





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()
snake_case_				=      logging.get_logger(__name__)


def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_=False				,							SCREAMING_SNAKE_CASE_=False		) ->  List[str]:
		lowerCamelCase :			Union[str, Any]    		='''backbone.''' if is_semantic else ''''''

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

				lowerCamelCase :			Tuple    		=in_proj_weight[
				    : config.hidden_size, :
				]
				lowerCamelCase :			Dict    		=q_bias
				lowerCamelCase :			List[Any]    		=in_proj_weight[
				    config.hidden_size : config.hidden_size * 2, :
				]
				lowerCamelCase :			Dict    		=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 :			Any    		=state_dict.pop(F"{prefix}blocks.{i}.gamma_1"		)
				lowerCamelCase :			List[str]    		=state_dict.pop(F"{prefix}blocks.{i}.gamma_2"		)

				lowerCamelCase :			str    		=gamma_a
				lowerCamelCase :			Optional[Any]    		=gamma_a

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

						lowerCamelCase :			str    		=table
						lowerCamelCase :			Tuple    		=index


def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  str:
		lowerCamelCase :			List[str]    		=dct.pop(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase :			Any    		=val


def        A__						(							) ->  Optional[int]:
		lowerCamelCase :			Union[str, Any]    		='''http://images.cocodataset.org/val2017/000000039769.jpg'''
		lowerCamelCase :			Any    		=Image.open(requests.get(SCREAMING_SNAKE_CASE_				,							stream=SCREAMING_SNAKE_CASE_		).raw		)
		return im


@torch.no_grad()
def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  List[str]:
		lowerCamelCase :			Optional[int]    		=BeitConfig()
		lowerCamelCase :			Any    		=False
		lowerCamelCase :			Tuple    		=False
		lowerCamelCase :			str    		='''huggingface/label-files'''
		# set config parameters based on URL
		if checkpoint_url[-9:-4] == "pt22k":
				# masked image modeling
				lowerCamelCase :			Optional[int]    		=True
				lowerCamelCase :			List[Any]    		=True
				lowerCamelCase :			Tuple    		=True
		elif checkpoint_url[-9:-4] == "ft22k":
				# intermediate fine-tuning on ImageNet-22k
				lowerCamelCase :			List[str]    		=True
				lowerCamelCase :			Optional[Any]    		=2_1_8_4_1
				lowerCamelCase :			str    		='''imagenet-22k-id2label.json'''
				lowerCamelCase :			str    		=json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							repo_type='''dataset'''		)				,							'''r'''		)		)
				lowerCamelCase :			Optional[int]    		={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[9_2_0_5]
				del idalabel[1_5_0_2_7]
				lowerCamelCase :			str    		=idalabel
				lowerCamelCase :			str    		={v: k for k, v in idalabel.items()}
		elif checkpoint_url[-8:-4] == "to1k":
				# fine-tuning on ImageNet-1k
				lowerCamelCase :			Optional[int]    		=True
				lowerCamelCase :			int    		=1_0_0_0
				lowerCamelCase :			str    		='''imagenet-1k-id2label.json'''
				lowerCamelCase :			List[Any]    		=json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							repo_type='''dataset'''		)				,							'''r'''		)		)
				lowerCamelCase :			Tuple    		={int(SCREAMING_SNAKE_CASE_		): v for k, v in idalabel.items()}
				lowerCamelCase :			Any    		=idalabel
				lowerCamelCase :			List[str]    		={v: k for k, v in idalabel.items()}
				if "384" in checkpoint_url:
						lowerCamelCase :			Union[str, Any]    		=3_8_4
				if "512" in checkpoint_url:
						lowerCamelCase :			Optional[Any]    		=5_1_2
		elif "ade20k" in checkpoint_url:
				# fine-tuning
				lowerCamelCase :			Tuple    		=True
				lowerCamelCase :			List[str]    		=1_5_0
				lowerCamelCase :			Tuple    		='''ade20k-id2label.json'''
				lowerCamelCase :			List[str]    		=json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							repo_type='''dataset'''		)				,							'''r'''		)		)
				lowerCamelCase :			Optional[Any]    		={int(SCREAMING_SNAKE_CASE_		): v for k, v in idalabel.items()}
				lowerCamelCase :			Tuple    		=idalabel
				lowerCamelCase :			Optional[int]    		={v: k for k, v in idalabel.items()}
				lowerCamelCase :			List[Any]    		=6_4_0
				lowerCamelCase :			List[Any]    		=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 :			Tuple    		=1_0_2_4
				lowerCamelCase :			Optional[Any]    		=4_0_9_6
				lowerCamelCase :			int    		=2_4
				lowerCamelCase :			Tuple    		=1_6
				if "ade20k" in checkpoint_url:
						lowerCamelCase :			Optional[int]    		=6_4_0
						lowerCamelCase :			List[Any]    		=[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
		lowerCamelCase :			str    		=torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_				,							map_location='''cpu'''				,							check_hash=SCREAMING_SNAKE_CASE_		)
		lowerCamelCase :			Any    		=state_dict['''model'''] if '''ade20k''' not in checkpoint_url else state_dict['''state_dict''']

