
import logging

import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEncoder,
    BertModel,
    BertPreTrainedModel,
)


lowercase  :					Union[str, Any]													=       logging.getLogger(__name__)




class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	def    _lowerCamelCase				(					self		:Optional[Any]     ,   a		:List[Any]     ,   a		:str     ,   a		:Tuple=None     ,   a		:List[str]=None						)		-> Tuple:
								__UpperCamelCase	:   Dict			   =					self.layer[current_layer](a     ,   a     ,   head_mask[current_layer]						)

								__UpperCamelCase	:   Optional[int]			   =					layer_outputs[0]

								return hidden_states




@add_start_docstrings(
    'The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top.'     ,						__lowercase     ,						)
class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	def __init__(					self		:List[Any]     ,   a		:int						)		-> str:
								super().__init__(a						)

								__UpperCamelCase	:   Dict			   =					BertEncoderWithPabee(a						)

								self.init_weights()
								__UpperCamelCase	:   Optional[int]			   =					0
								__UpperCamelCase	:   List[str]			   =					0
								__UpperCamelCase	:   List[str]			   =					0

								__UpperCamelCase	:   List[str]			   =					0







	def    _lowerCamelCase				(					self		:str     ,   a		:Union[str, Any]						)		-> Optional[Any]:
								__UpperCamelCase	:   str			   =					threshold







	def    _lowerCamelCase				(					self		:Dict     ,   a		:Dict						)		-> str:
								__UpperCamelCase	:   List[str]			   =					patience







	def    _lowerCamelCase				(					self		:str						)		-> Any:
								__UpperCamelCase	:   List[str]			   =					0
								__UpperCamelCase	:   Optional[int]			   =					0







	def    _lowerCamelCase				(					self		:int						)		-> Optional[int]:
								__UpperCamelCase	:   str			   =					self.inference_layers_num / self.inference_instances_num
								__UpperCamelCase	:   List[Any]			   =					(
								    f'*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ='
								    f' {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***'
								)
								print(a						)







	@add_start_docstrings_to_model_forward(a						)
	def    _lowerCamelCase				(					self		:Tuple     ,   a		:List[str]=None     ,   a		:str=None     ,   a		:Optional[Any]=None     ,   a		:Dict=None     ,   a		:int=None     ,   a		:Optional[Any]=None     ,   a		:Tuple=None     ,   a		:Dict=None     ,   a		:Dict=None     ,   a		:int=None     ,   a		:Tuple=False     ,   )		-> Dict:

								if input_ids is not None and inputs_embeds is not None:
															raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time"						)
								elif input_ids is not None:
															__UpperCamelCase	:   List[str]			   =					input_ids.size()
								elif inputs_embeds is not None:
															__UpperCamelCase	:   Dict			   =					inputs_embeds.size()[:-1]
								else:
															raise ValueError("You have to specify either input_ids or inputs_embeds"						)

								__UpperCamelCase	:   List[str]			   =					input_ids.device if input_ids is not None else inputs_embeds.device

								if attention_mask is None:
															__UpperCamelCase	:   List[str]			   =					torch.ones(a     ,   device=a						)
								if token_type_ids is None:
															__UpperCamelCase	:   int			   =					torch.zeros(a     ,   dtype=torch.long     ,   device=a						)

								# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
								# ourselves in which case we just need to make it broadcastable to all heads.
								__UpperCamelCase	:   torch.Tensor			   =					self.get_extended_attention_mask(a     ,   a     ,   a						)

								# If a 2D ou 3D attention mask is provided for the cross-attention
								# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
								if self.config.is_decoder and encoder_hidden_states is not None:
															__UpperCamelCase   ,						__UpperCamelCase   ,						__UpperCamelCase	:   List[Any]			   =					encoder_hidden_states.size()
															__UpperCamelCase	:   Tuple			   =					(encoder_batch_size, encoder_sequence_length)
															if encoder_attention_mask is None:
																						__UpperCamelCase	:   List[str]			   =					torch.ones(a     ,   device=a						)
															__UpperCamelCase	:   str			   =					self.invert_attention_mask(a						)
								else:
															__UpperCamelCase	:   Any			   =					None

