




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_ 		=  logging.get_logger(__name__)




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_=False       ,  lowercase_=False   )							->						Union[str, Any]:

    """simple docstring"""


    __UpperCamelCase			=    '''backbone.''' if is_semantic else ''''''

    __UpperCamelCase			=    []
    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        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_=False       ,  lowercase_=False   )							->						Tuple:

    """simple docstring"""


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

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

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

        __UpperCamelCase			=    gamma_a
        __UpperCamelCase			=    gamma_a

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

            __UpperCamelCase			=    table
            __UpperCamelCase			=    index




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_   )							->						Optional[int]:

    """simple docstring"""


    __UpperCamelCase			=    dct.pop(lowercase_   )
    __UpperCamelCase			=    val




def        __SCREAMING_SNAKE_CASE (	)							->						str:

    """simple docstring"""


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




@torch.no_grad()
def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						List[Any]:

    """simple docstring"""


    __UpperCamelCase			=    BeitConfig()
    __UpperCamelCase			=    False
    __UpperCamelCase			=    False
    __UpperCamelCase			=    '''huggingface/label-files'''
    # set config parameters based on URL
    if checkpoint_url[-9:-4] == "pt22k":
        # masked image modeling
        __UpperCamelCase			=    True
        __UpperCamelCase			=    True
        __UpperCamelCase			=    True
    elif checkpoint_url[-9:-4] == "ft22k":
        # intermediate fine-tuning on ImageNet-22k
        __UpperCamelCase			=    True
        __UpperCamelCase			=    2_18_41
        __UpperCamelCase			=    '''imagenet-22k-id2label.json'''
        __UpperCamelCase			=    json.load(open(hf_hub_download(lowercase_       ,  lowercase_       ,  repo_type='''dataset'''   )       ,  '''r'''   )   )
        __UpperCamelCase			=    {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[92_05]
        del idalabel[1_50_27]
        __UpperCamelCase			=    idalabel
        __UpperCamelCase			=    {v: k for k, v in idalabel.items()}
    elif checkpoint_url[-8:-4] == "to1k":
        # fine-tuning on ImageNet-1k
        __UpperCamelCase			=    True
        __UpperCamelCase			=    10_00
        __UpperCamelCase			=    '''imagenet-1k-id2label.json'''
        __UpperCamelCase			=    json.load(open(hf_hub_download(lowercase_       ,  lowercase_       ,  repo_type='''dataset'''   )       ,  '''r'''   )   )
        __UpperCamelCase			=    {int(lowercase_   ): v for k, v in idalabel.items()}
        __UpperCamelCase			=    idalabel
        __UpperCamelCase			=    {v: k for k, v in idalabel.items()}
        if "384" in checkpoint_url:
            __UpperCamelCase			=    3_84
        if "512" in checkpoint_url:
            __UpperCamelCase			=    5_12
    elif "ade20k" in checkpoint_url:
        # fine-tuning
        __UpperCamelCase			=    True
        __UpperCamelCase			=    1_50
        __UpperCamelCase			=    '''ade20k-id2label.json'''
        __UpperCamelCase			=    json.load(open(hf_hub_download(lowercase_       ,  lowercase_       ,  repo_type='''dataset'''   )       ,  '''r'''   )   )
        __UpperCamelCase			=    {int(lowercase_   ): v for k, v in idalabel.items()}
        __UpperCamelCase			=    idalabel
        __UpperCamelCase			=    {v: k for k, v in idalabel.items()}
        __UpperCamelCase			=    6_40
        __UpperCamelCase			=    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			=    10_24
        __UpperCamelCase			=    40_96
        __UpperCamelCase			=    24
        __UpperCamelCase			=    16
        if "ade20k" in checkpoint_url:
            __UpperCamelCase			=    6_40
            __UpperCamelCase			=    [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
    __UpperCamelCase			=    torch.hub.load_state_dict_from_url(lowercase_       ,  map_location='''cpu'''       ,  check_hash=lowercase_   )
    __UpperCamelCase			=    state_dict['''model'''] if '''ade20k''' not in checkpoint_url else state_dict['''state_dict''']

    __UpperCamelCase			=    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			=    state_dict.pop(lowercase_   )
            if key.startswith('''backbone.fpn'''   ):
                __UpperCamelCase			=    key.replace('''backbone.fpn'''       ,  '''fpn'''   )
            __UpperCamelCase			=    val

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

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

    __UpperCamelCase			=    image_processor(images=lowercase_       ,  return_tensors='''pt'''   )
    __UpperCamelCase			=    encoding['''pixel_values''']

