




'''simple docstring'''

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()
a    :							str										=    logging.get_logger(__name__)


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase=False, __UpperCAmelCase=False      )						->   Dict:

					'''simple docstring'''

					snake_case_		     =							'''backbone.''' if is_semantic else ''''''

					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'''),
					        (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 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase=False, __UpperCAmelCase=False      )						->   int:

					'''simple docstring'''

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

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

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

										snake_case_		     =							gamma_a
										snake_case_		     =							gamma_a


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )						->   Optional[int]:

					'''simple docstring'''

					snake_case_		     =							dct.pop(__UpperCAmelCase      )
					snake_case_		     =							val


def 						__magic_name__       (      )						->   Union[str, Any]:

					'''simple docstring'''

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


@torch.no_grad()
def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase=False      )						->   Dict:

					'''simple docstring'''

					snake_case_		     =							False if '''rvlcdip''' in checkpoint_url else True
					snake_case_		     =							BeitConfig(use_absolute_position_embeddings=__UpperCAmelCase, use_mask_token=__UpperCAmelCase      )

					# size of the architecture
					if "large" in checkpoint_url or "dit-l" in checkpoint_url:
										snake_case_		     =							1024
										snake_case_		     =							4096
										snake_case_		     =							24
										snake_case_		     =							16

					# labels
					if "rvlcdip" in checkpoint_url:
										snake_case_		     =							16
										snake_case_		     =							'''huggingface/label-files'''
										snake_case_		     =							'''rvlcdip-id2label.json'''
										snake_case_		     =							json.load(open(hf_hub_download(__UpperCAmelCase, __UpperCAmelCase, repo_type='''dataset'''      ), '''r'''      )      )
										snake_case_		     =							{int(__UpperCAmelCase      ): v for k, v in idalabel.items()}
										snake_case_		     =							idalabel
										snake_case_		     =							{v: k for k, v in idalabel.items()}

					# load state_dict of original model, remove and rename some keys
					snake_case_		     =							torch.hub.load_state_dict_from_url(__UpperCAmelCase, map_location='''cpu'''      )['''model''']

					snake_case_		     =							create_rename_keys(__UpperCAmelCase, has_lm_head=__UpperCAmelCase      )
					for src, dest in rename_keys:
										rename_key(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )
					read_in_q_k_v(__UpperCAmelCase, __UpperCAmelCase, has_lm_head=__UpperCAmelCase      )

					# load HuggingFace model
					snake_case_		     =							BeitForMaskedImageModeling(__UpperCAmelCase      ) if has_lm_head else BeitForImageClassification(__UpperCAmelCase      )
					model.eval()
					model.load_state_dict(__UpperCAmelCase      )

					# Check outputs on an image
					snake_case_		     =							BeitImageProcessor(
					    size=config.image_size, resample=PILImageResampling.BILINEAR, do_center_crop=__UpperCAmelCase      )
					snake_case_		     =							prepare_img()

					snake_case_		     =							image_processor(images=__UpperCAmelCase, return_tensors='''pt'''      )
					snake_case_		     =							encoding['''pixel_values''']

					snake_case_		     =							model(__UpperCAmelCase      )
					snake_case_		     =							outputs.logits

					# verify logits
					snake_case_		     =							[1, 16] if '''rvlcdip''' in checkpoint_url else [1, 196, 8192]
					assert logits.shape == torch.Size(__UpperCAmelCase      ), "Shape of logits not as expected"

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

					if push_to_hub:
										if has_lm_head:
															snake_case_		     =							'''dit-base''' if '''base''' in checkpoint_url else '''dit-large'''
										else:
															snake_case_		     =							'''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(__UpperCAmelCase, __UpperCAmelCase      ), organization='''nielsr''', commit_message='''Add image processor''', use_temp_dir=__UpperCAmelCase, )
										model.push_to_hub(
										    repo_path_or_name=Path(__UpperCAmelCase, __UpperCAmelCase      ), organization='''nielsr''', commit_message='''Add model''', use_temp_dir=__UpperCAmelCase, )


if __name__ == "__main__":
							a    :							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',
							)
							a    :							Any										=    parser.parse_args()
							convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)







'''simple docstring'''

import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader






class 		a							(  _lowerCamelCase	):


				def __init__(						self       :   Optional[int]			,							lowercase_       :   NestedDataStructureLike[PathLike]			,							lowercase_       :   Optional[NamedSplit] = None			,							lowercase_       :   Optional[Features] = None			,							lowercase_       :   str = None			,							lowercase_       :   bool = False			,							lowercase_       :   bool = False			,							lowercase_       :   Optional[str] = None			,							lowercase_       :   Optional[int] = None			,							**lowercase_       :   Optional[Any]			,							):
									super().__init__(
									    lowercase_			,							split=lowercase_			,							features=lowercase_			,							cache_dir=lowercase_			,							keep_in_memory=lowercase_			,							streaming=lowercase_			,							num_proc=lowercase_			,							**lowercase_			,							)
									snake_case_		     =							field
									snake_case_		     =							path_or_paths if isinstance(lowercase_			,							lowercase_   ) else {self.split: path_or_paths}
									snake_case_		     =							Json(
									    cache_dir=lowercase_			,							data_files=lowercase_			,							features=lowercase_			,							field=lowercase_			,							**lowercase_			,							)


