


import argparse
import pathlib
from pathlib import Path
from tempfile import TemporaryDirectory

import esm as esm_module
import torch
from esm.esmfold.va.misc import batch_encode_sequences as esmfold_encode_sequences
from esm.esmfold.va.pretrained import esmfold_va

from transformers.models.esm.configuration_esm import EsmConfig, EsmFoldConfig
from transformers.models.esm.modeling_esm import (
    EsmForMaskedLM,
    EsmForSequenceClassification,
    EsmIntermediate,
    EsmLayer,
    EsmOutput,
    EsmSelfAttention,
    EsmSelfOutput,
)
from transformers.models.esm.modeling_esmfold import EsmForProteinFolding
from transformers.models.esm.tokenization_esm import EsmTokenizer
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCamelCase   	=  logging.get_logger(__name__)

__lowerCamelCase   	=  [
    (
        '''protein1''',
        '''MNGTEGPNFYVPFSNATGVVRSPFEYPQYYLAEPWQFSMLAAYMFLLIVLGFPINFLTLYVTVQHKKLRTPLNYILLNLAVADLFMVLGGFTSTLYTSLHGYFVFGPTGCNLEGFFATLGGEIALWSLVVLAIERYVVVCKPMSNFRFGENHAIMGVAFTWVMALACAAPPLAGWSRYIPEGLQCSCGIDYYTLKPEVNNESFVIYMFVVHFTIPMIIIFFCYGQLVFTVKEAAAQQQESATTQKAEKEVTRMVIIMVIAFLICWVPYASVAFYIFTHQGSNFGPIFMTIPAFFAKSAAIYNPVIYIMMNKQFRNCMLTTICCGKNPLGDDEASATVSKTETSQVAPA''',
    ),
    ('''protein2''', '''MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLA'''),
    ('''protein3''', '''MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLAGG'''),
    ('''protein4''', '''MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLA'''),
]

__lowerCamelCase   	=  {
    '''esm1b_t33_650M_UR50S''': esm_module.pretrained.esm1b_t33_650M_UR50S,
    '''esm1v_t33_650M_UR90S_1''': esm_module.pretrained.esm1v_t33_650M_UR90S_1,
    '''esm1v_t33_650M_UR90S_2''': esm_module.pretrained.esm1v_t33_650M_UR90S_2,
    '''esm1v_t33_650M_UR90S_3''': esm_module.pretrained.esm1v_t33_650M_UR90S_3,
    '''esm1v_t33_650M_UR90S_4''': esm_module.pretrained.esm1v_t33_650M_UR90S_4,
    '''esm1v_t33_650M_UR90S_5''': esm_module.pretrained.esm1v_t33_650M_UR90S_5,
    '''esm2_t48_15B_UR50D''': esm_module.pretrained.esm2_t48_15B_UR50D,
    '''esm2_t36_3B_UR50D''': esm_module.pretrained.esm2_t36_3B_UR50D,
    '''esm2_t33_650M_UR50D''': esm_module.pretrained.esm2_t33_650M_UR50D,
    '''esm2_t30_150M_UR50D''': esm_module.pretrained.esm2_t30_150M_UR50D,
    '''esm2_t12_35M_UR50D''': esm_module.pretrained.esm2_t12_35M_UR50D,
    '''esm2_t6_8M_UR50D''': esm_module.pretrained.esm2_t6_8M_UR50D,
    '''esmfold_v1''': esmfold_va,
}

__lowerCamelCase   	=  list('''ARNDCQEGHILKMFPSTWYV''')

__lowerCamelCase   	=  restypes + ['''X''']
__lowerCamelCase   	=  restypes_with_x + ['''<pad>''', '''<mask>''', '''<cls>''', '''<sep>''', '''<eos>''']



def 			_a      (    ):
     with TemporaryDirectory() as tempdir:
          a_     :  int				      =     """\n""".join(__UpperCamelCase			)
          a_     :  Tuple				      =     Path(__UpperCamelCase			) / """vocab.txt"""
          vocab_file.write_text(__UpperCamelCase			)
          a_     :  int				      =     EsmTokenizer(vocab_file=str(__UpperCamelCase			)			)
     a_     :  Any				      =     0  # Overlaps with 'A' but that seems to be what they want
     return hf_tokenizer



