





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()
snake_case__   :				Optional[Any]						=	logging.get_logger(__name__)



def _a							(      lowerCamelCase:  Tuple   ,							lowerCamelCase:  List[Any]=False   ,							lowerCamelCase:  Dict=False					)      ->		int:







      '''simple docstring'''






      __A			  =							'''backbone.''' if is_semantic else ''''''

      __A			  =							[]
      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							(      lowerCamelCase:  Dict   ,							lowerCamelCase:  Optional[Any]   ,							lowerCamelCase:  List[Any]=False   ,							lowerCamelCase:  Dict=False					)      ->		List[str]:







      '''simple docstring'''






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

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

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

            __A			  =							gamma_a
            __A			  =							gamma_a

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

                  __A			  =							table
                  __A			  =							index



def _a							(      lowerCamelCase:  List[str]   ,							lowerCamelCase:  Dict   ,							lowerCamelCase:  List[str]					)      ->		Optional[int]:







      '''simple docstring'''






      __A			  =							dct.pop(lowerCamelCase					)
      __A			  =							val



def _a							(      )      ->		Dict:







      '''simple docstring'''






      __A			  =							'''http://images.cocodataset.org/val2017/000000039769.jpg'''
      __A			  =							Image.open(requests.get(lowerCamelCase   ,							stream=lowerCamelCase					).raw					)
      return im



@torch.no_grad()
def _a							(      lowerCamelCase:  Dict   ,							lowerCamelCase:  List[Any]					)      ->		Any:







      '''simple docstring'''






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

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

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

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

      __A			  =							image_processor(images=lowerCamelCase   ,							return_tensors='''pt'''					)
      __A			  =							encoding['''pixel_values''']

      __A			  =							model(lowerCamelCase					)
      __A			  =							outputs.logits

      # verify logits
      __A			  =							torch.Size([1, 10_00]					)
      if checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k'''					):
            __A			  =							torch.Size([1, 1_96, 81_92]					)
      elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k'''					):
            __A			  =							torch.Size([1, 1_96, 81_92]					)
      elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22k'''					):
            __A			  =							torch.Size([1, 2_18_41]					)
            __A			  =							torch.tensor([2.2_288, 2.4_671, 0.7_395]					)
            __A			  =							23_97
      elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22k'''					):
            __A			  =							torch.Size([1, 2_18_41]					)
            __A			  =							torch.tensor([1.6_881, -0.2_787, 0.5_901]					)
            __A			  =							23_96
      elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft1k'''					):
            __A			  =							torch.tensor([0.1_241, 0.0_798, -0.6_569]					)
            __A			  =							2_85
      elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22kto1k'''					):
            __A			  =							torch.tensor([-1.2_385, -1.0_987, -1.0_108]					)
            __A			  =							2_81
      elif checkpoint_url[:-4].endswith('''beit_base_patch16_384_pt22k_ft22kto1k'''					):
            __A			  =							torch.tensor([-1.5_303, -0.9_484, -0.3_147]					)
            __A			  =							7_61
      elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft1k'''					):
            __A			  =							torch.tensor([0.4_610, -0.0_928, 0.2_086]					)
            __A			  =							7_61
      elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22kto1k'''					):
            __A			  =							torch.tensor([-0.4_804, 0.6_257, -0.1_837]					)
            __A			  =							7_61
      elif checkpoint_url[:-4].endswith('''beit_large_patch16_384_pt22k_ft22kto1k'''					):
            __A			  =							torch.tensor([[-0.5_122, 0.5_117, -0.2_113]]					)
            __A			  =							7_61
      elif checkpoint_url[:-4].endswith('''beit_large_patch16_512_pt22k_ft22kto1k'''					):
            __A			  =							torch.tensor([-0.3_062, 0.7_261, 0.4_852]					)
            __A			  =							7_61
      elif checkpoint_url[:-4].endswith('''beit_base_patch16_640_pt22k_ft22ktoade20k'''					):
            __A			  =							(1, 1_50, 1_60, 1_60)
            __A			  =							torch.tensor(
                [
                    [[-4.9_225, -2.3_954, -3.0_522], [-2.8_822, -1.0_046, -1.7_561], [-2.9_549, -1.3_228, -2.1_347]],
                    [[-5.8_168, -3.4_129, -4.0_778], [-3.8_651, -2.2_214, -3.0_277], [-3.8_356, -2.4_643, -3.3_535]],
                    [[-0.0_078, 3.9_952, 4.0_754], [2.9_856, 4.6_944, 5.0_035], [3.2_413, 4.7_813, 4.9_969]],
                ]					)
      elif checkpoint_url[:-4].endswith('''beit_large_patch16_640_pt22k_ft22ktoade20k'''					):
            __A			  =							(1, 1_50, 1_60, 1_60)
            __A			  =							torch.tensor(
                [
                    [[-4.3_305, -2.3_049, -3.0_161], [-2.9_591, -1.5_305, -2.2_251], [-3.4_198, -1.8_004, -2.9_062]],
                    [[-5.8_922, -3.7_435, -4.3_978], [-4.2_063, -2.7_872, -3.4_755], [-4.2_791, -3.1_874, -4.1_681]],
                    [[0.9_895, 4.3_467, 4.7_663], [4.2_476, 5.6_830, 6.1_518], [4.5_550, 6.2_495, 6.5_154]],
                ]					)
      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]   ,							lowerCamelCase   ,							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]   ,							lowerCamelCase   ,							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(lowerCamelCase					).mkdir(exist_ok=lowerCamelCase					)
      print(F"""Saving model to {pytorch_dump_folder_path}"""					)
      model.save_pretrained(lowerCamelCase					)
      print(F"""Saving image processor to {pytorch_dump_folder_path}"""					)
      image_processor.save_pretrained(lowerCamelCase					)


if __name__ == "__main__":
       snake_case__   :				Optional[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.'
       )
       snake_case__   :				Any						=	parser.parse_args()
       convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case__   :				Any						=	{
    'configuration_camembert': ['CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CamembertConfig', 'CamembertOnnxConfig'],
}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__   :				List[Any]						=	['CamembertTokenizer']

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__   :				Dict						=	['CamembertTokenizerFast']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__   :				Any						=	[
           'CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'CamembertForCausalLM',
           'CamembertForMaskedLM',
           'CamembertForMultipleChoice',
           'CamembertForQuestionAnswering',
           'CamembertForSequenceClassification',
           'CamembertForTokenClassification',
           'CamembertModel',
           'CamembertPreTrainedModel',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__   :				Any						=	[
           'TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'TFCamembertForCausalLM',
           'TFCamembertForMaskedLM',
           'TFCamembertForMultipleChoice',
           'TFCamembertForQuestionAnswering',
           'TFCamembertForSequenceClassification',
           'TFCamembertForTokenClassification',
           'TFCamembertModel',
           'TFCamembertPreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_camembert import CamembertTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_camembert_fast import CamembertTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_camembert import (
                  CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  CamembertForCausalLM,
                  CamembertForMaskedLM,
                  CamembertForMultipleChoice,
                  CamembertForQuestionAnswering,
                  CamembertForSequenceClassification,
                  CamembertForTokenClassification,
                  CamembertModel,
                  CamembertPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_camembert import (
                  TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFCamembertForCausalLM,
                  TFCamembertForMaskedLM,
                  TFCamembertForMultipleChoice,
                  TFCamembertForQuestionAnswering,
                  TFCamembertForSequenceClassification,
                  TFCamembertForTokenClassification,
                  TFCamembertModel,
                  TFCamembertPreTrainedModel,
              )

else:
       import sys

       snake_case__   :				Any						=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





