



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

def 				_lowerCAmelCase   (     lowercase_	,			lowercase_=False	,			lowercase_=False		):
						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 				_lowerCAmelCase   (     lowercase_	,			lowercase_	,			lowercase_=False	,			lowercase_=False		):
						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 				_lowerCAmelCase   (     lowercase_	,			lowercase_	,			lowercase_		):
						UpperCAmelCase    =				dct.pop(lowercase_		)
						UpperCAmelCase    =				val

def 				_lowerCAmelCase   (     ):
						UpperCAmelCase    =				'http://images.cocodataset.org/val2017/000000039769.jpg'
						UpperCAmelCase    =				Image.open(requests.get(lowercase_	,			stream=lowercase_		).raw		)
						return im

@torch.no_grad()
def 				_lowerCAmelCase   (     lowercase_	,			lowercase_		):
						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(lowercase_	,			lowercase_	,			repo_type='dataset'		)	,			'r'		)		)
												UpperCAmelCase    =				{int(lowercase_		): 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(lowercase_	,			lowercase_	,			repo_type='dataset'		)	,			'r'		)		)
												UpperCAmelCase    =				{int(lowercase_		): 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(lowercase_	,			lowercase_	,			repo_type='dataset'		)	,			'r'		)		)
												UpperCAmelCase    =				{int(lowercase_		): 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(lowercase_	,			map_location='cpu'	,			check_hash=lowercase_		)
						UpperCAmelCase    =				state_dict['model'] if 'ade20k' not in checkpoint_url else state_dict['state_dict']

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

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

						# Check outputs on an image
						if is_semantic:
												UpperCAmelCase    =				BeitImageProcessor(size=config.image_size	,			do_center_crop=lowercase_		)
												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=lowercase_		)
												UpperCAmelCase    =				prepare_img()

						UpperCAmelCase    =				image_processor(images=lowercase_	,			return_tensors='pt'		)
						UpperCAmelCase    =				encoding['pixel_values']

						UpperCAmelCase    =				model(lowercase_		)
						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([2.2_2_8_8, 2.4_6_7_1, 0.7_3_9_5]		)
												UpperCAmelCase    =				2397
						elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22k'		):
												UpperCAmelCase    =				torch.Size([1, 21841]		)
												UpperCAmelCase    =				torch.tensor([1.6_8_8_1, -0.2_7_8_7, 0.5_9_0_1]		)
												UpperCAmelCase    =				2396
						elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft1k'		):
												UpperCAmelCase    =				torch.tensor([0.1_2_4_1, 0.0_7_9_8, -0.6_5_6_9]		)
												UpperCAmelCase    =				285
						elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22kto1k'		):
												UpperCAmelCase    =				torch.tensor([-1.2_3_8_5, -1.0_9_8_7, -1.0_1_0_8]		)
												UpperCAmelCase    =				281
						elif checkpoint_url[:-4].endswith('beit_base_patch16_384_pt22k_ft22kto1k'		):
												UpperCAmelCase    =				torch.tensor([-1.5_3_0_3, -0.9_4_8_4, -0.3_1_4_7]		)
												UpperCAmelCase    =				761
						elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft1k'		):
												UpperCAmelCase    =				torch.tensor([0.4_6_1_0, -0.0_9_2_8, 0.2_0_8_6]		)
												UpperCAmelCase    =				761
						elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22kto1k'		):
												UpperCAmelCase    =				torch.tensor([-0.4_8_0_4, 0.6_2_5_7, -0.1_8_3_7]		)
												UpperCAmelCase    =				761
						elif checkpoint_url[:-4].endswith('beit_large_patch16_384_pt22k_ft22kto1k'		):
												UpperCAmelCase    =				torch.tensor([[-0.5_1_2_2, 0.5_1_1_7, -0.2_1_1_3]]		)
												UpperCAmelCase    =				761
						elif checkpoint_url[:-4].endswith('beit_large_patch16_512_pt22k_ft22kto1k'		):
												UpperCAmelCase    =				torch.tensor([-0.3_0_6_2, 0.7_2_6_1, 0.4_8_5_2]		)
												UpperCAmelCase    =				761
						elif checkpoint_url[:-4].endswith('beit_base_patch16_640_pt22k_ft22ktoade20k'		):
												UpperCAmelCase    =				(1, 150, 160, 160)
												UpperCAmelCase    =				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'		):
												UpperCAmelCase    =				(1, 150, 160, 160)
												UpperCAmelCase    =				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]	,			lowercase_	,			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]	,			lowercase_	,			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(lowercase_		).mkdir(exist_ok=lowercase_		)
						print(F"""Saving model to {pytorch_dump_folder_path}"""		)
						model.save_pretrained(lowercase_		)
						print(F"""Saving image processor to {pytorch_dump_folder_path}"""		)
						image_processor.save_pretrained(lowercase_		)


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)










"""simple docstring"""


from math import exp

def 				_lowerCAmelCase   (     lowercase_		):
						return 8 * x - 2 * exp(-x		)

def 				_lowerCAmelCase   (     lowercase_	,			lowercase_	,			lowercase_		):
						UpperCAmelCase    =				lower_bound
						UpperCAmelCase    =				upper_bound
						for _ in range(0	,			lowercase_		):
												UpperCAmelCase   ,					UpperCAmelCase    =				xa, xa - (f(lowercase_		) * (xa - xa)) / (f(lowercase_		) - f(lowercase_		))
						return xa


if __name__ == "__main__":
				print(f'''Example: {secant_method(1, 3, 2)}''')





