






"""simple docstring"""







import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
	if is_torch_available():
		import torch
	if is_tf_available():
		import tensorflow as tf

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_codegen import CodeGenTokenizer


__a       :	Dict      						= logging.get_logger(__name__)

__a       :	int      						= {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

__a       :	Union[str, Any]      						= {
    'vocab_file': {
        'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json',
    },
    'merges_file': {
        'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'Salesforce/codegen-350M-mono': (
            'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json'
        ),
    },
}

__a       :	Optional[int]      						= {
    'Salesforce/codegen-350M-mono': 2048,
}







class        _SCREAMING_SNAKE_CASE						(  __a							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    =VOCAB_FILES_NAMES
							_SCREAMING_SNAKE_CASE    =PRETRAINED_VOCAB_FILES_MAP
							_SCREAMING_SNAKE_CASE    =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_SCREAMING_SNAKE_CASE    =['input_ids', 'attention_mask']
							_SCREAMING_SNAKE_CASE    =CodeGenTokenizer




							def __init__(   self:     str				,		__A:     List[str]=None				,		__A:     str=None				,		__A:     Any=None				,		__A:     List[Any]="<|endoftext|>"				,		__A:     int="<|endoftext|>"				,		__A:     List[Any]="<|endoftext|>"				,		__A:     int=False				,		**__A:     List[Any]				,		):

												'''simple docstring'''

												super().__init__(
												    a_				,		a_				,		tokenizer_file=a_				,		unk_token=a_				,		bos_token=a_				,		eos_token=a_				,		add_prefix_space=a_				,		**a_				,		)

												if kwargs.pop('''add_bos_token'''				,		a_    ):
																	a__          =  kwargs.pop('''name_or_path'''				,		''''''    )
																	raise ValueError(
																	    '''Currenty GPT2\'s fast tokenizer does NOT support adding a BOS token.'''
																	    '''Instead you should use GPT2\'s slow tokenizer class `CodeGenTokenizer` as follows: \n'''
																	    F'`CodeGenTokenizer.from_pretrained(\'{model_id}\')`\nor\n'
																	    F'`AutoTokenizer.from_pretrained(\'{model_id}\', use_fast=False)`\n'
																	    '''This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005.'''
																	    ''' so that the fast tokenizer works correctly.'''    )

												a__          =  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()    )
												if pre_tok_state.get('''add_prefix_space'''				,		a_    ) != add_prefix_space:
																	a__          =  getattr(a_				,		pre_tok_state.pop('''type'''    )    )
																	a__          =  add_prefix_space
																	a__          =  pre_tok_class(**a_    )

												a__          =  add_prefix_space




							def  lowercase		(   self:     List[Any]				,		*__A:     Tuple				,		**__A:     Optional[int]    ):

												'''simple docstring'''

												a__          =  kwargs.get('''is_split_into_words'''				,		a_    )
												assert self.add_prefix_space or not is_split_into_words, (
												    F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
												    "to use it with pretokenized inputs."
												)

												return super()._batch_encode_plus(*a_				,		**a_    )




							def  lowercase		(   self:     Optional[int]				,		*__A:     Dict				,		**__A:     Optional[int]    ):

												'''simple docstring'''

												a__          =  kwargs.get('''is_split_into_words'''				,		a_    )

												assert self.add_prefix_space or not is_split_into_words, (
												    F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
												    "to use it with pretokenized inputs."
												)

												return super()._encode_plus(*a_				,		**a_    )




							def  lowercase		(   self:     Optional[int]				,		__A:     str				,		__A:     Optional[str] = None    ):

												'''simple docstring'''

												a__          =  self._tokenizer.model.save(a_				,		name=a_    )
												return tuple(a_    )




							def  lowercase		(   self:     List[str]				,		__A:     Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"]				,		__A:     bool = False				,		__A:     bool = None				,		__A:     Optional[List[str]] = None				,		**__A:     Optional[int]				,		):

												'''simple docstring'''

												a__          =  super().decode(
												    token_ids=a_				,		skip_special_tokens=a_				,		clean_up_tokenization_spaces=a_				,		**a_				,		)

												if truncate_before_pattern is not None and len(a_    ) > 0:
																	a__          =  self.truncate(a_				,		a_    )

												return decoded_text





							def  lowercase		(   self:     List[Any]				,		__A:     List[str]				,		__A:     str    ):

												'''simple docstring'''

												def find_re(__A:     Union[str, Any]				,		__A:     List[Any]				,		__A:     List[str]    ):
																	a__          =  pattern.search(a_				,		a_    )
																	return m.start() if m else -1

												a__          =  [re.compile(a_				,		re.MULTILINE    ) for pattern in truncate_before_pattern]

												a__          =  list(re.finditer('''^print'''				,		a_				,		re.MULTILINE    )    )

												if len(a_    ) > 1:
																	a__          =  completion[: prints[1].start()]

												a__          =  list(re.finditer('''^def'''				,		a_				,		re.MULTILINE    )    )

												if len(a_    ) > 1:
																	a__          =  completion[: defs[1].start()]

												a__          =  0

												a__          =  [
												    pos for pos in [find_re(a_				,		a_				,		a_    ) for terminal in terminals] if pos != -1
												]

												if len(a_    ) > 0:
																	return completion[: min(a_    )]
												else:
																	return completion










"""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       :	int             = logging.get_logger(__name__)





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_=False ,     lowerCamelCase_=False):
					a__          =  '''backbone.''' if is_semantic else ''''''

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

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

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

										a__          =  gamma_a
										a__          =  gamma_a





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					a__          =  dct.pop(lowerCamelCase_)
					a__          =  val





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





@torch.no_grad()
def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_=False):
					a__          =  False if '''rvlcdip''' in checkpoint_url else True
					a__          =  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:
										a__          =  1024
										a__          =  4096
										a__          =  24
										a__          =  16

					# labels
					if "rvlcdip" in checkpoint_url:
										a__          =  16
										a__          =  '''huggingface/label-files'''
										a__          =  '''rvlcdip-id2label.json'''
										a__          =  json.load(open(hf_hub_download(lowerCamelCase_ ,     lowerCamelCase_ ,     repo_type='''dataset''') ,     '''r'''))
										a__          =  {int(lowerCamelCase_): v for k, v in idalabel.items()}
										a__          =  idalabel
										a__          =  {v: k for k, v in idalabel.items()}

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

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

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

					a__          =  image_processor(images=lowerCamelCase_ ,     return_tensors='''pt''')
					a__          =  encoding['''pixel_values''']

					a__          =  model(lowerCamelCase_)
					a__          =  outputs.logits

					# verify logits
					a__          =  [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:
															a__          =  '''dit-base''' if '''base''' in checkpoint_url else '''dit-large'''
										else:
															a__          =  '''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       :	Dict             = 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       :	List[Any]             = parser.parse_args()
	convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)


