
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				=logging.get_logger(__name__)





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__=False	, lowerCamelCase__=False    ):
				lowerCamelCase_						=			"backbone." if is_semantic else ""

				lowerCamelCase_						=			[]
				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_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__=False	, lowerCamelCase__=False    ):
				for i in range(config.num_hidden_layers    ):
								lowerCamelCase_						=			"backbone." if is_semantic else ""
								# queries, keys and values
								lowerCamelCase_						=			state_dict.pop(F'{prefix}blocks.{i}.attn.qkv.weight'    )
								lowerCamelCase_						=			state_dict.pop(F'{prefix}blocks.{i}.attn.q_bias'    )
								lowerCamelCase_						=			state_dict.pop(F'{prefix}blocks.{i}.attn.v_bias'    )

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

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

								lowerCamelCase_						=			gamma_a
								lowerCamelCase_						=			gamma_a





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    ):
				lowerCamelCase_						=			dct.pop(lowerCamelCase__    )
				lowerCamelCase_						=			val





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





@torch.no_grad()
def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__=False    ):
				lowerCamelCase_						=			False if "rvlcdip" in checkpoint_url else True
				lowerCamelCase_						=			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:
								lowerCamelCase_						=			1_0_2_4
								lowerCamelCase_						=			4_0_9_6
								lowerCamelCase_						=			2_4
								lowerCamelCase_						=			1_6

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

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

				lowerCamelCase_						=			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
				lowerCamelCase_						=			BeitForMaskedImageModeling(lowerCamelCase__    ) if has_lm_head else BeitForImageClassification(lowerCamelCase__    )
				model.eval()
				model.load_state_dict(lowerCamelCase__    )

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

				lowerCamelCase_						=			image_processor(images=lowerCamelCase__	, return_tensors="pt"    )
				lowerCamelCase_						=			encoding["pixel_values"]

				lowerCamelCase_						=			model(lowerCamelCase__    )
				lowerCamelCase_						=			outputs.logits

				# verify logits
				lowerCamelCase_						=			[1, 1_6] if "rvlcdip" in checkpoint_url else [1, 1_9_6, 8_1_9_2]
				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:
												lowerCamelCase_						=			"dit-base" if "base" in checkpoint_url else "dit-large"
								else:
												lowerCamelCase_						=			"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				=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				=parser.parse_args()
			convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
			from .tokenization_mbart import MBartTokenizer
else:
			__A				=None


__A				=logging.get_logger(__name__)


__A				={'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

__A				={
    '''vocab_file''': {
        '''facebook/mbart-large-en-ro''': (
            '''https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'''
        ),
        '''facebook/mbart-large-cc25''': (
            '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/mbart-large-en-ro''': '''https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json''',
        '''facebook/mbart-large-cc25''': '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json''',
    },
}

__A				={
    '''facebook/mbart-large-en-ro''': 1_0_2_4,
    '''facebook/mbart-large-cc25''': 1_0_2_4,
}

# fmt: off
__A				=['''ar_AR''', '''cs_CZ''', '''de_DE''', '''en_XX''', '''es_XX''', '''et_EE''', '''fi_FI''', '''fr_XX''', '''gu_IN''', '''hi_IN''', '''it_IT''', '''ja_XX''', '''kk_KZ''', '''ko_KR''', '''lt_LT''', '''lv_LV''', '''my_MM''', '''ne_NP''', '''nl_XX''', '''ro_RO''', '''ru_RU''', '''si_LK''', '''tr_TR''', '''vi_VN''', '''zh_CN''']




class     _SCREAMING_SNAKE_CASE     (      snake_case_						):
	lowerCAmelCase__         = VOCAB_FILES_NAMES
	lowerCAmelCase__         = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCAmelCase__         = PRETRAINED_VOCAB_FILES_MAP
	lowerCAmelCase__         = ['input_ids', 'attention_mask']
	lowerCAmelCase__         = MBartTokenizer

	lowerCAmelCase__         = []
	lowerCAmelCase__         = []



	def __init__(    self						,  lowercase=None						,  lowercase=None						,  lowercase="<s>"						,  lowercase="</s>"						,  lowercase="</s>"						,  lowercase="<s>"						,  lowercase="<unk>"						,  lowercase="<pad>"						,  lowercase="<mask>"						,  lowercase=None						,  lowercase=None						,  lowercase=None						,  **lowercase						,  )				->       Dict:
					# Mask token behave like a normal word, i.e. include the space before it
					lowerCamelCase_						=			AddedToken(lowercase						,  lstrip=lowercase						,  rstrip=lowercase ) if isinstance(lowercase						,  lowercase ) else mask_token