								# Prepare head mask if needed
								# 1.0 in head_mask indicate we keep the head
								# attention_probs has shape bsz x n_heads x N x N
								# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
								# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
								__UpperCamelCase	:   Union[str, Any]			   =					self.get_head_mask(a     ,   self.config.num_hidden_layers						)

								__UpperCamelCase	:   List[str]			   =					self.embeddings(
								    input_ids=a     ,   position_ids=a     ,   token_type_ids=a     ,   inputs_embeds=a						)
								__UpperCamelCase	:   Dict			   =					embedding_output

								if self.training:
															__UpperCamelCase	:   Dict			   =					[]
															for i in range(self.config.num_hidden_layers						):
																						__UpperCamelCase	:   List[Any]			   =					self.encoder.adaptive_forward(
																						    a     ,   current_layer=a     ,   attention_mask=a     ,   head_mask=a						)

																						__UpperCamelCase	:   str			   =					self.pooler(a						)
																						__UpperCamelCase	:   Optional[int]			   =					output_layers[i](output_dropout(a						)						)
																						res.append(a						)
								elif self.patience == 0:  # Use all layers for inference
															__UpperCamelCase	:   Dict			   =					self.encoder(
															    a     ,   attention_mask=a     ,   head_mask=a     ,   encoder_hidden_states=a     ,   encoder_attention_mask=a     ,   )
															__UpperCamelCase	:   List[Any]			   =					self.pooler(encoder_outputs[0]						)
															__UpperCamelCase	:   Optional[int]			   =					[output_layers[self.config.num_hidden_layers - 1](a						)]
								else:
															__UpperCamelCase	:   int			   =					0
															__UpperCamelCase	:   List[Any]			   =					None
															__UpperCamelCase	:   Dict			   =					0
															for i in range(self.config.num_hidden_layers						):
																						calculated_layer_num += 1
																						__UpperCamelCase	:   Optional[int]			   =					self.encoder.adaptive_forward(
																						    a     ,   current_layer=a     ,   attention_mask=a     ,   head_mask=a						)

																						__UpperCamelCase	:   Union[str, Any]			   =					self.pooler(a						)
																						__UpperCamelCase	:   List[Any]			   =					output_layers[i](a						)
																						if regression:
																													__UpperCamelCase	:   Any			   =					logits.detach()
																													if patient_result is not None:
																																				__UpperCamelCase	:   Optional[Any]			   =					patient_result.detach()
																													if (patient_result is not None) and torch.abs(patient_result - labels						) < self.regression_threshold:
																																				patient_counter += 1
																													else:
																																				__UpperCamelCase	:   Any			   =					0
																						else:
																													__UpperCamelCase	:   List[str]			   =					logits.detach().argmax(dim=1						)
																													if patient_result is not None:
																																				__UpperCamelCase	:   List[Any]			   =					patient_result.detach().argmax(dim=1						)
																													if (patient_result is not None) and torch.all(labels.eq(a						)						):
																																				patient_counter += 1
																													else:
																																				__UpperCamelCase	:   int			   =					0

																						__UpperCamelCase	:   Optional[int]			   =					logits
																						if patient_counter == self.patience:
																													break
															__UpperCamelCase	:   int			   =					[patient_result]
															self.inference_layers_num += calculated_layer_num
															self.inference_instances_num += 1

								return res




@add_start_docstrings(
    'Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of\n    the pooled output) e.g. for GLUE tasks. '     ,						__lowercase     ,						)
class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	def __init__(					self		:Union[str, Any]     ,   a		:int						)		-> Optional[Any]:
								super().__init__(a						)
								__UpperCamelCase	:   List[Any]			   =					config.num_labels

