
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     :				Optional[int]             =       logging.get_logger(__name__)
def       UpperCAmelCase__							(    __magic_name__  :       int							,					__magic_name__  :       Tuple=False							,					__magic_name__  :       Any=False					):


					'''simple docstring'''

					lowerCAmelCase						:   Union[str, Any]							=       '''backbone.''' if is_semantic else ''''''

					lowerCAmelCase						:   Dict							=       []
					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__							(    __magic_name__  :       Any							,					__magic_name__  :       int							,					__magic_name__  :       Optional[Any]=False							,					__magic_name__  :       List[Any]=False					):


					'''simple docstring'''

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

										lowerCAmelCase						:   Optional[int]							=       in_proj_weight[
										    : config.hidden_size, :
										]
										lowerCAmelCase						:   List[Any]							=       q_bias
										lowerCAmelCase						:   Optional[Any]							=       in_proj_weight[
										    config.hidden_size : config.hidden_size * 2, :
										]
										lowerCAmelCase						:   List[Any]							=       in_proj_weight[
										    -config.hidden_size :, :
										]
										lowerCAmelCase						:   Optional[int]							=       v_bias

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

										lowerCAmelCase						:   Union[str, Any]							=       gamma_a
										lowerCAmelCase						:   int							=       gamma_a

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

															lowerCAmelCase						:   List[Any]							=       table
															lowerCAmelCase						:   Dict							=       index
def       UpperCAmelCase__							(    __magic_name__  :       Tuple							,					__magic_name__  :       Tuple							,					__magic_name__  :       int					):


					'''simple docstring'''

					lowerCAmelCase						:   List[str]							=       dct.pop(__magic_name__					)
					lowerCAmelCase						:   Any							=       val
def       UpperCAmelCase__							(    ):


					'''simple docstring'''

					lowerCAmelCase						:   Optional[int]							=       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
					lowerCAmelCase						:   Union[str, Any]							=       Image.open(requests.get(__magic_name__							,					stream=__magic_name__					).raw					)
					return im
@torch.no_grad()
def       UpperCAmelCase__							(    __magic_name__  :       str							,					__magic_name__  :       str					):


					'''simple docstring'''

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

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

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

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

					lowerCAmelCase						:   Tuple							=       image_processor(images=__magic_name__							,					return_tensors='''pt'''					)
					lowerCAmelCase						:   Tuple							=       encoding['''pixel_values''']

					lowerCAmelCase						:   Union[str, Any]							=       model(__magic_name__					)
					lowerCAmelCase						:   Optional[int]							=       outputs.logits

					# verify logits
					lowerCAmelCase						:   Any							=       torch.Size([1, 10_00]					)
					if checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k'''					):
										lowerCAmelCase						:   int							=       torch.Size([1, 1_96, 81_92]					)
					elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k'''					):
										lowerCAmelCase						:   int							=       torch.Size([1, 1_96, 81_92]					)
					elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22k'''					):
										lowerCAmelCase						:   Any							=       torch.Size([1, 2_18_41]					)
										lowerCAmelCase						:   str							=       torch.tensor([2.2288, 2.4671, 0.7395]					)
										lowerCAmelCase						:   int							=       23_97
					elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22k'''					):
										lowerCAmelCase						:   Union[str, Any]							=       torch.Size([1, 2_18_41]					)
										lowerCAmelCase						:   List[Any]							=       torch.tensor([1.6881, -0.2787, 0.5901]					)
										lowerCAmelCase						:   Optional[int]							=       23_96
					elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft1k'''					):
										lowerCAmelCase						:   List[Any]							=       torch.tensor([0.1241, 0.0798, -0.6569]					)
										lowerCAmelCase						:   int							=       2_85
					elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22kto1k'''					):
										lowerCAmelCase						:   Dict							=       torch.tensor([-1.2385, -1.0987, -1.0108]					)
										lowerCAmelCase						:   List[str]							=       2_81
					elif checkpoint_url[:-4].endswith('''beit_base_patch16_384_pt22k_ft22kto1k'''					):
										lowerCAmelCase						:   Any							=       torch.tensor([-1.5303, -0.9484, -0.3147]					)
										lowerCAmelCase						:   str							=       7_61
					elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft1k'''					):
										lowerCAmelCase						:   Optional[int]							=       torch.tensor([0.4610, -0.0928, 0.2086]					)
										lowerCAmelCase						:   Dict							=       7_61
					elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22kto1k'''					):
										lowerCAmelCase						:   Union[str, Any]							=       torch.tensor([-0.4804, 0.6257, -0.1837]					)
										lowerCAmelCase						:   int							=       7_61
					elif checkpoint_url[:-4].endswith('''beit_large_patch16_384_pt22k_ft22kto1k'''					):
										lowerCAmelCase						:   int							=       torch.tensor([[-0.5122, 0.5117, -0.2113]]					)
										lowerCAmelCase						:   List[str]							=       7_61
					elif checkpoint_url[:-4].endswith('''beit_large_patch16_512_pt22k_ft22kto1k'''					):
										lowerCAmelCase						:   List[str]							=       torch.tensor([-0.3062, 0.7261, 0.4852]					)
										lowerCAmelCase						:   str							=       7_61
					elif checkpoint_url[:-4].endswith('''beit_base_patch16_640_pt22k_ft22ktoade20k'''					):
										lowerCAmelCase						:   str							=       (1, 1_50, 1_60, 1_60)
										lowerCAmelCase						:   str							=       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'''					):
										lowerCAmelCase						:   Dict							=       (1, 1_50, 1_60, 1_60)
										lowerCAmelCase						:   List[str]							=       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]							,					__magic_name__							,					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]							,					__magic_name__							,					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(__magic_name__					).mkdir(exist_ok=__magic_name__					)
					print(f'''Saving model to {pytorch_dump_folder_path}'''					)
					model.save_pretrained(__magic_name__					)
					print(f'''Saving image processor to {pytorch_dump_folder_path}'''					)
					image_processor.save_pretrained(__magic_name__					)


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE     :				Union[str, 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     :				Tuple             =       parser.parse_args()
		convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)


def       UpperCAmelCase__							(    __magic_name__  :       int = 99_80_01					):


					'''simple docstring'''

					lowerCAmelCase						:   Tuple							=       0
					for i in range(9_99							,					99							,					-1					):  # 3 digit numbers range from 999 down to 100
										for j in range(9_99							,					99							,					-1					):
															lowerCAmelCase						:   Any							=       str(i * j					)
															if product_string == product_string[::-1] and i * j < n:
																				lowerCAmelCase						:   Optional[int]							=       max(__magic_name__							,					i * j					)
					return answer


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