






"""simple docstring"""



import argparse
import json
from pathlib import Path

import requests
import torch
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    BeitConfig,
    BeitForImageClassification,
    BeitForMaskedImageModeling,
    BeitForSemanticSegmentation,
    BeitImageProcessor,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCAmelCase     :     Any            =	logging.get_logger(__name__)


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      Optional[int]       , snake_case    :      Any=False       , snake_case    :      Optional[Any]=False							)->						Union[str, Any]:






     '''simple docstring'''



     UpperCAmelCase__		:  int				=				"backbone." if is_semantic else ""

     UpperCAmelCase__		:  Any				=				[]
     for i in range(config.num_hidden_layers							):
          # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
          rename_keys.append((f'{prefix}blocks.{i}.norm1.weight', f'beit.encoder.layer.{i}.layernorm_before.weight')							)
          rename_keys.append((f'{prefix}blocks.{i}.norm1.bias', f'beit.encoder.layer.{i}.layernorm_before.bias')							)
          rename_keys.append(
              (f'{prefix}blocks.{i}.attn.proj.weight', f'beit.encoder.layer.{i}.attention.output.dense.weight')							)
          rename_keys.append(
              (f'{prefix}blocks.{i}.attn.proj.bias', f'beit.encoder.layer.{i}.attention.output.dense.bias')							)
          rename_keys.append((f'{prefix}blocks.{i}.norm2.weight', f'beit.encoder.layer.{i}.layernorm_after.weight')							)
          rename_keys.append((f'{prefix}blocks.{i}.norm2.bias', f'beit.encoder.layer.{i}.layernorm_after.bias')							)
          rename_keys.append((f'{prefix}blocks.{i}.mlp.fc1.weight', f'beit.encoder.layer.{i}.intermediate.dense.weight')							)
          rename_keys.append((f'{prefix}blocks.{i}.mlp.fc1.bias', f'beit.encoder.layer.{i}.intermediate.dense.bias')							)
          rename_keys.append((f'{prefix}blocks.{i}.mlp.fc2.weight', f'beit.encoder.layer.{i}.output.dense.weight')							)
          rename_keys.append((f'{prefix}blocks.{i}.mlp.fc2.bias', f'beit.encoder.layer.{i}.output.dense.bias')							)

     # projection layer + position embeddings
     rename_keys.extend(
         [
             (f'{prefix}cls_token', "beit.embeddings.cls_token"),
             (f'{prefix}patch_embed.proj.weight', "beit.embeddings.patch_embeddings.projection.weight"),
             (f'{prefix}patch_embed.proj.bias', "beit.embeddings.patch_embeddings.projection.bias"),
         ]							)

     if has_lm_head:
          # mask token + shared relative position bias + layernorm
          rename_keys.extend(
              [
                  ("mask_token", "beit.embeddings.mask_token"),
                  (
                      "rel_pos_bias.relative_position_bias_table",
                      "beit.encoder.relative_position_bias.relative_position_bias_table",
                  ),
                  (
                      "rel_pos_bias.relative_position_index",
                      "beit.encoder.relative_position_bias.relative_position_index",
                  ),
                  ("norm.weight", "layernorm.weight"),
                  ("norm.bias", "layernorm.bias"),
              ]							)
     elif is_semantic:
          # semantic segmentation classification heads
          rename_keys.extend(
              [
                  ("decode_head.conv_seg.weight", "decode_head.classifier.weight"),
                  ("decode_head.conv_seg.bias", "decode_head.classifier.bias"),
                  ("auxiliary_head.conv_seg.weight", "auxiliary_head.classifier.weight"),
                  ("auxiliary_head.conv_seg.bias", "auxiliary_head.classifier.bias"),
              ]							)
     else:
          # layernorm + classification head
          rename_keys.extend(
              [
                  ("fc_norm.weight", "beit.pooler.layernorm.weight"),
                  ("fc_norm.bias", "beit.pooler.layernorm.bias"),
                  ("head.weight", "classifier.weight"),
                  ("head.bias", "classifier.bias"),
              ]							)

     return rename_keys


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      Tuple       , snake_case    :      Dict       , snake_case    :      Dict=False       , snake_case    :      Union[str, Any]=False							)->						Any:






     '''simple docstring'''



     for i in range(config.num_hidden_layers							):
          UpperCAmelCase__		:  int				=				"backbone." if is_semantic else ""
          # queries, keys and values
          UpperCAmelCase__		:  Tuple				=				state_dict.pop(f'{prefix}blocks.{i}.attn.qkv.weight'							)
          UpperCAmelCase__		:  Any				=				state_dict.pop(f'{prefix}blocks.{i}.attn.q_bias'							)
          UpperCAmelCase__		:  Optional[Any]				=				state_dict.pop(f'{prefix}blocks.{i}.attn.v_bias'							)

          UpperCAmelCase__		:  Tuple				=				in_proj_weight[
              : config.hidden_size, :
          ]
          UpperCAmelCase__		:  List[Any]				=				q_bias
          UpperCAmelCase__		:  str				=				in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          UpperCAmelCase__		:  Dict				=				in_proj_weight[
              -config.hidden_size :, :
          ]
          UpperCAmelCase__		:  Dict				=				v_bias

          # gamma_1 and gamma_2
          # we call them lambda because otherwise they are renamed when using .from_pretrained
          UpperCAmelCase__		:  Union[str, Any]				=				state_dict.pop(f'{prefix}blocks.{i}.gamma_1'							)
          UpperCAmelCase__		:  int				=				state_dict.pop(f'{prefix}blocks.{i}.gamma_2'							)

          UpperCAmelCase__		:  int				=				gamma_a
          UpperCAmelCase__		:  Optional[int]				=				gamma_a

          # relative_position bias table + index
          if not has_lm_head:
               # each layer has its own relative position bias
               UpperCAmelCase__		:  Any				=				state_dict.pop(f'{prefix}blocks.{i}.attn.relative_position_bias_table'							)
               UpperCAmelCase__		:  str				=				state_dict.pop(f'{prefix}blocks.{i}.attn.relative_position_index'							)

               UpperCAmelCase__		:  List[Any]				=				table
               UpperCAmelCase__		:  Optional[Any]				=				index


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      str       , snake_case    :      Union[str, Any]       , snake_case    :      Any							)->						str:






     '''simple docstring'''



     UpperCAmelCase__		:  int				=				dct.pop(snake_case							)
     UpperCAmelCase__		:  List[Any]				=				val


def      SCREAMING_SNAKE_CASE__    (   )->						int:






     '''simple docstring'''



     UpperCAmelCase__		:  List[Any]				=				"http://images.cocodataset.org/val2017/000000039769.jpg"
     UpperCAmelCase__		:  List[Any]				=				Image.open(requests.get(snake_case       , stream=snake_case							).raw							)
     return im


@torch.no_grad()
def      SCREAMING_SNAKE_CASE__    (   snake_case    :      List[Any]       , snake_case    :      int							)->						Any:






     '''simple docstring'''



     UpperCAmelCase__		:  List[str]				=				BeitConfig()
     UpperCAmelCase__		:  Optional[int]				=				False
     UpperCAmelCase__		:  List[Any]				=				False
     UpperCAmelCase__		:  str				=				"huggingface/label-files"
     # set config parameters based on URL
     if checkpoint_url[-9:-4] == "pt22k":
          # masked image modeling
          UpperCAmelCase__		:  Dict				=				True
          UpperCAmelCase__		:  Optional[Any]				=				True
          UpperCAmelCase__		:  Optional[int]				=				True
     elif checkpoint_url[-9:-4] == "ft22k":
          # intermediate fine-tuning on ImageNet-22k
          UpperCAmelCase__		:  Optional[Any]				=				True
          UpperCAmelCase__		:  Dict				=				2_1841
          UpperCAmelCase__		:  Any				=				"imagenet-22k-id2label.json"
          UpperCAmelCase__		:  Tuple				=				json.load(open(hf_hub_download(snake_case       , snake_case       , repo_type="dataset"							)       , "r"							)							)
          UpperCAmelCase__		:  Dict				=				{int(snake_case							): v for k, v in idalabel.items()}
          # this dataset contains 21843 labels but the model only has 21841
          # we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
          del idalabel[9205]
          del idalabel[1_5027]
          UpperCAmelCase__		:  Union[str, Any]				=				idalabel
          UpperCAmelCase__		:  Optional[Any]				=				{v: k for k, v in idalabel.items()}
     elif checkpoint_url[-8:-4] == "to1k":
          # fine-tuning on ImageNet-1k
          UpperCAmelCase__		:  List[str]				=				True
          UpperCAmelCase__		:  Optional[int]				=				1000
          UpperCAmelCase__		:  List[str]				=				"imagenet-1k-id2label.json"
          UpperCAmelCase__		:  Any				=				json.load(open(hf_hub_download(snake_case       , snake_case       , repo_type="dataset"							)       , "r"							)							)
          UpperCAmelCase__		:  Optional[Any]				=				{int(snake_case							): v for k, v in idalabel.items()}
          UpperCAmelCase__		:  Any				=				idalabel
          UpperCAmelCase__		:  Optional[int]				=				{v: k for k, v in idalabel.items()}
          if "384" in checkpoint_url:
               UpperCAmelCase__		:  Dict				=				384
          if "512" in checkpoint_url:
               UpperCAmelCase__		:  List[Any]				=				512
     elif "ade20k" in checkpoint_url:
          # fine-tuning
          UpperCAmelCase__		:  str				=				True
          UpperCAmelCase__		:  Optional[Any]				=				150
          UpperCAmelCase__		:  str				=				"ade20k-id2label.json"
          UpperCAmelCase__		:  Union[str, Any]				=				json.load(open(hf_hub_download(snake_case       , snake_case       , repo_type="dataset"							)       , "r"							)							)
          UpperCAmelCase__		:  Any				=				{int(snake_case							): v for k, v in idalabel.items()}
          UpperCAmelCase__		:  Optional[Any]				=				idalabel
          UpperCAmelCase__		:  List[Any]				=				{v: k for k, v in idalabel.items()}
          UpperCAmelCase__		:  List[str]				=				640
          UpperCAmelCase__		:  int				=				True
     else:
          raise ValueError("Checkpoint not supported, URL should either end with 'pt22k', 'ft22k', 'to1k' or 'ade20k'"							)