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase			):
     a_     :  List[str]				      =     our_module.load_state_dict(original_module.state_dict()			)
     if status.missing_keys:
          raise ValueError(F'''Missing keys: {status.missing_keys}'''			)
     if status.unexpected_keys:
          raise ValueError(F'''Unexpected keys: {status.unexpected_keys}'''			)



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			):
     if model.startswith("""esmfold"""			):
          a_     :  Optional[Any]				      =     MODEL_MAPPING[model]()
     else:
          a_      ,      a_     :  Optional[int]				      =     MODEL_MAPPING[model]()
     esm.eval()  # disable dropout

     if model.startswith("""esmfold"""			):
          a_     :  List[str]				      =     esm.esm.embed_dim
          a_     :  Any				      =     esm.esm.num_layers
          a_     :  Any				      =     esm.esm.attention_heads
          a_     :  int				      =     4 * embed_dim
          a_     :  Any				      =     esm.esm.token_dropout
          a_     :  Union[str, Any]				      =     False  # This code path does not exist in ESM-2
          a_     :  Union[str, Any]				      =     """rotary"""
          a_     :  List[Any]				      =     True
          a_     :  str				      =     EsmFoldConfig()
          for key, val in esm.cfg.items():
               if hasattr(__UpperCamelCase	,	__UpperCamelCase			) and key != "trunk":
                    setattr(__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			)
          for key, val in esm.cfg.trunk.items():
               if hasattr(esmfold_config.trunk	,	__UpperCamelCase			) and key != "structure_module":
                    setattr(esmfold_config.trunk	,	__UpperCamelCase	,	__UpperCamelCase			)
          for key, val in esm.cfg.trunk.structure_module.items():
               if hasattr(esmfold_config.trunk.structure_module	,	__UpperCamelCase			):
                    setattr(esmfold_config.trunk.structure_module	,	__UpperCamelCase	,	__UpperCamelCase			)
     elif hasattr(__UpperCamelCase	,	"""args"""			):
          # Indicates an ESM-1b or ESM-1v model
          a_     :  Union[str, Any]				      =     esm.args.embed_dim
          a_     :  Optional[Any]				      =     esm.args.layers
          a_     :  Dict				      =     esm.args.attention_heads
          a_     :  Dict				      =     esm.args.ffn_embed_dim
          a_     :  Optional[Any]				      =     esm.args.token_dropout
          a_     :  Optional[int]				      =     True if esm.emb_layer_norm_before else False
          a_     :  Dict				      =     """absolute"""
          a_     :  Dict				      =     False
          a_     :  Tuple				      =     None
     else:
          # Indicates an ESM-2 model
          a_     :  Union[str, Any]				      =     esm.embed_dim
          a_     :  Optional[Any]				      =     esm.num_layers
          a_     :  int				      =     esm.attention_heads
          a_     :  Any				      =     4 * embed_dim  # This is hardcoded in ESM-2
          a_     :  Optional[Any]				      =     esm.token_dropout
          a_     :  Optional[int]				      =     False  # This code path does not exist in ESM-2
          a_     :  Union[str, Any]				      =     """rotary"""
          a_     :  List[Any]				      =     False
          a_     :  Union[str, Any]				      =     None

     if is_folding_model:
          a_     :  Any				      =     esm.esm.alphabet
     a_     :  int				      =     tuple(alphabet.all_toks			)
     a_     :  Tuple				      =     alphabet.mask_idx
     a_     :  int				      =     alphabet.padding_idx

     if is_folding_model:
          a_     :  Any				      =     esm.esm
     else:
          a_     :  Union[str, Any]				      =     esm

     a_     :  Dict				      =     EsmConfig(
         vocab_size=original_esm_model.embed_tokens.num_embeddings	,	mask_token_id=__UpperCamelCase	,	hidden_size=__UpperCamelCase	,	num_hidden_layers=__UpperCamelCase	,	num_attention_heads=__UpperCamelCase	,	intermediate_size=__UpperCamelCase	,	max_position_embeddings=1_0_2_6	,	layer_norm_eps=1E-5	,	attention_probs_dropout_prob=0.0	,	hidden_dropout_prob=0.0	,	pad_token_id=__UpperCamelCase	,	emb_layer_norm_before=__UpperCamelCase	,	token_dropout=__UpperCamelCase	,	position_embedding_type=__UpperCamelCase	,	is_folding_model=__UpperCamelCase	,	esmfold_config=__UpperCamelCase	,	vocab_list=__UpperCamelCase	,	)
     if classification_head:
          a_     :  Any				      =     esm.classification_heads["""mnli"""].out_proj.weight.shape[0]
     print("""Our ESM config:"""	,	__UpperCamelCase			)

