


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()
lowerCAmelCase					 =	logging.get_logger(__name__)







def  _lowerCamelCase(			lowercase__     ,				lowercase__=False     ,				lowercase__=False			)		->			Union[str, Any]:

						'''simple docstring'''







						__lowercase=    'backbone.' if is_semantic else ''

						__lowercase=    []
						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(			lowercase__     ,				lowercase__     ,				lowercase__=False     ,				lowercase__=False			)		->			Dict:

						'''simple docstring'''







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

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

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

												__lowercase=    gamma_a
												__lowercase=    gamma_a

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

																		__lowercase=    table
																		__lowercase=    index







def  _lowerCamelCase(			lowercase__     ,				lowercase__     ,				lowercase__			)		->			List[str]:

						'''simple docstring'''







						__lowercase=    dct.pop(_SCREAMING_SNAKE_CASE			)
						__lowercase=    val







def  _lowerCamelCase(			)		->			int:

						'''simple docstring'''







						__lowercase=    'http://images.cocodataset.org/val2017/000000039769.jpg'
						__lowercase=    Image.open(requests.get(_SCREAMING_SNAKE_CASE     ,				stream=_SCREAMING_SNAKE_CASE			).raw			)
						return im







@torch.no_grad()
def  _lowerCamelCase(			lowercase__     ,				lowercase__			)		->			Optional[int]:

						'''simple docstring'''







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

						__lowercase=    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():
																		__lowercase=    state_dict.pop(_SCREAMING_SNAKE_CASE			)
																		if key.startswith('backbone.fpn'			):
																								__lowercase=    key.replace('backbone.fpn'     ,				'fpn'			)
																		__lowercase=    val

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

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

						__lowercase=    image_processor(images=_SCREAMING_SNAKE_CASE     ,				return_tensors='pt'			)
						__lowercase=    encoding['pixel_values']

						__lowercase=    model(_SCREAMING_SNAKE_CASE			)
						__lowercase=    outputs.logits

						# verify logits
						__lowercase=    torch.Size([1, 1_0_0_0]			)
						if checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k'			):
												__lowercase=    torch.Size([1, 1_9_6, 8_1_9_2]			)
						elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k'			):
												__lowercase=    torch.Size([1, 1_9_6, 8_1_9_2]			)
						elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22k'			):
												__lowercase=    torch.Size([1, 2_1_8_4_1]			)
												__lowercase=    torch.tensor([2.2288, 2.4671, 0.7395]			)
												__lowercase=    2_3_9_7
						elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22k'			):
												__lowercase=    torch.Size([1, 2_1_8_4_1]			)
												__lowercase=    torch.tensor([1.6881, -0.2787, 0.5901]			)
												__lowercase=    2_3_9_6
						elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft1k'			):
												__lowercase=    torch.tensor([0.1241, 0.0798, -0.6569]			)
												__lowercase=    2_8_5
						elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22kto1k'			):
												__lowercase=    torch.tensor([-1.2385, -1.0987, -1.0108]			)
												__lowercase=    2_8_1
						elif checkpoint_url[:-4].endswith('beit_base_patch16_384_pt22k_ft22kto1k'			):
												__lowercase=    torch.tensor([-1.5303, -0.9484, -0.3147]			)
												__lowercase=    7_6_1
						elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft1k'			):
												__lowercase=    torch.tensor([0.4610, -0.0928, 0.2086]			)
												__lowercase=    7_6_1
						elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22kto1k'			):
												__lowercase=    torch.tensor([-0.4804, 0.6257, -0.1837]			)
												__lowercase=    7_6_1
						elif checkpoint_url[:-4].endswith('beit_large_patch16_384_pt22k_ft22kto1k'			):
												__lowercase=    torch.tensor([[-0.5122, 0.5117, -0.2113]]			)
												__lowercase=    7_6_1
						elif checkpoint_url[:-4].endswith('beit_large_patch16_512_pt22k_ft22kto1k'			):
												__lowercase=    torch.tensor([-0.3062, 0.7261, 0.4852]			)
												__lowercase=    7_6_1
						elif checkpoint_url[:-4].endswith('beit_base_patch16_640_pt22k_ft22ktoade20k'			):
												__lowercase=    (1, 1_5_0, 1_6_0, 1_6_0)
												__lowercase=    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'			):
												__lowercase=    (1, 1_5_0, 1_6_0, 1_6_0)
												__lowercase=    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]     ,				_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__":
	lowerCAmelCase					 =	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.'''
	)
	lowerCAmelCase					 =	parser.parse_args()
	convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)










from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available





@dataclass
# Copied from diffusers.pipelines.stable_diffusion.__init__.StableDiffusionPipelineOutput with Stable->Alt
class    A					(     A_				):
							UpperCamelCase_     :							Union[List[PIL.Image.Image], np.ndarray]
							UpperCamelCase_     :							Optional[List[bool]]


if is_transformers_available() and is_torch_available():
	from .modeling_roberta_series import RobertaSeriesModelWithTransformation
	from .pipeline_alt_diffusion import AltDiffusionPipeline
	from .pipeline_alt_diffusion_imgaimg import AltDiffusionImgaImgPipeline






