


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_  :	Dict							  =							logging.get_logger(__name__)







def   lowerCamelCase(      a__		,a__=False		,a__=False):
				_SCREAMING_SNAKE_CASE	     ='''backbone.''' if is_semantic else ''''''

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

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

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

								_SCREAMING_SNAKE_CASE	     =gamma_a
								_SCREAMING_SNAKE_CASE	     =gamma_a

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

												_SCREAMING_SNAKE_CASE	     =table
												_SCREAMING_SNAKE_CASE	     =index







def   lowerCamelCase(      a__		,a__		,a__):
				_SCREAMING_SNAKE_CASE	     =dct.pop(a__)
				_SCREAMING_SNAKE_CASE	     =val







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







@torch.no_grad()
def   lowerCamelCase(      a__		,a__):
				_SCREAMING_SNAKE_CASE	     =BeitConfig()
				_SCREAMING_SNAKE_CASE	     =False
				_SCREAMING_SNAKE_CASE	     =False
				_SCREAMING_SNAKE_CASE	     ='''huggingface/label-files'''
				# set config parameters based on URL
				if checkpoint_url[-9:-4] == "pt22k":
								# masked image modeling
								_SCREAMING_SNAKE_CASE	     =True
								_SCREAMING_SNAKE_CASE	     =True
								_SCREAMING_SNAKE_CASE	     =True
				elif checkpoint_url[-9:-4] == "ft22k":
								# intermediate fine-tuning on ImageNet-22k
								_SCREAMING_SNAKE_CASE	     =True
								_SCREAMING_SNAKE_CASE	     =2_1841
								_SCREAMING_SNAKE_CASE	     ='''imagenet-22k-id2label.json'''
								_SCREAMING_SNAKE_CASE	     =json.load(open(hf_hub_download(a__		,a__		,repo_type='''dataset''')		,'''r'''))
								_SCREAMING_SNAKE_CASE	     ={int(a__): 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[1_5027]
								_SCREAMING_SNAKE_CASE	     =idalabel
								_SCREAMING_SNAKE_CASE	     ={v: k for k, v in idalabel.items()}
				elif checkpoint_url[-8:-4] == "to1k":
								# fine-tuning on ImageNet-1k
								_SCREAMING_SNAKE_CASE	     =True
								_SCREAMING_SNAKE_CASE	     =1000
								_SCREAMING_SNAKE_CASE	     ='''imagenet-1k-id2label.json'''
								_SCREAMING_SNAKE_CASE	     =json.load(open(hf_hub_download(a__		,a__		,repo_type='''dataset''')		,'''r'''))
								_SCREAMING_SNAKE_CASE	     ={int(a__): v for k, v in idalabel.items()}
								_SCREAMING_SNAKE_CASE	     =idalabel
								_SCREAMING_SNAKE_CASE	     ={v: k for k, v in idalabel.items()}
								if "384" in checkpoint_url:
												_SCREAMING_SNAKE_CASE	     =384
								if "512" in checkpoint_url:
												_SCREAMING_SNAKE_CASE	     =512
				elif "ade20k" in checkpoint_url:
								# fine-tuning
								_SCREAMING_SNAKE_CASE	     =True
								_SCREAMING_SNAKE_CASE	     =150
								_SCREAMING_SNAKE_CASE	     ='''ade20k-id2label.json'''
								_SCREAMING_SNAKE_CASE	     =json.load(open(hf_hub_download(a__		,a__		,repo_type='''dataset''')		,'''r'''))
								_SCREAMING_SNAKE_CASE	     ={int(a__): v for k, v in idalabel.items()}
								_SCREAMING_SNAKE_CASE	     =idalabel
								_SCREAMING_SNAKE_CASE	     ={v: k for k, v in idalabel.items()}
								_SCREAMING_SNAKE_CASE	     =640
								_SCREAMING_SNAKE_CASE	     =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:
								_SCREAMING_SNAKE_CASE	     =1024
								_SCREAMING_SNAKE_CASE	     =4096
								_SCREAMING_SNAKE_CASE	     =24
								_SCREAMING_SNAKE_CASE	     =16
								if "ade20k" in checkpoint_url:
												_SCREAMING_SNAKE_CASE	     =640
												_SCREAMING_SNAKE_CASE	     =[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
				_SCREAMING_SNAKE_CASE	     =torch.hub.load_state_dict_from_url(a__		,map_location='''cpu'''		,check_hash=a__)
				_SCREAMING_SNAKE_CASE	     =state_dict['''model'''] if '''ade20k''' not in checkpoint_url else state_dict['''state_dict''']