     if model.startswith("""esmfold"""			):
          a_     :  Any				      =     EsmForProteinFolding
     elif classification_head:
          a_     :  Tuple				      =     EsmForSequenceClassification
     else:
          a_     :  Optional[int]				      =     EsmForMaskedLM
     a_     :  Optional[Any]				      =     model_class(__UpperCamelCase			)
     model.eval()

     # Now let's copy all the weights.
     # Embeddings
     a_     :  Union[str, Any]				      =     original_esm_model.embed_tokens.weight
     if position_embedding_type == "absolute":
          a_     :  str				      =     original_esm_model.embed_positions.weight

     if config.emb_layer_norm_before:
          a_     :  List[Any]				      =     original_esm_model.emb_layer_norm_before.weight
          a_     :  Union[str, Any]				      =     original_esm_model.emb_layer_norm_before.bias

     a_     :  Optional[int]				      =     original_esm_model.emb_layer_norm_after.weight
     a_     :  Optional[int]				      =     original_esm_model.emb_layer_norm_after.bias

     for i in range(config.num_hidden_layers			):
          # Encoder: start of layer
          a_     :  EsmLayer				      =     model.esm.encoder.layer[i]
          # esm_layer: TransformerSentenceEncoderLayer = original_esm_model.layers[i]
          a_     :  Dict				      =     original_esm_model.layers[i]

          # self attention
          a_     :  EsmSelfAttention				      =     layer.attention.self
          assert (
              esm_layer.self_attn.k_proj.weight.data.shape
              == esm_layer.self_attn.q_proj.weight.data.shape
              == esm_layer.self_attn.v_proj.weight.data.shape
              == torch.Size((config.hidden_size, config.hidden_size)			)
          )

          a_     :  Union[str, Any]				      =     esm_layer.self_attn.q_proj.weight
          a_     :  int				      =     esm_layer.self_attn.q_proj.bias
          a_     :  List[str]				      =     esm_layer.self_attn.k_proj.weight
          a_     :  List[Any]				      =     esm_layer.self_attn.k_proj.bias
          a_     :  Union[str, Any]				      =     esm_layer.self_attn.v_proj.weight
          a_     :  Dict				      =     esm_layer.self_attn.v_proj.bias

          if getattr(esm_layer.self_attn	,	"""rot_emb"""	,	__UpperCamelCase			) is not None:
               # Matt: Although inv_freq is not a trainable weight, it is computed at model init and cached.
               # During the training of ESM-2 the model was converted to float16 precision, which also converts
               # the inv_freq tensor, and the loss of precision remains even if the model is loaded later as float32.
               # If we recompute inv_freq without this loss of precision then we will get subtly different rotary
               # embeddings, which are enough to cause significant discrepancies in model outputs. To avoid this,
               # we make sure the new model copies the data from the old inv_freq.
               a_     :  int				      =     esm_layer.self_attn.rot_emb.inv_freq

          # LayerNorm changes for pre-activation
          a_     :  str				      =     esm_layer.self_attn_layer_norm.weight
          a_     :  str				      =     esm_layer.self_attn_layer_norm.bias
          a_     :  Optional[Any]				      =     esm_layer.final_layer_norm.weight
          a_     :  Tuple				      =     esm_layer.final_layer_norm.bias

          # self-attention output
          a_     :  EsmSelfOutput				      =     layer.attention.output
          assert self_output.dense.weight.shape == esm_layer.self_attn.out_proj.weight.shape
          a_     :  Any				      =     esm_layer.self_attn.out_proj.weight
          a_     :  str				      =     esm_layer.self_attn.out_proj.bias

          # intermediate
          a_     :  EsmIntermediate				      =     layer.intermediate
          assert intermediate.dense.weight.shape == esm_layer.fca.weight.shape
          a_     :  int				      =     esm_layer.fca.weight
          a_     :  Tuple				      =     esm_layer.fca.bias

