'''simple docstring'''




import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer






def __lowerCAmelCase (							UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__=True				,  UpperCamelCase__="pt"				)	->    List[Any]:
    __lowerCamelCase		=  {'''add_prefix_space''': True} if isinstance(UpperCamelCase__				,  UpperCamelCase__				) and not line.startswith(''' '''				) else {}
    __lowerCamelCase		=  padding_side
    return tokenizer(
        [line]				,  max_length=UpperCamelCase__				,  padding='''max_length''' if pad_to_max_length else None				,  truncation=UpperCamelCase__				,  return_tensors=UpperCamelCase__				,  add_special_tokens=UpperCamelCase__				,  **UpperCamelCase__				,  )






def __lowerCAmelCase (							UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__=None				,  )	->    Tuple:
    __lowerCamelCase		=  input_ids.ne(UpperCamelCase__				).any(dim=0				)
    if attention_mask is None:
        return input_ids[:, keep_column_mask]
    else:
        return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])




class     a__      (   UpperCAmelCase__ ):






     def __init__( self		:		Dict  ,				a		:		Optional[Any]  ,				a		:		int  ,				a		:		Optional[int]  ,				a		:		Union[str, Any]  ,				a		:		Optional[int]="train"  ,				a		:		List[str]=None  ,				a		:		List[str]=None  ,				a		:		List[str]=None  ,				a		:		Dict=""  ,				):





         """simple docstring"""



         super().__init__()
         __lowerCamelCase		=  Path(a    ).joinpath(type_path + '''.source'''    )
         __lowerCamelCase		=  Path(a    ).joinpath(type_path + '''.target'''    )
         __lowerCamelCase		=  self.get_char_lens(self.src_file    )
         __lowerCamelCase		=  max_source_length
         __lowerCamelCase		=  max_target_length
         assert min(self.src_lens    ) > 0, f"""found empty line in {self.src_file}"""
         __lowerCamelCase		=  tokenizer
         __lowerCamelCase		=  prefix
         if n_obs is not None:
             __lowerCamelCase		=  self.src_lens[:n_obs]
         __lowerCamelCase		=  src_lang
         __lowerCamelCase		=  tgt_lang






     def __len__( self		:		Dict    ):





         """simple docstring"""



         return len(self.src_lens    )






     def __getitem__( self		:		int  ,				a		:		Any    ):





         """simple docstring"""



         __lowerCamelCase		=  index + 1  # linecache starts at 1
         __lowerCamelCase		=  self.prefix + linecache.getline(str(self.src_file    )  ,				a    ).rstrip('''\n'''    )
         __lowerCamelCase		=  linecache.getline(str(self.tgt_file    )  ,				a    ).rstrip('''\n'''    )
         assert source_line, f"""empty source line for index {index}"""
         assert tgt_line, f"""empty tgt line for index {index}"""

         # Need to add eos token manually for T5
         if isinstance(self.tokenizer  ,				a    ):
             source_line += self.tokenizer.eos_token
             tgt_line += self.tokenizer.eos_token

         # Pad source and target to the right
         __lowerCamelCase		=  (
             self.tokenizer.question_encoder if isinstance(self.tokenizer  ,				a    ) else self.tokenizer
         )
         __lowerCamelCase		=  self.tokenizer.generator if isinstance(self.tokenizer  ,				a    ) else self.tokenizer

         __lowerCamelCase		=  encode_line(a  ,				a  ,				self.max_source_length  ,				'''right'''    )
         __lowerCamelCase		=  encode_line(a  ,				a  ,				self.max_target_length  ,				'''right'''    )

         __lowerCamelCase		=  source_inputs['''input_ids'''].squeeze()
         __lowerCamelCase		=  target_inputs['''input_ids'''].squeeze()
         __lowerCamelCase		=  source_inputs['''attention_mask'''].squeeze()
         return {
             "input_ids": source_ids,
             "attention_mask": src_mask,
             "decoder_input_ids": target_ids,
         }






     @staticmethod
     def    SCREAMING_SNAKE_CASE__   ( a		:		Any    ):





         """simple docstring"""



         return [len(a    ) for x in Path(a    ).open().readlines()]






     def    SCREAMING_SNAKE_CASE__   ( self		:		Tuple  ,				a		:		Optional[Any]    ):





         """simple docstring"""



         __lowerCamelCase		=  torch.stack([x['''input_ids'''] for x in batch]    )
         __lowerCamelCase		=  torch.stack([x['''attention_mask'''] for x in batch]    )
         __lowerCamelCase		=  torch.stack([x['''decoder_input_ids'''] for x in batch]    )
         __lowerCamelCase		=  (
             self.tokenizer.generator.pad_token_id
             if isinstance(self.tokenizer  ,				a    )
             else self.tokenizer.pad_token_id
         )
         __lowerCamelCase		=  (
             self.tokenizer.question_encoder.pad_token_id
             if isinstance(self.tokenizer  ,				a    )
             else self.tokenizer.pad_token_id
         )
         __lowerCamelCase		=  trim_batch(a  ,				a    )
         __lowerCamelCase	,  __lowerCamelCase		=  trim_batch(a  ,				a  ,				attention_mask=a    )
         __lowerCamelCase		=  {
             '''input_ids''': source_ids,
             '''attention_mask''': source_mask,
             '''decoder_input_ids''': y,
         }
         return batch
__UpperCAmelCase     							=getLogger(__name__)