     # size of the architecture
     if "base" in checkpoint_url:
          pass
     elif "large" in checkpoint_url:
          UpperCAmelCase__		:  List[Any]				=				1024
          UpperCAmelCase__		:  Tuple				=				4096
          UpperCAmelCase__		:  List[Any]				=				24
          UpperCAmelCase__		:  Union[str, Any]				=				16
          if "ade20k" in checkpoint_url:
               UpperCAmelCase__		:  Tuple				=				640
               UpperCAmelCase__		:  List[str]				=				[7, 11, 15, 23]
     else:
          raise ValueError("Should either find 'base' or 'large' in checkpoint URL"							)

     # load state_dict of original model, remove and rename some keys
     UpperCAmelCase__		:  Optional[Any]				=				torch.hub.load_state_dict_from_url(snake_case       , map_location="cpu"       , check_hash=snake_case							)
     UpperCAmelCase__		:  Dict				=				state_dict["model"] if "ade20k" not in checkpoint_url else state_dict["state_dict"]

     UpperCAmelCase__		:  str				=				create_rename_keys(snake_case       , has_lm_head=snake_case       , is_semantic=snake_case							)
     for src, dest in rename_keys:
          rename_key(snake_case       , snake_case       , snake_case							)
     read_in_q_k_v(snake_case       , snake_case       , has_lm_head=snake_case       , is_semantic=snake_case							)
     if is_semantic:
          # add prefix to decoder keys
          for key, val in state_dict.copy().items():
               UpperCAmelCase__		:  Optional[Any]				=				state_dict.pop(snake_case							)
               if key.startswith("backbone.fpn"							):
                    UpperCAmelCase__		:  List[Any]				=				key.replace("backbone.fpn"       , "fpn"							)
               UpperCAmelCase__		:  List[str]				=				val

    # load HuggingFace model
     if checkpoint_url[-9:-4] == "pt22k":
          UpperCAmelCase__		:  List[str]				=				BeitForMaskedImageModeling(snake_case							)
     elif "ade20k" in checkpoint_url:
          UpperCAmelCase__		:  List[str]				=				BeitForSemanticSegmentation(snake_case							)
     else:
          UpperCAmelCase__		:  str				=				BeitForImageClassification(snake_case							)
     model.eval()
     model.load_state_dict(snake_case							)

     # Check outputs on an image
     if is_semantic:
          UpperCAmelCase__		:  Any				=				BeitImageProcessor(size=config.image_size       , do_center_crop=snake_case							)
          UpperCAmelCase__		:  int				=				load_dataset("hf-internal-testing/fixtures_ade20k"       , split="test"							)
          UpperCAmelCase__		:  int				=				Image.open(ds[0]["file"]							)
     else:
          UpperCAmelCase__		:  Union[str, Any]				=				BeitImageProcessor(
              size=config.image_size       , resample=PILImageResampling.BILINEAR       , do_center_crop=snake_case							)
          UpperCAmelCase__		:  List[Any]				=				prepare_img()

