

'''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()
_lowerCamelCase	 =			logging.get_logger(__name__)


def       a__    (  _SCREAMING_SNAKE_CASE					:    Union[str, Any]     ,							_SCREAMING_SNAKE_CASE					:    Optional[Any]=False     ,							_SCREAMING_SNAKE_CASE					:    Optional[Any]=False							)			-> Optional[int]:


	"""simple docstring"""





	UpperCAmelCase_			:	Optional[int]      =   "backbone." if is_semantic else ""

	UpperCAmelCase_			:	Optional[int]      =   []
	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__    (  _SCREAMING_SNAKE_CASE					:    int     ,							_SCREAMING_SNAKE_CASE					:    List[str]     ,							_SCREAMING_SNAKE_CASE					:    Dict=False     ,							_SCREAMING_SNAKE_CASE					:    str=False							)			-> Union[str, Any]:


	"""simple docstring"""





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

		UpperCAmelCase_			:	Any      =   in_proj_weight[
		    : config.hidden_size, :
		]
		UpperCAmelCase_			:	List[str]      =   q_bias
		UpperCAmelCase_			:	List[Any]      =   in_proj_weight[
		    config.hidden_size : config.hidden_size * 2, :
		]
		UpperCAmelCase_			:	str      =   in_proj_weight[
		    -config.hidden_size :, :
		]
		UpperCAmelCase_			:	Any      =   v_bias

		# gamma_1 and gamma_2
		# we call them lambda because otherwise they are renamed when using .from_pretrained
		UpperCAmelCase_			:	List[str]      =   state_dict.pop(F'''{prefix}blocks.{i}.gamma_1'''							)
		UpperCAmelCase_			:	Optional[Any]      =   state_dict.pop(F'''{prefix}blocks.{i}.gamma_2'''							)

		UpperCAmelCase_			:	List[str]      =   gamma_a
		UpperCAmelCase_			:	int      =   gamma_a

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

			UpperCAmelCase_			:	List[str]      =   table
			UpperCAmelCase_			:	Dict      =   index


def       a__    (  _SCREAMING_SNAKE_CASE					:    Tuple     ,							_SCREAMING_SNAKE_CASE					:    Dict     ,							_SCREAMING_SNAKE_CASE					:    Optional[Any]							)			-> List[str]:


	"""simple docstring"""





	UpperCAmelCase_			:	Union[str, Any]      =   dct.pop(_SCREAMING_SNAKE_CASE							)
	UpperCAmelCase_			:	Tuple      =   val


def       a__    (  )			-> Tuple:


	"""simple docstring"""





	UpperCAmelCase_			:	List[str]      =   "http://images.cocodataset.org/val2017/000000039769.jpg"
	UpperCAmelCase_			:	Dict      =   Image.open(requests.get(_SCREAMING_SNAKE_CASE     ,							stream=_SCREAMING_SNAKE_CASE							).raw							)
	return im


@torch.no_grad()
def       a__    (  _SCREAMING_SNAKE_CASE					:    Optional[int]     ,							_SCREAMING_SNAKE_CASE					:    Tuple							)			-> Any:


	"""simple docstring"""





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

	UpperCAmelCase_			:	List[Any]      =   create_rename_keys(_SCREAMING_SNAKE_CASE     ,							has_lm_head=_SCREAMING_SNAKE_CASE     ,							is_semantic=_SCREAMING_SNAKE_CASE							)
	for src, dest in rename_keys:
		rename_key(_SCREAMING_SNAKE_CASE     ,							_SCREAMING_SNAKE_CASE     ,							_SCREAMING_SNAKE_CASE							)
	read_in_q_k_v(_SCREAMING_SNAKE_CASE     ,							_SCREAMING_SNAKE_CASE     ,							has_lm_head=_SCREAMING_SNAKE_CASE     ,							is_semantic=_SCREAMING_SNAKE_CASE							)
	if is_semantic:
		# add prefix to decoder keys
		for key, val in state_dict.copy().items():
			UpperCAmelCase_			:	Optional[int]      =   state_dict.pop(_SCREAMING_SNAKE_CASE							)
			if key.startswith("backbone.fpn"							):
				UpperCAmelCase_			:	int      =   key.replace("backbone.fpn"     ,							"fpn"							)
			UpperCAmelCase_			:	List[str]      =   val

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

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

	UpperCAmelCase_			:	Optional[int]      =   image_processor(images=_SCREAMING_SNAKE_CASE     ,							return_tensors="pt"							)
	UpperCAmelCase_			:	List[str]      =   encoding["pixel_values"]

	UpperCAmelCase_			:	List[Any]      =   model(_SCREAMING_SNAKE_CASE							)
	UpperCAmelCase_			:	Tuple      =   outputs.logits

