


'''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()
_lowercase      :      Any            =					logging.get_logger(__name__)


def 				lowerCamelCase__				(	A       :      List[Any]				,       A       :      int=False				,       A       :      Dict=False					):




						'''simple docstring'''






						UpperCAmelCase					    =     '''backbone.''' if is_semantic else ''''''

						UpperCAmelCase					    =     []
						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__				(	A       :      Tuple				,       A       :      Union[str, Any]				,       A       :      List[Any]=False				,       A       :      Optional[int]=False					):




						'''simple docstring'''






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

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

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

												UpperCAmelCase					    =     gamma_a
												UpperCAmelCase					    =     gamma_a


def 				lowerCamelCase__				(	A       :      Tuple				,       A       :      Tuple				,       A       :      Optional[int]					):




						'''simple docstring'''






						UpperCAmelCase					    =     dct.pop(A					)
						UpperCAmelCase					    =     val


def 				lowerCamelCase__				(	):




						'''simple docstring'''






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


@torch.no_grad()
def 				lowerCamelCase__				(	A       :      List[str]				,       A       :      Optional[Any]				,       A       :      Any=False					):




						'''simple docstring'''






						UpperCAmelCase					    =     False if '''rvlcdip''' in checkpoint_url else True
						UpperCAmelCase					    =     BeitConfig(use_absolute_position_embeddings=A				,       use_mask_token=A					)

						# size of the architecture
						if "large" in checkpoint_url or "dit-l" in checkpoint_url:
												UpperCAmelCase					    =     10_24
												UpperCAmelCase					    =     40_96
												UpperCAmelCase					    =     24
												UpperCAmelCase					    =     16

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

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

						UpperCAmelCase					    =     create_rename_keys(A				,       has_lm_head=A					)
						for src, dest in rename_keys:
												rename_key(A				,       A				,       A					)
						read_in_q_k_v(A				,       A				,       has_lm_head=A					)

						# load HuggingFace model
						UpperCAmelCase					    =     BeitForMaskedImageModeling(A					) if has_lm_head else BeitForImageClassification(A					)
						model.eval()
						model.load_state_dict(A					)

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

						UpperCAmelCase					    =     image_processor(images=A				,       return_tensors='''pt'''					)
						UpperCAmelCase					    =     encoding['''pixel_values''']

						UpperCAmelCase					    =     model(A					)
						UpperCAmelCase					    =     outputs.logits

						# verify logits
						UpperCAmelCase					    =     [1, 16] if '''rvlcdip''' in checkpoint_url else [1, 1_96, 81_92]
						assert logits.shape == torch.Size(A					), "Shape of logits 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 push_to_hub:
												if has_lm_head:
																		UpperCAmelCase					    =     '''dit-base''' if '''base''' in checkpoint_url else '''dit-large'''
												else:
																		UpperCAmelCase					    =     '''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(A				,       A					)				,       organization='''nielsr'''				,       commit_message='''Add image processor'''				,       use_temp_dir=A				,       )
												model.push_to_hub(
												    repo_path_or_name=Path(A				,       A					)				,       organization='''nielsr'''				,       commit_message='''Add model'''				,       use_temp_dir=A				,       )


if __name__ == "__main__":
	_lowercase      :      int            =					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      :      Any            =					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_torch_available


_lowercase      :      List[Any]            =					{
    """configuration_x_clip""": [
        """XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XCLIPConfig""",
        """XCLIPTextConfig""",
        """XCLIPVisionConfig""",
    ],
    """processing_x_clip""": ["""XCLIPProcessor"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase      :      List[str]            =					[
	    """XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """XCLIPModel""",
	    """XCLIPPreTrainedModel""",
	    """XCLIPTextModel""",
	    """XCLIPVisionModel""",
	]

if TYPE_CHECKING:
	from .configuration_x_clip import (
	    XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    XCLIPConfig,
	    XCLIPTextConfig,
	    XCLIPVisionConfig,
	)
	from .processing_x_clip import XCLIPProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_x_clip import (
		    XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    XCLIPModel,
		    XCLIPPreTrainedModel,
		    XCLIPTextModel,
		    XCLIPVisionModel,
		)

else:
	import sys

	_lowercase      :      List[Any]            =					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





