


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       :       Any       	= logging.get_logger(__name__)


def    a		(							SCREAMING_SNAKE_CASE_					:						Optional[Any]							,	SCREAMING_SNAKE_CASE_					:						str=False							,	SCREAMING_SNAKE_CASE_					:						Optional[Any]=False				):






       """simple docstring"""





       UpperCamelCase			:		Any      		=						'''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    a		(							SCREAMING_SNAKE_CASE_					:						Tuple							,	SCREAMING_SNAKE_CASE_					:						str							,	SCREAMING_SNAKE_CASE_					:						Union[str, Any]=False							,	SCREAMING_SNAKE_CASE_					:						Optional[Any]=False				):






       """simple docstring"""





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

              UpperCamelCase			:		Union[str, Any]      		=						in_proj_weight[
                  : config.hidden_size, :
              ]
              UpperCamelCase			:		Union[str, Any]      		=						q_bias
              UpperCamelCase			:		List[str]      		=						in_proj_weight[
                  config.hidden_size : config.hidden_size * 2, :
              ]
              UpperCamelCase			:		Union[str, Any]      		=						in_proj_weight[
                  -config.hidden_size :, :
              ]
              UpperCamelCase			:		Any      		=						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			:		List[str]      		=						state_dict.pop(F"""{prefix}blocks.{i}.gamma_2"""				)

              UpperCamelCase			:		List[Any]      		=						gamma_a
              UpperCamelCase			:		Optional[Any]      		=						gamma_a

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

                     UpperCamelCase			:		Optional[Any]      		=						table
                     UpperCamelCase			:		int      		=						index


def    a		(							SCREAMING_SNAKE_CASE_					:						str							,	SCREAMING_SNAKE_CASE_					:						Dict							,	SCREAMING_SNAKE_CASE_					:						Tuple				):






       """simple docstring"""





       UpperCamelCase			:		Dict      		=						dct.pop(lowercase_				)
       UpperCamelCase			:		List[Any]      		=						val


def    a		(							):






       """simple docstring"""





       UpperCamelCase			:		int      		=						'''http://images.cocodataset.org/val2017/000000039769.jpg'''
       UpperCamelCase			:		Union[str, Any]      		=						Image.open(requests.get(lowercase_							,	stream=lowercase_				).raw				)
       return im


@torch.no_grad()
def    a		(							SCREAMING_SNAKE_CASE_					:						str							,	SCREAMING_SNAKE_CASE_					:						int				):






       """simple docstring"""





       UpperCamelCase			:		Dict      		=						BeitConfig()
       UpperCamelCase			:		List[Any]      		=						False
       UpperCamelCase			:		int      		=						False
       UpperCamelCase			:		Optional[int]      		=						'''huggingface/label-files'''
       # set config parameters based on URL
       if checkpoint_url[-9:-4] == "pt22k":
              # masked image modeling
              UpperCamelCase			:		str      		=						True
              UpperCamelCase			:		Union[str, Any]      		=						True
              UpperCamelCase			:		Tuple      		=						True
       elif checkpoint_url[-9:-4] == "ft22k":
              # intermediate fine-tuning on ImageNet-22k
              UpperCamelCase			:		Tuple      		=						True
              UpperCamelCase			:		Any      		=						2_1_8_4_1
              UpperCamelCase			:		int      		=						'''imagenet-22k-id2label.json'''
              UpperCamelCase			:		Union[str, Any]      		=						json.load(open(hf_hub_download(lowercase_							,	lowercase_							,	repo_type='''dataset'''				)							,	'''r'''				)				)
              UpperCamelCase			:		Optional[int]      		=						{int(lowercase_				): 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]
              UpperCamelCase			:		Tuple      		=						idalabel
              UpperCamelCase			:		Union[str, Any]      		=						{v: k for k, v in idalabel.items()}
       elif checkpoint_url[-8:-4] == "to1k":
              # fine-tuning on ImageNet-1k
              UpperCamelCase			:		List[Any]      		=						True
              UpperCamelCase			:		Optional[int]      		=						1_0_0_0
              UpperCamelCase			:		Dict      		=						'''imagenet-1k-id2label.json'''
              UpperCamelCase			:		Any      		=						json.load(open(hf_hub_download(lowercase_							,	lowercase_							,	repo_type='''dataset'''				)							,	'''r'''				)				)
              UpperCamelCase			:		List[str]      		=						{int(lowercase_				): v for k, v in idalabel.items()}
              UpperCamelCase			:		str      		=						idalabel
              UpperCamelCase			:		int      		=						{v: k for k, v in idalabel.items()}
              if "384" in checkpoint_url:
                     UpperCamelCase			:		Tuple      		=						3_8_4
              if "512" in checkpoint_url:
                     UpperCamelCase			:		List[Any]      		=						5_1_2
       elif "ade20k" in checkpoint_url:
              # fine-tuning
              UpperCamelCase			:		Dict      		=						True
              UpperCamelCase			:		List[str]      		=						1_5_0
              UpperCamelCase			:		int      		=						'''ade20k-id2label.json'''
              UpperCamelCase			:		Union[str, Any]      		=						json.load(open(hf_hub_download(lowercase_							,	lowercase_							,	repo_type='''dataset'''				)							,	'''r'''				)				)
              UpperCamelCase			:		List[Any]      		=						{int(lowercase_				): v for k, v in idalabel.items()}
              UpperCamelCase			:		Dict      		=						idalabel
              UpperCamelCase			:		Optional[int]      		=						{v: k for k, v in idalabel.items()}
              UpperCamelCase			:		int      		=						6_4_0
              UpperCamelCase			:		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:
              UpperCamelCase			:		Any      		=						1_0_2_4
              UpperCamelCase			:		Optional[int]      		=						4_0_9_6
              UpperCamelCase			:		Tuple      		=						2_4
              UpperCamelCase			:		Union[str, Any]      		=						1_6
              if "ade20k" in checkpoint_url:
                     UpperCamelCase			:		Optional[Any]      		=						6_4_0
                     UpperCamelCase			:		Optional[int]      		=						[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
       UpperCamelCase			:		Optional[Any]      		=						torch.hub.load_state_dict_from_url(lowercase_							,	map_location='''cpu'''							,	check_hash=lowercase_				)
       UpperCamelCase			:		Any      		=						state_dict['''model'''] if '''ade20k''' not in checkpoint_url else state_dict['''state_dict''']

