




'''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       =							logging.get_logger(__name__)


def  _snake_case       (					A			,   A=False			,   A=False	)						->   Tuple:
							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  _snake_case       (					A			,   A			,   A=False			,   A=False	)						->   str:



							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  _snake_case       (					A			,   A			,   A	)						->   Any:
							lowerCAmelCase__	       =     dct.pop(A	)
							lowerCAmelCase__	       =     val


def  _snake_case       (					)						->   Union[str, Any]:
							lowerCAmelCase__	       =     """http://images.cocodataset.org/val2017/000000039769.jpg"""
							lowerCAmelCase__	       =     Image.open(requests.get(A			,   stream=A	).raw	)
							return im


@torch.no_grad()
def  _snake_case       (					A			,   A			,   A=False	)						->   int:
							lowerCAmelCase__	       =     False if """rvlcdip""" in checkpoint_url else True
							lowerCAmelCase__	       =     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:
														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(A			,   A			,   repo_type='''dataset'''	)			,   '''r'''	)	)
														lowerCAmelCase__	       =     {int(A	): 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(A			,   map_location='''cpu'''	)["""model"""]

							lowerCAmelCase__	       =     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
							lowerCAmelCase__	       =     BeitForMaskedImageModeling(A	) if has_lm_head else BeitForImageClassification(A	)
							model.eval()
							model.load_state_dict(A	)

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

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

							lowerCAmelCase__	       =     model(A	)
							lowerCAmelCase__	       =     outputs.logits

							# verify logits
							lowerCAmelCase__	       =     [1, 16] if """rvlcdip""" in checkpoint_url else [1, 196, 8192]
							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:
																					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(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__":
		__UpperCAmelCase       =							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       =							parser.parse_args()
		convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)



'''simple docstring'''






from __future__ import annotations




class 		a__    :





							'''simple docstring'''






							def __init__(		self ,   lowerCamelCase_       )	->							None:
														lowerCAmelCase__	       =     order

														# a_{0} ... a_{k}
														lowerCAmelCase__	       =     [1.0] + [0.0] * order
														# b_{0} ... b_{k}
														lowerCAmelCase__	       =     [1.0] + [0.0] * order

														# x[n-1] ... x[n-k]
														lowerCAmelCase__	       =     [0.0] * self.order
														# y[n-1] ... y[n-k]
														lowerCAmelCase__	       =     [0.0] * self.order







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							None:
														if len(lowerCamelCase_       ) < self.order:
																					lowerCAmelCase__	       =     [1.0, *a_coeffs]

														if len(lowerCamelCase_       ) != self.order + 1:
																					lowerCAmelCase__	       =     (
																					    F"""Expected a_coeffs to have {self.order + 1} elements """
																					    F"""for {self.order}-order filter, got {len(lowerCamelCase_       )}"""
																					)
																					raise ValueError(lowerCamelCase_       )

														if len(lowerCamelCase_       ) != self.order + 1:
																					lowerCAmelCase__	       =     (
																					    F"""Expected b_coeffs to have {self.order + 1} elements """
																					    F"""for {self.order}-order filter, got {len(lowerCamelCase_       )}"""
																					)
																					raise ValueError(lowerCamelCase_       )

														lowerCAmelCase__	       =     a_coeffs
														lowerCAmelCase__	       =     b_coeffs







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							float:
														lowerCAmelCase__	       =     0.0

														# Start at index 1 and do index 0 at the end.
														for i in range(1 ,   self.order + 1       ):
																					result += (
																					    self.b_coeffs[i] * self.input_history[i - 1]
																					    - self.a_coeffs[i] * self.output_history[i - 1]
																					)

														lowerCAmelCase__	       =     (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

														lowerCAmelCase__	       =     self.input_history[:-1]
														lowerCAmelCase__	       =     self.output_history[:-1]

														lowerCAmelCase__	       =     sample
														lowerCAmelCase__	       =     result

														return result