
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()
__SCREAMING_SNAKE_CASE			:   Dict         =     logging.get_logger(__name__)
def     UpperCAmelCase__							(       __magic_name__						:     Tuple							,  __magic_name__						:     Optional[Any]=False							,  __magic_name__						:     Optional[Any]=False  ):


     '''simple docstring'''

     lowerCAmelCase		:					Union[str, Any]  	=     '''backbone.''' if is_semantic else ''''''

     lowerCAmelCase		:					List[str]  	=     []
     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     UpperCAmelCase__							(       __magic_name__						:     Tuple							,  __magic_name__						:     Optional[Any]							,  __magic_name__						:     Tuple=False							,  __magic_name__						:     int=False  ):


     '''simple docstring'''

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

          lowerCAmelCase		:					Optional[Any]  	=     in_proj_weight[
              : config.hidden_size, :
          ]
          lowerCAmelCase		:					Tuple  	=     q_bias
          lowerCAmelCase		:					Union[str, Any]  	=     in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          lowerCAmelCase		:					Union[str, Any]  	=     in_proj_weight[
              -config.hidden_size :, :
          ]
          lowerCAmelCase		:					List[Any]  	=     v_bias

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

          lowerCAmelCase		:					Dict  	=     gamma_a
          lowerCAmelCase		:					Tuple  	=     gamma_a

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

               lowerCAmelCase		:					str  	=     table
               lowerCAmelCase		:					int  	=     index
def     UpperCAmelCase__							(       __magic_name__						:     int							,  __magic_name__						:     int							,  __magic_name__						:     Tuple  ):


     '''simple docstring'''

     lowerCAmelCase		:					Tuple  	=     dct.pop(__magic_name__  )
     lowerCAmelCase		:					Tuple  	=     val
def     UpperCAmelCase__							(       ):


     '''simple docstring'''

     lowerCAmelCase		:					Optional[Any]  	=     '''http://images.cocodataset.org/val2017/000000039769.jpg'''
     lowerCAmelCase		:					str  	=     Image.open(requests.get(__magic_name__							,  stream=__magic_name__  ).raw  )
     return im
@torch.no_grad()
def     UpperCAmelCase__							(       __magic_name__						:     Any							,  __magic_name__						:     Union[str, Any]  ):


     '''simple docstring'''

