




'''simple docstring'''






import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
A_			      =		logging.get_logger(__name__)






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   Union[str, Any]:
      snake_case__       :   Tuple	      =    'huggingface/label-files'
      snake_case__       :   Union[str, Any]	      =    'imagenet-1k-id2label.json'
      snake_case__       :   str	      =    json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      repo_type='dataset'		)				,      'r'		)		)
      snake_case__       :   Tuple	      =    {int(__SCREAMING_SNAKE_CASE		): v for k, v in idalabel.items()}
      snake_case__       :   Optional[Any]	      =    {v: k for k, v in idalabel.items()}

      snake_case__       :   Tuple	      =    'std_conv' if 'bit' in model_name else False

      # note that when using BiT as backbone for ViT-hybrid checkpoints,
      # one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
      # config.conv_layer = "std_conv_same"
      snake_case__       :   str	      =    BitConfig(
          conv_layer=__SCREAMING_SNAKE_CASE				,      num_labels=1000				,      idalabel=__SCREAMING_SNAKE_CASE				,      labelaid=__SCREAMING_SNAKE_CASE				,      )

      return config






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   Optional[Any]:
      if "stem.conv" in name:
            snake_case__       :   str	      =    name.replace('stem.conv'				,      'bit.embedder.convolution'		)
      if "blocks" in name:
            snake_case__       :   Any	      =    name.replace('blocks'				,      'layers'		)
      if "head.fc" in name:
            snake_case__       :   Any	      =    name.replace('head.fc'				,      'classifier.1'		)
      if name.startswith('norm'		):
            snake_case__       :   Optional[int]	      =    'bit.' + name
      if "bit" not in name and "classifier" not in name:
            snake_case__       :   str	      =    'bit.encoder.' + name

      return name






def 		UpperCamelCase__      (		)    ->   Any:
      snake_case__       :   Any	      =    'http://images.cocodataset.org/val2017/000000039769.jpg'
      snake_case__       :   Dict	      =    Image.open(requests.get(__SCREAMING_SNAKE_CASE				,      stream=__SCREAMING_SNAKE_CASE		).raw		)
      return im






@torch.no_grad()
def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE=False		)    ->   Optional[Any]:
      snake_case__       :   Tuple	      =    get_config(__SCREAMING_SNAKE_CASE		)

      # load original model from timm
      snake_case__       :   Tuple	      =    create_model(__SCREAMING_SNAKE_CASE				,      pretrained=__SCREAMING_SNAKE_CASE		)
      timm_model.eval()

      # load state_dict of original model
      snake_case__       :   Tuple	      =    timm_model.state_dict()
      for key in state_dict.copy().keys():
            snake_case__       :   int	      =    state_dict.pop(__SCREAMING_SNAKE_CASE		)
            snake_case__       :   List[Any]	      =    val.squeeze() if 'head' in key else val

      # load HuggingFace model
      snake_case__       :   List[Any]	      =    BitForImageClassification(__SCREAMING_SNAKE_CASE		)
      model.eval()
      model.load_state_dict(__SCREAMING_SNAKE_CASE		)

      # create image processor
      snake_case__       :   Tuple	      =    create_transform(**resolve_data_config({}				,      model=__SCREAMING_SNAKE_CASE		)		)
      snake_case__       :   Optional[Any]	      =    transform.transforms

      snake_case__       :   Any	      =    {
          'bilinear': PILImageResampling.BILINEAR,
          'bicubic': PILImageResampling.BICUBIC,
          'nearest': PILImageResampling.NEAREST,
      }

      snake_case__       :   Optional[int]	      =    BitImageProcessor(
          do_resize=__SCREAMING_SNAKE_CASE				,      size={'shortest_edge': timm_transforms[0].size}				,      resample=pillow_resamplings[timm_transforms[0].interpolation.value]				,      do_center_crop=__SCREAMING_SNAKE_CASE				,      crop_size={'height': timm_transforms[1].size[0], 'width': timm_transforms[1].size[1]}				,      do_normalize=__SCREAMING_SNAKE_CASE				,      image_mean=timm_transforms[-1].mean.tolist()				,      image_std=timm_transforms[-1].std.tolist()				,      )

      snake_case__       :   Dict	      =    prepare_img()
      snake_case__       :   Union[str, Any]	      =    transform(__SCREAMING_SNAKE_CASE		).unsqueeze(0		)
      snake_case__       :   Dict	      =    processor(__SCREAMING_SNAKE_CASE				,      return_tensors='pt'		).pixel_values