          # output
          a_     :  EsmOutput				      =     layer.output
          assert bert_output.dense.weight.shape == esm_layer.fca.weight.shape
          a_     :  List[str]				      =     esm_layer.fca.weight
          a_     :  Any				      =     esm_layer.fca.bias
          # end of layer

     if is_folding_model:
          a_     :  Union[str, Any]				      =     esm.esm_s_combine.data
          a_     :  int				      =     esm.afa_to_esm.data
          transfer_and_check_weights(esm.embedding	,	model.embedding			)
          transfer_and_check_weights(esm.esm_s_mlp	,	model.esm_s_mlp			)
          transfer_and_check_weights(esm.trunk	,	model.trunk			)
          transfer_and_check_weights(esm.distogram_head	,	model.distogram_head			)
          transfer_and_check_weights(esm.ptm_head	,	model.ptm_head			)
          transfer_and_check_weights(esm.lm_head	,	model.lm_head			)
          transfer_and_check_weights(esm.lddt_head	,	model.lddt_head			)

     elif classification_head:
          a_     :  Tuple				      =     esm.esm.classification_heads["""mnli"""].dense.weight
          a_     :  Dict				      =     esm.classification_heads["""mnli"""].dense.bias
          a_     :  Dict				      =     esm.classification_heads["""mnli"""].out_proj.weight
          a_     :  Optional[Any]				      =     esm.classification_heads["""mnli"""].out_proj.bias
     else:
          # LM Head
          a_     :  str				      =     esm.lm_head.dense.weight
          a_     :  List[str]				      =     esm.lm_head.dense.bias
          a_     :  Dict				      =     esm.lm_head.layer_norm.weight
          a_     :  List[Any]				      =     esm.lm_head.layer_norm.bias
          a_     :  Tuple				      =     esm.lm_head.weight
          a_     :  Optional[Any]				      =     esm.lm_head.bias

     # Contact prediction head
     transfer_and_check_weights(esm.contact_head	,	model.esm.contact_head			)

     # Prepare data (first 2 sequences from ESMStructuralSplitDataset superfamily / 4)
     if is_folding_model:
          # Folding models aren't trained on masked inputs and don't like mask tokens.
          a_     :  List[str]				      =     SAMPLE_DATA[:2]
     else:
          a_     :  Tuple				      =     SAMPLE_DATA

     if is_folding_model:
          a_     :  str				      =     get_esmfold_tokenizer()
          a_     :  Tuple				      =     hf_tokenizer(
              [row[1] for row in sample_data]	,	return_tensors="""pt"""	,	padding=__UpperCamelCase	,	add_special_tokens=__UpperCamelCase			)
          a_      ,      a_      ,      a_      ,      a_      ,      a_     :  List[Any]				      =     esmfold_encode_sequences([row[1] for row in sample_data]			)
          a_     :  Dict				      =     torch.all(hf_tokens["""input_ids"""] == esmfold_aas			) and torch.all(
              hf_tokens["""attention_mask"""] == esmfold_mask			)
     else:
          # Let's check that we get the same results.
          a_     :  List[str]				      =     alphabet.get_batch_converter()
          a_      ,      a_      ,      a_     :  Dict				      =     batch_converter(__UpperCamelCase			)
          # Prepare tokenizer and make sure it matches
          with TemporaryDirectory() as tempdir:
               a_     :  Union[str, Any]				      =     """\n""".join(alphabet.all_toks			)
               a_     :  str				      =     Path(__UpperCamelCase			) / """vocab.txt"""
               vocab_file.write_text(__UpperCamelCase			)
               a_     :  int				      =     EsmTokenizer(vocab_file=str(__UpperCamelCase			)			)

          a_     :  List[str]				      =     hf_tokenizer([row[1] for row in sample_data]	,	return_tensors="""pt"""	,	padding=__UpperCamelCase			)
          a_     :  Optional[Any]				      =     torch.all(hf_tokens["""input_ids"""] == batch_tokens			)

     print("""Do both models tokenizers output the same tokens?"""	,	"""🔥""" if success else """💩"""			)
     if not success:
          raise Exception("""Tokenization does not match!"""			)

