






'''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()
UpperCamelCase    :      Tuple  			=		logging.get_logger(__name__)
def 						A__			(				__lowerCAmelCase		:			int   ,    __lowerCAmelCase		:			Any=False   ,    __lowerCAmelCase		:			List[str]=False						):
						lowerCamelCase__         =  """backbone.""" if is_semantic else """"""

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

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

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

												lowerCamelCase__         =  gamma_a
												lowerCamelCase__         =  gamma_a
def 						A__			(				__lowerCAmelCase		:			Any   ,    __lowerCAmelCase		:			Any   ,    __lowerCAmelCase		:			Optional[Any]						):
						lowerCamelCase__         =  dct.pop(__lowerCAmelCase						)
						lowerCamelCase__         =  val
def 						A__			(				):
						lowerCamelCase__         =  """http://images.cocodataset.org/val2017/000000039769.jpg"""
						lowerCamelCase__         =  Image.open(requests.get(__lowerCAmelCase   ,    stream=__lowerCAmelCase						).raw						)
						return im
@torch.no_grad()
def 						A__			(				__lowerCAmelCase		:			int   ,    __lowerCAmelCase		:			List[str]   ,    __lowerCAmelCase		:			List[str]=False						):
						lowerCamelCase__         =  False if """rvlcdip""" in checkpoint_url else True
						lowerCamelCase__         =  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:
												lowerCamelCase__         =  1024
												lowerCamelCase__         =  4096
												lowerCamelCase__         =  24
												lowerCamelCase__         =  16

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

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

						lowerCamelCase__         =  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
						lowerCamelCase__         =  BeitForMaskedImageModeling(__lowerCAmelCase						) if has_lm_head else BeitForImageClassification(__lowerCAmelCase						)
						model.eval()
						model.load_state_dict(__lowerCAmelCase						)

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

						lowerCamelCase__         =  image_processor(images=__lowerCAmelCase   ,    return_tensors="""pt"""						)
						lowerCamelCase__         =  encoding["""pixel_values"""]

						lowerCamelCase__         =  model(__lowerCAmelCase						)
						lowerCamelCase__         =  outputs.logits

						# verify logits
						lowerCamelCase__         =  [1, 16] if """rvlcdip""" in checkpoint_url else [1, 196, 8192]
						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:
																		lowerCamelCase__         =  """dit-base""" if """base""" in checkpoint_url else """dit-large"""
												else:
																		lowerCamelCase__         =  """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__":
							UpperCamelCase    :      Dict  			=		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',
							)
							UpperCamelCase    :      Optional[int]  			=		parser.parse_args()
							convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)









'''simple docstring'''







import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
							from PIL import Image

							from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast




@require_vision
class 		UpperCamelCase__    (unittest.TestCase			):




		'''simple docstring'''





		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  tempfile.mkdtemp()

								lowerCamelCase__         =  BlipImageProcessor()
								lowerCamelCase__         =  BertTokenizer.from_pretrained("""hf-internal-testing/tiny-random-BertModel"""       )

								lowerCamelCase__         =  BlipProcessor(_lowerCAmelCase   ,_lowerCAmelCase       )

								processor.save_pretrained(self.tmpdirname       )






		def 			UpperCamelCase_					(      self   ,**_lowerCAmelCase       ):
								return AutoProcessor.from_pretrained(self.tmpdirname   ,**_lowerCAmelCase       ).tokenizer






		def 			UpperCamelCase_					(      self   ,**_lowerCAmelCase       ):
								return AutoProcessor.from_pretrained(self.tmpdirname   ,**_lowerCAmelCase       ).image_processor






		def 			UpperCamelCase_					(      self       ):
								shutil.rmtree(self.tmpdirname       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  [np.random.randint(2_55   ,size=(3, 30, 4_00)   ,dtype=np.uinta       )]

								lowerCamelCase__         =  [Image.fromarray(np.moveaxis(_lowerCAmelCase   ,0   ,-1       )       ) for x in image_inputs]

								return image_inputs






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  BlipProcessor(tokenizer=self.get_tokenizer()   ,image_processor=self.get_image_processor()       )
								processor.save_pretrained(self.tmpdirname       )