								__UpperCamelCase	:   Tuple			   =					BertModelWithPabee(a						)
								__UpperCamelCase	:   List[Any]			   =					nn.Dropout(config.hidden_dropout_prob						)
								__UpperCamelCase	:   Tuple			   =					nn.ModuleList(
								    [nn.Linear(config.hidden_size     ,   self.config.num_labels						) for _ in range(config.num_hidden_layers						)]						)

								self.init_weights()







	@add_start_docstrings_to_model_forward(a						)
	def    _lowerCamelCase				(					self		:List[Any]     ,   a		:Dict=None     ,   a		:Optional[Any]=None     ,   a		:int=None     ,   a		:List[Any]=None     ,   a		:Tuple=None     ,   a		:str=None     ,   a		:Optional[Any]=None     ,   )		-> str:
								__UpperCamelCase	:   str			   =					self.bert(
								    input_ids=a     ,   attention_mask=a     ,   token_type_ids=a     ,   position_ids=a     ,   head_mask=a     ,   inputs_embeds=a     ,   output_dropout=self.dropout     ,   output_layers=self.classifiers     ,   regression=self.num_labels == 1     ,   )

								__UpperCamelCase	:   Any			   =					(logits[-1],)

								if labels is not None:
															__UpperCamelCase	:   Optional[int]			   =					None
															__UpperCamelCase	:   Dict			   =					0
															for ix, logits_item in enumerate(a						):
																						if self.num_labels == 1:
																													#  We are doing regression
																													__UpperCamelCase	:   List[Any]			   =					MSELoss()
																													__UpperCamelCase	:   Union[str, Any]			   =					loss_fct(logits_item.view(-1						)     ,   labels.view(-1						)						)
																						else:
																													__UpperCamelCase	:   Optional[Any]			   =					CrossEntropyLoss()
																													__UpperCamelCase	:   List[str]			   =					loss_fct(logits_item.view(-1     ,   self.num_labels						)     ,   labels.view(-1						)						)
																						if total_loss is None:
																													__UpperCamelCase	:   List[str]			   =					loss
																						else:
																													total_loss += loss * (ix + 1)
																						total_weights += ix + 1
															__UpperCamelCase	:   int			   =					(total_loss / total_weights,) + outputs

								return outputs

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  :					Dict													=       logging.get_logger(__name__)




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Optional[Any]						,  _lowerCamelCase :			Union[str, Any]=False						,  _lowerCamelCase :			Optional[int]=False)					->    Any:






							'''simple docstring'''





							__UpperCamelCase	:   Optional[int]			   =					"backbone." if is_semantic else ""

							__UpperCamelCase	:   Optional[Any]			   =					[]
							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					(		_lowerCamelCase :			Tuple						,  _lowerCamelCase :			List[str]						,  _lowerCamelCase :			Union[str, Any]=False						,  _lowerCamelCase :			Optional[int]=False)					->    List[str]:






							'''simple docstring'''





							for i in range(config.num_hidden_layers):
														__UpperCamelCase	:   List[Any]			   =					"backbone." if is_semantic else ""
														# queries, keys and values
														__UpperCamelCase	:   Tuple			   =					state_dict.pop(F'{prefix}blocks.{i}.attn.qkv.weight')
														__UpperCamelCase	:   Optional[Any]			   =					state_dict.pop(F'{prefix}blocks.{i}.attn.q_bias')
														__UpperCamelCase	:   int			   =					state_dict.pop(F'{prefix}blocks.{i}.attn.v_bias')

														__UpperCamelCase	:   Optional[int]			   =					in_proj_weight[
														    : config.hidden_size, :
														]
														__UpperCamelCase	:   Any			   =					q_bias
														__UpperCamelCase	:   Dict			   =					in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														__UpperCamelCase	:   List[Any]			   =					in_proj_weight[
														    -config.hidden_size :, :
														]
														__UpperCamelCase	:   Tuple			   =					v_bias

