


"""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()
__a			 =   logging.get_logger(__name__)






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase=False	,			_UpperCamelCase=False ):





						"""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 ):





						"""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 ):





						"""simple docstring"""

						UpperCAmelCase__		=    dct.pop(_UpperCamelCase )
						UpperCAmelCase__		=    val






def 					a__ (       ):





						"""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 ):





						"""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__		=    21841
												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[15027]
												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, 21841] )
												UpperCAmelCase__		=    torch.tensor([2.2288, 2.4671, 0.7395] )
												UpperCAmelCase__		=    2397
						elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22k''' ):
												UpperCAmelCase__		=    torch.Size([1, 21841] )
												UpperCAmelCase__		=    torch.tensor([1.6881, -0.2787, 0.5901] )
												UpperCAmelCase__		=    2396
						elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft1k''' ):
												UpperCAmelCase__		=    torch.tensor([0.1241, 0.0798, -0.6569] )
												UpperCAmelCase__		=    285
						elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22kto1k''' ):
												UpperCAmelCase__		=    torch.tensor([-1.2385, -1.0987, -1.0108] )
												UpperCAmelCase__		=    281
						elif checkpoint_url[:-4].endswith('''beit_base_patch16_384_pt22k_ft22kto1k''' ):
												UpperCAmelCase__		=    torch.tensor([-1.5303, -0.9484, -0.3147] )
												UpperCAmelCase__		=    761
						elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft1k''' ):
												UpperCAmelCase__		=    torch.tensor([0.4610, -0.0928, 0.2086] )
												UpperCAmelCase__		=    761
						elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22kto1k''' ):
												UpperCAmelCase__		=    torch.tensor([-0.4804, 0.6257, -0.1837] )
												UpperCAmelCase__		=    761
						elif checkpoint_url[:-4].endswith('''beit_large_patch16_384_pt22k_ft22kto1k''' ):
												UpperCAmelCase__		=    torch.tensor([[-0.5122, 0.5117, -0.2113]] )
												UpperCAmelCase__		=    761
						elif checkpoint_url[:-4].endswith('''beit_large_patch16_512_pt22k_ft22kto1k''' ):
												UpperCAmelCase__		=    torch.tensor([-0.3062, 0.7261, 0.4852] )
												UpperCAmelCase__		=    761
						elif checkpoint_url[:-4].endswith('''beit_base_patch16_640_pt22k_ft22ktoade20k''' ):
												UpperCAmelCase__		=    (1, 150, 160, 160)
												UpperCAmelCase__		=    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''' ):
												UpperCAmelCase__		=    (1, 150, 160, 160)
												UpperCAmelCase__		=    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__":
						__a			 =   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."
						)
						__a			 =   parser.parse_args()
						convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)



"""simple docstring"""







import safs
class 							lowerCamelCase       (			safs.SaFileSystem ):


							"""simple docstring"""