


import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType





class   SCREAMING_SNAKE_CASE_			(a__						):




       '''simple docstring'''



       _a   							=							["image_processor", "tokenizer"]
       _a   							=							"LayoutLMv2ImageProcessor"
       _a   							=							("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")



       def __init__(						self				:				Any		,				__a				:				str=None		,				__a				:				List[Any]=None		,				**__a				:				Optional[int]							)					->int:
          if "feature_extractor" in kwargs:
             warnings.warn(
                 """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                 """ instead."""		,				__a		,				)
             lowerCamelCase_						:   str		= kwargs.pop("""feature_extractor"""							)

          lowerCamelCase_						:   List[Any]		= image_processor if image_processor is not None else feature_extractor
          if image_processor is None:
             raise ValueError("""You need to specify an `image_processor`."""							)
          if tokenizer is None:
             raise ValueError("""You need to specify a `tokenizer`."""							)

          super().__init__(__a		,				__a							)



       def __call__(						self				:				List[str]		,				__a				:				List[Any]		,				__a				:				Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None		,				__a				:				Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None		,				__a				:				Union[List[List[int]], List[List[List[int]]]] = None		,				__a				:				Optional[Union[List[int], List[List[int]]]] = None		,				__a				:				bool = True		,				__a				:				Union[bool, str, PaddingStrategy] = False		,				__a				:				Union[bool, str, TruncationStrategy] = None		,				__a				:				Optional[int] = None		,				__a				:				int = 0		,				__a				:				Optional[int] = None		,				__a				:				Optional[bool] = None		,				__a				:				Optional[bool] = None		,				__a				:				bool = False		,				__a				:				bool = False		,				__a				:				bool = False		,				__a				:				bool = False		,				__a				:				bool = True		,				__a				:				Optional[Union[str, TensorType]] = None		,				**__a				:				Any		,				)					->BatchEncoding:
          # verify input
          if self.image_processor.apply_ocr and (boxes is not None):
             raise ValueError(
                 """You cannot provide bounding boxes """
                 """if you initialized the image processor with apply_ocr set to True."""							)

          if self.image_processor.apply_ocr and (word_labels is not None):
             raise ValueError(
                 """You cannot provide word labels if you initialized the image processor with apply_ocr set to True."""							)

          if return_overflowing_tokens is True and return_offsets_mapping is False:
             raise ValueError("""You cannot return overflowing tokens without returning the offsets mapping."""							)

          # first, apply the image processor
          lowerCamelCase_						:   Tuple		= self.image_processor(images=__a		,				return_tensors=__a							)

          # second, apply the tokenizer
          if text is not None and self.image_processor.apply_ocr and text_pair is None:
             if isinstance(__a		,				__a							):
                lowerCamelCase_						:   Optional[Any]		= [text]  # add batch dimension (as the image processor always adds a batch dimension)
             lowerCamelCase_						:   Dict		= features["""words"""]

          lowerCamelCase_						:   int		= self.tokenizer(
              text=text if text is not None else features["""words"""]		,				text_pair=text_pair if text_pair is not None else None		,				boxes=boxes if boxes is not None else features["""boxes"""]		,				word_labels=__a		,				add_special_tokens=__a		,				padding=__a		,				truncation=__a		,				max_length=__a		,				stride=__a		,				pad_to_multiple_of=__a		,				return_token_type_ids=__a		,				return_attention_mask=__a		,				return_overflowing_tokens=__a		,				return_special_tokens_mask=__a		,				return_offsets_mapping=__a		,				return_length=__a		,				verbose=__a		,				return_tensors=__a		,				**__a		,				)

          # add pixel values
          lowerCamelCase_						:   Any		= features.pop("""pixel_values"""							)
          if return_overflowing_tokens is True:
             lowerCamelCase_						:   Dict		= self.get_overflowing_images(__a		,				encoded_inputs["""overflow_to_sample_mapping"""]							)
          lowerCamelCase_						:   Optional[int]		= images

          return encoded_inputs



       def 				_lowerCAmelCase							(						self				:				Optional[Any]		,				__a				:				str		,				__a				:				Union[str, Any]							)					->List[str]:
          # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
          lowerCamelCase_						:   Dict		= []
          for sample_idx in overflow_to_sample_mapping:
             images_with_overflow.append(images[sample_idx]							)

          if len(__a							) != len(__a							):
             raise ValueError(
                 """Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"""
                 F''' {len(__a							)} and {len(__a							)}'''							)

          return images_with_overflow



       def 				_lowerCAmelCase							(						self				:				Dict		,				*__a				:				Dict		,				**__a				:				int							)					->int:
          return self.tokenizer.batch_decode(*__a		,				**__a							)



       def 				_lowerCAmelCase							(						self				:				List[Any]		,				*__a				:				List[str]		,				**__a				:				int							)					->List[Any]:
          return self.tokenizer.decode(*__a		,				**__a							)