def __lowerCAmelCase (							UpperCamelCase__				)	->    Dict:
    return list(itertools.chain.from_iterable(UpperCamelCase__				)				)






def __lowerCAmelCase (							UpperCamelCase__				)	->    None:
    __lowerCamelCase		=  get_git_info()
    save_json(UpperCamelCase__				,  os.path.join(UpperCamelCase__				,  '''git_log.json'''				)				)






def __lowerCAmelCase (							UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__=4				,  **UpperCamelCase__				)	->    Tuple:
    with open(UpperCamelCase__				,  '''w'''				) as f:
        json.dump(UpperCamelCase__				,  UpperCamelCase__				,  indent=UpperCamelCase__				,  **UpperCamelCase__				)






def __lowerCAmelCase (							UpperCamelCase__				)	->    Optional[Any]:
    with open(UpperCamelCase__				) as f:
        return json.load(UpperCamelCase__				)






def __lowerCAmelCase (							)	->    Union[str, Any]:
    __lowerCamelCase		=  git.Repo(search_parent_directories=UpperCamelCase__				)
    __lowerCamelCase		=  {
        '''repo_id''': str(UpperCamelCase__				),
        '''repo_sha''': str(repo.head.object.hexsha				),
        '''repo_branch''': str(repo.active_branch				),
        '''hostname''': str(socket.gethostname()				),
    }
    return repo_infos






def __lowerCAmelCase (							UpperCamelCase__				,  UpperCamelCase__				)	->    List:
    return list(map(UpperCamelCase__				,  UpperCamelCase__				)				)






def __lowerCAmelCase (							UpperCamelCase__				,  UpperCamelCase__				)	->    Dict:
    with open(UpperCamelCase__				,  '''wb'''				) as f:
        return pickle.dump(UpperCamelCase__				,  UpperCamelCase__				)






def __lowerCAmelCase (							UpperCamelCase__				)	->    Dict:

    def remove_articles(UpperCamelCase__				):
        return re.sub(r'''\b(a|an|the)\b'''				,  ''' '''				,  UpperCamelCase__				)

    def white_space_fix(UpperCamelCase__				):
        return " ".join(text.split()				)

    def remove_punc(UpperCamelCase__				):
        __lowerCamelCase		=  set(string.punctuation				)
        return "".join(ch for ch in text if ch not in exclude				)

    def lower(UpperCamelCase__				):
        return text.lower()

    return white_space_fix(remove_articles(remove_punc(lower(UpperCamelCase__				)				)				)				)






def __lowerCAmelCase (							UpperCamelCase__				,  UpperCamelCase__				)	->    str:
    __lowerCamelCase		=  normalize_answer(UpperCamelCase__				).split()
    __lowerCamelCase		=  normalize_answer(UpperCamelCase__				).split()
    __lowerCamelCase		=  Counter(UpperCamelCase__				) & Counter(UpperCamelCase__				)
    __lowerCamelCase		=  sum(common.values()				)
    if num_same == 0:
        return 0
    __lowerCamelCase		=  1.0 * num_same / len(UpperCamelCase__				)
    __lowerCamelCase		=  1.0 * num_same / len(UpperCamelCase__				)
    __lowerCamelCase		=  (2 * precision * recall) / (precision + recall)
    return fa






def __lowerCAmelCase (							UpperCamelCase__				,  UpperCamelCase__				)	->    Optional[Any]:
    return normalize_answer(UpperCamelCase__				) == normalize_answer(UpperCamelCase__				)






def __lowerCAmelCase (							UpperCamelCase__				,  UpperCamelCase__				)	->    Dict:
    assert len(UpperCamelCase__				) == len(UpperCamelCase__				)
    __lowerCamelCase		=  0
    for hypo, pred in zip(UpperCamelCase__				,  UpperCamelCase__				):
        em += exact_match_score(UpperCamelCase__				,  UpperCamelCase__				)
    if len(UpperCamelCase__				) > 0:
        em /= len(UpperCamelCase__				)
    return {"em": em}






def __lowerCAmelCase (							UpperCamelCase__				)	->    Tuple:
    return model_prefix.startswith('''rag'''				)






def __lowerCAmelCase (							UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				)	->    Union[str, Any]:
    __lowerCamelCase		=  {p: p for p in extra_params}
    # T5 models don't have `dropout` param, they have `dropout_rate` instead
    __lowerCamelCase		=  '''dropout_rate'''
    for p in extra_params:
        if getattr(UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				):
            if not hasattr(UpperCamelCase__				,  UpperCamelCase__				) and not hasattr(UpperCamelCase__				,  equivalent_param[p]				):
                logger.info('''config doesn\'t have a `{}` attribute'''.format(UpperCamelCase__				)				)
                delattr(UpperCamelCase__				,  UpperCamelCase__				)
                continue
            __lowerCamelCase		=  p if hasattr(UpperCamelCase__				,  UpperCamelCase__				) else equivalent_param[p]
            setattr(UpperCamelCase__				,  UpperCamelCase__				,  getattr(UpperCamelCase__				,  UpperCamelCase__				)				)
            delattr(UpperCamelCase__				,  UpperCamelCase__				)
    return hparams, config


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)