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

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

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

       UpperCamelCase			:		Optional[int]      		=						image_processor(images=lowercase_							,	return_tensors='''pt'''				)
       UpperCamelCase			:		Tuple      		=						encoding['''pixel_values''']

       UpperCamelCase			:		Optional[int]      		=						model(lowercase_				)
       UpperCamelCase			:		Tuple      		=						outputs.logits

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


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




def    UpperCamelCase(					lowercase_							)	->				str:





  '''simple docstring'''





  if len(lowercase_							) == 0 or not all(len(lowercase_							) == 2 for pair in coordinates							):
    raise ValueError("""The program cannot work out a fitting polynomial."""							)

  if len({tuple(lowercase_							) for pair in coordinates}							) != len(lowercase_							):
    raise ValueError("""The program cannot work out a fitting polynomial."""							)

  snake_case_       						=      {x for x, _ in coordinates}
  if len(lowercase_							) == 1:
    return f'''x={coordinates[0][0]}'''

  if len(lowercase_							) != len(lowercase_							):
    raise ValueError("""The program cannot work out a fitting polynomial."""							)

  snake_case_       						=      len(lowercase_							)

  snake_case_       						=      0
  snake_case_       						=      []
  # put the x and x to the power values in a matrix
  while count_of_line < x:
    snake_case_       						=      0
    snake_case_       						=      coordinates[count_of_line][0]
    snake_case_       						=      []
    while count_in_line < x:
      count_line.append(a ** (x - (count_in_line + 1))							)
      count_in_line += 1
    matrix.append(lowercase_							)
    count_of_line += 1

  snake_case_       						=      0
  # put the y values into a vector
  snake_case_       						=      []
  while count_of_line < x:
    vector.append(coordinates[count_of_line][1]							)
    count_of_line += 1

  snake_case_       						=      0

  while count < x:
    snake_case_       						=      0
    while zahlen < x:
      if count == zahlen:
        zahlen += 1
      if zahlen == x:
        break
      snake_case_       						=      matrix[zahlen][count] / matrix[count][count]
      for counting_columns, item in enumerate(matrix[count]							):
        # manipulating all the values in the matrix
        matrix[zahlen][counting_columns] -= item * bruch
      # manipulating the values in the vector
      vector[zahlen] -= vector[count] * bruch
      zahlen += 1
    count += 1

  snake_case_       						=      0
  # make solutions
  snake_case_       						=      []
  while count < x:
    solution.append(str(vector[count] / matrix[count][count]							)							)
    count += 1

  snake_case_       						=      0
  snake_case_       						=      """f(x)="""

  while count < x:
    snake_case_       						=      solution[count].split("""E"""							)
    if len(lowercase_							) > 1:
      snake_case_       						=      f'''{remove_e[0]}*10^{remove_e[1]}'''
    solved += f'''x^{x - (count + 1)}*{solution[count]}'''
    if count + 1 != x:
      solved += "+"
    count += 1

  return solved


if __name__ == "__main__":
    print(points_to_polynomial([]))
    print(points_to_polynomial([[]]))
    print(points_to_polynomial([[1, 0], [2, 0], [3, 0]]))
    print(points_to_polynomial([[1, 1], [2, 1], [3, 1]]))
    print(points_to_polynomial([[1, 3], [2, 3], [3, 3]]))
    print(points_to_polynomial([[1, 1], [2, 2], [3, 3]]))
    print(points_to_polynomial([[1, 1], [2, 4], [3, 9]]))
    print(points_to_polynomial([[1, 3], [2, 6], [3, 11]]))
    print(points_to_polynomial([[1, -3], [2, -6], [3, -11]]))
    print(points_to_polynomial([[1, 5], [2, 2], [3, 9]]))