				def     A_					(						self       :   Optional[Any]   ):
									# Build iterable dataset
									if self.streaming:
														snake_case_		     =							self.builder.as_streaming_dataset(split=self.split   )
									# Build regular (map-style) dataset
									else:
														snake_case_		     =							None
														snake_case_		     =							None
														snake_case_		     =							None
														snake_case_		     =							None

														self.builder.download_and_prepare(
														    download_config=lowercase_			,							download_mode=lowercase_			,							verification_mode=lowercase_			,							base_path=lowercase_			,							num_proc=self.num_proc			,							)
														snake_case_		     =							self.builder.as_dataset(
														    split=self.split			,							verification_mode=lowercase_			,							in_memory=self.keep_in_memory   )
									return dataset







class 		a							:


				def __init__(						self       :   Dict			,							lowercase_       :   Dataset			,							lowercase_       :   Union[PathLike, BinaryIO]			,							lowercase_       :   Optional[int] = None			,							lowercase_       :   Optional[int] = None			,							**lowercase_       :   List[str]			,							):
									if num_proc is not None and num_proc <= 0:
														raise ValueError(F"num_proc {num_proc} must be an integer > 0."   )

									snake_case_		     =							dataset
									snake_case_		     =							path_or_buf
									snake_case_		     =							batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
									snake_case_		     =							num_proc
									snake_case_		     =							'''utf-8'''
									snake_case_		     =							to_json_kwargs


				def     A_					(						self       :   str   ):
									snake_case_		     =							self.to_json_kwargs.pop('''path_or_buf'''			,							lowercase_   )
									snake_case_		     =							self.to_json_kwargs.pop('''orient'''			,							'''records'''   )
									snake_case_		     =							self.to_json_kwargs.pop('''lines'''			,							True if orient == '''records''' else False   )
									snake_case_		     =							self.to_json_kwargs.pop('''index'''			,							False if orient in ['''split''', '''table'''] else True   )
									snake_case_		     =							self.to_json_kwargs.pop('''compression'''			,							lowercase_   )

									if compression not in [None, "infer", "gzip", "bz2", "xz"]:
														raise NotImplementedError(F"`datasets` currently does not support {compression} compression"   )

									if isinstance(self.path_or_buf			,							(str, bytes, os.PathLike)   ):
														with fsspec.open(self.path_or_buf			,							'''wb'''			,							compression=lowercase_   ) as buffer:
																			snake_case_		     =							self._write(file_obj=lowercase_			,							orient=lowercase_			,							lines=lowercase_			,							index=lowercase_			,							**self.to_json_kwargs   )
									else:
														if compression:
																			raise NotImplementedError(
																			    F"The compression parameter is not supported when writing to a buffer, but compression={compression}"
																			    ''' was passed. Please provide a local path instead.'''   )
														snake_case_		     =							self._write(
														    file_obj=self.path_or_buf			,							orient=lowercase_			,							lines=lowercase_			,							index=lowercase_			,							**self.to_json_kwargs   )
									return written


				def     A_					(						self       :   List[str]			,							lowercase_       :   Dict   ):
									snake_case_     ,snake_case_     ,snake_case_     ,snake_case_     ,snake_case_		     =							args

									snake_case_		     =							query_table(
									    table=self.dataset.data			,							key=slice(lowercase_			,							offset + self.batch_size   )			,							indices=self.dataset._indices			,							)
									snake_case_		     =							batch.to_pandas().to_json(
									    path_or_buf=lowercase_			,							orient=lowercase_			,							lines=lowercase_			,							index=lowercase_			,							**lowercase_   )
									if not json_str.endswith('''\n'''   ):
														json_str += "\n"
									return json_str.encode(self.encoding   )


				def     A_					(						self       :   Optional[int]			,							lowercase_       :   BinaryIO			,							lowercase_       :   List[Any]			,							lowercase_       :   str			,							lowercase_       :   int			,							**lowercase_       :   Optional[int]			,							):
									snake_case_		     =							0

									if self.num_proc is None or self.num_proc == 1:
														for offset in logging.tqdm(
														    range(0			,							len(self.dataset   )			,							self.batch_size   )			,							unit='''ba'''			,							disable=not logging.is_progress_bar_enabled()			,							desc='''Creating json from Arrow format'''			,							):
																			snake_case_		     =							self._batch_json((offset, orient, lines, index, to_json_kwargs)   )
																			written += file_obj.write(lowercase_   )
									else:
														snake_case_     ,snake_case_		     =							len(self.dataset   ), self.batch_size
														with multiprocessing.Pool(self.num_proc   ) as pool:
																			for json_str in logging.tqdm(
																			    pool.imap(
																			        self._batch_json			,							[(offset, orient, lines, index, to_json_kwargs) for offset in range(0			,							lowercase_			,							lowercase_   )]			,							)			,							total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size			,							unit='''ba'''			,							disable=not logging.is_progress_bar_enabled()			,							desc='''Creating json from Arrow format'''			,							):
																								written += file_obj.write(lowercase_   )

									return written