     UpperCAmelCase__		:  Any				=				image_processor(images=snake_case       , return_tensors="pt"							)
     UpperCAmelCase__		:  List[Any]				=				encoding["pixel_values"]

     UpperCAmelCase__		:  Optional[int]				=				model(snake_case							)
     UpperCAmelCase__		:  Any				=				outputs.logits

     # verify logits
     UpperCAmelCase__		:  Optional[Any]				=				torch.Size([1, 1000]							)
     if checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k"							):
          UpperCAmelCase__		:  Dict				=				torch.Size([1, 196, 8192]							)
     elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k"							):
          UpperCAmelCase__		:  int				=				torch.Size([1, 196, 8192]							)
     elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft22k"							):
          UpperCAmelCase__		:  str				=				torch.Size([1, 2_1841]							)
          UpperCAmelCase__		:  str				=				torch.tensor([2.2288, 2.4671, 0.7395]							)
          UpperCAmelCase__		:  List[Any]				=				2397
     elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft22k"							):
          UpperCAmelCase__		:  int				=				torch.Size([1, 2_1841]							)
          UpperCAmelCase__		:  List[Any]				=				torch.tensor([1.6881, -0.2787, 0.5901]							)
          UpperCAmelCase__		:  Dict				=				2396
     elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft1k"							):
          UpperCAmelCase__		:  Tuple				=				torch.tensor([0.1241, 0.0798, -0.6569]							)
          UpperCAmelCase__		:  List[Any]				=				285
     elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft22kto1k"							):
          UpperCAmelCase__		:  str				=				torch.tensor([-1.2385, -1.0987, -1.0108]							)
          UpperCAmelCase__		:  List[str]				=				281
     elif checkpoint_url[:-4].endswith("beit_base_patch16_384_pt22k_ft22kto1k"							):
          UpperCAmelCase__		:  Any				=				torch.tensor([-1.5303, -0.9484, -0.3147]							)
          UpperCAmelCase__		:  Dict				=				761
     elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft1k"							):
          UpperCAmelCase__		:  Optional[Any]				=				torch.tensor([0.4610, -0.0928, 0.2086]							)
          UpperCAmelCase__		:  Union[str, Any]				=				761
     elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft22kto1k"							):
          UpperCAmelCase__		:  List[Any]				=				torch.tensor([-0.4804, 0.6257, -0.1837]							)
          UpperCAmelCase__		:  List[Any]				=				761
     elif checkpoint_url[:-4].endswith("beit_large_patch16_384_pt22k_ft22kto1k"							):
          UpperCAmelCase__		:  Dict				=				torch.tensor([[-0.5122, 0.5117, -0.2113]]							)
          UpperCAmelCase__		:  int				=				761
     elif checkpoint_url[:-4].endswith("beit_large_patch16_512_pt22k_ft22kto1k"							):
          UpperCAmelCase__		:  Any				=				torch.tensor([-0.3062, 0.7261, 0.4852]							)
          UpperCAmelCase__		:  Optional[Any]				=				761
     elif checkpoint_url[:-4].endswith("beit_base_patch16_640_pt22k_ft22ktoade20k"							):
          UpperCAmelCase__		:  List[str]				=				(1, 150, 160, 160)
          UpperCAmelCase__		:  List[str]				=				torch.tensor(
              [
                  [[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
                  [[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
                  [[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
              ]							)
     elif checkpoint_url[:-4].endswith("beit_large_patch16_640_pt22k_ft22ktoade20k"							):
          UpperCAmelCase__		:  str				=				(1, 150, 160, 160)
          UpperCAmelCase__		:  Any				=				torch.tensor(
              [
                  [[-4.3305, -2.3049, -3.0161], [-2.9591, -1.5305, -2.2251], [-3.4198, -1.8004, -2.9062]],
                  [[-5.8922, -3.7435, -4.3978], [-4.2063, -2.7872, -3.4755], [-4.2791, -3.1874, -4.1681]],
                  [[0.9895, 4.3467, 4.7663], [4.2476, 5.6830, 6.1518], [4.5550, 6.2495, 6.5154]],
              ]							)
     else:
          raise ValueError("Can't verify logits as model is not supported"							)

     assert logits.shape == expected_shape, "Shape of logits not as expected"
     if not has_lm_head:
          if is_semantic:
               assert torch.allclose(
                   logits[0, :3, :3, :3]       , snake_case       , atol=1E-3							), "First elements of logits not as expected"
          else:
               print("Predicted class idx:"       , logits.argmax(-1							).item()							)
               assert torch.allclose(
                   logits[0, :3]       , snake_case       , atol=1E-3							), "First elements of logits not as expected"
               assert logits.argmax(-1							).item() == expected_class_idx, "Predicted class index not as expected"

