




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


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase=False, __UpperCAmelCase=False      )						->   Optional[Any]:

					'''simple docstring'''

					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 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase=False, __UpperCAmelCase=False      )						->   Any:

					'''simple docstring'''

					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 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )						->   Optional[int]:

					'''simple docstring'''

					snake_case_		     =							dct.pop(lowerCAmelCase__      )
					snake_case_		     =							val


def 						__magic_name__       (      )						->   Optional[Any]:

					'''simple docstring'''

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


@torch.no_grad()
def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase=False      )						->   List[Any]:

					'''simple docstring'''

					snake_case_		     =							False if '''rvlcdip''' in checkpoint_url else True
					snake_case_		     =							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:
										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(lowerCAmelCase__, lowerCAmelCase__, repo_type='''dataset'''      ), '''r'''      )      )
										snake_case_		     =							{int(lowerCAmelCase__      ): 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(lowerCAmelCase__, map_location='''cpu'''      )['''model''']

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

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

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

					snake_case_		     =							model(lowerCAmelCase__      )
					snake_case_		     =							outputs.logits

					# verify logits
					snake_case_		     =							[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:
															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(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__":
							a    :							Optional[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',
							)
							a    :							Dict										=    parser.parse_args()
							convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)



"""simple docstring"""


import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


A_    							=					logging.getLogger(__name__)




@dataclass
@add_start_docstrings(TrainingArguments.__doc__			)
class  __lowerCamelCase  ( lowerCAmelCase			):
				a__:      bool          =    field(default=lowerCAmelCase						,	metadata={'help': 'Whether to use SortishSampler or not.'}			)
				a__:      bool          =    field(
				    default=lowerCAmelCase						,	metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'}			)
				a__:      Optional[int]          =    field(
				    default=lowerCAmelCase						,	metadata={
				        'help': (
				            'The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '
				            'to the `max_length` value of the model configuration.'
				        )
				    }						,	)
				a__:      Optional[int]          =    field(
				    default=lowerCAmelCase						,	metadata={
				        'help': (
				            'The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '
				            'to the `num_beams` value of the model configuration.'
				        )
				    }						,	)
				a__:      Optional[Union[str, Path, GenerationConfig]]          =    field(
				    default=lowerCAmelCase						,	metadata={
				        'help': 'Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'
				    }						,	)




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  super().to_dict()
							for k, v in d.items():
										if isinstance(UpperCAmelCase							,						UpperCAmelCase   ):
													lowerCamelCase_					=  v.to_dict()
							return d