		lowerCamelCase :			Dict    		=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():
						lowerCamelCase :			Any    		=state_dict.pop(SCREAMING_SNAKE_CASE_		)
						if key.startswith('''backbone.fpn'''		):
								lowerCamelCase :			List[Any]    		=key.replace('''backbone.fpn'''				,							'''fpn'''		)
						lowerCamelCase :			List[Any]    		=val

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

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

		lowerCamelCase :			Optional[int]    		=image_processor(images=SCREAMING_SNAKE_CASE_				,							return_tensors='''pt'''		)
		lowerCamelCase :			Any    		=encoding['''pixel_values''']

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

		# verify logits
		lowerCamelCase :			Any    		=torch.Size([1, 1_0_0_0]		)
		if checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k'''		):
				lowerCamelCase :			Optional[Any]    		=torch.Size([1, 1_9_6, 8_1_9_2]		)
		elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k'''		):
				lowerCamelCase :			str    		=torch.Size([1, 1_9_6, 8_1_9_2]		)
		elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22k'''		):
				lowerCamelCase :			str    		=torch.Size([1, 2_1_8_4_1]		)
				lowerCamelCase :			Dict    		=torch.tensor([2.2_2_8_8, 2.4_6_7_1, 0.7_3_9_5]		)
				lowerCamelCase :			List[Any]    		=2_3_9_7
		elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22k'''		):
				lowerCamelCase :			List[Any]    		=torch.Size([1, 2_1_8_4_1]		)
				lowerCamelCase :			List[Any]    		=torch.tensor([1.6_8_8_1, -0.2_7_8_7, 0.5_9_0_1]		)
				lowerCamelCase :			str    		=2_3_9_6
		elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft1k'''		):
				lowerCamelCase :			int    		=torch.tensor([0.1_2_4_1, 0.0_7_9_8, -0.6_5_6_9]		)
				lowerCamelCase :			Optional[int]    		=2_8_5
		elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22kto1k'''		):
				lowerCamelCase :			Tuple    		=torch.tensor([-1.2_3_8_5, -1.0_9_8_7, -1.0_1_0_8]		)
				lowerCamelCase :			List[Any]    		=2_8_1
		elif checkpoint_url[:-4].endswith('''beit_base_patch16_384_pt22k_ft22kto1k'''		):
				lowerCamelCase :			str    		=torch.tensor([-1.5_3_0_3, -0.9_4_8_4, -0.3_1_4_7]		)
				lowerCamelCase :			Optional[Any]    		=7_6_1
		elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft1k'''		):
				lowerCamelCase :			Dict    		=torch.tensor([0.4_6_1_0, -0.0_9_2_8, 0.2_0_8_6]		)
				lowerCamelCase :			Optional[Any]    		=7_6_1
		elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22kto1k'''		):
				lowerCamelCase :			Dict    		=torch.tensor([-0.4_8_0_4, 0.6_2_5_7, -0.1_8_3_7]		)
				lowerCamelCase :			int    		=7_6_1
