



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 						_UpperCAmelCase							( snake_case      ,		snake_case=False      ,		snake_case=False     ):





					"""simple docstring"""

					_lowerCAmelCase         =       """backbone.""" if is_semantic else """"""

					_lowerCAmelCase         =       []
					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							( snake_case      ,		snake_case      ,		snake_case=False      ,		snake_case=False     ):





					"""simple docstring"""

					for i in range(config.num_hidden_layers     ):
										_lowerCAmelCase         =       """backbone.""" if is_semantic else """"""
										# queries, keys and values
										_lowerCAmelCase         =       state_dict.pop(F'{prefix}blocks.{i}.attn.qkv.weight'     )
										_lowerCAmelCase         =       state_dict.pop(F'{prefix}blocks.{i}.attn.q_bias'     )
										_lowerCAmelCase         =       state_dict.pop(F'{prefix}blocks.{i}.attn.v_bias'     )

										_lowerCAmelCase         =       in_proj_weight[
										    : config.hidden_size, :
										]
										_lowerCAmelCase         =       q_bias
										_lowerCAmelCase         =       in_proj_weight[
										    config.hidden_size : config.hidden_size * 2, :
										]
										_lowerCAmelCase         =       in_proj_weight[
										    -config.hidden_size :, :
										]
										_lowerCAmelCase         =       v_bias

										# gamma_1 and gamma_2
										# we call them lambda because otherwise they are renamed when using .from_pretrained
										_lowerCAmelCase         =       state_dict.pop(F'{prefix}blocks.{i}.gamma_1'     )
										_lowerCAmelCase         =       state_dict.pop(F'{prefix}blocks.{i}.gamma_2'     )

										_lowerCAmelCase         =       gamma_a
										_lowerCAmelCase         =       gamma_a

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

															_lowerCAmelCase         =       table
															_lowerCAmelCase         =       index


def 						_UpperCAmelCase							( snake_case      ,		snake_case      ,		snake_case     ):





					"""simple docstring"""

					_lowerCAmelCase         =       dct.pop(snake_case     )
					_lowerCAmelCase         =       val


def 						_UpperCAmelCase							( ):





					"""simple docstring"""

					_lowerCAmelCase         =       """http://images.cocodataset.org/val2017/000000039769.jpg"""
					_lowerCAmelCase         =       Image.open(requests.get(snake_case      ,		stream=snake_case     ).raw     )
					return im


@torch.no_grad()
def 						_UpperCAmelCase							( snake_case      ,		snake_case     ):





					"""simple docstring"""

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

					_lowerCAmelCase         =       create_rename_keys(snake_case      ,		has_lm_head=snake_case      ,		is_semantic=snake_case     )
					for src, dest in rename_keys:
										rename_key(snake_case      ,		snake_case      ,		snake_case     )
					read_in_q_k_v(snake_case      ,		snake_case      ,		has_lm_head=snake_case      ,		is_semantic=snake_case     )
					if is_semantic:
										# add prefix to decoder keys
										for key, val in state_dict.copy().items():
															_lowerCAmelCase         =       state_dict.pop(snake_case     )
															if key.startswith("""backbone.fpn"""     ):
																				_lowerCAmelCase         =       key.replace("""backbone.fpn"""      ,		"""fpn"""     )
															_lowerCAmelCase         =       val

    # load HuggingFace model
					if checkpoint_url[-9:-4] == "pt22k":
										_lowerCAmelCase         =       BeitForMaskedImageModeling(snake_case     )
					elif "ade20k" in checkpoint_url:
										_lowerCAmelCase         =       BeitForSemanticSegmentation(snake_case     )
					else:
										_lowerCAmelCase         =       BeitForImageClassification(snake_case     )
					model.eval()
					model.load_state_dict(snake_case     )

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

					_lowerCAmelCase         =       image_processor(images=snake_case      ,		return_tensors="""pt"""     )
					_lowerCAmelCase         =       encoding["""pixel_values"""]

					_lowerCAmelCase         =       model(snake_case     )
					_lowerCAmelCase         =       outputs.logits

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


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 numpy as np
from PIL import Image

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    snake_case_      ,			snake_case_      ,			snake_case_			:     List[str]        =    rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
    return 0.2989 * r + 0.5870 * g + 0.1140 * b

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    return (gray > 1_2_7) & (gray <= 2_5_5)

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    snake_case_			:     Any        =    np.zeros_like(__SCREAMING_SNAKE_CASE							)
    snake_case_			:     Tuple        =    np.zeros(
        (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)							)

    # Copy image to padded image
    snake_case_			:     Union[str, Any]        =    image

    # Iterate over image & apply kernel
    for x in range(image.shape[1]							):
        for y in range(image.shape[0]							):
            snake_case_			:     List[str]        =    (
                kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
            ).sum()
            snake_case_			:     Dict        =    int(summation == 5							)
    return output


# kernel to be applied
a_           =    np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

if __name__ == "__main__":
  # read original image
  a_           =    np.array(Image.open(R"..\image_data\lena.jpg"))
  # Apply erosion operation to a binary image
  a_           =    erosion(grayabinary(rgbagray(image)), structuring_element)
  # Save the output image
  a_           =    Image.fromarray(output).convert("RGB")
  pil_img.save("result_erosion.png")




