
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase  :					List[Any]													=       logging.get_logger(__name__)

lowercase  :					str													=       {
    'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/config.json',
    'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/config.json',
    'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/config.json',
    'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/config.json',
    'bert-base-multilingual-uncased': 'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json',
    'bert-base-multilingual-cased': 'https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json',
    'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/config.json',
    'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/config.json',
    'bert-large-uncased-whole-word-masking': (
        'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json'
    ),
    'bert-large-cased-whole-word-masking': (
        'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json'
    ),
    'bert-large-uncased-whole-word-masking-finetuned-squad': (
        'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json'
    ),
    'bert-large-cased-whole-word-masking-finetuned-squad': (
        'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json'
    ),
    'bert-base-cased-finetuned-mrpc': 'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json',
    'bert-base-german-dbmdz-cased': 'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json',
    'bert-base-german-dbmdz-uncased': 'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json',
    'cl-tohoku/bert-base-japanese': 'https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json',
    'cl-tohoku/bert-base-japanese-whole-word-masking': (
        'https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json'
    ),
    'cl-tohoku/bert-base-japanese-char': (
        'https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json'
    ),
    'cl-tohoku/bert-base-japanese-char-whole-word-masking': (
        'https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json'
    ),
    'TurkuNLP/bert-base-finnish-cased-v1': (
        'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json'
    ),
    'TurkuNLP/bert-base-finnish-uncased-v1': (
        'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json'
    ),
    'wietsedv/bert-base-dutch-cased': 'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json',
    # See all BERT models at https://huggingface.co/models?filter=bert
}




class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	_A								=     'bert'







	def __init__(					self		:Tuple     ,   a		:Optional[Any]=3_0_5_2_2     ,   a		:Dict=7_6_8     ,   a		:List[Any]=1_2     ,   a		:Optional[int]=1_2     ,   a		:int=3_0_7_2     ,   a		:Dict="gelu"     ,   a		:List[str]=0.1     ,   a		:Tuple=0.1     ,   a		:str=5_1_2     ,   a		:Union[str, Any]=2     ,   a		:Optional[Any]=0.02     ,   a		:Tuple=1E-1_2     ,   a		:Union[str, Any]=0     ,   a		:str="absolute"     ,   a		:Any=True     ,   a		:Dict=None     ,   **a		:str     ,   )		-> int:
								super().__init__(pad_token_id=a     ,   **a						)

								__UpperCamelCase	:   int			   =					vocab_size
								__UpperCamelCase	:   Optional[Any]			   =					hidden_size
								__UpperCamelCase	:   int			   =					num_hidden_layers
								__UpperCamelCase	:   int			   =					num_attention_heads
								__UpperCamelCase	:   Optional[Any]			   =					hidden_act
								__UpperCamelCase	:   Tuple			   =					intermediate_size
								__UpperCamelCase	:   Any			   =					hidden_dropout_prob
								__UpperCamelCase	:   List[str]			   =					attention_probs_dropout_prob
								__UpperCamelCase	:   Union[str, Any]			   =					max_position_embeddings
								__UpperCamelCase	:   Union[str, Any]			   =					type_vocab_size
								__UpperCamelCase	:   Optional[Any]			   =					initializer_range
								__UpperCamelCase	:   List[str]			   =					layer_norm_eps
								__UpperCamelCase	:   Union[str, Any]			   =					position_embedding_type
								__UpperCamelCase	:   Optional[Any]			   =					use_cache
								__UpperCamelCase	:   Optional[Any]			   =					classifier_dropout




class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	@property
	def    _lowerCamelCase				(					self		:Tuple						)		-> Mapping[str, Mapping[int, str]]:
								if self.task == "multiple-choice":
															__UpperCamelCase	:   int			   =					{0: "batch", 1: "choice", 2: "sequence"}
								else:
															__UpperCamelCase	:   Any			   =					{0: "batch", 1: "sequence"}
								return OrderedDict(
								    [
								        ("input_ids", dynamic_axis),
								        ("attention_mask", dynamic_axis),
								        ("token_type_ids", dynamic_axis),
								    ]						)

