
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()
_UpperCAmelCase    			=					logging.get_logger(__name__)


def 		_lowerCamelCase       (   _a			,   _a=False			,   _a=False  ):
  """simple docstring"""




  _lowerCamelCase      =						"backbone." if is_semantic else ""

  _lowerCamelCase      =						[]
  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       (   _a			,   _a			,   _a=False			,   _a=False  ):
  """simple docstring"""




  for i in range(config.num_hidden_layers  ):
    _lowerCamelCase      =						"backbone." if is_semantic else ""
    # queries, keys and values
    _lowerCamelCase      =						state_dict.pop(F'''{prefix}blocks.{i}.attn.qkv.weight'''  )
    _lowerCamelCase      =						state_dict.pop(F'''{prefix}blocks.{i}.attn.q_bias'''  )
    _lowerCamelCase      =						state_dict.pop(F'''{prefix}blocks.{i}.attn.v_bias'''  )

    _lowerCamelCase      =						in_proj_weight[
        : config.hidden_size, :
    ]
    _lowerCamelCase      =						q_bias
    _lowerCamelCase      =						in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    _lowerCamelCase      =						in_proj_weight[
        -config.hidden_size :, :
    ]
    _lowerCamelCase      =						v_bias

    # gamma_1 and gamma_2
    # we call them lambda because otherwise they are renamed when using .from_pretrained
    _lowerCamelCase      =						state_dict.pop(F'''{prefix}blocks.{i}.gamma_1'''  )
    _lowerCamelCase      =						state_dict.pop(F'''{prefix}blocks.{i}.gamma_2'''  )

    _lowerCamelCase      =						gamma_a
    _lowerCamelCase      =						gamma_a

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

      _lowerCamelCase      =						table
      _lowerCamelCase      =						index


def 		_lowerCamelCase       (   _a			,   _a			,   _a  ):
  """simple docstring"""




  _lowerCamelCase      =						dct.pop(_a  )
  _lowerCamelCase      =						val


def 		_lowerCamelCase       (   ):
  """simple docstring"""




  _lowerCamelCase      =						"http://images.cocodataset.org/val2017/000000039769.jpg"
  _lowerCamelCase      =						Image.open(requests.get(_a			,   stream=_a  ).raw  )
  return im


@torch.no_grad()
def 		_lowerCamelCase       (   _a			,   _a  ):
  """simple docstring"""




  _lowerCamelCase      =						BeitConfig()
  _lowerCamelCase      =						False
  _lowerCamelCase      =						False
  _lowerCamelCase      =						"huggingface/label-files"
  # set config parameters based on URL
  if checkpoint_url[-9:-4] == "pt22k":
    # masked image modeling
    _lowerCamelCase      =						True
    _lowerCamelCase      =						True
    _lowerCamelCase      =						True
  elif checkpoint_url[-9:-4] == "ft22k":
    # intermediate fine-tuning on ImageNet-22k
    _lowerCamelCase      =						True
    _lowerCamelCase      =						2_1_8_4_1
    _lowerCamelCase      =						"imagenet-22k-id2label.json"
    _lowerCamelCase      =						json.load(open(hf_hub_download(_a			,   _a			,   repo_type='''dataset'''  )			,   '''r'''  )  )
    _lowerCamelCase      =						{int(_a  ): 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[9_2_0_5]
    del idalabel[1_5_0_2_7]
    _lowerCamelCase      =						idalabel
    _lowerCamelCase      =						{v: k for k, v in idalabel.items()}
  elif checkpoint_url[-8:-4] == "to1k":
    # fine-tuning on ImageNet-1k
    _lowerCamelCase      =						True
    _lowerCamelCase      =						1_0_0_0
    _lowerCamelCase      =						"imagenet-1k-id2label.json"
    _lowerCamelCase      =						json.load(open(hf_hub_download(_a			,   _a			,   repo_type='''dataset'''  )			,   '''r'''  )  )
    _lowerCamelCase      =						{int(_a  ): v for k, v in idalabel.items()}
    _lowerCamelCase      =						idalabel
    _lowerCamelCase      =						{v: k for k, v in idalabel.items()}
    if "384" in checkpoint_url:
      _lowerCamelCase      =						3_8_4
    if "512" in checkpoint_url:
      _lowerCamelCase      =						5_1_2
  elif "ade20k" in checkpoint_url:
    # fine-tuning
    _lowerCamelCase      =						True
    _lowerCamelCase      =						1_5_0
    _lowerCamelCase      =						"ade20k-id2label.json"
    _lowerCamelCase      =						json.load(open(hf_hub_download(_a			,   _a			,   repo_type='''dataset'''  )			,   '''r'''  )  )
    _lowerCamelCase      =						{int(_a  ): v for k, v in idalabel.items()}
    _lowerCamelCase      =						idalabel
    _lowerCamelCase      =						{v: k for k, v in idalabel.items()}
    _lowerCamelCase      =						6_4_0
    _lowerCamelCase      =						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      =						1_0_2_4
    _lowerCamelCase      =						4_0_9_6
    _lowerCamelCase      =						2_4
    _lowerCamelCase      =						1_6
    if "ade20k" in checkpoint_url:
      _lowerCamelCase      =						6_4_0
      _lowerCamelCase      =						[7, 1_1, 1_5, 2_3]
  else:
    raise ValueError('''Should either find \'base\' or \'large\' in checkpoint URL'''  )

