


'''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()
__SCREAMING_SNAKE_CASE				:							str		       =		logging.get_logger(__name__)







def 			a_							(  UpperCamelCase_	,     UpperCamelCase_=False	,     UpperCamelCase_=False   ):
							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_							(  UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_=False	,     UpperCamelCase_=False   ):
							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_							(  UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_   ):
							A_ 						=		dct.pop(UpperCamelCase_   )
							A_ 						=		val







def 			a_							(  ):
							A_ 						=		"http://images.cocodataset.org/val2017/000000039769.jpg"
							A_ 						=		Image.open(requests.get(UpperCamelCase_	,     stream=UpperCamelCase_   ).raw   )
							return im







@torch.no_grad()
def 			a_							(  UpperCamelCase_	,     UpperCamelCase_   ):
							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_1_8_4_1
														A_ 						=		"imagenet-22k-id2label.json"
														A_ 						=		json.load(open(hf_hub_download(UpperCamelCase_	,     UpperCamelCase_	,     repo_type="dataset"   )	,     "r"   )   )
														A_ 						=		{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_2_0_5]
														del idalabel[1_5_0_2_7]
														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_ 						=		1_0_0_0
														A_ 						=		"imagenet-1k-id2label.json"
														A_ 						=		json.load(open(hf_hub_download(UpperCamelCase_	,     UpperCamelCase_	,     repo_type="dataset"   )	,     "r"   )   )
														A_ 						=		{int(UpperCamelCase_   ): 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_8_4
														if "512" in checkpoint_url:
																					A_ 						=		5_1_2
							elif "ade20k" in checkpoint_url:
														# fine-tuning
														A_ 						=		True
														A_ 						=		1_5_0
														A_ 						=		"ade20k-id2label.json"
														A_ 						=		json.load(open(hf_hub_download(UpperCamelCase_	,     UpperCamelCase_	,     repo_type="dataset"   )	,     "r"   )   )
														A_ 						=		{int(UpperCamelCase_   ): v for k, v in idalabel.items()}
														A_ 						=		idalabel
														A_ 						=		{v: k for k, v in idalabel.items()}
														A_ 						=		6_4_0
														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_ 						=		1_0_2_4
														A_ 						=		4_0_9_6
														A_ 						=		2_4
														A_ 						=		1_6
														if "ade20k" in checkpoint_url:
																					A_ 						=		6_4_0
																					A_ 						=		[7, 1_1, 1_5, 2_3]
							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(UpperCamelCase_	,     map_location="cpu"	,     check_hash=UpperCamelCase_   )
							A_ 						=		state_dict["model"] if "ade20k" not in checkpoint_url else state_dict["state_dict"]

							A_ 						=		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():
																					A_ 						=		state_dict.pop(UpperCamelCase_   )
																					if key.startswith("backbone.fpn"   ):
																												A_ 						=		key.replace("backbone.fpn"	,     "fpn"   )
																					A_ 						=		val

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

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

							A_ 						=		image_processor(images=UpperCamelCase_	,     return_tensors="pt"   )
							A_ 						=		encoding["pixel_values"]

							A_ 						=		model(UpperCamelCase_   )
							A_ 						=		outputs.logits

							# verify logits
							A_ 						=		torch.Size([1, 1_0_0_0]   )
							if checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k"   ):
														A_ 						=		torch.Size([1, 1_9_6, 8_1_9_2]   )
							elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k"   ):
														A_ 						=		torch.Size([1, 1_9_6, 8_1_9_2]   )
							elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft22k"   ):
														A_ 						=		torch.Size([1, 2_1_8_4_1]   )
														A_ 						=		torch.tensor([2.2288, 2.4671, 0.7395]   )
														A_ 						=		2_3_9_7
							elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft22k"   ):
														A_ 						=		torch.Size([1, 2_1_8_4_1]   )
														A_ 						=		torch.tensor([1.6881, -0.2787, 0.5901]   )
														A_ 						=		2_3_9_6
							elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft1k"   ):
														A_ 						=		torch.tensor([0.1241, 0.0798, -0.6569]   )
														A_ 						=		2_8_5
							elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft22kto1k"   ):
														A_ 						=		torch.tensor([-1.2385, -1.0987, -1.0108]   )
														A_ 						=		2_8_1
							elif checkpoint_url[:-4].endswith("beit_base_patch16_384_pt22k_ft22kto1k"   ):
														A_ 						=		torch.tensor([-1.5303, -0.9484, -0.3147]   )
														A_ 						=		7_6_1
							elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft1k"   ):
														A_ 						=		torch.tensor([0.4610, -0.0928, 0.2086]   )
														A_ 						=		7_6_1
							elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft22kto1k"   ):
														A_ 						=		torch.tensor([-0.4804, 0.6257, -0.1837]   )
														A_ 						=		7_6_1
							elif checkpoint_url[:-4].endswith("beit_large_patch16_384_pt22k_ft22kto1k"   ):
														A_ 						=		torch.tensor([[-0.5122, 0.5117, -0.2113]]   )
														A_ 						=		7_6_1
							elif checkpoint_url[:-4].endswith("beit_large_patch16_512_pt22k_ft22kto1k"   ):
														A_ 						=		torch.tensor([-0.3062, 0.7261, 0.4852]   )
														A_ 						=		7_6_1
							elif checkpoint_url[:-4].endswith("beit_base_patch16_640_pt22k_ft22ktoade20k"   ):
														A_ 						=		(1, 1_5_0, 1_6_0, 1_6_0)
														A_ 						=		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"   ):
														A_ 						=		(1, 1_5_0, 1_6_0, 1_6_0)
														A_ 						=		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__":
							__SCREAMING_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.'''
							)
							__SCREAMING_SNAKE_CASE				:							List[Any]		       =		parser.parse_args()
							convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)









'''simple docstring'''
def 			a_							(  UpperCamelCase_	,     UpperCamelCase_ = 1_0_0_0   ):
							if (not isinstance(UpperCamelCase_	,     UpperCamelCase_   )) or (digit_position <= 0):
														raise ValueError("Digit position must be a positive integer"   )
							elif (not isinstance(UpperCamelCase_	,     UpperCamelCase_   )) or (precision < 0):
														raise ValueError("Precision must be a nonnegative integer"   )

							# compute an approximation of (16 ** (n - 1)) * pi whose fractional part is mostly
							# accurate
							A_ 						=		(
							    4 * _subsum(UpperCamelCase_	,     1	,     UpperCamelCase_   )
							    - 2 * _subsum(UpperCamelCase_	,     4	,     UpperCamelCase_   )
							    - _subsum(UpperCamelCase_	,     5	,     UpperCamelCase_   )
							    - _subsum(UpperCamelCase_	,     6	,     UpperCamelCase_   )
							)

							# return the first hex digit of the fractional part of the result
							return hex(int((sum_result % 1) * 1_6   )   )[2:]







def 			a_							(  UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_   ):
							A_ 						=		0.0
							for sum_index in range(digit_pos_to_extract + precision   ):
														A_ 						=		8 * sum_index + denominator_addend
														if sum_index < digit_pos_to_extract:
																					# if the exponential term is an integer and we mod it by the denominator
																					# before dividing, only the integer part of the sum will change;
																					# the fractional part will not
																					A_ 						=		pow(
																					    1_6	,     digit_pos_to_extract - 1 - sum_index	,     UpperCamelCase_   )
														else:
																					A_ 						=		pow(1_6	,     digit_pos_to_extract - 1 - sum_index   )
														total += exponential_term / denominator
							return total


if __name__ == "__main__":
							import doctest

							doctest.testmod()





