




"""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		 =      logging.get_logger(__name__)

def 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase=False ,     lowerCamelCase=False						):
						'''simple docstring'''


						snake_case__    :   Optional[Any]							     =   '''backbone.''' if is_semantic else ''''''

						snake_case__    :   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 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase=False ,     lowerCamelCase=False						):
						'''simple docstring'''


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

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

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

																		snake_case__    :   Optional[Any]							     =   table
																		snake_case__    :   List[str]							     =   index

def 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase						):
						'''simple docstring'''


						snake_case__    :   str							     =   dct.pop(lowerCamelCase						)
						snake_case__    :   List[Any]							     =   val

def 						lowerCAmelCase							(	):
						'''simple docstring'''


						snake_case__    :   List[str]							     =   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
						snake_case__    :   Any							     =   Image.open(requests.get(lowerCamelCase ,     stream=lowerCamelCase						).raw						)
						return im

@torch.no_grad()
def 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase						):
						'''simple docstring'''


						snake_case__    :   Optional[int]							     =   BeitConfig()
						snake_case__    :   List[Any]							     =   False
						snake_case__    :   Optional[Any]							     =   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__    :   int							     =   True
												snake_case__    :   Any							     =   True
												snake_case__    :   Union[str, Any]							     =   True
						elif checkpoint_url[-9:-4] == "ft22k":
												# intermediate fine-tuning on ImageNet-22k
												snake_case__    :   Any							     =   True
												snake_case__    :   Union[str, Any]							     =   21841
												snake_case__    :   int							     =   '''imagenet-22k-id2label.json'''
												snake_case__    :   Optional[Any]							     =   json.load(open(hf_hub_download(lowerCamelCase ,     lowerCamelCase ,     repo_type='''dataset'''						) ,     '''r'''						)						)
												snake_case__    :   Optional[int]							     =   {int(lowerCamelCase						): 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[15027]
												snake_case__    :   Optional[int]							     =   idalabel
												snake_case__    :   List[Any]							     =   {v: k for k, v in idalabel.items()}
						elif checkpoint_url[-8:-4] == "to1k":
												# fine-tuning on ImageNet-1k
												snake_case__    :   str							     =   True
												snake_case__    :   str							     =   1000
												snake_case__    :   List[Any]							     =   '''imagenet-1k-id2label.json'''
												snake_case__    :   Any							     =   json.load(open(hf_hub_download(lowerCamelCase ,     lowerCamelCase ,     repo_type='''dataset'''						) ,     '''r'''						)						)
												snake_case__    :   Optional[Any]							     =   {int(lowerCamelCase						): v for k, v in idalabel.items()}
												snake_case__    :   List[Any]							     =   idalabel
												snake_case__    :   Tuple							     =   {v: k for k, v in idalabel.items()}
												if "384" in checkpoint_url:
																		snake_case__    :   Union[str, Any]							     =   384
												if "512" in checkpoint_url:
																		snake_case__    :   Union[str, Any]							     =   512
						elif "ade20k" in checkpoint_url:
												# fine-tuning
												snake_case__    :   Tuple							     =   True
												snake_case__    :   Union[str, Any]							     =   150
												snake_case__    :   List[Any]							     =   '''ade20k-id2label.json'''
												snake_case__    :   List[Any]							     =   json.load(open(hf_hub_download(lowerCamelCase ,     lowerCamelCase ,     repo_type='''dataset'''						) ,     '''r'''						)						)
												snake_case__    :   int							     =   {int(lowerCamelCase						): v for k, v in idalabel.items()}
												snake_case__    :   Any							     =   idalabel
												snake_case__    :   Union[str, Any]							     =   {v: k for k, v in idalabel.items()}
												snake_case__    :   int							     =   640
												snake_case__    :   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:
												snake_case__    :   str							     =   1024
												snake_case__    :   str							     =   4096
												snake_case__    :   Optional[Any]							     =   24
												snake_case__    :   Optional[Any]							     =   16
												if "ade20k" in checkpoint_url:
																		snake_case__    :   Any							     =   640
																		snake_case__    :   List[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
						snake_case__    :   Tuple							     =   torch.hub.load_state_dict_from_url(lowerCamelCase ,     map_location='''cpu''' ,     check_hash=lowerCamelCase						)
						snake_case__    :   List[str]							     =   state_dict['''model'''] if '''ade20k''' not in checkpoint_url else state_dict['''state_dict''']

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

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

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

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

						snake_case__    :   Union[str, Any]							     =   model(lowerCamelCase						)
						snake_case__    :   Optional[Any]							     =   outputs.logits