  # load state_dict of original model, remove and rename some keys
  _lowerCamelCase      =						torch.hub.load_state_dict_from_url(_a			,   map_location='''cpu'''			,   check_hash=_a  )
  _lowerCamelCase      =						state_dict["model"] if "ade20k" not in checkpoint_url else state_dict["state_dict"]

  _lowerCamelCase      =						create_rename_keys(_a			,   has_lm_head=_a			,   is_semantic=_a  )
  for src, dest in rename_keys:
    rename_key(_a			,   _a			,   _a  )
  read_in_q_k_v(_a			,   _a			,   has_lm_head=_a			,   is_semantic=_a  )
  if is_semantic:
    # add prefix to decoder keys
    for key, val in state_dict.copy().items():
      _lowerCamelCase      =						state_dict.pop(_a  )
      if key.startswith('''backbone.fpn'''  ):
        _lowerCamelCase      =						key.replace('''backbone.fpn'''			,   '''fpn'''  )
      _lowerCamelCase      =						val

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

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

  _lowerCamelCase      =						image_processor(images=_a			,   return_tensors='''pt'''  )
  _lowerCamelCase      =						encoding["pixel_values"]

  _lowerCamelCase      =						model(_a  )
  _lowerCamelCase      =						outputs.logits

  # verify logits
  _lowerCamelCase      =						torch.Size([1, 1_0_0_0]  )
  if checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k'''  ):
    _lowerCamelCase      =						torch.Size([1, 1_9_6, 8_1_9_2]  )
  elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k'''  ):
    _lowerCamelCase      =						torch.Size([1, 1_9_6, 8_1_9_2]  )
  elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22k'''  ):
    _lowerCamelCase      =						torch.Size([1, 2_1_8_4_1]  )
    _lowerCamelCase      =						torch.tensor([2.2288, 2.4671, 0.7395]  )
    _lowerCamelCase      =						2_3_9_7
  elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22k'''  ):
    _lowerCamelCase      =						torch.Size([1, 2_1_8_4_1]  )
    _lowerCamelCase      =						torch.tensor([1.6881, -0.2787, 0.5901]  )
    _lowerCamelCase      =						2_3_9_6
  elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft1k'''  ):
    _lowerCamelCase      =						torch.tensor([0.1241, 0.0798, -0.6569]  )
    _lowerCamelCase      =						2_8_5
  elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22kto1k'''  ):
    _lowerCamelCase      =						torch.tensor([-1.2385, -1.0987, -1.0108]  )
    _lowerCamelCase      =						2_8_1
  elif checkpoint_url[:-4].endswith('''beit_base_patch16_384_pt22k_ft22kto1k'''  ):
    _lowerCamelCase      =						torch.tensor([-1.5303, -0.9484, -0.3147]  )
    _lowerCamelCase      =						7_6_1
  elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft1k'''  ):
    _lowerCamelCase      =						torch.tensor([0.4610, -0.0928, 0.2086]  )
    _lowerCamelCase      =						7_6_1
  elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22kto1k'''  ):
    _lowerCamelCase      =						torch.tensor([-0.4804, 0.6257, -0.1837]  )
    _lowerCamelCase      =						7_6_1
  elif checkpoint_url[:-4].endswith('''beit_large_patch16_384_pt22k_ft22kto1k'''  ):
    _lowerCamelCase      =						torch.tensor([[-0.5122, 0.5117, -0.2113]]  )
    _lowerCamelCase      =						7_6_1
  elif checkpoint_url[:-4].endswith('''beit_large_patch16_512_pt22k_ft22kto1k'''  ):
    _lowerCamelCase      =						torch.tensor([-0.3062, 0.7261, 0.4852]  )
    _lowerCamelCase      =						7_6_1
  elif checkpoint_url[:-4].endswith('''beit_base_patch16_640_pt22k_ft22ktoade20k'''  ):
    _lowerCamelCase      =						(1, 1_5_0, 1_6_0, 1_6_0)
    _lowerCamelCase      =						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      =						(1, 1_5_0, 1_6_0, 1_6_0)
    _lowerCamelCase      =						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]			,   _a			,   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]			,   _a			,   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(_a  ).mkdir(exist_ok=_a  )
  print(F'''Saving model to {pytorch_dump_folder_path}'''  )
  model.save_pretrained(_a  )
  print(F'''Saving image processor to {pytorch_dump_folder_path}'''  )
  image_processor.save_pretrained(_a  )


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






"""simple docstring"""




def 				a__			(					lowerCAmelCase						:							int		):






     '''simple docstring'''
     if number != int(lowerCAmelCase		):
          raise ValueError("factorial() only accepts integral values"		)
     if number < 0:
          raise ValueError("factorial() not defined for negative values"		)
     UpperCAmelCase__				:			int							=    1
     for i in range(1							,     number + 1		):
          value *= i
     return value



def 				a__			(					lowerCAmelCase						:							int		):






     '''simple docstring'''
     if not isinstance(lowerCAmelCase							,     lowerCAmelCase		):
          raise ValueError("factorial() only accepts integral values"		)
     if n < 0:
          raise ValueError("factorial() not defined for negative values"		)
     return 1 if n == 0 or n == 1 else n * factorial(n - 1		)


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    A__   :						Any 							=     int(input("""Enter a positive integer: """).strip() or 0)
    print(f"""factorial{n} is {factorial(n)}""")



