'''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()
_A		:			Optional[int]	      =logging.get_logger(__name__)







def 							__UpperCamelCase   (				__UpperCAmelCase     :				Optional[int]   ,      __UpperCAmelCase     :				Any=False   ,      __UpperCAmelCase     :				Union[str, Any]=False )       ->  int:
  lowercase							:       List[str]											=      '''backbone.''' if is_semantic else ''''''

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

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

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

    lowercase							:       str											=      gamma_a
    lowercase							:       Dict											=      gamma_a

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

      lowercase							:       List[Any]											=      table
      lowercase							:       Tuple											=      index







def 							__UpperCamelCase   (				__UpperCAmelCase     :				List[Any]   ,      __UpperCAmelCase     :				Optional[int]   ,      __UpperCAmelCase     :				Optional[int] )       ->  Optional[Any]:
  lowercase							:       List[Any]											=      dct.pop(__UpperCAmelCase )
  lowercase							:       List[str]											=      val







def 							__UpperCamelCase   (				)       ->  int:
  lowercase							:       Union[str, Any]											=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
  lowercase							:       Optional[Any]											=      Image.open(requests.get(__UpperCAmelCase   ,      stream=__UpperCAmelCase ).raw )
  return im







@torch.no_grad()
def 							__UpperCamelCase   (				__UpperCAmelCase     :				List[Any]   ,      __UpperCAmelCase     :				Any )       ->  Optional[int]:
  lowercase							:       Optional[int]											=      BeitConfig()
  lowercase							:       Union[str, Any]											=      False
  lowercase							:       Optional[Any]											=      False
  lowercase							:       Optional[Any]											=      '''huggingface/label-files'''
  # set config parameters based on URL
  if checkpoint_url[-9:-4] == "pt22k":
    # masked image modeling
    lowercase							:       List[str]											=      True
    lowercase							:       List[Any]											=      True
    lowercase							:       int											=      True
  elif checkpoint_url[-9:-4] == "ft22k":
    # intermediate fine-tuning on ImageNet-22k
    lowercase							:       Tuple											=      True
    lowercase							:       Optional[int]											=      21_841
    lowercase							:       List[str]											=      '''imagenet-22k-id2label.json'''
    lowercase							:       Optional[int]											=      json.load(open(hf_hub_download(__UpperCAmelCase   ,      __UpperCAmelCase   ,      repo_type='''dataset''' )   ,      '''r''' ) )
    lowercase							:       str											=      {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[9_205]
    del idalabel[15_027]
    lowercase							:       str											=      idalabel
    lowercase							:       Dict											=      {v: k for k, v in idalabel.items()}
  elif checkpoint_url[-8:-4] == "to1k":
    # fine-tuning on ImageNet-1k
    lowercase							:       Dict											=      True
    lowercase							:       Union[str, Any]											=      1_000
    lowercase							:       Tuple											=      '''imagenet-1k-id2label.json'''
    lowercase							:       int											=      json.load(open(hf_hub_download(__UpperCAmelCase   ,      __UpperCAmelCase   ,      repo_type='''dataset''' )   ,      '''r''' ) )
    lowercase							:       Optional[int]											=      {int(__UpperCAmelCase ): v for k, v in idalabel.items()}
    lowercase							:       str											=      idalabel
    lowercase							:       Optional[int]											=      {v: k for k, v in idalabel.items()}
    if "384" in checkpoint_url:
      lowercase							:       str											=      384
    if "512" in checkpoint_url:
      lowercase							:       Optional[int]											=      512
  elif "ade20k" in checkpoint_url:
    # fine-tuning
    lowercase							:       Union[str, Any]											=      True
    lowercase							:       int											=      150
    lowercase							:       Union[str, Any]											=      '''ade20k-id2label.json'''
    lowercase							:       int											=      json.load(open(hf_hub_download(__UpperCAmelCase   ,      __UpperCAmelCase   ,      repo_type='''dataset''' )   ,      '''r''' ) )
    lowercase							:       Dict											=      {int(__UpperCAmelCase ): v for k, v in idalabel.items()}
    lowercase							:       List[str]											=      idalabel
    lowercase							:       Optional[int]											=      {v: k for k, v in idalabel.items()}
    lowercase							:       int											=      640
    lowercase							:       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:
    lowercase							:       Optional[Any]											=      1_024
    lowercase							:       List[Any]											=      4_096
    lowercase							:       Optional[int]											=      24
    lowercase							:       List[Any]											=      16
    if "ade20k" in checkpoint_url:
      lowercase							:       int											=      640
      lowercase							:       Any											=      [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
  lowercase							:       Tuple											=      torch.hub.load_state_dict_from_url(__UpperCAmelCase   ,      map_location='''cpu'''   ,      check_hash=__UpperCAmelCase )
  lowercase							:       Any											=      state_dict['''model'''] if '''ade20k''' not in checkpoint_url else state_dict['''state_dict''']

  lowercase							:       Optional[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():
      lowercase							:       Optional[int]											=      state_dict.pop(__UpperCAmelCase )
      if key.startswith('''backbone.fpn''' ):
        lowercase							:       Union[str, Any]											=      key.replace('''backbone.fpn'''   ,      '''fpn''' )
      lowercase							:       Tuple											=      val