      # verify pixel values
      assert torch.allclose(__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)

      # verify logits
      with torch.no_grad():
            snake_case__       :   List[str]	      =    model(__SCREAMING_SNAKE_CASE		)
            snake_case__       :   Dict	      =    outputs.logits

      print('Logits:'				,      logits[0, :3]		)
      print('Predicted class:'				,      model.config.idalabel[logits.argmax(-1		).item()]		)
      snake_case__       :   List[str]	      =    timm_model(__SCREAMING_SNAKE_CASE		)
      assert timm_logits.shape == outputs.logits.shape
      assert torch.allclose(__SCREAMING_SNAKE_CASE				,      outputs.logits				,      atol=1E-3		)
      print('Looks ok!'		)

      if pytorch_dump_folder_path is not None:
            Path(__SCREAMING_SNAKE_CASE		).mkdir(exist_ok=__SCREAMING_SNAKE_CASE		)
            print(f"Saving model {model_name} and processor to {pytorch_dump_folder_path}"		)
            model.save_pretrained(__SCREAMING_SNAKE_CASE		)
            processor.save_pretrained(__SCREAMING_SNAKE_CASE		)

      if push_to_hub:
            print(f"Pushing model {model_name} and processor to the hub"		)
            model.push_to_hub(f"ybelkada/{model_name}"		)
            processor.push_to_hub(f"ybelkada/{model_name}"		)


if __name__ == "__main__":
      A_			      =		argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--model_name",
          default="resnetv2_50x1_bitm",
          type=str,
          help="Name of the BiT timm model you'd like to convert.",
      )
      parser.add_argument(
          "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
      )
      parser.add_argument(
          "--push_to_hub",
          action="store_true",
          help="Whether to push the model to the hub.",
      )

      A_			      =		parser.parse_args()
      convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


A_			      =		{
    "configuration_roberta_prelayernorm": [
        "ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "RobertaPreLayerNormConfig",
        "RobertaPreLayerNormOnnxConfig",
    ],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A_			      =		[
          "ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
          "RobertaPreLayerNormForCausalLM",
          "RobertaPreLayerNormForMaskedLM",
          "RobertaPreLayerNormForMultipleChoice",
          "RobertaPreLayerNormForQuestionAnswering",
          "RobertaPreLayerNormForSequenceClassification",
          "RobertaPreLayerNormForTokenClassification",
          "RobertaPreLayerNormModel",
          "RobertaPreLayerNormPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A_			      =		[
          "TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFRobertaPreLayerNormForCausalLM",
          "TFRobertaPreLayerNormForMaskedLM",
          "TFRobertaPreLayerNormForMultipleChoice",
          "TFRobertaPreLayerNormForQuestionAnswering",
          "TFRobertaPreLayerNormForSequenceClassification",
          "TFRobertaPreLayerNormForTokenClassification",
          "TFRobertaPreLayerNormMainLayer",
          "TFRobertaPreLayerNormModel",
          "TFRobertaPreLayerNormPreTrainedModel",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A_			      =		[
          "FlaxRobertaPreLayerNormForCausalLM",
          "FlaxRobertaPreLayerNormForMaskedLM",
          "FlaxRobertaPreLayerNormForMultipleChoice",
          "FlaxRobertaPreLayerNormForQuestionAnswering",
          "FlaxRobertaPreLayerNormForSequenceClassification",
          "FlaxRobertaPreLayerNormForTokenClassification",
          "FlaxRobertaPreLayerNormModel",
          "FlaxRobertaPreLayerNormPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_roberta_prelayernorm import (
          ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
          RobertaPreLayerNormConfig,
          RobertaPreLayerNormOnnxConfig,
      )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roberta_prelayernorm import (
                ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
                RobertaPreLayerNormForCausalLM,
                RobertaPreLayerNormForMaskedLM,
                RobertaPreLayerNormForMultipleChoice,
                RobertaPreLayerNormForQuestionAnswering,
                RobertaPreLayerNormForSequenceClassification,
                RobertaPreLayerNormForTokenClassification,
                RobertaPreLayerNormModel,
                RobertaPreLayerNormPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roberta_prelayernorm import (
                TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRobertaPreLayerNormForCausalLM,
                TFRobertaPreLayerNormForMaskedLM,
                TFRobertaPreLayerNormForMultipleChoice,
                TFRobertaPreLayerNormForQuestionAnswering,
                TFRobertaPreLayerNormForSequenceClassification,
                TFRobertaPreLayerNormForTokenClassification,
                TFRobertaPreLayerNormMainLayer,
                TFRobertaPreLayerNormModel,
                TFRobertaPreLayerNormPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roberta_prelayernorm import (
                FlaxRobertaPreLayerNormForCausalLM,
                FlaxRobertaPreLayerNormForMaskedLM,
                FlaxRobertaPreLayerNormForMultipleChoice,
                FlaxRobertaPreLayerNormForQuestionAnswering,
                FlaxRobertaPreLayerNormForSequenceClassification,
                FlaxRobertaPreLayerNormForTokenClassification,
                FlaxRobertaPreLayerNormModel,
                FlaxRobertaPreLayerNormPreTrainedModel,
            )

else:
      import sys

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