					super().__init__(
					    vocab_file=lowercase						,  tokenizer_file=lowercase						,  bos_token=lowercase						,  eos_token=lowercase						,  sep_token=lowercase						,  cls_token=lowercase						,  unk_token=lowercase						,  pad_token=lowercase						,  mask_token=lowercase						,  src_lang=lowercase						,  tgt_lang=lowercase						,  additional_special_tokens=lowercase						,  **lowercase						,  )

					lowerCamelCase_						=			vocab_file
					lowerCamelCase_						=			False if not self.vocab_file else True

					lowerCamelCase_						=			FAIRSEQ_LANGUAGE_CODES.copy()

					if additional_special_tokens is not None:
									# Only add those special tokens if they are not already there.
									_additional_special_tokens.extend(
									    [t for t in additional_special_tokens if t not in _additional_special_tokens] )

					self.add_special_tokens({"additional_special_tokens": _additional_special_tokens} )
					lowerCamelCase_						=			{
					    lang_code: self.convert_tokens_to_ids(lowercase ) for lang_code in FAIRSEQ_LANGUAGE_CODES
					}

					lowerCamelCase_						=			src_lang if src_lang is not None else "en_XX"
					lowerCamelCase_						=			self.convert_tokens_to_ids(self._src_lang )
					lowerCamelCase_						=			tgt_lang
					self.set_src_lang_special_tokens(self._src_lang )



	@property
	def     SCREAMING_SNAKE_CASE_(    self )				->       str:
					return self._src_lang



	@src_lang.setter
	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       None:
					lowerCamelCase_						=			new_src_lang
					self.set_src_lang_special_tokens(self._src_lang )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase = None )				->       List[int]:
					if token_ids_a is None:
									return self.prefix_tokens + token_ids_a + self.suffix_tokens
					# We don't expect to process pairs, but leave the pair logic for API consistency
					return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase = None )				->       List[int]:
					lowerCamelCase_						=			[self.sep_token_id]
					lowerCamelCase_						=			[self.cls_token_id]

					if token_ids_a is None:
									return len(cls + token_ids_a + sep ) * [0]
					return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  **lowercase )				->       List[Any]:
					if src_lang is None or tgt_lang is None:
									raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model" )
					lowerCamelCase_						=			src_lang
					lowerCamelCase_						=			self(lowercase						,  add_special_tokens=lowercase						,  return_tensors=lowercase						,  **lowercase )
					lowerCamelCase_						=			self.convert_tokens_to_ids(lowercase )
					lowerCamelCase_						=			tgt_lang_id
					return inputs



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase = "en_XX"						,  lowercase = None						,  lowercase = "ro_RO"						,  **lowercase						,  )				->       BatchEncoding:
					lowerCamelCase_						=			src_lang
					lowerCamelCase_						=			tgt_lang
					return super().prepare_seqaseq_batch(lowercase						,  lowercase						,  **lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Dict:
					return self.set_src_lang_special_tokens(self.src_lang )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[Any]:
					return self.set_tgt_lang_special_tokens(self.tgt_lang )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       None:
					lowerCamelCase_						=			self.convert_tokens_to_ids(lowercase )
					lowerCamelCase_						=			[]
					lowerCamelCase_						=			[self.eos_token_id, self.cur_lang_code]

					lowerCamelCase_						=			self.convert_ids_to_tokens(self.prefix_tokens )
					lowerCamelCase_						=			self.convert_ids_to_tokens(self.suffix_tokens )

					lowerCamelCase_						=			processors.TemplateProcessing(
					    single=prefix_tokens_str + ["$A"] + suffix_tokens_str						,  pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str						,  special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str						,  self.prefix_tokens + self.suffix_tokens ) )						,  )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       None:
					lowerCamelCase_						=			self.convert_tokens_to_ids(lowercase )
					lowerCamelCase_						=			[]
					lowerCamelCase_						=			[self.eos_token_id, self.cur_lang_code]

					lowerCamelCase_						=			self.convert_ids_to_tokens(self.prefix_tokens )
					lowerCamelCase_						=			self.convert_ids_to_tokens(self.suffix_tokens )

					lowerCamelCase_						=			processors.TemplateProcessing(
					    single=prefix_tokens_str + ["$A"] + suffix_tokens_str						,  pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str						,  special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str						,  self.prefix_tokens + self.suffix_tokens ) )						,  )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase = None )				->       Tuple[str]:
					if not self.can_save_slow_tokenizer:
									raise ValueError(
									    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
									    "tokenizer." )

					if not os.path.isdir(lowercase ):
									logger.error(f'Vocabulary path ({save_directory}) should be a directory.' )
									return
					lowerCamelCase_						=			os.path.join(
					    lowercase						,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

					if os.path.abspath(self.vocab_file ) != os.path.abspath(lowercase ):
									copyfile(self.vocab_file						,  lowercase )

					return (out_vocab_file,)