						# verify logits
						snake_case__    :   Any							     =   torch.Size([1, 1000]						)
						if checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k'''						):
												snake_case__    :   List[str]							     =   torch.Size([1, 196, 8192]						)
						elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k'''						):
												snake_case__    :   List[str]							     =   torch.Size([1, 196, 8192]						)
						elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22k'''						):
												snake_case__    :   Tuple							     =   torch.Size([1, 21841]						)
												snake_case__    :   List[Any]							     =   torch.tensor([2.2_288, 2.4_671, 0.7_395]						)
												snake_case__    :   List[str]							     =   2397
						elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22k'''						):
												snake_case__    :   Any							     =   torch.Size([1, 21841]						)
												snake_case__    :   Any							     =   torch.tensor([1.6_881, -0.2_787, 0.5_901]						)
												snake_case__    :   str							     =   2396
						elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft1k'''						):
												snake_case__    :   Union[str, Any]							     =   torch.tensor([0.1_241, 0.0_798, -0.6_569]						)
												snake_case__    :   str							     =   285
						elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22kto1k'''						):
												snake_case__    :   Optional[int]							     =   torch.tensor([-1.2_385, -1.0_987, -1.0_108]						)
												snake_case__    :   List[str]							     =   281
						elif checkpoint_url[:-4].endswith('''beit_base_patch16_384_pt22k_ft22kto1k'''						):
												snake_case__    :   Tuple							     =   torch.tensor([-1.5_303, -0.9_484, -0.3_147]						)
												snake_case__    :   List[Any]							     =   761
						elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft1k'''						):
												snake_case__    :   Tuple							     =   torch.tensor([0.4_610, -0.0_928, 0.2_086]						)
												snake_case__    :   Dict							     =   761
						elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22kto1k'''						):
												snake_case__    :   Dict							     =   torch.tensor([-0.4_804, 0.6_257, -0.1_837]						)
												snake_case__    :   Dict							     =   761
						elif checkpoint_url[:-4].endswith('''beit_large_patch16_384_pt22k_ft22kto1k'''						):
												snake_case__    :   Any							     =   torch.tensor([[-0.5_122, 0.5_117, -0.2_113]]						)
												snake_case__    :   Optional[Any]							     =   761
						elif checkpoint_url[:-4].endswith('''beit_large_patch16_512_pt22k_ft22kto1k'''						):
												snake_case__    :   Tuple							     =   torch.tensor([-0.3_062, 0.7_261, 0.4_852]						)
												snake_case__    :   List[str]							     =   761
						elif checkpoint_url[:-4].endswith('''beit_base_patch16_640_pt22k_ft22ktoade20k'''						):
												snake_case__    :   Any							     =   (1, 150, 160, 160)
												snake_case__    :   Dict							     =   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__    :   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] ,     lowerCamelCase ,     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] ,     lowerCamelCase ,     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(lowerCamelCase						).mkdir(exist_ok=lowerCamelCase						)
						print(f'''Saving model to {pytorch_dump_folder_path}'''						)
						model.save_pretrained(lowerCamelCase						)
						print(f'''Saving image processor to {pytorch_dump_folder_path}'''						)
						image_processor.save_pretrained(lowerCamelCase						)


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








"""simple docstring"""





from __future__ import annotations

from collections.abc import Iterator
from typing import Any

class  __lowercase    :


				'''simple docstring'''


				def __init__(				self				:       List[Any] ,SCREAMING_SNAKE_CASE_				:       Any ):
										snake_case__    :   Any							     =   data
										snake_case__    :   Node | None							     =   None





				def __str__(				self				:       Union[str, Any] ):
										return F'''{self.data}'''







class  __lowercase    :


				'''simple docstring'''


				def __init__(				self				:       Optional[int] ):
										snake_case__    :   Node | None							     =   None
										snake_case__    :   Node | None							     =   None

				def __iter__(				self				:       Optional[int] ):
										snake_case__    :   Optional[int]							     =   self.front
										while node:
																yield node.data
																snake_case__    :   Optional[int]							     =   node.next

				def __len__(				self				:       Any ):
										return len(tuple(iter(self ) ) )

				def __str__(				self				:       Union[str, Any] ):
										return " <- ".join(str(SCREAMING_SNAKE_CASE_ ) for item in self )

				def  _lowerCamelCase   (				self				:       List[str] ):
										return len(self ) == 0

				def  _lowerCamelCase   (				self				:       Optional[int] ,SCREAMING_SNAKE_CASE_				:       Any ):
										snake_case__    :   int							     =   Node(SCREAMING_SNAKE_CASE_ )
										if self.is_empty():
																snake_case__    :   List[Any]							     =   node
										else:
																assert isinstance(self.rear ,SCREAMING_SNAKE_CASE_ )
																snake_case__    :   Union[str, Any]							     =   node
																snake_case__    :   int							     =   node

				def  _lowerCamelCase   (				self				:       int ):
										if self.is_empty():
																raise IndexError('''dequeue from empty queue''' )
										assert isinstance(self.front ,SCREAMING_SNAKE_CASE_ )
										snake_case__    :   List[str]							     =   self.front
										snake_case__    :   str							     =   self.front.next
										if self.front is None:
																snake_case__    :   Optional[Any]							     =   None
										return node.data





				def  _lowerCamelCase   (				self				:       Optional[int] ):
										snake_case__    :   Optional[Any]							     =   None


if __name__ == "__main__":
							from doctest import testmod

							testmod()