	# verify logits
	UpperCAmelCase_			:	Tuple      =   torch.Size([1, 10_00]							)
	if checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k"							):
		UpperCAmelCase_			:	Dict      =   torch.Size([1, 1_96, 81_92]							)
	elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k"							):
		UpperCAmelCase_			:	Dict      =   torch.Size([1, 1_96, 81_92]							)
	elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft22k"							):
		UpperCAmelCase_			:	str      =   torch.Size([1, 2_18_41]							)
		UpperCAmelCase_			:	Tuple      =   torch.tensor([2.2_288, 2.4_671, 0.7_395]							)
		UpperCAmelCase_			:	Dict      =   23_97
	elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft22k"							):
		UpperCAmelCase_			:	List[str]      =   torch.Size([1, 2_18_41]							)
		UpperCAmelCase_			:	Optional[int]      =   torch.tensor([1.6_881, -0.2_787, 0.5_901]							)
		UpperCAmelCase_			:	List[str]      =   23_96
	elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft1k"							):
		UpperCAmelCase_			:	int      =   torch.tensor([0.1_241, 0.0_798, -0.6_569]							)
		UpperCAmelCase_			:	Union[str, Any]      =   2_85
	elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft22kto1k"							):
		UpperCAmelCase_			:	Union[str, Any]      =   torch.tensor([-1.2_385, -1.0_987, -1.0_108]							)
		UpperCAmelCase_			:	Tuple      =   2_81
	elif checkpoint_url[:-4].endswith("beit_base_patch16_384_pt22k_ft22kto1k"							):
		UpperCAmelCase_			:	str      =   torch.tensor([-1.5_303, -0.9_484, -0.3_147]							)
		UpperCAmelCase_			:	Any      =   7_61
	elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft1k"							):
		UpperCAmelCase_			:	str      =   torch.tensor([0.4_610, -0.0_928, 0.2_086]							)
		UpperCAmelCase_			:	List[Any]      =   7_61
	elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft22kto1k"							):
		UpperCAmelCase_			:	Union[str, Any]      =   torch.tensor([-0.4_804, 0.6_257, -0.1_837]							)
		UpperCAmelCase_			:	Any      =   7_61
	elif checkpoint_url[:-4].endswith("beit_large_patch16_384_pt22k_ft22kto1k"							):
		UpperCAmelCase_			:	List[Any]      =   torch.tensor([[-0.5_122, 0.5_117, -0.2_113]]							)
		UpperCAmelCase_			:	Any      =   7_61
	elif checkpoint_url[:-4].endswith("beit_large_patch16_512_pt22k_ft22kto1k"							):
		UpperCAmelCase_			:	Optional[Any]      =   torch.tensor([-0.3_062, 0.7_261, 0.4_852]							)
		UpperCAmelCase_			:	Dict      =   7_61
	elif checkpoint_url[:-4].endswith("beit_base_patch16_640_pt22k_ft22ktoade20k"							):
		UpperCAmelCase_			:	Dict      =   (1, 1_50, 1_60, 1_60)
		UpperCAmelCase_			:	int      =   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_			:	List[Any]      =   (1, 1_50, 1_60, 1_60)
		UpperCAmelCase_			:	List[str]      =   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]     ,							_SCREAMING_SNAKE_CASE     ,							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]     ,							_SCREAMING_SNAKE_CASE     ,							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(_SCREAMING_SNAKE_CASE							).mkdir(exist_ok=_SCREAMING_SNAKE_CASE							)
	print(F'''Saving model to {pytorch_dump_folder_path}'''							)
	model.save_pretrained(_SCREAMING_SNAKE_CASE							)
	print(F'''Saving image processor to {pytorch_dump_folder_path}'''							)
	image_processor.save_pretrained(_SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
					_lowerCamelCase	 =			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."""
					)
					_lowerCamelCase	 =			parser.parse_args()
					convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)







'''simple docstring'''
from __future__ import annotations


def       a__    (  _SCREAMING_SNAKE_CASE					:    int							)			-> set[int]:


	"""simple docstring"""





	UpperCAmelCase_			:	Dict      =   {1}
	UpperCAmelCase_			:	int      =   [1]
	for _ in range(2     ,							depth + 1							):
		UpperCAmelCase_			:	Dict      =   [*previous_coefficients, 0]
		UpperCAmelCase_			:	Tuple      =   [0, *previous_coefficients]
		UpperCAmelCase_			:	Dict      =   []
		for x, y in zip(_SCREAMING_SNAKE_CASE     ,							_SCREAMING_SNAKE_CASE							):
			coefficients.add(x + y							)
			previous_coefficients.append(x + y							)
	return coefficients


def       a__    (  _SCREAMING_SNAKE_CASE					:    set[int]							)			-> set[int]:


	"""simple docstring"""





	UpperCAmelCase_			:	List[Any]      =   set()
	for number in unique_coefficients:
		UpperCAmelCase_			:	Tuple      =   2
		UpperCAmelCase_			:	Tuple      =   number
		while divisor**2 <= copy_number:
			UpperCAmelCase_			:	List[Any]      =   0
			while copy_number % divisor == 0:
				copy_number //= divisor
				multiplicity += 1
			if multiplicity >= 2:
				non_squarefrees.add(_SCREAMING_SNAKE_CASE							)
				break
			divisor += 1

	return unique_coefficients.difference(_SCREAMING_SNAKE_CASE							)


def       a__    (  _SCREAMING_SNAKE_CASE					:    int = 51							)			-> int:


	"""simple docstring"""





	UpperCAmelCase_			:	Optional[Any]      =   get_pascal_triangle_unique_coefficients(_SCREAMING_SNAKE_CASE							)
	UpperCAmelCase_			:	Union[str, Any]      =   get_squarefrees(_SCREAMING_SNAKE_CASE							)
	return sum(_SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
					print(f"""{solution() = }""")




