






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()
snake_case    				=		logging.get_logger()


def   lowerCamelCase__			(     lowercase						,     lowercase						,     lowercase						,     lowercase						,     lowercase = True				):

  """simple docstring"""






  print(F'''Converting {name}...'''				)

  with torch.no_grad():
    if hidden_sizes == 128:
      if name[-1] == "S":
        SCREAMING_SNAKE_CASE       :      Dict							=    timm.create_model("levit_128s"						,     pretrained=lowerCamelCase__				)
      else:
        SCREAMING_SNAKE_CASE       :      Optional[int]							=    timm.create_model("levit_128"						,     pretrained=lowerCamelCase__				)
    if hidden_sizes == 192:
      SCREAMING_SNAKE_CASE       :      str							=    timm.create_model("levit_192"						,     pretrained=lowerCamelCase__				)
    if hidden_sizes == 256:
      SCREAMING_SNAKE_CASE       :      Optional[int]							=    timm.create_model("levit_256"						,     pretrained=lowerCamelCase__				)
    if hidden_sizes == 384:
      SCREAMING_SNAKE_CASE       :      Optional[Any]							=    timm.create_model("levit_384"						,     pretrained=lowerCamelCase__				)

    from_model.eval()
    SCREAMING_SNAKE_CASE       :      Optional[Any]							=    LevitForImageClassificationWithTeacher(lowerCamelCase__				).eval()
    SCREAMING_SNAKE_CASE       :      Optional[Any]							=    OrderedDict()

    SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    from_model.state_dict()
    SCREAMING_SNAKE_CASE       :      Dict							=    list(from_model.state_dict().keys()				)
    SCREAMING_SNAKE_CASE       :      List[Any]							=    list(our_model.state_dict().keys()				)
    print(len(lowerCamelCase__				)						,     len(lowerCamelCase__				)				)
    for i in range(len(lowerCamelCase__				)				):
      SCREAMING_SNAKE_CASE       :      int							=    weights[og_keys[i]]
    our_model.load_state_dict(lowerCamelCase__				)

    SCREAMING_SNAKE_CASE       :      Tuple							=    torch.randn((2, 3, 224, 224)				)
    SCREAMING_SNAKE_CASE       :      Optional[Any]							=    from_model(lowerCamelCase__				)
    SCREAMING_SNAKE_CASE       :      List[Any]							=    our_model(lowerCamelCase__				).logits

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

  SCREAMING_SNAKE_CASE       :      str							=    name
  print(lowerCamelCase__				)

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

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


def   lowerCamelCase__			(     lowercase						,     lowercase = None						,     lowercase = True				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      str							=    "imagenet-1k-id2label.json"
  SCREAMING_SNAKE_CASE       :      Optional[int]							=    1000
  SCREAMING_SNAKE_CASE       :      int							=    (1, num_labels)

  SCREAMING_SNAKE_CASE       :      int							=    "huggingface/label-files"
  SCREAMING_SNAKE_CASE       :      Optional[int]							=    num_labels
  SCREAMING_SNAKE_CASE       :      Optional[Any]							=    json.load(open(hf_hub_download(lowerCamelCase__						,     lowerCamelCase__						,     repo_type="dataset"				)						,     "r"				)				)
  SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    {int(lowerCamelCase__				): v for k, v in idalabel.items()}

  SCREAMING_SNAKE_CASE       :      List[Any]							=    idalabel
  SCREAMING_SNAKE_CASE       :      Optional[int]							=    {v: k for k, v in idalabel.items()}

  SCREAMING_SNAKE_CASE       :      Optional[Any]							=    partial(lowerCamelCase__						,     num_labels=lowerCamelCase__						,     idalabel=lowerCamelCase__						,     labelaid=lowerCamelCase__				)

  SCREAMING_SNAKE_CASE       :      List[str]							=    {
      "levit-128S": 128,
      "levit-128": 128,
      "levit-192": 192,
      "levit-256": 256,
      "levit-384": 384,
  }

  SCREAMING_SNAKE_CASE       :      Any							=    {
      "levit-128S": ImageNetPreTrainedConfig(
          hidden_sizes=[128, 256, 384]						,     num_attention_heads=[4, 6, 8]						,     depths=[2, 3, 4]						,     key_dim=[16, 16, 16]						,     drop_path_rate=0						,     ),
      "levit-128": ImageNetPreTrainedConfig(
          hidden_sizes=[128, 256, 384]						,     num_attention_heads=[4, 8, 12]						,     depths=[4, 4, 4]						,     key_dim=[16, 16, 16]						,     drop_path_rate=0						,     ),
      "levit-192": ImageNetPreTrainedConfig(
          hidden_sizes=[192, 288, 384]						,     num_attention_heads=[3, 5, 6]						,     depths=[4, 4, 4]						,     key_dim=[32, 32, 32]						,     drop_path_rate=0						,     ),
      "levit-256": ImageNetPreTrainedConfig(
          hidden_sizes=[256, 384, 512]						,     num_attention_heads=[4, 6, 8]						,     depths=[4, 4, 4]						,     key_dim=[32, 32, 32]						,     drop_path_rate=0						,     ),
      "levit-384": ImageNetPreTrainedConfig(
          hidden_sizes=[384, 512, 768]						,     num_attention_heads=[6, 9, 12]						,     depths=[4, 4, 4]						,     key_dim=[32, 32, 32]						,     drop_path_rate=0.1						,     ),
  }

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


if __name__ == "__main__":
    snake_case    				=		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""",
    )

    snake_case    				=		parser.parse_args()
    snake_case    				=		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)














# Copyright 2022 The HuggingFace Team and The OpenBMB Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


snake_case    				=		{
    """configuration_cpmant""": ["""CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CpmAntConfig"""],
    """tokenization_cpmant""": ["""CpmAntTokenizer"""],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case    				=		[
       """CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """CpmAntForCausalLM""",
       """CpmAntModel""",
       """CpmAntPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
   from .tokenization_cpmant import CpmAntTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_cpmant import (
          CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
          CpmAntForCausalLM,
          CpmAntModel,
          CpmAntPreTrainedModel,
      )


else:
   import sys

   snake_case    				=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






