




import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase__ :   Dict					=							logging.get_logger()





@dataclass
class        lowerCamelCase_							:


 '''simple docstring'''







 lowercase_				    =    42
 lowercase_				    =    field(default_factory=_SCREAMING_SNAKE_CASE					)
 lowercase_				    =    field(default_factory=_SCREAMING_SNAKE_CASE					)



 def       lowerCAmelCase_  (							self  :		Any				,				_lowerCAmelCase  :		Union[str, Any]				,				_lowerCAmelCase  :		Tensor				,				_lowerCAmelCase  :		Tensor						):
       SCREAMING_SNAKE_CASE_							=			len(list(m.modules()						)						) == 1 or isinstance(_lowerCAmelCase				,				nn.Convad						) or isinstance(_lowerCAmelCase				,				nn.BatchNormad						)
       if has_not_submodules:
             self.traced.append(_lowerCAmelCase						)



 def __call__(							self  :		Any				,				_lowerCAmelCase  :		Tensor						):
       for m in self.module.modules():
             self.handles.append(m.register_forward_hook(self._forward_hook						)						)
       self.module(_lowerCAmelCase						)
       [x.remove() for x in self.handles]
       return self




 @property
 def       lowerCAmelCase_  (							self  :		Optional[int]						):
       # check the len of the state_dict keys to see if we have learnable params
       return list(filter(lambda _lowerCAmelCase						: len(list(x.state_dict().keys()						)						) > 0				,				self.traced						)						)





@dataclass
class        lowerCamelCase_							:


 '''simple docstring'''







 lowercase_				    =    42
 lowercase_				    =    42
 lowercase_				    =    0
 lowercase_				    =    field(default_factory=_SCREAMING_SNAKE_CASE					)
 lowercase_				    =    field(default_factory=_SCREAMING_SNAKE_CASE					)



 def __call__(							self  :		Tuple				,				_lowerCAmelCase  :		Tensor						):
       SCREAMING_SNAKE_CASE_							=			Tracker(self.dest						)(_lowerCAmelCase						).parametrized
       SCREAMING_SNAKE_CASE_							=			Tracker(self.src						)(_lowerCAmelCase						).parametrized

       SCREAMING_SNAKE_CASE_							=			list(filter(lambda _lowerCAmelCase						: type(_lowerCAmelCase						) not in self.src_skip				,				_lowerCAmelCase						)						)
       SCREAMING_SNAKE_CASE_							=			list(filter(lambda _lowerCAmelCase						: type(_lowerCAmelCase						) not in self.dest_skip				,				_lowerCAmelCase						)						)

       if len(_lowerCAmelCase						) != len(_lowerCAmelCase						):
             raise Exception(
                 F"Numbers of operations are different. Source module has {len(_lowerCAmelCase						)} operations while"
                 F" destination module has {len(_lowerCAmelCase						)}."						)

       for dest_m, src_m in zip(_lowerCAmelCase				,				_lowerCAmelCase						):
             dest_m.load_state_dict(src_m.state_dict()						)
             if self.verbose == 1:
                   print(F"Transfered from={src_m} to={dest_m}"						)


def 					UpperCAmelCase_							(      __UpperCAmelCase						:	str			,			__UpperCAmelCase						:	ResNetConfig			,			__UpperCAmelCase						:	Path			,			__UpperCAmelCase						:	bool = True							)					-> Union[str, Any]:
      print(f"Converting {name}..."							)
      with torch.no_grad():
            SCREAMING_SNAKE_CASE_							=			timm.create_model(__UpperCAmelCase			,			pretrained=__UpperCAmelCase							).eval()
            SCREAMING_SNAKE_CASE_							=			ResNetForImageClassification(__UpperCAmelCase							).eval()
            SCREAMING_SNAKE_CASE_							=			ModuleTransfer(src=__UpperCAmelCase			,			dest=__UpperCAmelCase							)
            SCREAMING_SNAKE_CASE_							=			torch.randn((1, 3, 2_24, 2_24)							)
            module_transfer(__UpperCAmelCase							)