														# gamma_1 and gamma_2
														# we call them lambda because otherwise they are renamed when using .from_pretrained
														__UpperCamelCase	:   Union[str, Any]			   =					state_dict.pop(F'{prefix}blocks.{i}.gamma_1')
														__UpperCamelCase	:   Optional[int]			   =					state_dict.pop(F'{prefix}blocks.{i}.gamma_2')

														__UpperCamelCase	:   Any			   =					gamma_a
														__UpperCamelCase	:   List[Any]			   =					gamma_a




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			str						,  _lowerCamelCase :			List[str]						,  _lowerCamelCase :			Optional[Any])					->    Any:






							'''simple docstring'''





							__UpperCamelCase	:   str			   =					dct.pop(_lowerCamelCase)
							__UpperCamelCase	:   str			   =					val




def 	_SCREAMING_SNAKE_CASE					(		)					->    Optional[int]:






							'''simple docstring'''





							__UpperCamelCase	:   Any			   =					"http://images.cocodataset.org/val2017/000000039769.jpg"
							__UpperCamelCase	:   Dict			   =					Image.open(requests.get(_lowerCamelCase						,  stream=_lowerCamelCase).raw)
							return im




@torch.no_grad()
def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Optional[int]						,  _lowerCamelCase :			List[str]						,  _lowerCamelCase :			Dict=False)					->    str:






							'''simple docstring'''





							__UpperCamelCase	:   str			   =					False if "rvlcdip" in checkpoint_url else True
							__UpperCamelCase	:   int			   =					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:
														__UpperCamelCase	:   Optional[int]			   =					1_024
														__UpperCamelCase	:   Union[str, Any]			   =					4_096
														__UpperCamelCase	:   Tuple			   =					24
														__UpperCamelCase	:   int			   =					16

							# labels
							if "rvlcdip" in checkpoint_url:
														__UpperCamelCase	:   List[str]			   =					16
														__UpperCamelCase	:   str			   =					"huggingface/label-files"
														__UpperCamelCase	:   str			   =					"rvlcdip-id2label.json"
														__UpperCamelCase	:   Tuple			   =					json.load(open(hf_hub_download(_lowerCamelCase						,  _lowerCamelCase						,  repo_type="dataset")						,  "r"))
														__UpperCamelCase	:   Dict			   =					{int(_lowerCamelCase): v for k, v in idalabel.items()}
														__UpperCamelCase	:   Optional[Any]			   =					idalabel
														__UpperCamelCase	:   List[Any]			   =					{v: k for k, v in idalabel.items()}

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

							__UpperCamelCase	:   Optional[Any]			   =					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
							__UpperCamelCase	:   List[Any]			   =					BeitForMaskedImageModeling(_lowerCamelCase) if has_lm_head else BeitForImageClassification(_lowerCamelCase)
							model.eval()
							model.load_state_dict(_lowerCamelCase)

							# Check outputs on an image
							__UpperCamelCase	:   Dict			   =					BeitImageProcessor(
							    size=config.image_size						,  resample=PILImageResampling.BILINEAR						,  do_center_crop=_lowerCamelCase)
							__UpperCamelCase	:   Union[str, Any]			   =					prepare_img()

							__UpperCamelCase	:   Union[str, Any]			   =					image_processor(images=_lowerCamelCase						,  return_tensors="pt")
							__UpperCamelCase	:   str			   =					encoding["pixel_values"]

							__UpperCamelCase	:   str			   =					model(_lowerCamelCase)
							__UpperCamelCase	:   Union[str, Any]			   =					outputs.logits

							# verify logits
							__UpperCamelCase	:   Tuple			   =					[1, 16] if "rvlcdip" in checkpoint_url else [1, 196, 8_192]
							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:
																					__UpperCamelCase	:   List[str]			   =					"dit-base" if "base" in checkpoint_url else "dit-large"
														else:
																					__UpperCamelCase	:   Optional[Any]			   =					"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__":
		lowercase  :					Optional[Any]													=       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  :					Tuple													=       parser.parse_args()
		convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)