     Path(snake_case							).mkdir(exist_ok=snake_case							)
     print(f'Saving model to {pytorch_dump_folder_path}'							)
     model.save_pretrained(snake_case							)
     print(f'Saving image processor to {pytorch_dump_folder_path}'							)
     image_processor.save_pretrained(snake_case							)


if __name__ == "__main__":
 _lowerCAmelCase     :     Tuple            =	argparse.ArgumentParser()

 parser.add_argument(
     """--checkpoint_url""",
     default="""https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22kto1k.pth""",
     type=str,
     help="""URL to the original PyTorch checkpoint (.pth file).""",
 )
 parser.add_argument(
     """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
 )
 _lowerCAmelCase     :     Optional[Any]            =	parser.parse_args()
 convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)









import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class      UpperCamelCase_				(   UpperCamelCase__			):
							lowerCamelCase_							=    ["image_processor", "tokenizer"]
							lowerCamelCase_							=    "AutoImageProcessor"
							lowerCamelCase_							=    "AutoTokenizer"


							def __init__(							self			:Any							, __A			:List[str]=None							, __A			:Dict=None							, **__A			:List[str]						)      ->      List[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					None
									if "feature_extractor" in kwargs:
											warnings.warn(
											    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
											    """ instead."""							, __A							, )
											SCREAMING_SNAKE_CASE__								=					kwargs.pop("""feature_extractor"""						)

									SCREAMING_SNAKE_CASE__								=					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						)
									SCREAMING_SNAKE_CASE__								=					self.image_processor
									SCREAMING_SNAKE_CASE__								=					False


							def __call__(							self			:List[str]							, *__A			:int							, **__A			:Optional[int]						)      ->      Optional[Any]:



									"""simple docstring"""



									if self._in_target_context_manager:
											return self.current_processor(*__A							, **__A						)

									SCREAMING_SNAKE_CASE__								=					kwargs.pop("""images"""							, __A						)
									SCREAMING_SNAKE_CASE__								=					kwargs.pop("""text"""							, __A						)
									if len(__A						) > 0:
											SCREAMING_SNAKE_CASE__								=					args[0]
											SCREAMING_SNAKE_CASE__								=					args[1:]

									if images is None and text is None:
											raise ValueError("""You need to specify either an `images` or `text` input to process."""						)

									if images is not None:
											SCREAMING_SNAKE_CASE__								=					self.image_processor(__A							, *__A							, **__A						)
									if text is not None:
											SCREAMING_SNAKE_CASE__								=					self.tokenizer(__A							, **__A						)

									if text is None:
											return inputs
									elif images is None:
											return encodings
									else:
											SCREAMING_SNAKE_CASE__								=					encodings["""input_ids"""]
											return inputs


							def    _snake_case     (							self			:Any							, *__A			:Optional[Any]							, **__A			:int						)      ->      List[Any]:



									"""simple docstring"""



									return self.tokenizer.batch_decode(*__A							, **__A						)


							def    _snake_case     (							self			:str							, *__A			:Optional[int]							, **__A			:List[Any]						)      ->      List[str]:



									"""simple docstring"""



									return self.tokenizer.decode(*__A							, **__A						)


							@contextmanager
							def    _snake_case     (							self			:Dict						)      ->      List[str]:



									"""simple docstring"""



									warnings.warn(
									    """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
									    """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
									    """your images inputs, or in a separate call."""						)
									SCREAMING_SNAKE_CASE__								=					True
									SCREAMING_SNAKE_CASE__								=					self.tokenizer
									yield
									SCREAMING_SNAKE_CASE__								=					self.image_processor
									SCREAMING_SNAKE_CASE__								=					False


							@property
							def    _snake_case     (							self			:List[str]						)      ->      int:



									"""simple docstring"""



									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    _snake_case     (							self			:str						)      ->      List[Any]:



									"""simple docstring"""



									warnings.warn(
									    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""							, __A							, )
									return self.image_processor