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					:   str							=		logging.get_logger(__name__)





def 	__lowerCamelCase							(UpperCAmelCase__     :							List[Any]      , UpperCAmelCase__     :							str=False      , UpperCAmelCase__     :							Union[str, Any]=False  ):
					SCREAMING_SNAKE_CASE    			=       "backbone." if is_semantic else ""

					SCREAMING_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 	__lowerCamelCase							(UpperCAmelCase__     :							List[Any]      , UpperCAmelCase__     :							List[str]      , UpperCAmelCase__     :							Any=False      , UpperCAmelCase__     :							Union[str, Any]=False  ):
					for i in range(config.num_hidden_layers  ):
										SCREAMING_SNAKE_CASE    			=       "backbone." if is_semantic else ""
										# queries, keys and values
										SCREAMING_SNAKE_CASE    			=       state_dict.pop(F"{prefix}blocks.{i}.attn.qkv.weight"  )
										SCREAMING_SNAKE_CASE    			=       state_dict.pop(F"{prefix}blocks.{i}.attn.q_bias"  )
										SCREAMING_SNAKE_CASE    			=       state_dict.pop(F"{prefix}blocks.{i}.attn.v_bias"  )

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

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

										SCREAMING_SNAKE_CASE    			=       gamma_a
										SCREAMING_SNAKE_CASE    			=       gamma_a





def 	__lowerCamelCase							(UpperCAmelCase__     :							List[Any]      , UpperCAmelCase__     :							List[str]      , UpperCAmelCase__     :							Optional[int]  ):
					SCREAMING_SNAKE_CASE    			=       dct.pop(UpperCAmelCase__  )
					SCREAMING_SNAKE_CASE    			=       val





def 	__lowerCamelCase							():
					SCREAMING_SNAKE_CASE    			=       "http://images.cocodataset.org/val2017/000000039769.jpg"
					SCREAMING_SNAKE_CASE    			=       Image.open(requests.get(UpperCAmelCase__      , stream=UpperCAmelCase__  ).raw  )
					return im





@torch.no_grad()
def 	__lowerCamelCase							(UpperCAmelCase__     :							Optional[Any]      , UpperCAmelCase__     :							str      , UpperCAmelCase__     :							Dict=False  ):
					SCREAMING_SNAKE_CASE    			=       False if "rvlcdip" in checkpoint_url else True
					SCREAMING_SNAKE_CASE    			=       BeitConfig(use_absolute_position_embeddings=UpperCAmelCase__      , use_mask_token=UpperCAmelCase__  )

					# size of the architecture
					if "large" in checkpoint_url or "dit-l" in checkpoint_url:
										SCREAMING_SNAKE_CASE    			=       1_0_2_4
										SCREAMING_SNAKE_CASE    			=       4_0_9_6
										SCREAMING_SNAKE_CASE    			=       2_4
										SCREAMING_SNAKE_CASE    			=       1_6

					# labels
					if "rvlcdip" in checkpoint_url:
										SCREAMING_SNAKE_CASE    			=       1_6
										SCREAMING_SNAKE_CASE    			=       "huggingface/label-files"
										SCREAMING_SNAKE_CASE    			=       "rvlcdip-id2label.json"
										SCREAMING_SNAKE_CASE    			=       json.load(open(hf_hub_download(UpperCAmelCase__      , UpperCAmelCase__      , repo_type="dataset"  )      , "r"  )  )
										SCREAMING_SNAKE_CASE    			=       {int(UpperCAmelCase__  ): v for k, v in idalabel.items()}
										SCREAMING_SNAKE_CASE    			=       idalabel
										SCREAMING_SNAKE_CASE    			=       {v: k for k, v in idalabel.items()}

					# load state_dict of original model, remove and rename some keys
					SCREAMING_SNAKE_CASE    			=       torch.hub.load_state_dict_from_url(UpperCAmelCase__      , map_location="cpu"  )["model"]

					SCREAMING_SNAKE_CASE    			=       create_rename_keys(UpperCAmelCase__      , has_lm_head=UpperCAmelCase__  )
					for src, dest in rename_keys:
										rename_key(UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__  )
					read_in_q_k_v(UpperCAmelCase__      , UpperCAmelCase__      , has_lm_head=UpperCAmelCase__  )