     with torch.no_grad():
          if is_folding_model:
               # Let's test the model in parts
               # ESMFold always converts the ESM stem to float16, which requires float16 ops
               # that don't exist on CPU. Therefore, to test it we need to run it on GPU. However,
               # ESMFold is what we in the community call a "big boy" and so we desperately avoid putting both the
               # original and the converted model on the GPU at the same time.
               a_     :  Dict				      =     esm.cuda().infer([row[1] for row in sample_data]			)
               a_     :  Union[str, Any]				      =     model.cuda()(
                   input_ids=hf_tokens["""input_ids"""].cuda()	,	attention_mask=hf_tokens["""attention_mask"""].cuda()			)
          else:
               a_     :  Dict				      =     model(**__UpperCamelCase	,	output_hidden_states=__UpperCamelCase			)
               a_     :  Tuple				      =     our_output["""logits"""]
               if classification_head:
                    a_     :  Optional[int]				      =     esm.model.classification_heads["""mnli"""](esm.extract_features(__UpperCamelCase			)			)
               else:
                    a_     :  Union[str, Any]				      =     esm(hf_tokens["""input_ids"""]	,	repr_layers=list(range(9_9_9			)			)			)
                    a_     :  Optional[Any]				      =     their_output["""logits"""]

          if is_folding_model:
               a_     :  List[str]				      =     torch.max(torch.abs(our_output["""positions"""] - their_output["""positions"""]			)			).item()
               a_     :  Optional[int]				      =     torch.allclose(our_output["""positions"""]	,	their_output["""positions"""]	,	atol=1E-5			)
          else:
               a_     :  Optional[int]				      =     torch.max(torch.abs(our_output - their_output			)			).item()
               a_     :  str				      =     torch.allclose(__UpperCamelCase	,	__UpperCamelCase	,	atol=1E-5			)

          print(F'''max_absolute_diff = {max_absolute_diff}'''			)  # ~ 1e-5
          print("""Do both models output the same tensors?"""	,	"""🔥""" if success else """💩"""			)

          if not success:
               raise Exception("""Something went wRoNg"""			)

          if not is_folding_model:
               # Let's check contact prediction too
               a_     :  Any				      =     model.predict_contacts(hf_tokens["""input_ids"""]	,	hf_tokens["""attention_mask"""]			)
               a_     :  List[Any]				      =     esm.predict_contacts(hf_tokens["""input_ids"""]			)
               a_     :  Optional[Any]				      =     torch.max(torch.abs(our_output - their_output			)			).item()
               a_     :  Optional[int]				      =     torch.allclose(__UpperCamelCase	,	__UpperCamelCase	,	atol=1E-5			)

               print("""Contact prediction testing:"""			)
               print(F'''max_absolute_diff = {max_absolute_diff}'''			)  # ~ 1e-5
               print("""Do both models output the same tensors?"""	,	"""🔥""" if success else """💩"""			)

               if not success:
                    raise Exception("""Something went wRoNg"""			)

          pathlib.Path(__UpperCamelCase			).mkdir(parents=__UpperCamelCase	,	exist_ok=__UpperCamelCase			)
          print(F'''Saving model to {pytorch_dump_folder_path}'''			)
          model.save_pretrained(__UpperCamelCase			)

          del esm  # Free up some memory before continuing

     print(F'''Saving tokenizer to {pytorch_dump_folder_path}'''			)
     hf_tokenizer.save_pretrained(__UpperCamelCase			)

     if push_to_repo:
          model.push_to_hub(repo_id=__UpperCamelCase	,	use_auth_token=__UpperCamelCase			)
          hf_tokenizer.push_to_hub(repo_id=__UpperCamelCase	,	use_auth_token=__UpperCamelCase			)


if __name__ == "__main__":
       __lowerCamelCase   	=  argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--pytorch_dump_folder_path''', type=str, required=True, help='''Path to the output PyTorch model.'''
       )
       parser.add_argument(
           '''--classification_head''', action='''store_true''', help='''Whether to convert a final classification head.'''
       )
       parser.add_argument('''--model''', default=None, type=str, required=True, help='''Name of model to convert.''')
       parser.add_argument('''--push_to_repo''', type=str, help='''Repo to upload to (including username!).''')
       parser.add_argument('''--auth_token''', type=str, help='''HuggingFace auth token.''')
       __lowerCamelCase   	=  parser.parse_args()
       convert_esm_checkpoint_to_pytorch(
           args.model, args.pytorch_dump_folder_path, args.classification_head, args.push_to_repo, args.auth_token
       )









