

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_			      =      logging.get_logger(__name__)


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__=False    ,						SCREAMING_SNAKE_CASE__=False			):
						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     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__=False    ,						SCREAMING_SNAKE_CASE__=False			):
						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     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			):
						snake_case_  =      dct.pop(SCREAMING_SNAKE_CASE__			)
						snake_case_  =      val


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


@torch.no_grad()
def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__=False			):
						snake_case_  =      False if '''rvlcdip''' in checkpoint_url else True
						snake_case_  =      BeitConfig(use_absolute_position_embeddings=SCREAMING_SNAKE_CASE__    ,						use_mask_token=SCREAMING_SNAKE_CASE__			)

						# 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(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						repo_type='''dataset'''			)    ,						'''r'''			)			)
												snake_case_  =      {int(SCREAMING_SNAKE_CASE__			): 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(SCREAMING_SNAKE_CASE__    ,						map_location='''cpu'''			)['''model''']

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

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

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

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

						snake_case_  =      model(SCREAMING_SNAKE_CASE__			)
						snake_case_  =      outputs.logits

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

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

						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(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			)    ,						organization='''nielsr'''    ,						commit_message='''Add image processor'''    ,						use_temp_dir=SCREAMING_SNAKE_CASE__    ,						)
												model.push_to_hub(
												    repo_path_or_name=Path(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			)    ,						organization='''nielsr'''    ,						commit_message='''Add model'''    ,						use_temp_dir=SCREAMING_SNAKE_CASE__    ,						)


if __name__ == "__main__":
					lowerCAmelCase_			      =      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_			      =      parser.parse_args()
					convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)




"""simple docstring"""







import contextlib
import importlib
import io
import unittest

import transformers

# Try to import everything from transformers to ensure every object can be loaded.
from transformers import *  # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from transformers.utils import ContextManagers, find_labels, is_flax_available, is_tf_available, is_torch_available


if is_torch_available():
			from transformers import BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification

if is_tf_available():
			from transformers import TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification

if is_flax_available():
			from transformers import FlaxBertForPreTraining, FlaxBertForQuestionAnswering, FlaxBertForSequenceClassification


A		=						DUMMY_UNKNOWN_IDENTIFIER
# An actual model hosted on huggingface.co

A		=						'main'
# Default branch name
A		=						'f2c752cfc5c0ab6f4bdec59acea69eefbee381c2'
# One particular commit (not the top of `main`)
A		=						'aaaaaaa'
# This commit does not exist, so we should 404.

A		=						'd9e9f15bc825e4b2c9249e9578f884bbcb5e3684'
# Sha-1 of config.json on the top of `main`, for checking purposes
A		=						'4b243c475af8d0a7754e87d7d096c92e5199ec2fe168a2ee7998e3b8e9bcb1d3'

@contextlib.contextmanager
def  __SCREAMING_SNAKE_CASE		(			):
						"""simple docstring"""






						print("Welcome!" )
						yield
						print("Bye!" )

@contextlib.contextmanager
def  __SCREAMING_SNAKE_CASE		(			):
						"""simple docstring"""






						print("Bonjour!" )
						yield
						print("Au revoir!" )

class       _a  ( unittest.TestCase):



			def 				__lowercase					(			self							:				Dict		)							->					Optional[int]:
									# If the spec is missing, importlib would not be able to import the module dynamically.
									assert transformers.__spec__ is not None
									assert importlib.util.find_spec("transformers"		) is not None



class       _a  ( unittest.TestCase):



			@unittest.mock.patch("sys.stdout"   ,	new_callable=io.StringIO		)
			def 				__lowercase					(			self							:				Union[str, Any]   ,	_lowercase							:				Optional[int]		)							->					Optional[Any]:
									with ContextManagers([]		):
															print("Transformers are awesome!"		)
									# The print statement adds a new line at the end of the output
									self.assertEqual(mock_stdout.getvalue()   ,	"Transformers are awesome!\n"		)



			@unittest.mock.patch("sys.stdout"   ,	new_callable=io.StringIO		)
			def 				__lowercase					(			self							:				Any   ,	_lowercase							:				Any		)							->					Dict:
									with ContextManagers([context_en()]		):
															print("Transformers are awesome!"		)
									# The output should be wrapped with an English welcome and goodbye
									self.assertEqual(mock_stdout.getvalue()   ,	"Welcome!\nTransformers are awesome!\nBye!\n"		)



			@unittest.mock.patch("sys.stdout"   ,	new_callable=io.StringIO		)
			def 				__lowercase					(			self							:				str   ,	_lowercase							:				List[Any]		)							->					Optional[int]:
									with ContextManagers([context_fr(), context_en()]		):
															print("Transformers are awesome!"		)
									# The output should be wrapped with an English and French welcome and goodbye
									self.assertEqual(mock_stdout.getvalue()   ,	"Bonjour!\nWelcome!\nTransformers are awesome!\nBye!\nAu revoir!\n"		)



			@require_torch
			def 				__lowercase					(			self							:				Any		)							->					Union[str, Any]:
									self.assertEqual(find_labels(_lowercase		)   ,	["labels"]		)
									self.assertEqual(find_labels(_lowercase		)   ,	["labels", "next_sentence_label"]		)
									self.assertEqual(find_labels(_lowercase		)   ,	["start_positions", "end_positions"]		)

									class       _a  ( SCREAMING_SNAKE_CASE__):
												pass



									self.assertEqual(find_labels(_lowercase		)   ,	["labels"]		)



			@require_tf
			def 				__lowercase					(			self							:				Optional[Any]		)							->					int:
									self.assertEqual(find_labels(_lowercase		)   ,	["labels"]		)
									self.assertEqual(find_labels(_lowercase		)   ,	["labels", "next_sentence_label"]		)
									self.assertEqual(find_labels(_lowercase		)   ,	["start_positions", "end_positions"]		)

									class       _a  ( SCREAMING_SNAKE_CASE__):
												pass



									self.assertEqual(find_labels(_lowercase		)   ,	["labels"]		)



			@require_flax
			def 				__lowercase					(			self							:				Tuple		)							->					List[Any]:
									# Flax models don't have labels
									self.assertEqual(find_labels(_lowercase		)   ,	[]		)
									self.assertEqual(find_labels(_lowercase		)   ,	[]		)
									self.assertEqual(find_labels(_lowercase		)   ,	[]		)

									class       _a  ( SCREAMING_SNAKE_CASE__):
												pass



									self.assertEqual(find_labels(_lowercase		)   ,	[]		)