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

      SCREAMING_SNAKE_CASE_							=			f"resnet{'-'.join(name.split('resnet'							)							)}"
      print(__UpperCAmelCase							)

      if push_to_hub:
            our_model.push_to_hub(
                repo_path_or_name=save_directory / checkpoint_name			,			commit_message='Add model'			,			use_temp_dir=__UpperCAmelCase			,			)

            # we can use the convnext one
            SCREAMING_SNAKE_CASE_							=			AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'							)
            image_processor.push_to_hub(
                repo_path_or_name=save_directory / checkpoint_name			,			commit_message='Add image processor'			,			use_temp_dir=__UpperCAmelCase			,			)

            print(f"Pushed {checkpoint_name}"							)

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	Path			,			__UpperCAmelCase						:	str = None			,			__UpperCAmelCase						:	bool = True							)					-> Union[str, Any]:
      SCREAMING_SNAKE_CASE_							=			'imagenet-1k-id2label.json'
      SCREAMING_SNAKE_CASE_							=			10_00
      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_							=			{
          'resnet18': ImageNetPreTrainedConfig(
              depths=[2, 2, 2, 2]			,			hidden_sizes=[64, 1_28, 2_56, 5_12]			,			layer_type='basic'							),
          'resnet26': ImageNetPreTrainedConfig(
              depths=[2, 2, 2, 2]			,			hidden_sizes=[2_56, 5_12, 10_24, 20_48]			,			layer_type='bottleneck'							),
          'resnet34': ImageNetPreTrainedConfig(
              depths=[3, 4, 6, 3]			,			hidden_sizes=[64, 1_28, 2_56, 5_12]			,			layer_type='basic'							),
          'resnet50': ImageNetPreTrainedConfig(
              depths=[3, 4, 6, 3]			,			hidden_sizes=[2_56, 5_12, 10_24, 20_48]			,			layer_type='bottleneck'							),
          'resnet101': ImageNetPreTrainedConfig(
              depths=[3, 4, 23, 3]			,			hidden_sizes=[2_56, 5_12, 10_24, 20_48]			,			layer_type='bottleneck'							),
          'resnet152': ImageNetPreTrainedConfig(
              depths=[3, 8, 36, 3]			,			hidden_sizes=[2_56, 5_12, 10_24, 20_48]			,			layer_type='bottleneck'							),
      }

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


if __name__ == "__main__":
   lowerCamelCase__ :   List[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 resnet* architecture,'
           ' currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted.'
       ),
   )
   parser.add_argument(
       '--pytorch_dump_folder_path',
       default=None,
       type=Path,
       required=True,
       help='Path to the output PyTorch model directory.',
   )
   parser.add_argument(
       '--push_to_hub',
       default=True,
       type=bool,
       required=False,
       help='If True, push model and image processor to the hub.',
   )

   lowerCamelCase__ :   List[Any]					=							parser.parse_args()
   lowerCamelCase__ :   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)