    __UpperCamelCase			=    model(lowercase_   )
    __UpperCamelCase			=    outputs.logits

    # verify logits
    __UpperCamelCase			=    torch.Size([1, 10_00]   )
    if checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k'''   ):
        __UpperCamelCase			=    torch.Size([1, 1_96, 81_92]   )
    elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k'''   ):
        __UpperCamelCase			=    torch.Size([1, 1_96, 81_92]   )
    elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22k'''   ):
        __UpperCamelCase			=    torch.Size([1, 2_18_41]   )
        __UpperCamelCase			=    torch.tensor([2.2288, 2.4671, 0.7395]   )
        __UpperCamelCase			=    23_97
    elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22k'''   ):
        __UpperCamelCase			=    torch.Size([1, 2_18_41]   )
        __UpperCamelCase			=    torch.tensor([1.6881, -0.2787, 0.5901]   )
        __UpperCamelCase			=    23_96
    elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft1k'''   ):
        __UpperCamelCase			=    torch.tensor([0.1241, 0.0798, -0.6569]   )
        __UpperCamelCase			=    2_85
    elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22kto1k'''   ):
        __UpperCamelCase			=    torch.tensor([-1.2385, -1.0987, -1.0108]   )
        __UpperCamelCase			=    2_81
    elif checkpoint_url[:-4].endswith('''beit_base_patch16_384_pt22k_ft22kto1k'''   ):
        __UpperCamelCase			=    torch.tensor([-1.5303, -0.9484, -0.3147]   )
        __UpperCamelCase			=    7_61
    elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft1k'''   ):
        __UpperCamelCase			=    torch.tensor([0.4610, -0.0928, 0.2086]   )
        __UpperCamelCase			=    7_61
    elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22kto1k'''   ):
        __UpperCamelCase			=    torch.tensor([-0.4804, 0.6257, -0.1837]   )
        __UpperCamelCase			=    7_61
    elif checkpoint_url[:-4].endswith('''beit_large_patch16_384_pt22k_ft22kto1k'''   ):
        __UpperCamelCase			=    torch.tensor([[-0.5122, 0.5117, -0.2113]]   )
        __UpperCamelCase			=    7_61
    elif checkpoint_url[:-4].endswith('''beit_large_patch16_512_pt22k_ft22kto1k'''   ):
        __UpperCamelCase			=    torch.tensor([-0.3062, 0.7261, 0.4852]   )
        __UpperCamelCase			=    7_61
    elif checkpoint_url[:-4].endswith('''beit_base_patch16_640_pt22k_ft22ktoade20k'''   ):
        __UpperCamelCase			=    (1, 1_50, 1_60, 1_60)
        __UpperCamelCase			=    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			=    (1, 1_50, 1_60, 1_60)
        __UpperCamelCase			=    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__":
 a_ 		=  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_ 		=  parser.parse_args()
 convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)










"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowercase          = logging.get_logger(__name__)

__lowercase          = {
    """squeezebert/squeezebert-uncased""": (
        """https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json"""
    ),
    """squeezebert/squeezebert-mnli""": """https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json""",
    """squeezebert/squeezebert-mnli-headless""": (
        """https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json"""
    ),
}






class 		_A    (    _a    ):







    """simple docstring"""




    UpperCAmelCase			:				Tuple      =			SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
    UpperCAmelCase			:				Any      =			"""squeezebert"""






    def __init__(   self							:       List[str]							,					__UpperCAmelCase							:       Tuple=30522							,					__UpperCAmelCase							:       Tuple=768							,					__UpperCAmelCase							:       int=12							,					__UpperCAmelCase							:       str=12							,					__UpperCAmelCase							:       List[Any]=3072							,					__UpperCAmelCase							:       Dict="gelu"							,					__UpperCAmelCase							:       List[Any]=0.1							,					__UpperCAmelCase							:       Optional[int]=0.1							,					__UpperCAmelCase							:       Optional[Any]=512							,					__UpperCAmelCase							:       int=2							,					__UpperCAmelCase							:       Dict=0.02							,					__UpperCAmelCase							:       Optional[int]=1e-12							,					__UpperCAmelCase							:       Optional[Any]=0							,					__UpperCAmelCase							:       Optional[Any]=768							,					__UpperCAmelCase							:       Tuple=4							,					__UpperCAmelCase							:       Dict=4							,					__UpperCAmelCase							:       str=4							,					__UpperCAmelCase							:       List[Any]=1							,					__UpperCAmelCase							:       Optional[int]=4							,					__UpperCAmelCase							:       Optional[int]=4							,					**__UpperCAmelCase							:       Tuple							,					):
      super().__init__(pad_token_id=__UpperCAmelCase							,					**__UpperCAmelCase)

      a		:						List[Any] 				=     vocab_size
      a		:						List[Any] 				=     hidden_size
      a		:						Dict 				=     num_hidden_layers
      a		:						int 				=     num_attention_heads
      a		:						Dict 				=     hidden_act
      a		:						Tuple 				=     intermediate_size
      a		:						int 				=     hidden_dropout_prob
      a		:						List[Any] 				=     attention_probs_dropout_prob
      a		:						Union[str, Any] 				=     max_position_embeddings
      a		:						Union[str, Any] 				=     type_vocab_size
      a		:						Optional[Any] 				=     initializer_range
      a		:						Any 				=     layer_norm_eps
      a		:						Any 				=     embedding_size
      a		:						Tuple 				=     q_groups
      a		:						List[str] 				=     k_groups
      a		:						str 				=     v_groups
      a		:						Optional[Any] 				=     post_attention_groups
      a		:						List[str] 				=     intermediate_groups
      a		:						Optional[int] 				=     output_groups






class 		_A    (    _a    ):







    """simple docstring"""




    @property
    def       __snake_case				(   self							:       Optional[Any]):
      if self.task == "multiple-choice":
        a		:						Optional[int] 				=     {0: "batch", 1: "choice", 2: "sequence"}
      else:
        a		:						Tuple 				=     {0: "batch", 1: "sequence"}
      return OrderedDict(
          [
              ("input_ids", dynamic_axis),
              ("attention_mask", dynamic_axis),
              ("token_type_ids", dynamic_axis),
          ])






