



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


def 							__UpperCAmelCase				(				UpperCamelCase__				:Tuple   ,				UpperCamelCase__				:Union[str, Any]=False   ,				UpperCamelCase__				:Optional[Any]=False			)		->						List[str]:
				snake_case__      :   str      =    '''backbone.''' if is_semantic else ''''''

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

								snake_case__      :   Dict      =    in_proj_weight[
								    : config.hidden_size, :
								]
								snake_case__      :   List[str]      =    q_bias
								snake_case__      :   Optional[int]      =    in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								snake_case__      :   Dict      =    in_proj_weight[
								    -config.hidden_size :, :
								]
								snake_case__      :   str      =    v_bias

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

								snake_case__      :   List[Any]      =    gamma_a
								snake_case__      :   str      =    gamma_a

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

												snake_case__      :   Tuple      =    table
												snake_case__      :   Union[str, Any]      =    index


def 							__UpperCAmelCase				(				UpperCamelCase__				:Union[str, Any]   ,				UpperCamelCase__				:Tuple   ,				UpperCamelCase__				:Any			)		->						Dict:
				snake_case__      :   List[str]      =    dct.pop(UpperCamelCase__			)
				snake_case__      :   int      =    val


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


@torch.no_grad()
def 							__UpperCAmelCase				(				UpperCamelCase__				:Optional[int]   ,				UpperCamelCase__				:List[Any]			)		->						List[str]:
				snake_case__      :   int      =    BeitConfig()
				snake_case__      :   List[Any]      =    False
				snake_case__      :   int      =    False
				snake_case__      :   Optional[Any]      =    '''huggingface/label-files'''
				# set config parameters based on URL
				if checkpoint_url[-9:-4] == "pt22k":
								# masked image modeling
								snake_case__      :   Any      =    True
								snake_case__      :   List[str]      =    True
								snake_case__      :   Tuple      =    True
				elif checkpoint_url[-9:-4] == "ft22k":
								# intermediate fine-tuning on ImageNet-22k
								snake_case__      :   Union[str, Any]      =    True
								snake_case__      :   str      =    2_1841
								snake_case__      :   Optional[int]      =    '''imagenet-22k-id2label.json'''
								snake_case__      :   Optional[Any]      =    json.load(open(hf_hub_download(UpperCamelCase__   ,				UpperCamelCase__   ,				repo_type='''dataset'''			)   ,				'''r'''			)			)
								snake_case__      :   Union[str, Any]      =    {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[9205]
								del idalabel[1_5027]
								snake_case__      :   Optional[int]      =    idalabel
								snake_case__      :   int      =    {v: k for k, v in idalabel.items()}
				elif checkpoint_url[-8:-4] == "to1k":
								# fine-tuning on ImageNet-1k
								snake_case__      :   Tuple      =    True
								snake_case__      :   str      =    1000
								snake_case__      :   List[Any]      =    '''imagenet-1k-id2label.json'''
								snake_case__      :   List[Any]      =    json.load(open(hf_hub_download(UpperCamelCase__   ,				UpperCamelCase__   ,				repo_type='''dataset'''			)   ,				'''r'''			)			)
								snake_case__      :   Any      =    {int(UpperCamelCase__			): v for k, v in idalabel.items()}
								snake_case__      :   Any      =    idalabel
								snake_case__      :   Dict      =    {v: k for k, v in idalabel.items()}
								if "384" in checkpoint_url:
												snake_case__      :   List[Any]      =    384
								if "512" in checkpoint_url:
												snake_case__      :   Optional[Any]      =    512
				elif "ade20k" in checkpoint_url:
								# fine-tuning
								snake_case__      :   List[str]      =    True
								snake_case__      :   Optional[int]      =    150
								snake_case__      :   Dict      =    '''ade20k-id2label.json'''
								snake_case__      :   List[str]      =    json.load(open(hf_hub_download(UpperCamelCase__   ,				UpperCamelCase__   ,				repo_type='''dataset'''			)   ,				'''r'''			)			)
								snake_case__      :   Union[str, Any]      =    {int(UpperCamelCase__			): v for k, v in idalabel.items()}
								snake_case__      :   Optional[int]      =    idalabel
								snake_case__      :   Dict      =    {v: k for k, v in idalabel.items()}
								snake_case__      :   int      =    640
								snake_case__      :   Union[str, Any]      =    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:
								snake_case__      :   Optional[Any]      =    1024
								snake_case__      :   List[Any]      =    4096
								snake_case__      :   List[Any]      =    24
								snake_case__      :   Optional[Any]      =    16
								if "ade20k" in checkpoint_url:
												snake_case__      :   List[str]      =    640
												snake_case__      :   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
				snake_case__      :   List[str]      =    torch.hub.load_state_dict_from_url(UpperCamelCase__   ,				map_location='''cpu'''   ,				check_hash=UpperCamelCase__			)
				snake_case__      :   List[Any]      =    state_dict['''model'''] if '''ade20k''' not in checkpoint_url else state_dict['''state_dict''']

				snake_case__      :   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():
												snake_case__      :   Tuple      =    state_dict.pop(UpperCamelCase__			)
												if key.startswith('''backbone.fpn'''			):
																snake_case__      :   Dict      =    key.replace('''backbone.fpn'''   ,				'''fpn'''			)
												snake_case__      :   List[str]      =    val