		elif checkpoint_url[:-4].endswith('''beit_large_patch16_384_pt22k_ft22kto1k'''		):
				lowerCamelCase :			Tuple    		=torch.tensor([[-0.5_1_2_2, 0.5_1_1_7, -0.2_1_1_3]]		)
				lowerCamelCase :			Optional[int]    		=7_6_1
		elif checkpoint_url[:-4].endswith('''beit_large_patch16_512_pt22k_ft22kto1k'''		):
				lowerCamelCase :			Tuple    		=torch.tensor([-0.3_0_6_2, 0.7_2_6_1, 0.4_8_5_2]		)
				lowerCamelCase :			List[str]    		=7_6_1
		elif checkpoint_url[:-4].endswith('''beit_base_patch16_640_pt22k_ft22ktoade20k'''		):
				lowerCamelCase :			Dict    		=(1, 1_5_0, 1_6_0, 1_6_0)
				lowerCamelCase :			List[Any]    		=torch.tensor(
				    [
				        [[-4.9_2_2_5, -2.3_9_5_4, -3.0_5_2_2], [-2.8_8_2_2, -1.0_0_4_6, -1.7_5_6_1], [-2.9_5_4_9, -1.3_2_2_8, -2.1_3_4_7]],
				        [[-5.8_1_6_8, -3.4_1_2_9, -4.0_7_7_8], [-3.8_6_5_1, -2.2_2_1_4, -3.0_2_7_7], [-3.8_3_5_6, -2.4_6_4_3, -3.3_5_3_5]],
				        [[-0.0_0_7_8, 3.9_9_5_2, 4.0_7_5_4], [2.9_8_5_6, 4.6_9_4_4, 5.0_0_3_5], [3.2_4_1_3, 4.7_8_1_3, 4.9_9_6_9]],
				    ]		)
		elif checkpoint_url[:-4].endswith('''beit_large_patch16_640_pt22k_ft22ktoade20k'''		):
				lowerCamelCase :			List[str]    		=(1, 1_5_0, 1_6_0, 1_6_0)
				lowerCamelCase :			Dict    		=torch.tensor(
				    [
				        [[-4.3_3_0_5, -2.3_0_4_9, -3.0_1_6_1], [-2.9_5_9_1, -1.5_3_0_5, -2.2_2_5_1], [-3.4_1_9_8, -1.8_0_0_4, -2.9_0_6_2]],
				        [[-5.8_9_2_2, -3.7_4_3_5, -4.3_9_7_8], [-4.2_0_6_3, -2.7_8_7_2, -3.4_7_5_5], [-4.2_7_9_1, -3.1_8_7_4, -4.1_6_8_1]],
				        [[0.9_8_9_5, 4.3_4_6_7, 4.7_6_6_3], [4.2_4_7_6, 5.6_8_3_0, 6.1_5_1_8], [4.5_5_5_0, 6.2_4_9_5, 6.5_1_5_4]],
				    ]		)
		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__":
				snake_case_				=      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.'''
				)
				snake_case_				=      parser.parse_args()
				convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)








import pytest

from datasets.utils.version import Version


@pytest.mark.parametrize(
    '''other, expected_equality'''				,							[
        (Version('''1.0.0'''		), True),
        ('''1.0.0''', True),
        (Version('''2.0.0'''		), False),
        ('''2.0.0''', False),
        ('''1''', False),
        ('''a''', False),
        (1, False),
        (None, False),
    ]				,							)
def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  Optional[int]:
		lowerCamelCase :			str    		=Version('''1.0.0'''		)
		assert (version == other) is expected_equality
		assert (version != other) is not expected_equality
		assert (hash(SCREAMING_SNAKE_CASE_		) == hash(SCREAMING_SNAKE_CASE_		)) is expected_equality