								lowerCamelCase__         =  self.get_tokenizer(bos_token="""(BOS)"""   ,eos_token="""(EOS)"""       )
								lowerCamelCase__         =  self.get_image_processor(do_normalize=_lowerCAmelCase   ,padding_value=1.0       )

								lowerCamelCase__         =  BlipProcessor.from_pretrained(
								    self.tmpdirname   ,bos_token="""(BOS)"""   ,eos_token="""(EOS)"""   ,do_normalize=_lowerCAmelCase   ,padding_value=1.0       )

								self.assertEqual(processor.tokenizer.get_vocab()   ,tokenizer_add_kwargs.get_vocab()       )
								self.assertIsInstance(processor.tokenizer   ,_lowerCAmelCase       )

								self.assertEqual(processor.image_processor.to_json_string()   ,image_processor_add_kwargs.to_json_string()       )
								self.assertIsInstance(processor.image_processor   ,_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.get_image_processor()
								lowerCamelCase__         =  self.get_tokenizer()

								lowerCamelCase__         =  BlipProcessor(tokenizer=_lowerCAmelCase   ,image_processor=_lowerCAmelCase       )

								lowerCamelCase__         =  self.prepare_image_inputs()

								lowerCamelCase__         =  image_processor(_lowerCAmelCase   ,return_tensors="""np"""       )
								lowerCamelCase__         =  processor(images=_lowerCAmelCase   ,return_tensors="""np"""       )

								for key in input_feat_extract.keys():
														self.assertAlmostEqual(input_feat_extract[key].sum()   ,input_processor[key].sum()   ,delta=1E-2       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.get_image_processor()
								lowerCamelCase__         =  self.get_tokenizer()

								lowerCamelCase__         =  BlipProcessor(tokenizer=_lowerCAmelCase   ,image_processor=_lowerCAmelCase       )

								lowerCamelCase__         =  """lower newer"""

								lowerCamelCase__         =  processor(text=_lowerCAmelCase       )

								lowerCamelCase__         =  tokenizer(_lowerCAmelCase   ,return_token_type_ids=_lowerCAmelCase       )

								for key in encoded_tok.keys():
														self.assertListEqual(encoded_tok[key]   ,encoded_processor[key]       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.get_image_processor()
								lowerCamelCase__         =  self.get_tokenizer()

								lowerCamelCase__         =  BlipProcessor(tokenizer=_lowerCAmelCase   ,image_processor=_lowerCAmelCase       )

								lowerCamelCase__         =  """lower newer"""
								lowerCamelCase__         =  self.prepare_image_inputs()

								lowerCamelCase__         =  processor(text=_lowerCAmelCase   ,images=_lowerCAmelCase       )

								self.assertListEqual(list(inputs.keys()       )   ,["""pixel_values""", """input_ids""", """attention_mask"""]       )

								# test if it raises when no input is passed
								with pytest.raises(_lowerCAmelCase       ):
														processor()






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.get_image_processor()
								lowerCamelCase__         =  self.get_tokenizer()

								lowerCamelCase__         =  BlipProcessor(tokenizer=_lowerCAmelCase   ,image_processor=_lowerCAmelCase       )

								lowerCamelCase__         =  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

								lowerCamelCase__         =  processor.batch_decode(_lowerCAmelCase       )
								lowerCamelCase__         =  tokenizer.batch_decode(_lowerCAmelCase       )

								self.assertListEqual(_lowerCAmelCase   ,_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.get_image_processor()
								lowerCamelCase__         =  self.get_tokenizer()

								lowerCamelCase__         =  BlipProcessor(tokenizer=_lowerCAmelCase   ,image_processor=_lowerCAmelCase       )

								lowerCamelCase__         =  """lower newer"""
								lowerCamelCase__         =  self.prepare_image_inputs()

								lowerCamelCase__         =  processor(text=_lowerCAmelCase   ,images=_lowerCAmelCase       )

								# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
								self.assertListEqual(list(inputs.keys()       )   ,["""pixel_values""", """input_ids""", """attention_mask"""]       )