     lowerCAmelCase		:					Any  	=     BeitConfig()
     lowerCAmelCase		:					Tuple  	=     False
     lowerCAmelCase		:					str  	=     False
     lowerCAmelCase		:					List[Any]  	=     '''huggingface/label-files'''
     # set config parameters based on URL
     if checkpoint_url[-9:-4] == "pt22k":
          # masked image modeling
          lowerCAmelCase		:					str  	=     True
          lowerCAmelCase		:					Optional[int]  	=     True
          lowerCAmelCase		:					Tuple  	=     True
     elif checkpoint_url[-9:-4] == "ft22k":
          # intermediate fine-tuning on ImageNet-22k
          lowerCAmelCase		:					str  	=     True
          lowerCAmelCase		:					Optional[int]  	=     2_18_41
          lowerCAmelCase		:					Dict  	=     '''imagenet-22k-id2label.json'''
          lowerCAmelCase		:					int  	=     json.load(open(hf_hub_download(__magic_name__							,  __magic_name__							,  repo_type='''dataset'''  )							,  '''r'''  )  )
          lowerCAmelCase		:					List[str]  	=     {int(__magic_name__  ): 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[92_05]
          del idalabel[1_50_27]
          lowerCAmelCase		:					Optional[int]  	=     idalabel
          lowerCAmelCase		:					Any  	=     {v: k for k, v in idalabel.items()}
     elif checkpoint_url[-8:-4] == "to1k":
          # fine-tuning on ImageNet-1k
          lowerCAmelCase		:					Tuple  	=     True
          lowerCAmelCase		:					List[Any]  	=     10_00
          lowerCAmelCase		:					int  	=     '''imagenet-1k-id2label.json'''
          lowerCAmelCase		:					List[Any]  	=     json.load(open(hf_hub_download(__magic_name__							,  __magic_name__							,  repo_type='''dataset'''  )							,  '''r'''  )  )
          lowerCAmelCase		:					Any  	=     {int(__magic_name__  ): v for k, v in idalabel.items()}
          lowerCAmelCase		:					Dict  	=     idalabel
          lowerCAmelCase		:					List[Any]  	=     {v: k for k, v in idalabel.items()}
          if "384" in checkpoint_url:
               lowerCAmelCase		:					Tuple  	=     3_84
          if "512" in checkpoint_url:
               lowerCAmelCase		:					List[Any]  	=     5_12
     elif "ade20k" in checkpoint_url:
          # fine-tuning
          lowerCAmelCase		:					Any  	=     True
          lowerCAmelCase		:					Union[str, Any]  	=     1_50
          lowerCAmelCase		:					Any  	=     '''ade20k-id2label.json'''
          lowerCAmelCase		:					Optional[Any]  	=     json.load(open(hf_hub_download(__magic_name__							,  __magic_name__							,  repo_type='''dataset'''  )							,  '''r'''  )  )
          lowerCAmelCase		:					Tuple  	=     {int(__magic_name__  ): v for k, v in idalabel.items()}
          lowerCAmelCase		:					List[str]  	=     idalabel
          lowerCAmelCase		:					Optional[Any]  	=     {v: k for k, v in idalabel.items()}
          lowerCAmelCase		:					Any  	=     6_40
          lowerCAmelCase		:					Tuple  	=     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:
          lowerCAmelCase		:					Optional[int]  	=     10_24
          lowerCAmelCase		:					Tuple  	=     40_96
          lowerCAmelCase		:					Dict  	=     24
          lowerCAmelCase		:					Dict  	=     16
          if "ade20k" in checkpoint_url:
               lowerCAmelCase		:					Dict  	=     6_40
               lowerCAmelCase		:					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
     lowerCAmelCase		:					Any  	=     torch.hub.load_state_dict_from_url(__magic_name__							,  map_location='''cpu'''							,  check_hash=__magic_name__  )
     lowerCAmelCase		:					Optional[int]  	=     state_dict['''model'''] if '''ade20k''' not in checkpoint_url else state_dict['''state_dict''']

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

    # load HuggingFace model
     if checkpoint_url[-9:-4] == "pt22k":
          lowerCAmelCase		:					str  	=     BeitForMaskedImageModeling(__magic_name__  )
     elif "ade20k" in checkpoint_url:
          lowerCAmelCase		:					Optional[int]  	=     BeitForSemanticSegmentation(__magic_name__  )
     else:
          lowerCAmelCase		:					Optional[Any]  	=     BeitForImageClassification(__magic_name__  )
     model.eval()
     model.load_state_dict(__magic_name__  )

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

     lowerCAmelCase		:					int  	=     image_processor(images=__magic_name__							,  return_tensors='''pt'''  )
     lowerCAmelCase		:					Optional[int]  	=     encoding['''pixel_values''']

     lowerCAmelCase		:					Any  	=     model(__magic_name__  )
     lowerCAmelCase		:					Tuple  	=     outputs.logits

