



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_      			=  logging.get_logger(__name__)







def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase=False				,   __UpperCAmelCase=False      )			->			Optional[int]:



						'''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 							A     (						__UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase=False				,   __UpperCAmelCase=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 							A     (						__UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase      )			->			List[Any]:



						'''simple docstring'''






						UpperCAmelCase_					=	dct.pop(__UpperCAmelCase      )
						UpperCAmelCase_					=	val







def 							A     (						)			->			int:



						'''simple docstring'''






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







@torch.no_grad()
def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase      )			->			List[str]:



						'''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_1841
												UpperCAmelCase_					=	'''imagenet-22k-id2label.json'''
												UpperCAmelCase_					=	json.load(open(hf_hub_download(__UpperCAmelCase				,   __UpperCAmelCase				,   repo_type='''dataset'''      )				,   '''r'''      )      )
												UpperCAmelCase_					=	{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]
												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_					=	1000
												UpperCAmelCase_					=	'''imagenet-1k-id2label.json'''
												UpperCAmelCase_					=	json.load(open(hf_hub_download(__UpperCAmelCase				,   __UpperCAmelCase				,   repo_type='''dataset'''      )				,   '''r'''      )      )
												UpperCAmelCase_					=	{int(__UpperCAmelCase      ): v for k, v in idalabel.items()}
												UpperCAmelCase_					=	idalabel
												UpperCAmelCase_					=	{v: k for k, v in idalabel.items()}
												if "384" in checkpoint_url:
																		UpperCAmelCase_					=	384
												if "512" in checkpoint_url:
																		UpperCAmelCase_					=	512
						elif "ade20k" in checkpoint_url:
												# fine-tuning
												UpperCAmelCase_					=	True
												UpperCAmelCase_					=	150
												UpperCAmelCase_					=	'''ade20k-id2label.json'''
												UpperCAmelCase_					=	json.load(open(hf_hub_download(__UpperCAmelCase				,   __UpperCAmelCase				,   repo_type='''dataset'''      )				,   '''r'''      )      )
												UpperCAmelCase_					=	{int(__UpperCAmelCase      ): v for k, v in idalabel.items()}
												UpperCAmelCase_					=	idalabel
												UpperCAmelCase_					=	{v: k for k, v in idalabel.items()}
												UpperCAmelCase_					=	640
												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_					=	1024
												UpperCAmelCase_					=	4096
												UpperCAmelCase_					=	24
												UpperCAmelCase_					=	16
												if "ade20k" in checkpoint_url:
																		UpperCAmelCase_					=	640
																		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(__UpperCAmelCase				,   map_location='''cpu'''				,   check_hash=__UpperCAmelCase      )
						UpperCAmelCase_					=	state_dict['''model'''] if '''ade20k''' not in checkpoint_url else state_dict['''state_dict''']

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

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

						# Check outputs on an image
						if is_semantic:
												UpperCAmelCase_					=	BeitImageProcessor(size=config.image_size				,   do_center_crop=__UpperCAmelCase      )
												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=__UpperCAmelCase      )
												UpperCAmelCase_					=	prepare_img()

						UpperCAmelCase_					=	image_processor(images=__UpperCAmelCase				,   return_tensors='''pt'''      )
						UpperCAmelCase_					=	encoding['''pixel_values''']

						UpperCAmelCase_					=	model(__UpperCAmelCase      )
						UpperCAmelCase_					=	outputs.logits

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


UpperCamelCase_      			=  {
    "configuration_mobilenet_v1": [
        "MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileNetV1Config",
        "MobileNetV1OnnxConfig",
    ],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_      			=  ["MobileNetV1FeatureExtractor"]
							UpperCamelCase_      			=  ["MobileNetV1ImageProcessor"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_      			=  [
							    "MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "MobileNetV1ForImageClassification",
							    "MobileNetV1Model",
							    "MobileNetV1PreTrainedModel",
							    "load_tf_weights_in_mobilenet_v1",
							]


if TYPE_CHECKING:
							from .configuration_mobilenet_va import (
							    MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    MobileNetVaConfig,
							    MobileNetVaOnnxConfig,
							)

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
														from .image_processing_mobilenet_va import MobileNetVaImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mobilenet_va import (
														    MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MobileNetVaForImageClassification,
														    MobileNetVaModel,
														    MobileNetVaPreTrainedModel,
														    load_tf_weights_in_mobilenet_va,
														)


else:
							import sys

							UpperCamelCase_      			=  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