# Copyright 2021 The HuggingFace Inc. 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 packaging import version

from .. import __version__
from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
from .doc import (
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    copy_func,
    replace_return_docstrings,
)
from .generic import (
    ContextManagers,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    TensorType,
    add_model_info_to_auto_map,
    cached_property,
    can_return_loss,
    expand_dims,
    find_labels,
    flatten_dict,
    infer_framework,
    is_jax_tensor,
    is_numpy_array,
    is_tensor,
    is_tf_symbolic_tensor,
    is_tf_tensor,
    is_torch_device,
    is_torch_dtype,
    is_torch_tensor,
    reshape,
    squeeze,
    strtobool,
    tensor_size,
    to_numpy,
    to_py_obj,
    transpose,
    working_or_temp_dir,
)
from .hub import (
    CLOUDFRONT_DISTRIB_PREFIX,
    DISABLE_TELEMETRY,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    EntryNotFoundError,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    cached_file,
    default_cache_path,
    define_sagemaker_information,
    download_url,
    extract_commit_hash,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    has_file,
    http_user_agent,
    is_offline_mode,
    is_remote_url,
    move_cache,
    send_example_telemetry,
    try_to_load_from_cache,
)
from .import_utils import (
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    TORCH_FX_REQUIRED_VERSION,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    _LazyModule,
    ccl_version,
    direct_transformers_import,
    get_torch_version,
    is_accelerate_available,
    is_apex_available,
    is_bitsandbytes_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_cython_available,
    is_datasets_available,
    is_decord_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_jieba_available,
    is_jumanpp_available,
    is_kenlm_available,
    is_keras_nlp_available,
    is_librosa_available,
    is_natten_available,
    is_ninja_available,
    is_onnx_available,
    is_openai_available,
    is_optimum_available,
    is_pandas_available,
    is_peft_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytest_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sacremoses_available,
    is_safetensors_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_sudachi_available,
    is_tensorflow_probability_available,
    is_tensorflow_text_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_bfaa_cpu_available,
    is_torch_bfaa_gpu_available,
    is_torch_compile_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_neuroncore_available,
    is_torch_tensorrt_fx_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_torchdistx_available,
    is_torchdynamo_available,
    is_torchvision_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    requires_backends,
    torch_only_method,
)


lowerCamelCase__ :   str					=							'pytorch_model.bin'
lowerCamelCase__ :   int					=							'pytorch_model.bin.index.json'
lowerCamelCase__ :   List[Any]					=							'adapter_config.json'
lowerCamelCase__ :   Optional[int]					=							'adapter_model.bin'
lowerCamelCase__ :   Dict					=							'adapter_model.safetensors'
lowerCamelCase__ :   List[str]					=							'tf_model.h5'
lowerCamelCase__ :   Tuple					=							'tf_model.h5.index.json'
lowerCamelCase__ :   Optional[int]					=							'model.ckpt'
lowerCamelCase__ :   Any					=							'flax_model.msgpack'
lowerCamelCase__ :   List[Any]					=							'flax_model.msgpack.index.json'
lowerCamelCase__ :   int					=							'model.safetensors'
lowerCamelCase__ :   str					=							'model.safetensors.index.json'
lowerCamelCase__ :   Union[str, Any]					=							'config.json'
lowerCamelCase__ :   List[Any]					=							'preprocessor_config.json'
lowerCamelCase__ :   Optional[int]					=							FEATURE_EXTRACTOR_NAME
lowerCamelCase__ :   Any					=							'generation_config.json'
lowerCamelCase__ :   Any					=							'modelcard.json'

lowerCamelCase__ :   List[str]					=							'▁'
lowerCamelCase__ :   Union[str, Any]					=							SENTENCEPIECE_UNDERLINE  # Kept for backward compatibility

lowerCamelCase__ :   Optional[int]					=							[
    [[0, 1, 0, 1], [1, 0, 0, 1]]
] * 2  # Needs to have 0s and 1s only since XLM uses it for langs too.
lowerCamelCase__ :   str					=							[[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
lowerCamelCase__ :   Dict					=							[[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	Optional[int]							)					-> int:
      if version.parse(__UpperCAmelCase							) < version.parse(__UpperCAmelCase							):
            if "dev" in min_version:
                  SCREAMING_SNAKE_CASE_							=			(
                      'This example requires a source install from HuggingFace Transformers (see '
                      '`https://huggingface.co/docs/transformers/installation#install-from-source`),'
                  )
            else:
                  SCREAMING_SNAKE_CASE_							=			f"This example requires a minimum version of {min_version},"
            error_message += f" but the version found is {__version__}.\n"
            raise ImportError(
                error_message
                + 'Check out https://github.com/huggingface/transformers/tree/main/examples#important-note for the examples corresponding to other '
                'versions of HuggingFace Transformers.'							)