


'''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__  =     logging.get_logger(__name__)



def 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase=False    ,       UpperCAmelCase=False     ):






       """simple docstring"""





       snake_case__					:	Any          =					"""backbone.""" if is_semantic else """"""

       snake_case__					:	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 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase=False    ,       UpperCAmelCase=False     ):






       """simple docstring"""





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

              snake_case__					:	Dict          =					in_proj_weight[
                  : config.hidden_size, :
              ]
              snake_case__					:	Any          =					q_bias
              snake_case__					:	Optional[int]          =					in_proj_weight[
                  config.hidden_size : config.hidden_size * 2, :
              ]
              snake_case__					:	Optional[Any]          =					in_proj_weight[
                  -config.hidden_size :, :
              ]
              snake_case__					:	Tuple          =					v_bias

              # gamma_1 and gamma_2
              # we call them lambda because otherwise they are renamed when using .from_pretrained
              snake_case__					:	int          =					state_dict.pop(f"""{prefix}blocks.{i}.gamma_1"""     )
              snake_case__					:	Any          =					state_dict.pop(f"""{prefix}blocks.{i}.gamma_2"""     )

              snake_case__					:	str          =					gamma_a
              snake_case__					:	Optional[int]          =					gamma_a

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

                     snake_case__					:	str          =					table
                     snake_case__					:	Union[str, Any]          =					index



def 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase     ):






       """simple docstring"""





       snake_case__					:	Tuple          =					dct.pop(UpperCAmelCase     )
       snake_case__					:	List[str]          =					val



def 				lowerCAmelCase__	(       ):






       """simple docstring"""





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



@torch.no_grad()
def 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase     ):






       """simple docstring"""





       snake_case__					:	str          =					BeitConfig()
       snake_case__					:	int          =					False
       snake_case__					:	List[str]          =					False
       snake_case__					:	Dict          =					"""huggingface/label-files"""
       # set config parameters based on URL
       if checkpoint_url[-9:-4] == "pt22k":
              # masked image modeling
              snake_case__					:	Optional[int]          =					True
              snake_case__					:	int          =					True
              snake_case__					:	int          =					True
       elif checkpoint_url[-9:-4] == "ft22k":
              # intermediate fine-tuning on ImageNet-22k
              snake_case__					:	Dict          =					True
              snake_case__					:	int          =					2_1841
              snake_case__					:	int          =					"""imagenet-22k-id2label.json"""
              snake_case__					:	Dict          =					json.load(open(hf_hub_download(UpperCAmelCase    ,       UpperCAmelCase    ,       repo_type="""dataset"""     )    ,       """r"""     )     )
              snake_case__					:	Dict          =					{int(UpperCAmelCase     ): 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]
              snake_case__					:	Tuple          =					idalabel
              snake_case__					:	List[str]          =					{v: k for k, v in idalabel.items()}
       elif checkpoint_url[-8:-4] == "to1k":
              # fine-tuning on ImageNet-1k
              snake_case__					:	Optional[Any]          =					True
              snake_case__					:	Union[str, Any]          =					1000
              snake_case__					:	Optional[int]          =					"""imagenet-1k-id2label.json"""
              snake_case__					:	Tuple          =					json.load(open(hf_hub_download(UpperCAmelCase    ,       UpperCAmelCase    ,       repo_type="""dataset"""     )    ,       """r"""     )     )
              snake_case__					:	int          =					{int(UpperCAmelCase     ): v for k, v in idalabel.items()}
              snake_case__					:	Tuple          =					idalabel
              snake_case__					:	str          =					{v: k for k, v in idalabel.items()}
              if "384" in checkpoint_url:
                     snake_case__					:	List[str]          =					384
              if "512" in checkpoint_url:
                     snake_case__					:	str          =					512
       elif "ade20k" in checkpoint_url:
              # fine-tuning
              snake_case__					:	Optional[Any]          =					True
              snake_case__					:	List[str]          =					150
              snake_case__					:	List[str]          =					"""ade20k-id2label.json"""
              snake_case__					:	Optional[int]          =					json.load(open(hf_hub_download(UpperCAmelCase    ,       UpperCAmelCase    ,       repo_type="""dataset"""     )    ,       """r"""     )     )
              snake_case__					:	int          =					{int(UpperCAmelCase     ): v for k, v in idalabel.items()}
              snake_case__					:	Dict          =					idalabel
              snake_case__					:	Optional[Any]          =					{v: k for k, v in idalabel.items()}
              snake_case__					:	Union[str, Any]          =					640
              snake_case__					:	List[str]          =					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:
              snake_case__					:	Optional[Any]          =					1024
              snake_case__					:	Dict          =					4096
              snake_case__					:	Any          =					24
              snake_case__					:	List[str]          =					16
              if "ade20k" in checkpoint_url:
                     snake_case__					:	List[str]          =					640
                     snake_case__					:	int          =					[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
       snake_case__					:	List[Any]          =					torch.hub.load_state_dict_from_url(UpperCAmelCase    ,       map_location="""cpu"""    ,       check_hash=UpperCAmelCase     )
       snake_case__					:	Any          =					state_dict["""model"""] if """ade20k""" not in checkpoint_url else state_dict["""state_dict"""]

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

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

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

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

       snake_case__					:	Optional[int]          =					model(UpperCAmelCase     )
       snake_case__					:	Any          =					outputs.logits