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
lowercase  :					Dict													=       logging.get_logger(__name__)




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Optional[Any]						,  _lowerCamelCase :			Union[str, Any]=False						,  _lowerCamelCase :			Optional[int]=False)					->    Any:






							'''simple docstring'''





							__UpperCamelCase	:   Optional[int]			   =					"backbone." if is_semantic else ""

							__UpperCamelCase	:   Optional[Any]			   =					[]
							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"),
							        (F'{prefix}pos_embed', "beit.embeddings.position_embeddings"),
							    ])

							if has_lm_head:
														# mask token + layernorm
														rename_keys.extend(
														    [
														        ("mask_token", "beit.embeddings.mask_token"),
														        ("norm.weight", "layernorm.weight"),
														        ("norm.bias", "layernorm.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 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Tuple						,  _lowerCamelCase :			List[str]						,  _lowerCamelCase :			Union[str, Any]=False						,  _lowerCamelCase :			Optional[int]=False)					->    List[str]:






							'''simple docstring'''





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

														__UpperCamelCase	:   Optional[int]			   =					in_proj_weight[
														    : config.hidden_size, :
														]
														__UpperCamelCase	:   Any			   =					q_bias
														__UpperCamelCase	:   Dict			   =					in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														__UpperCamelCase	:   List[Any]			   =					in_proj_weight[
														    -config.hidden_size :, :
														]
														__UpperCamelCase	:   Tuple			   =					v_bias

														# gamma_1 and gamma_2
														# we call them lambda because otherwise they are renamed when using .from_pretrained
														__UpperCamelCase	:   Union[str, Any]			   =					state_dict.pop(F'{prefix}blocks.{i}.gamma_1')
														__UpperCamelCase	:   Optional[int]			   =					state_dict.pop(F'{prefix}blocks.{i}.gamma_2')

														__UpperCamelCase	:   Any			   =					gamma_a
														__UpperCamelCase	:   List[Any]			   =					gamma_a




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			str						,  _lowerCamelCase :			List[str]						,  _lowerCamelCase :			Optional[Any])					->    Any:






							'''simple docstring'''





							__UpperCamelCase	:   str			   =					dct.pop(_lowerCamelCase)
							__UpperCamelCase	:   str			   =					val




def 	_SCREAMING_SNAKE_CASE					(		)					->    Optional[int]:






							'''simple docstring'''





							__UpperCamelCase	:   Any			   =					"http://images.cocodataset.org/val2017/000000039769.jpg"
							__UpperCamelCase	:   Dict			   =					Image.open(requests.get(_lowerCamelCase						,  stream=_lowerCamelCase).raw)
							return im




@torch.no_grad()
def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Optional[int]						,  _lowerCamelCase :			List[str]						,  _lowerCamelCase :			Dict=False)					->    str:






							'''simple docstring'''





							__UpperCamelCase	:   str			   =					False if "rvlcdip" in checkpoint_url else True
							__UpperCamelCase	:   int			   =					BeitConfig(use_absolute_position_embeddings=_lowerCamelCase						,  use_mask_token=_lowerCamelCase)

							# size of the architecture
							if "large" in checkpoint_url or "dit-l" in checkpoint_url:
														__UpperCamelCase	:   Optional[int]			   =					1_024
														__UpperCamelCase	:   Union[str, Any]			   =					4_096
														__UpperCamelCase	:   Tuple			   =					24
														__UpperCamelCase	:   int			   =					16

