




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 		a_							( __lowerCAmelCase				):
  lowerCAmelCase__										=      []
  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 		a_							( __lowerCAmelCase   , __lowerCAmelCase				):
  lowerCAmelCase__										=      []
  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 		a_							( __lowerCAmelCase				):
  lowerCAmelCase__										=      []
  token.append((F"""cvt.encoder.stages.{idx}.cls_token""", '''stage2.cls_token''')				)
  return token
def 		a_							( ):
  lowerCAmelCase__										=      []
  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 		a_							( __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase				):
  lowerCAmelCase__										=      '''imagenet-1k-id2label.json'''
  lowerCAmelCase__										=      10_00

  lowerCAmelCase__										=      '''huggingface/label-files'''
  lowerCAmelCase__										=      num_labels
  lowerCAmelCase__										=      json.load(open(cached_download(hf_hub_url(__lowerCAmelCase   , __lowerCAmelCase   , repo_type='''dataset'''				)				)   , '''r'''				)				)
  lowerCAmelCase__										=      {int(__lowerCAmelCase				): v for k, v in idalabel.items()}

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

  lowerCAmelCase__										=      lowerCAmelCase__										=      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":
    lowerCAmelCase__										=      [1, 2, 10]

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

  # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
  else:
    lowerCAmelCase__										=      [2, 2, 20]
    lowerCAmelCase__										=      [3, 12, 16]
    lowerCAmelCase__										=      [1_92, 7_68, 10_24]

  lowerCAmelCase__										=      CvtForImageClassification(__lowerCAmelCase				)
  lowerCAmelCase__										=      AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''				)
  lowerCAmelCase__										=      image_size
  lowerCAmelCase__										=      torch.load(__lowerCAmelCase   , map_location=torch.device('''cpu'''				)				)

  lowerCAmelCase__										=      OrderedDict()
  lowerCAmelCase__										=      []

  for idx in range(len(config.depth				)				):
    if config.cls_token[idx]:
      lowerCAmelCase__										=      list_of_state_dict + cls_token(__lowerCAmelCase				)
    lowerCAmelCase__										=      list_of_state_dict + embeddings(__lowerCAmelCase				)
    for cnt in range(config.depth[idx]				):
      lowerCAmelCase__										=      list_of_state_dict + attention(__lowerCAmelCase   , __lowerCAmelCase				)

  lowerCAmelCase__										=      list_of_state_dict + final()
  for gg in list_of_state_dict:
    print(__lowerCAmelCase				)
  for i in range(len(__lowerCAmelCase				)				):
    lowerCAmelCase__										=      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__":
      __magic_name__  :  List[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."""
      )

      __magic_name__  :  Optional[Any]					=       parser.parse_args()
      convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)







import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
__magic_name__  :  Tuple					=       data_utils.TransfoXLTokenizer
__magic_name__  :  int					=       data_utils.TransfoXLCorpus
__magic_name__  :  List[str]					=       data_utils
__magic_name__  :  str					=       data_utils
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase				):
  if transfo_xl_dataset_file:
    # Convert a pre-processed corpus (see original TensorFlow repo)
    with open(__lowerCAmelCase   , '''rb'''				) as fp:
      lowerCAmelCase__										=      pickle.load(__lowerCAmelCase   , encoding='''latin1'''				)
    # Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
    lowerCAmelCase__										=      pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''pretrained_vocab_file''']
    print(F"""Save vocabulary to {pytorch_vocab_dump_path}"""				)
    lowerCAmelCase__										=      corpus.vocab.__dict__
    torch.save(__lowerCAmelCase   , __lowerCAmelCase				)

    lowerCAmelCase__										=      corpus.__dict__
    corpus_dict_no_vocab.pop('''vocab'''   , __lowerCAmelCase				)
    lowerCAmelCase__										=      pytorch_dump_folder_path + '''/''' + CORPUS_NAME
    print(F"""Save dataset to {pytorch_dataset_dump_path}"""				)
    torch.save(__lowerCAmelCase   , __lowerCAmelCase				)

  if tf_checkpoint_path:
    # Convert a pre-trained TensorFlow model
    lowerCAmelCase__										=      os.path.abspath(__lowerCAmelCase				)
    lowerCAmelCase__										=      os.path.abspath(__lowerCAmelCase				)

    print(F"""Converting Transformer XL checkpoint from {tf_path} with config at {config_path}."""				)
    # Initialise PyTorch model
    if transfo_xl_config_file == "":
      lowerCAmelCase__										=      TransfoXLConfig()
    else:
      lowerCAmelCase__										=      TransfoXLConfig.from_json_file(__lowerCAmelCase				)
    print(F"""Building PyTorch model from configuration: {config}"""				)
    lowerCAmelCase__										=      TransfoXLLMHeadModel(__lowerCAmelCase				)

    lowerCAmelCase__										=      load_tf_weights_in_transfo_xl(__lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase				)
    # Save pytorch-model
    lowerCAmelCase__										=      os.path.join(__lowerCAmelCase   , __lowerCAmelCase				)
    lowerCAmelCase__										=      os.path.join(__lowerCAmelCase   , __lowerCAmelCase				)
    print(F"""Save PyTorch model to {os.path.abspath(__lowerCAmelCase				)}"""				)
    torch.save(model.state_dict()   , __lowerCAmelCase				)
    print(F"""Save configuration file to {os.path.abspath(__lowerCAmelCase				)}"""				)
    with open(__lowerCAmelCase   , '''w'''   , encoding='''utf-8'''				) as f:
      f.write(config.to_json_string()				)


if __name__ == "__main__":
      __magic_name__  :  int					=       argparse.ArgumentParser()
      parser.add_argument(
          """--pytorch_dump_folder_path""",
          default=None,
          type=str,
          required=True,
          help="""Path to the folder to store the PyTorch model or dataset/vocab.""",
      )
      parser.add_argument(
          """--tf_checkpoint_path""",
          default="""""",
          type=str,
          help="""An optional path to a TensorFlow checkpoint path to be converted.""",
      )
      parser.add_argument(
          """--transfo_xl_config_file""",
          default="""""",
          type=str,
          help=(
              """An optional config json file corresponding to the pre-trained BERT model. \n"""
              """This specifies the model architecture."""
          ),
      )
      parser.add_argument(
          """--transfo_xl_dataset_file""",
          default="""""",
          type=str,
          help="""An optional dataset file to be converted in a vocabulary.""",
      )
      __magic_name__  :  Optional[Any]					=       parser.parse_args()
      convert_transfo_xl_checkpoint_to_pytorch(
          args.tf_checkpoint_path,
          args.transfo_xl_config_file,
          args.pytorch_dump_folder_path,
          args.transfo_xl_dataset_file,
      )