       # verify logits
       snake_case__					:	List[str]          =					torch.Size([1, 1000]     )
       if checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k"""     ):
              snake_case__					:	str          =					torch.Size([1, 196, 8192]     )
       elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k"""     ):
              snake_case__					:	Dict          =					torch.Size([1, 196, 8192]     )
       elif checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k_ft22k"""     ):
              snake_case__					:	Optional[int]          =					torch.Size([1, 2_1841]     )
              snake_case__					:	int          =					torch.tensor([2.2_2_8_8, 2.4_6_7_1, 0.7_3_9_5]     )
              snake_case__					:	Optional[Any]          =					2397
       elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k_ft22k"""     ):
              snake_case__					:	Tuple          =					torch.Size([1, 2_1841]     )
              snake_case__					:	Union[str, Any]          =					torch.tensor([1.6_8_8_1, -0.2_7_8_7, 0.5_9_0_1]     )
              snake_case__					:	Tuple          =					2396
       elif checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k_ft1k"""     ):
              snake_case__					:	str          =					torch.tensor([0.1_2_4_1, 0.0_7_9_8, -0.6_5_6_9]     )
              snake_case__					:	List[str]          =					285
       elif checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k_ft22kto1k"""     ):
              snake_case__					:	Any          =					torch.tensor([-1.2_3_8_5, -1.0_9_8_7, -1.0_1_0_8]     )
              snake_case__					:	Any          =					281
       elif checkpoint_url[:-4].endswith("""beit_base_patch16_384_pt22k_ft22kto1k"""     ):
              snake_case__					:	int          =					torch.tensor([-1.5_3_0_3, -0.9_4_8_4, -0.3_1_4_7]     )
              snake_case__					:	Any          =					761
       elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k_ft1k"""     ):
              snake_case__					:	Union[str, Any]          =					torch.tensor([0.4_6_1_0, -0.0_9_2_8, 0.2_0_8_6]     )
              snake_case__					:	Union[str, Any]          =					761
       elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k_ft22kto1k"""     ):
              snake_case__					:	List[Any]          =					torch.tensor([-0.4_8_0_4, 0.6_2_5_7, -0.1_8_3_7]     )
              snake_case__					:	Tuple          =					761
       elif checkpoint_url[:-4].endswith("""beit_large_patch16_384_pt22k_ft22kto1k"""     ):
              snake_case__					:	Any          =					torch.tensor([[-0.5_1_2_2, 0.5_1_1_7, -0.2_1_1_3]]     )
              snake_case__					:	str          =					761
       elif checkpoint_url[:-4].endswith("""beit_large_patch16_512_pt22k_ft22kto1k"""     ):
              snake_case__					:	Optional[Any]          =					torch.tensor([-0.3_0_6_2, 0.7_2_6_1, 0.4_8_5_2]     )
              snake_case__					:	Tuple          =					761
       elif checkpoint_url[:-4].endswith("""beit_base_patch16_640_pt22k_ft22ktoade20k"""     ):
              snake_case__					:	int          =					(1, 150, 160, 160)
              snake_case__					:	int          =					torch.tensor(
                  [
                      [[-4.9_2_2_5, -2.3_9_5_4, -3.0_5_2_2], [-2.8_8_2_2, -1.0_0_4_6, -1.7_5_6_1], [-2.9_5_4_9, -1.3_2_2_8, -2.1_3_4_7]],
                      [[-5.8_1_6_8, -3.4_1_2_9, -4.0_7_7_8], [-3.8_6_5_1, -2.2_2_1_4, -3.0_2_7_7], [-3.8_3_5_6, -2.4_6_4_3, -3.3_5_3_5]],
                      [[-0.0_0_7_8, 3.9_9_5_2, 4.0_7_5_4], [2.9_8_5_6, 4.6_9_4_4, 5.0_0_3_5], [3.2_4_1_3, 4.7_8_1_3, 4.9_9_6_9]],
                  ]     )
       elif checkpoint_url[:-4].endswith("""beit_large_patch16_640_pt22k_ft22ktoade20k"""     ):
              snake_case__					:	List[str]          =					(1, 150, 160, 160)
              snake_case__					:	int          =					torch.tensor(
                  [
                      [[-4.3_3_0_5, -2.3_0_4_9, -3.0_1_6_1], [-2.9_5_9_1, -1.5_3_0_5, -2.2_2_5_1], [-3.4_1_9_8, -1.8_0_0_4, -2.9_0_6_2]],
                      [[-5.8_9_2_2, -3.7_4_3_5, -4.3_9_7_8], [-4.2_0_6_3, -2.7_8_7_2, -3.4_7_5_5], [-4.2_7_9_1, -3.1_8_7_4, -4.1_6_8_1]],
                      [[0.9_8_9_5, 4.3_4_6_7, 4.7_6_6_3], [4.2_4_7_6, 5.6_8_3_0, 6.1_5_1_8], [4.5_5_5_0, 6.2_4_9_5, 6.5_1_5_4]],
                  ]     )
       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]    ,       UpperCAmelCase    ,       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]    ,       UpperCAmelCase    ,       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(UpperCAmelCase     ).mkdir(exist_ok=UpperCAmelCase     )
       print(f"""Saving model to {pytorch_dump_folder_path}"""     )
       model.save_pretrained(UpperCAmelCase     )
       print(f"""Saving image processor to {pytorch_dump_folder_path}"""     )
       image_processor.save_pretrained(UpperCAmelCase     )


if __name__ == "__main__":
     lowerCAmelCase__  =     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__  =     parser.parse_args()
     convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)








'''simple docstring'''
def 				lowerCAmelCase__	(       UpperCAmelCase = 30     ):






       """simple docstring"""





       snake_case__					:	Tuple          =					exponent + 2
       snake_case__					:	List[Any]          =					(1 + 5**0.5) / 2
       snake_case__					:	Tuple          =					(phi**fibonacci_index - (phi - 1) ** fibonacci_index) / 5**0.5

       return int(UpperCAmelCase     )


if __name__ == "__main__":
     print(f"""{solution() = }""")