					# load HuggingFace model
					SCREAMING_SNAKE_CASE    			=       BeitForMaskedImageModeling(UpperCAmelCase__  ) if has_lm_head else BeitForImageClassification(UpperCAmelCase__  )
					model.eval()
					model.load_state_dict(UpperCAmelCase__  )

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

					SCREAMING_SNAKE_CASE    			=       image_processor(images=UpperCAmelCase__      , return_tensors="pt"  )
					SCREAMING_SNAKE_CASE    			=       encoding["pixel_values"]

					SCREAMING_SNAKE_CASE    			=       model(UpperCAmelCase__  )
					SCREAMING_SNAKE_CASE    			=       outputs.logits

					# verify logits
					SCREAMING_SNAKE_CASE    			=       [1, 1_6] if "rvlcdip" in checkpoint_url else [1, 1_9_6, 8_1_9_2]
					assert logits.shape == torch.Size(UpperCAmelCase__  ), "Shape of logits not as expected"

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

					if push_to_hub:
										if has_lm_head:
															SCREAMING_SNAKE_CASE    			=       "dit-base" if "base" in checkpoint_url else "dit-large"
										else:
															SCREAMING_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(UpperCAmelCase__      , UpperCAmelCase__  )      , organization="nielsr"      , commit_message="Add image processor"      , use_temp_dir=UpperCAmelCase__      , )
										model.push_to_hub(
										    repo_path_or_name=Path(UpperCAmelCase__      , UpperCAmelCase__  )      , organization="nielsr"      , commit_message="Add model"      , use_temp_dir=UpperCAmelCase__      , )


if __name__ == "__main__":
							_lowerCamelCase					:   Tuple							=		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					:   Any							=		parser.parse_args()
							convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)





from __future__ import annotations

from typing import Dict

from ...configuration_utils import PretrainedConfig


_lowerCamelCase					:   Tuple							=		{
    '''susnato/ernie-m-base_pytorch''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json''',
    '''susnato/ernie-m-large_pytorch''': '''https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json''',
}


class 						lowercase    (     a     ):
							lowercase__						:       Optional[Any]								=							"""ernie_m"""
							lowercase__						:       Dict[str, str]								=							{"dropout": "classifier_dropout", "num_classes": "num_labels"}




							def __init__(						self   :					Optional[int]	,							_UpperCamelCase   :					int = 250_002	,							_UpperCamelCase   :					int = 768	,							_UpperCamelCase   :					int = 12	,							_UpperCamelCase   :					int = 12	,							_UpperCamelCase   :					int = 3_072	,							_UpperCamelCase   :					str = "gelu"	,							_UpperCamelCase   :					float = 0.1	,							_UpperCamelCase   :					float = 0.1	,							_UpperCamelCase   :					int = 514	,							_UpperCamelCase   :					float = 0.0_2	,							_UpperCamelCase   :					int = 1	,							_UpperCamelCase   :					float = 1e-05	,							_UpperCamelCase   :					int=None	,							_UpperCamelCase   :					int=False	,							_UpperCamelCase   :					int=0.0	,							**_UpperCamelCase   :					Union[str, Any]	,							)					->				Optional[Any]:



												'''simple docstring'''

												super().__init__(pad_token_id=_UpperCamelCase	,							**_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       vocab_size
												SCREAMING_SNAKE_CASE    			=       hidden_size
												SCREAMING_SNAKE_CASE    			=       num_hidden_layers
												SCREAMING_SNAKE_CASE    			=       num_attention_heads
												SCREAMING_SNAKE_CASE    			=       intermediate_size
												SCREAMING_SNAKE_CASE    			=       hidden_act
												SCREAMING_SNAKE_CASE    			=       hidden_dropout_prob
												SCREAMING_SNAKE_CASE    			=       attention_probs_dropout_prob
												SCREAMING_SNAKE_CASE    			=       max_position_embeddings
												SCREAMING_SNAKE_CASE    			=       initializer_range
												SCREAMING_SNAKE_CASE    			=       layer_norm_eps
												SCREAMING_SNAKE_CASE    			=       classifier_dropout
												SCREAMING_SNAKE_CASE    			=       is_decoder
												SCREAMING_SNAKE_CASE    			=       act_dropout