     # verify logits
     lowerCAmelCase		:					List[str]  	=     torch.Size([1, 10_00]  )
     if checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k'''  ):
          lowerCAmelCase		:					Dict  	=     torch.Size([1, 1_96, 81_92]  )
     elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k'''  ):
          lowerCAmelCase		:					str  	=     torch.Size([1, 1_96, 81_92]  )
     elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22k'''  ):
          lowerCAmelCase		:					Dict  	=     torch.Size([1, 2_18_41]  )
          lowerCAmelCase		:					str  	=     torch.tensor([2.2288, 2.4671, 0.7395]  )
          lowerCAmelCase		:					Dict  	=     23_97
     elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22k'''  ):
          lowerCAmelCase		:					Tuple  	=     torch.Size([1, 2_18_41]  )
          lowerCAmelCase		:					int  	=     torch.tensor([1.6881, -0.2787, 0.5901]  )
          lowerCAmelCase		:					Tuple  	=     23_96
     elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft1k'''  ):
          lowerCAmelCase		:					Optional[int]  	=     torch.tensor([0.1241, 0.0798, -0.6569]  )
          lowerCAmelCase		:					List[str]  	=     2_85
     elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22kto1k'''  ):
          lowerCAmelCase		:					List[Any]  	=     torch.tensor([-1.2385, -1.0987, -1.0108]  )
          lowerCAmelCase		:					List[Any]  	=     2_81
     elif checkpoint_url[:-4].endswith('''beit_base_patch16_384_pt22k_ft22kto1k'''  ):
          lowerCAmelCase		:					Optional[int]  	=     torch.tensor([-1.5303, -0.9484, -0.3147]  )
          lowerCAmelCase		:					List[Any]  	=     7_61
     elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft1k'''  ):
          lowerCAmelCase		:					Any  	=     torch.tensor([0.4610, -0.0928, 0.2086]  )
          lowerCAmelCase		:					Optional[int]  	=     7_61
     elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22kto1k'''  ):
          lowerCAmelCase		:					str  	=     torch.tensor([-0.4804, 0.6257, -0.1837]  )
          lowerCAmelCase		:					Union[str, Any]  	=     7_61
     elif checkpoint_url[:-4].endswith('''beit_large_patch16_384_pt22k_ft22kto1k'''  ):
          lowerCAmelCase		:					Dict  	=     torch.tensor([[-0.5122, 0.5117, -0.2113]]  )
          lowerCAmelCase		:					Optional[int]  	=     7_61
     elif checkpoint_url[:-4].endswith('''beit_large_patch16_512_pt22k_ft22kto1k'''  ):
          lowerCAmelCase		:					Optional[Any]  	=     torch.tensor([-0.3062, 0.7261, 0.4852]  )
          lowerCAmelCase		:					Any  	=     7_61
     elif checkpoint_url[:-4].endswith('''beit_base_patch16_640_pt22k_ft22ktoade20k'''  ):
          lowerCAmelCase		:					str  	=     (1, 1_50, 1_60, 1_60)
          lowerCAmelCase		:					Dict  	=     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'''  ):
          lowerCAmelCase		:					str  	=     (1, 1_50, 1_60, 1_60)
          lowerCAmelCase		:					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]							,  __magic_name__							,  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]							,  __magic_name__							,  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(__magic_name__  ).mkdir(exist_ok=__magic_name__  )
     print(f'''Saving model to {pytorch_dump_folder_path}'''  )
     model.save_pretrained(__magic_name__  )
     print(f'''Saving image processor to {pytorch_dump_folder_path}'''  )
     image_processor.save_pretrained(__magic_name__  )


if __name__ == "__main__":
  __SCREAMING_SNAKE_CASE			:   List[Any]         =     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.'
  )
  __SCREAMING_SNAKE_CASE			:   int         =     parser.parse_args()
  convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)


from ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
		from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore


class 		__magic_name__   (						snake_case    ):
		_lowerCAmelCase		=	c_float





		@property
		def       _A							(							self    :	int				):
							return sizeof(self.value				)



def     UpperCAmelCase__							(       __magic_name__						:     int							,  __magic_name__						:     int							,  __magic_name__						:     int = 0  ):


					'''simple docstring'''

					if dimension <= 0:
										lowerCAmelCase		:					Tuple  	=     fixed_dimension

					dimension -= num_token_to_add
					return dimension
def     UpperCAmelCase__							(       __magic_name__						:     int							,  __magic_name__						:     ParameterFormat  ):


					'''simple docstring'''

					return num_parameters * dtype.size
def     UpperCAmelCase__							(       __magic_name__						:     str  ):


					'''simple docstring'''

					from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

					try:
										return AutoProcessor.from_pretrained(__magic_name__  )
					except (ValueError, OSError, KeyError):
										lowerCAmelCase	,							lowerCAmelCase		:					Any  	=     None, None
										try:
															lowerCAmelCase		:					Union[str, Any]  	=     AutoTokenizer.from_pretrained(__magic_name__  )
										except (OSError, KeyError):
															pass
										try:
															lowerCAmelCase		:					List[Any]  	=     AutoFeatureExtractor.from_pretrained(__magic_name__  )
										except (OSError, KeyError):
															pass

										if tokenizer is not None and feature_extractor is not None:
															raise ValueError(
															    f'''Couldn\'t auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor.'''  )
										elif tokenizer is None and feature_extractor is None:
															return None
										elif tokenizer is not None:
															return tokenizer
										else:
															return feature_extractor