       @property
       def 				_lowerCAmelCase							(						self				:				int							)					->List[str]:
          return ["input_ids", "bbox", "attention_mask", "image"]



       @property
       def 				_lowerCAmelCase							(						self				:				int							)					->str:
          warnings.warn(
              """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""		,				__a		,				)
          return self.image_processor_class




       @property
       def 				_lowerCAmelCase							(						self				:				int							)					->List[Any]:
          warnings.warn(
              """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""		,				__a		,				)
          return self.image_processor









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()
snake_case__				:	Dict					       =					logging.get_logger(__name__)


def    __lowerCamelCase   (      A__						: Optional[Any]					)     ->    List[str]:
   lowerCamelCase_						:   int		= """huggingface/label-files"""
   lowerCamelCase_						:   Dict		= """imagenet-1k-id2label.json"""
   lowerCamelCase_						:   Optional[Any]		= json.load(open(hf_hub_download(A__						,						A__						,						repo_type="""dataset"""					)						,						"""r"""					)					)
   lowerCamelCase_						:   str		= {int(A__					): v for k, v in idalabel.items()}
   lowerCamelCase_						:   List[Any]		= {v: k for k, v in idalabel.items()}

   lowerCamelCase_						:   str		= """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"
   lowerCamelCase_						:   Optional[Any]		= BitConfig(
       conv_layer=A__						,						num_labels=1000						,						idalabel=A__						,						labelaid=A__						,						)

   return config


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

   return name


def    __lowerCamelCase   (      )     ->    List[Any]:
   lowerCamelCase_						:   List[Any]		= """http://images.cocodataset.org/val2017/000000039769.jpg"""
   lowerCamelCase_						:   Optional[Any]		= Image.open(requests.get(A__						,						stream=A__					).raw					)
   return im


@torch.no_grad()
def    __lowerCamelCase   (      A__						: List[Any]						,						A__						: List[str]						,						A__						: Tuple=False					)     ->    List[str]:
   lowerCamelCase_						:   Optional[Any]		= get_config(A__					)

   # load original model from timm
   lowerCamelCase_						:   Optional[Any]		= create_model(A__						,						pretrained=A__					)
   timm_model.eval()

   # load state_dict of original model
   lowerCamelCase_						:   Optional[int]		= timm_model.state_dict()
   for key in state_dict.copy().keys():
      lowerCamelCase_						:   int		= state_dict.pop(A__					)
      lowerCamelCase_						:   Union[str, Any]		= val.squeeze() if """head""" in key else val

   # load HuggingFace model
   lowerCamelCase_						:   Tuple		= BitForImageClassification(A__					)
   model.eval()
   model.load_state_dict(A__					)

   # create image processor
   lowerCamelCase_						:   Optional[Any]		= create_transform(**resolve_data_config({}						,						model=A__					)					)
   lowerCamelCase_						:   List[Any]		= transform.transforms

   lowerCamelCase_						:   List[Any]		= {
       """bilinear""": PILImageResampling.BILINEAR,
       """bicubic""": PILImageResampling.BICUBIC,
       """nearest""": PILImageResampling.NEAREST,
   }

   lowerCamelCase_						:   List[str]		= BitImageProcessor(
       do_resize=A__						,						size={"""shortest_edge""": timm_transforms[0].size}						,						resample=pillow_resamplings[timm_transforms[0].interpolation.value]						,						do_center_crop=A__						,						crop_size={"""height""": timm_transforms[1].size[0], """width""": timm_transforms[1].size[1]}						,						do_normalize=A__						,						image_mean=timm_transforms[-1].mean.tolist()						,						image_std=timm_transforms[-1].std.tolist()						,						)

   lowerCamelCase_						:   int		= prepare_img()
   lowerCamelCase_						:   int		= transform(A__					).unsqueeze(0					)
   lowerCamelCase_						:   List[str]		= processor(A__						,						return_tensors="""pt"""					).pixel_values

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

   # verify logits
   with torch.no_grad():
      lowerCamelCase_						:   str		= model(A__					)
      lowerCamelCase_						:   int		= outputs.logits

   print("""Logits:"""						,						logits[0, :3]					)
   print("""Predicted class:"""						,						model.config.idalabel[logits.argmax(-1					).item()]					)
   lowerCamelCase_						:   List[Any]		= timm_model(A__					)
   assert timm_logits.shape == outputs.logits.shape
   assert torch.allclose(A__						,						outputs.logits						,						atol=1e-3					)
   print("""Looks ok!"""					)

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

   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__":
      snake_case__				:	Dict					       =					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.',
      )

      snake_case__				:	int					       =					parser.parse_args()
      convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