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

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

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

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

				_SCREAMING_SNAKE_CASE	     =model(a__)
				_SCREAMING_SNAKE_CASE	     =outputs.logits

				# verify logits
				_SCREAMING_SNAKE_CASE	     =torch.Size([1, 1000])
				if checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k'''):
								_SCREAMING_SNAKE_CASE	     =torch.Size([1, 196, 8192])
				elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k'''):
								_SCREAMING_SNAKE_CASE	     =torch.Size([1, 196, 8192])
				elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22k'''):
								_SCREAMING_SNAKE_CASE	     =torch.Size([1, 2_1841])
								_SCREAMING_SNAKE_CASE	     =torch.tensor([2.2288, 2.4671, 0.7395])
								_SCREAMING_SNAKE_CASE	     =2397
				elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22k'''):
								_SCREAMING_SNAKE_CASE	     =torch.Size([1, 2_1841])
								_SCREAMING_SNAKE_CASE	     =torch.tensor([1.6881, -0.2787, 0.5901])
								_SCREAMING_SNAKE_CASE	     =2396
				elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft1k'''):
								_SCREAMING_SNAKE_CASE	     =torch.tensor([0.1241, 0.0798, -0.6569])
								_SCREAMING_SNAKE_CASE	     =285
				elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22kto1k'''):
								_SCREAMING_SNAKE_CASE	     =torch.tensor([-1.2385, -1.0987, -1.0108])
								_SCREAMING_SNAKE_CASE	     =281
				elif checkpoint_url[:-4].endswith('''beit_base_patch16_384_pt22k_ft22kto1k'''):
								_SCREAMING_SNAKE_CASE	     =torch.tensor([-1.5303, -0.9484, -0.3147])
								_SCREAMING_SNAKE_CASE	     =761
				elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft1k'''):
								_SCREAMING_SNAKE_CASE	     =torch.tensor([0.4610, -0.0928, 0.2086])
								_SCREAMING_SNAKE_CASE	     =761
				elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22kto1k'''):
								_SCREAMING_SNAKE_CASE	     =torch.tensor([-0.4804, 0.6257, -0.1837])
								_SCREAMING_SNAKE_CASE	     =761
				elif checkpoint_url[:-4].endswith('''beit_large_patch16_384_pt22k_ft22kto1k'''):
								_SCREAMING_SNAKE_CASE	     =torch.tensor([[-0.5122, 0.5117, -0.2113]])
								_SCREAMING_SNAKE_CASE	     =761
				elif checkpoint_url[:-4].endswith('''beit_large_patch16_512_pt22k_ft22kto1k'''):
								_SCREAMING_SNAKE_CASE	     =torch.tensor([-0.3062, 0.7261, 0.4852])
								_SCREAMING_SNAKE_CASE	     =761
				elif checkpoint_url[:-4].endswith('''beit_base_patch16_640_pt22k_ft22ktoade20k'''):
								_SCREAMING_SNAKE_CASE	     =(1, 150, 160, 160)
								_SCREAMING_SNAKE_CASE	     =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'''):
								_SCREAMING_SNAKE_CASE	     =(1, 150, 160, 160)
								_SCREAMING_SNAKE_CASE	     =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]		,a__		,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]		,a__		,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(a__).mkdir(exist_ok=a__)
				print(f"Saving model to {pytorch_dump_folder_path}")
				model.save_pretrained(a__)
				print(f"Saving image processor to {pytorch_dump_folder_path}")
				image_processor.save_pretrained(a__)


if __name__ == "__main__":
							snake_case_  :	Any							  =							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_  :	int							  =							parser.parse_args()
							convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)




from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PretrainedConfig, PreTrainedTokenizer
from ...onnx import OnnxConfig, PatchingSpec
from ...utils import TensorType, is_torch_available, logging


__A				    =						logging.get_logger(__name__)

__A				    =						{
    '''microsoft/layoutlm-base-uncased''': (
        '''https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/config.json'''
    ),
    '''microsoft/layoutlm-large-uncased''': (
        '''https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/config.json'''
    ),
}






class 		lowercase						( snake_case__):






