



'''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()
lowerCAmelCase	:		Tuple				=   logging.get_logger(__name__)



def   __lowerCAmelCase					(							lowerCamelCase   :						str				,		lowerCamelCase   :						Any=False				,		lowerCamelCase   :						int=False					):






	'''simple docstring'''




	__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   __lowerCAmelCase					(							lowerCamelCase   :						Any				,		lowerCamelCase   :						Optional[Any]				,		lowerCamelCase   :						str=False				,		lowerCamelCase   :						List[Any]=False					):






	'''simple docstring'''




	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   __lowerCAmelCase					(							lowerCamelCase   :						Any				,		lowerCamelCase   :						Any				,		lowerCamelCase   :						Any					):






	'''simple docstring'''




	__lowerCAmelCase												=  dct.pop(lowerCamelCase					)
	__lowerCAmelCase												=  val



def   __lowerCAmelCase					(							):






	'''simple docstring'''




	__lowerCAmelCase												=  "http://images.cocodataset.org/val2017/000000039769.jpg"
	__lowerCAmelCase												=  Image.open(requests.get(lowerCamelCase				,		stream=lowerCamelCase					).raw					)
	return im



@torch.no_grad()
def   __lowerCAmelCase					(							lowerCamelCase   :						str				,		lowerCamelCase   :						List[str]				,		lowerCamelCase   :						Union[str, Any]=False					):






	'''simple docstring'''




	__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												=  10_24
		__lowerCAmelCase												=  40_96
		__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, 1_96, 81_92]
	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__":
	lowerCAmelCase	:		Union[str, 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''',
	)
	lowerCAmelCase	:		int				=   parser.parse_args()
	convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)




'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase	:		Union[str, Any]				=   {
    '''configuration_distilbert''': [
        '''DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''DistilBertConfig''',
        '''DistilBertOnnxConfig''',
    ],
    '''tokenization_distilbert''': ['''DistilBertTokenizer'''],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase	:		Tuple				=   ['''DistilBertTokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase	:		Tuple				=   [
	    '''DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''DistilBertForMaskedLM''',
	    '''DistilBertForMultipleChoice''',
	    '''DistilBertForQuestionAnswering''',
	    '''DistilBertForSequenceClassification''',
	    '''DistilBertForTokenClassification''',
	    '''DistilBertModel''',
	    '''DistilBertPreTrainedModel''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase	:		List[str]				=   [
	    '''TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFDistilBertForMaskedLM''',
	    '''TFDistilBertForMultipleChoice''',
	    '''TFDistilBertForQuestionAnswering''',
	    '''TFDistilBertForSequenceClassification''',
	    '''TFDistilBertForTokenClassification''',
	    '''TFDistilBertMainLayer''',
	    '''TFDistilBertModel''',
	    '''TFDistilBertPreTrainedModel''',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase	:		Optional[int]				=   [
	    '''FlaxDistilBertForMaskedLM''',
	    '''FlaxDistilBertForMultipleChoice''',
	    '''FlaxDistilBertForQuestionAnswering''',
	    '''FlaxDistilBertForSequenceClassification''',
	    '''FlaxDistilBertForTokenClassification''',
	    '''FlaxDistilBertModel''',
	    '''FlaxDistilBertPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_distilbert import (
	    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    DistilBertConfig,
	    DistilBertOnnxConfig,
	)
	from .tokenization_distilbert import DistilBertTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_distilbert_fast import DistilBertTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_distilbert import (
		    DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DistilBertForMaskedLM,
		    DistilBertForMultipleChoice,
		    DistilBertForQuestionAnswering,
		    DistilBertForSequenceClassification,
		    DistilBertForTokenClassification,
		    DistilBertModel,
		    DistilBertPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_distilbert import (
		    TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFDistilBertForMaskedLM,
		    TFDistilBertForMultipleChoice,
		    TFDistilBertForQuestionAnswering,
		    TFDistilBertForSequenceClassification,
		    TFDistilBertForTokenClassification,
		    TFDistilBertMainLayer,
		    TFDistilBertModel,
		    TFDistilBertPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_distilbert import (
		    FlaxDistilBertForMaskedLM,
		    FlaxDistilBertForMultipleChoice,
		    FlaxDistilBertForQuestionAnswering,
		    FlaxDistilBertForSequenceClassification,
		    FlaxDistilBertForTokenClassification,
		    FlaxDistilBertModel,
		    FlaxDistilBertPreTrainedModel,
		)

else:
	import sys

	lowerCAmelCase	:		List[str]				=   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)