'''simple docstring'''





import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class     lowercase  ( UpperCAmelCase_):
      __lowercase					=	['''image_processor''', '''tokenizer''']
      __lowercase					=	'''LayoutLMv2ImageProcessor'''
      __lowercase					=	('''LayoutLMv2Tokenizer''', '''LayoutLMv2TokenizerFast''')






      def __init__(self			,	a__=None			,	a__=None			,	**a__       )							-> Any:
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			None
         if "feature_extractor" in kwargs:
            warnings.warn(
                '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                ''' instead.'''			,	a__			,	)
            SCREAMING_SNAKE_CASE__      :					List[Any]							    =			kwargs.pop('''feature_extractor'''       )

         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			image_processor if image_processor is not None else feature_extractor
         if image_processor is None:
            raise ValueError('''You need to specify an `image_processor`.'''       )
         if tokenizer is None:
            raise ValueError('''You need to specify a `tokenizer`.'''       )

         super().__init__(a__			,	a__       )






      def __call__(self			,	a__			,	a__ = None			,	a__ = None			,	a__ = None			,	a__ = None			,	a__ = True			,	a__ = False			,	a__ = False			,	a__ = None			,	a__ = 0			,	a__ = None			,	a__ = None			,	a__ = None			,	a__ = False			,	a__ = False			,	a__ = False			,	a__ = False			,	a__ = True			,	a__ = None			,	**a__			,	)							-> BatchEncoding:
         # verify input
         if self.image_processor.apply_ocr and (boxes is not None):
            raise ValueError(
                '''You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.'''       )

         if self.image_processor.apply_ocr and (word_labels is not None):
            raise ValueError(
                '''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'''       )

         if return_overflowing_tokens is True and return_offsets_mapping is False:
            raise ValueError('''You cannot return overflowing tokens without returning the offsets mapping.'''       )

         # first, apply the image processor
         SCREAMING_SNAKE_CASE__      :					str							    =			self.image_processor(images=a__			,	return_tensors=a__       )

         # second, apply the tokenizer
         if text is not None and self.image_processor.apply_ocr and text_pair is None:
            if isinstance(a__			,	a__       ):
               SCREAMING_SNAKE_CASE__      :					Tuple							    =			[text]  # add batch dimension (as the image processor always adds a batch dimension)
            SCREAMING_SNAKE_CASE__      :					Any							    =			features['''words''']

         SCREAMING_SNAKE_CASE__      :					int							    =			self.tokenizer(
             text=text if text is not None else features['''words''']			,	text_pair=text_pair if text_pair is not None else None			,	boxes=boxes if boxes is not None else features['''boxes''']			,	word_labels=a__			,	add_special_tokens=a__			,	padding=a__			,	truncation=a__			,	max_length=a__			,	stride=a__			,	pad_to_multiple_of=a__			,	return_token_type_ids=a__			,	return_attention_mask=a__			,	return_overflowing_tokens=a__			,	return_special_tokens_mask=a__			,	return_offsets_mapping=a__			,	return_length=a__			,	verbose=a__			,	return_tensors=a__			,	**a__			,	)

         # add pixel values
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			features.pop('''pixel_values'''       )
         if return_overflowing_tokens is True:
            SCREAMING_SNAKE_CASE__      :					Tuple							    =			self.get_overflowing_images(a__			,	encoded_inputs['''overflow_to_sample_mapping''']       )
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			images

         return encoded_inputs






      def  A__(self			,	a__			,	a__       )							-> str:
         # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
         SCREAMING_SNAKE_CASE__      :					int							    =			[]
         for sample_idx in overflow_to_sample_mapping:
            images_with_overflow.append(images[sample_idx]       )

         if len(a__       ) != len(a__       ):
            raise ValueError(
                '''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
                f""" {len(a__       )} and {len(a__       )}"""       )

         return images_with_overflow






      def  A__(self			,	*a__			,	**a__       )							-> List[Any]:
         return self.tokenizer.batch_decode(*a__			,	**a__       )






      def  A__(self			,	*a__			,	**a__       )							-> Optional[Any]:
         return self.tokenizer.decode(*a__			,	**a__       )






      @property
      def  A__(self       )							-> Tuple:
         return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]






      @property
      def  A__(self       )							-> str:
         warnings.warn(
             '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''			,	a__			,	)
         return self.image_processor_class






      @property
      def  A__(self       )							-> Dict:
         warnings.warn(
             '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''			,	a__			,	)
         return self.image_processor