	"""simple docstring"""


	a__      :					Dict		   =							"layoutlm"




	def __init__(	self	:						List[Any]						,  __UpperCAmelCase	:						int=30_522						,  __UpperCAmelCase	:						List[Any]=768						,  __UpperCAmelCase	:						Optional[int]=12						,  __UpperCAmelCase	:						List[str]=12						,  __UpperCAmelCase	:						int=3_072						,  __UpperCAmelCase	:						str="gelu"						,  __UpperCAmelCase	:						str=0.1						,  __UpperCAmelCase	:						Any=0.1						,  __UpperCAmelCase	:						Union[str, Any]=512						,  __UpperCAmelCase	:						Any=2						,  __UpperCAmelCase	:						Any=0.02						,  __UpperCAmelCase	:						Dict=1E-12						,  __UpperCAmelCase	:						Optional[int]=0						,  __UpperCAmelCase	:						List[str]="absolute"						,  __UpperCAmelCase	:						List[Any]=True						,  __UpperCAmelCase	:						Optional[int]=1_024						,  **__UpperCAmelCase	:						int						,  )			-> Union[str, Any]:
							super().__init__(pad_token_id=__UpperCAmelCase						,  **__UpperCAmelCase     )
							UpperCAmelCase_=   vocab_size
							UpperCAmelCase_=   hidden_size
							UpperCAmelCase_=   num_hidden_layers
							UpperCAmelCase_=   num_attention_heads
							UpperCAmelCase_=   hidden_act
							UpperCAmelCase_=   intermediate_size
							UpperCAmelCase_=   hidden_dropout_prob
							UpperCAmelCase_=   attention_probs_dropout_prob
							UpperCAmelCase_=   max_position_embeddings
							UpperCAmelCase_=   type_vocab_size
							UpperCAmelCase_=   initializer_range
							UpperCAmelCase_=   layer_norm_eps
							UpperCAmelCase_=   position_embedding_type
							UpperCAmelCase_=   use_cache
							UpperCAmelCase_=   max_ad_position_embeddings






class 		lowercase						( snake_case__):






	"""simple docstring"""


	def __init__(	self	:						List[str]						,  __UpperCAmelCase	:						PretrainedConfig						,  __UpperCAmelCase	:						str = "default"						,  __UpperCAmelCase	:						List[PatchingSpec] = None						,  )			-> str:
							super().__init__(__UpperCAmelCase						,  task=__UpperCAmelCase						,  patching_specs=__UpperCAmelCase     )
							UpperCAmelCase_=   config.max_ad_position_embeddings - 1




	@property
	def   _SCREAMING_SNAKE_CASE		(	self	:						Optional[Any]     )			-> Mapping[str, Mapping[int, str]]:
							return OrderedDict(
							    [
							        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
							        ("""bbox""", {0: """batch""", 1: """sequence"""}),
							        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
							        ("""token_type_ids""", {0: """batch""", 1: """sequence"""}),
							    ]     )




	def   _SCREAMING_SNAKE_CASE		(	self	:						Any						,  __UpperCAmelCase	:						PreTrainedTokenizer						,  __UpperCAmelCase	:						int = -1						,  __UpperCAmelCase	:						int = -1						,  __UpperCAmelCase	:						bool = False						,  __UpperCAmelCase	:						Optional[TensorType] = None						,  )			-> Mapping[str, Any]:
							UpperCAmelCase_=   super().generate_dummy_inputs(
							    __UpperCAmelCase						,  batch_size=__UpperCAmelCase						,  seq_length=__UpperCAmelCase						,  is_pair=__UpperCAmelCase						,  framework=__UpperCAmelCase     )

							# Generate a dummy bbox
							UpperCAmelCase_=   [48, 84, 73, 128]

							if not framework == TensorType.PYTORCH:
													raise NotImplementedError("""Exporting LayoutLM to ONNX is currently only supported for PyTorch."""     )

							if not is_torch_available():
													raise ValueError("""Cannot generate dummy inputs without PyTorch installed."""     )
							import torch

							UpperCAmelCase_,   UpperCAmelCase_=   input_dict["""input_ids"""].shape
							UpperCAmelCase_=   torch.tensor([*[box] * seq_length]     ).tile(__UpperCAmelCase						,  1						,  1     )
							return input_dict