    # load HuggingFace model
				if checkpoint_url[-9:-4] == "pt22k":
								snake_case__      :   Any      =    BeitForMaskedImageModeling(UpperCamelCase__			)
				elif "ade20k" in checkpoint_url:
								snake_case__      :   Tuple      =    BeitForSemanticSegmentation(UpperCamelCase__			)
				else:
								snake_case__      :   int      =    BeitForImageClassification(UpperCamelCase__			)
				model.eval()
				model.load_state_dict(UpperCamelCase__			)

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

				snake_case__      :   int      =    image_processor(images=UpperCamelCase__   ,				return_tensors='''pt'''			)
				snake_case__      :   Dict      =    encoding['''pixel_values''']

				snake_case__      :   Optional[int]      =    model(UpperCamelCase__			)
				snake_case__      :   str      =    outputs.logits

				# verify logits
				snake_case__      :   str      =    torch.Size([1, 1000]			)
				if checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k'''			):
								snake_case__      :   Optional[Any]      =    torch.Size([1, 196, 8192]			)
				elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k'''			):
								snake_case__      :   str      =    torch.Size([1, 196, 8192]			)
				elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22k'''			):
								snake_case__      :   Tuple      =    torch.Size([1, 2_1841]			)
								snake_case__      :   Optional[int]      =    torch.tensor([2.2_288, 2.4_671, 0.7_395]			)
								snake_case__      :   Dict      =    2397
				elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22k'''			):
								snake_case__      :   int      =    torch.Size([1, 2_1841]			)
								snake_case__      :   Optional[Any]      =    torch.tensor([1.6_881, -0.2_787, 0.5_901]			)
								snake_case__      :   Any      =    2396
				elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft1k'''			):
								snake_case__      :   str      =    torch.tensor([0.1_241, 0.0_798, -0.6_569]			)
								snake_case__      :   int      =    285
				elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22kto1k'''			):
								snake_case__      :   Optional[Any]      =    torch.tensor([-1.2_385, -1.0_987, -1.0_108]			)
								snake_case__      :   Any      =    281
				elif checkpoint_url[:-4].endswith('''beit_base_patch16_384_pt22k_ft22kto1k'''			):
								snake_case__      :   Optional[Any]      =    torch.tensor([-1.5_303, -0.9_484, -0.3_147]			)
								snake_case__      :   Dict      =    761
				elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft1k'''			):
								snake_case__      :   Dict      =    torch.tensor([0.4_610, -0.0_928, 0.2_086]			)
								snake_case__      :   List[str]      =    761
				elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22kto1k'''			):
								snake_case__      :   List[Any]      =    torch.tensor([-0.4_804, 0.6_257, -0.1_837]			)
								snake_case__      :   List[str]      =    761
				elif checkpoint_url[:-4].endswith('''beit_large_patch16_384_pt22k_ft22kto1k'''			):
								snake_case__      :   Dict      =    torch.tensor([[-0.5_122, 0.5_117, -0.2_113]]			)
								snake_case__      :   Tuple      =    761
				elif checkpoint_url[:-4].endswith('''beit_large_patch16_512_pt22k_ft22kto1k'''			):
								snake_case__      :   int      =    torch.tensor([-0.3_062, 0.7_261, 0.4_852]			)
								snake_case__      :   Any      =    761
				elif checkpoint_url[:-4].endswith('''beit_base_patch16_640_pt22k_ft22ktoade20k'''			):
								snake_case__      :   int      =    (1, 150, 160, 160)
								snake_case__      :   Any      =    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'''			):
								snake_case__      :   Optional[Any]      =    (1, 150, 160, 160)
								snake_case__      :   Union[str, Any]      =    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]   ,				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__":
				_lowercase       :						int						=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."
				)
				_lowercase       :						int						=parser.parse_args()
				convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)









'''simple docstring'''



import pytest

from datasets.utils.version import Version


@pytest.mark.parametrize(
    '''other, expected_equality'''   ,				[
        (Version('''1.0.0'''			), True),
        ('''1.0.0''', True),
        (Version('''2.0.0'''			), False),
        ('''2.0.0''', False),
        ('''1''', False),
        ('''a''', False),
        (1, False),
        (None, False),
    ]   ,				)
def 							__UpperCAmelCase				(				UpperCamelCase__				:Optional[int]   ,				UpperCamelCase__				:int			)		->						List[str]:
				snake_case__      :   Optional[int]      =    Version('''1.0.0'''			)
				assert (version == other) is expected_equality
				assert (version != other) is not expected_equality
				assert (hash(UpperCamelCase__			) == hash(UpperCamelCase__			)) is expected_equality




