{file_idx}_{batch_idx}''', self._cast_table(snake_case__				)
           except ValueError as e:
             logger.error(F'''Failed to read file \'{file}\' with error {type(snake_case__				)}: {e}'''				)
             raise