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

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

  lowercase							:       Optional[int]											=      image_processor(images=__UpperCAmelCase   ,      return_tensors='''pt''' )
  lowercase							:       List[Any]											=      encoding['''pixel_values''']

  lowercase							:       List[str]											=      model(__UpperCAmelCase )
  lowercase							:       int											=      outputs.logits

  # verify logits
  lowercase							:       List[Any]											=      torch.Size([1, 1_000] )
  if checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k''' ):
    lowercase							:       str											=      torch.Size([1, 196, 8_192] )
  elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k''' ):
    lowercase							:       List[Any]											=      torch.Size([1, 196, 8_192] )
  elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22k''' ):
    lowercase							:       Dict											=      torch.Size([1, 21_841] )
    lowercase							:       Any											=      torch.tensor([2.2288, 2.4671, 0.7395] )
    lowercase							:       Any											=      2_397
  elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22k''' ):
    lowercase							:       Union[str, Any]											=      torch.Size([1, 21_841] )
    lowercase							:       int											=      torch.tensor([1.6881, -0.2787, 0.5901] )
    lowercase							:       Dict											=      2_396
  elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft1k''' ):
    lowercase							:       Optional[int]											=      torch.tensor([0.1241, 0.0798, -0.6569] )
    lowercase							:       Dict											=      285
  elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22kto1k''' ):
    lowercase							:       Optional[int]											=      torch.tensor([-1.2385, -1.0987, -1.0108] )
    lowercase							:       Any											=      281
  elif checkpoint_url[:-4].endswith('''beit_base_patch16_384_pt22k_ft22kto1k''' ):
    lowercase							:       int											=      torch.tensor([-1.5303, -0.9484, -0.3147] )
    lowercase							:       Union[str, Any]											=      761
  elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft1k''' ):
    lowercase							:       int											=      torch.tensor([0.4610, -0.0928, 0.2086] )
    lowercase							:       Tuple											=      761
  elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22kto1k''' ):
    lowercase							:       Union[str, Any]											=      torch.tensor([-0.4804, 0.6257, -0.1837] )
    lowercase							:       Tuple											=      761
  elif checkpoint_url[:-4].endswith('''beit_large_patch16_384_pt22k_ft22kto1k''' ):
    lowercase							:       Any											=      torch.tensor([[-0.5122, 0.5117, -0.2113]] )
    lowercase							:       List[Any]											=      761
  elif checkpoint_url[:-4].endswith('''beit_large_patch16_512_pt22k_ft22kto1k''' ):
    lowercase							:       Any											=      torch.tensor([-0.3062, 0.7261, 0.4852] )
    lowercase							:       Dict											=      761
  elif checkpoint_url[:-4].endswith('''beit_base_patch16_640_pt22k_ft22ktoade20k''' ):
    lowercase							:       List[str]											=      (1, 150, 160, 160)
    lowercase							:       List[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''' ):
    lowercase							:       Any											=      (1, 150, 160, 160)
    lowercase							:       Tuple											=      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]   ,      __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__":
 _A		:			Dict	      =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.'''
 )
 _A		:			Any	      =parser.parse_args()
 convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_A		:			int	      ={'''configuration_cvt''': ['''CVT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CvtConfig''']}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _A		:			str	      =[
     '''CVT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''CvtForImageClassification''',
     '''CvtModel''',
     '''CvtPreTrainedModel''',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _A		:			Optional[Any]	      =[
     '''TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''TFCvtForImageClassification''',
     '''TFCvtModel''',
     '''TFCvtPreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_cvt import CVT_PRETRAINED_CONFIG_ARCHIVE_MAP, CvtConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_cvt import (
      CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
      CvtForImageClassification,
      CvtModel,
      CvtPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_cvt import (
      TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFCvtForImageClassification,
      TFCvtModel,
      TFCvtPreTrainedModel,
  )


else:
 import sys

 _A		:			Tuple	      =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)