							# labels
							if "rvlcdip" in checkpoint_url:
														__UpperCamelCase	:   List[str]			   =					16
														__UpperCamelCase	:   str			   =					"huggingface/label-files"
														__UpperCamelCase	:   str			   =					"rvlcdip-id2label.json"
														__UpperCamelCase	:   Tuple			   =					json.load(open(hf_hub_download(_lowerCamelCase						,  _lowerCamelCase						,  repo_type="dataset")						,  "r"))
														__UpperCamelCase	:   Dict			   =					{int(_lowerCamelCase): v for k, v in idalabel.items()}
														__UpperCamelCase	:   Optional[Any]			   =					idalabel
														__UpperCamelCase	:   List[Any]			   =					{v: k for k, v in idalabel.items()}

							# load state_dict of original model, remove and rename some keys
							__UpperCamelCase	:   str			   =					torch.hub.load_state_dict_from_url(_lowerCamelCase						,  map_location="cpu")["model"]

							__UpperCamelCase	:   Optional[Any]			   =					create_rename_keys(_lowerCamelCase						,  has_lm_head=_lowerCamelCase)
							for src, dest in rename_keys:
														rename_key(_lowerCamelCase						,  _lowerCamelCase						,  _lowerCamelCase)
							read_in_q_k_v(_lowerCamelCase						,  _lowerCamelCase						,  has_lm_head=_lowerCamelCase)

							# load HuggingFace model
							__UpperCamelCase	:   List[Any]			   =					BeitForMaskedImageModeling(_lowerCamelCase) if has_lm_head else BeitForImageClassification(_lowerCamelCase)
							model.eval()
							model.load_state_dict(_lowerCamelCase)

							# Check outputs on an image
							__UpperCamelCase	:   Dict			   =					BeitImageProcessor(
							    size=config.image_size						,  resample=PILImageResampling.BILINEAR						,  do_center_crop=_lowerCamelCase)
							__UpperCamelCase	:   Union[str, Any]			   =					prepare_img()

							__UpperCamelCase	:   Union[str, Any]			   =					image_processor(images=_lowerCamelCase						,  return_tensors="pt")
							__UpperCamelCase	:   str			   =					encoding["pixel_values"]

							__UpperCamelCase	:   str			   =					model(_lowerCamelCase)
							__UpperCamelCase	:   Union[str, Any]			   =					outputs.logits

							# verify logits
							__UpperCamelCase	:   Tuple			   =					[1, 16] if "rvlcdip" in checkpoint_url else [1, 196, 8_192]
							assert logits.shape == torch.Size(_lowerCamelCase), "Shape of logits not as expected"

							Path(_lowerCamelCase).mkdir(exist_ok=_lowerCamelCase)
							print(F'Saving model to {pytorch_dump_folder_path}')
							model.save_pretrained(_lowerCamelCase)
							print(F'Saving image processor to {pytorch_dump_folder_path}')
							image_processor.save_pretrained(_lowerCamelCase)

							if push_to_hub:
														if has_lm_head:
																					__UpperCamelCase	:   List[str]			   =					"dit-base" if "base" in checkpoint_url else "dit-large"
														else:
																					__UpperCamelCase	:   Optional[Any]			   =					"dit-base-finetuned-rvlcdip" if "dit-b" in checkpoint_url else "dit-large-finetuned-rvlcdip"
														image_processor.push_to_hub(
														    repo_path_or_name=Path(_lowerCamelCase						,  _lowerCamelCase)						,  organization="nielsr"						,  commit_message="Add image processor"						,  use_temp_dir=_lowerCamelCase						,  )
														model.push_to_hub(
														    repo_path_or_name=Path(_lowerCamelCase						,  _lowerCamelCase)						,  organization="nielsr"						,  commit_message="Add model"						,  use_temp_dir=_lowerCamelCase						,  )


if __name__ == "__main__":
		lowercase  :					Optional[Any]													=       argparse.ArgumentParser()

		parser.add_argument(
		    '--checkpoint_url',
		    default='https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.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.'
		)
		parser.add_argument(
		    '--push_to_hub',
		    action='store_true',
		)
		lowercase  :					Tuple													=       parser.parse_args()
		convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)