




'''simple docstring'''


import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
				import torch

class      A       (						_a       ):
							lowercase_							     =							'char'
							lowercase_							     =							'bpe'
							lowercase_							     =							'wp'



_snake_case : Optional[int]  				=						(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)

class      A       (						_a       ):
							lowercase_							     =							['image_processor', 'char_tokenizer']
							lowercase_							     =							'ViTImageProcessor'
							lowercase_							     =							'MgpstrTokenizer'







							def __init__(    self			:    Dict				,     lowerCAmelCase_			:    Optional[Any]=None				,     lowerCAmelCase_			:    str=None				,     **lowerCAmelCase_			:    List[Any]   )     ->				Optional[Any]:


														"""simple docstring"""


														_a      	=							None
														if "feature_extractor" in kwargs:
																					warnings.warn(
																					    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																					    ''' instead.'''				,     lowerCAmelCase_				,     )
																					_a      	=							kwargs.pop('''feature_extractor'''   )

														_a      	=							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`.'''   )

														_a      	=							tokenizer
														_a      	=							AutoTokenizer.from_pretrained('''gpt2'''   )
														_a      	=							AutoTokenizer.from_pretrained('''bert-base-uncased'''   )

														super().__init__(lowerCAmelCase_				,     lowerCAmelCase_   )







							def __call__(    self			:    str				,     lowerCAmelCase_			:    List[Any]=None				,     lowerCAmelCase_			:    Union[str, Any]=None				,     lowerCAmelCase_			:    Any=None				,     **lowerCAmelCase_			:    Tuple   )     ->				Optional[Any]:


														"""simple docstring"""


														if images is None and text is None:
																					raise ValueError('''You need to specify either an `images` or `text` input to process.'''   )

														if images is not None:
																					_a      	=							self.image_processor(lowerCAmelCase_				,     return_tensors=lowerCAmelCase_				,     **lowerCAmelCase_   )
														if text is not None:
																					_a      	=							self.char_tokenizer(lowerCAmelCase_				,     return_tensors=lowerCAmelCase_				,     **lowerCAmelCase_   )

														if text is None:
																					return inputs
														elif images is None:
																					return encodings
														else:
																					_a      	=							encodings['''input_ids''']
																					return inputs







							def   __lowerCAmelCase     (    self			:    Optional[int]				,     lowerCAmelCase_			:    Optional[Any]   )     ->				List[Any]:


														"""simple docstring"""


														_a	,				_a	,				_a      	=							sequences
														_a      	=							char_preds.size(0   )

														_a	,				_a      	=							self._decode_helper(lowerCAmelCase_				,     '''char'''   )
														_a	,				_a      	=							self._decode_helper(lowerCAmelCase_				,     '''bpe'''   )
														_a	,				_a      	=							self._decode_helper(lowerCAmelCase_				,     '''wp'''   )

														_a      	=							[]
														_a      	=							[]
														for i in range(lowerCAmelCase_   ):
																					_a      	=							[char_scores[i], bpe_scores[i], wp_scores[i]]
																					_a      	=							[char_strs[i], bpe_strs[i], wp_strs[i]]
																					_a      	=							scores.index(max(lowerCAmelCase_   )   )
																					final_strs.append(strs[max_score_index]   )
																					final_scores.append(scores[max_score_index]   )

														_a      	=							{}
														_a      	=							final_strs
														_a      	=							final_scores
														_a      	=							char_strs
														_a      	=							bpe_strs
														_a      	=							wp_strs
														return out







							def   __lowerCAmelCase     (    self			:    Optional[int]				,     lowerCAmelCase_			:    Dict				,     lowerCAmelCase_			:    Optional[Any]   )     ->				Dict:


														"""simple docstring"""


														if format == DecodeType.CHARACTER:
																					_a      	=							self.char_decode
																					_a      	=							1
																					_a      	=							'''[s]'''
														elif format == DecodeType.BPE:
																					_a      	=							self.bpe_decode
																					_a      	=							2
																					_a      	=							'''#'''
														elif format == DecodeType.WORDPIECE:
																					_a      	=							self.wp_decode
																					_a      	=							1_02
																					_a      	=							'''[SEP]'''
														else:
																					raise ValueError(F'Format {format} is not supported.'   )

														_a	,				_a      	=							[], []
														_a      	=							pred_logits.size(0   )
														_a      	=							pred_logits.size(1   )
														_a	,				_a      	=							pred_logits.topk(1				,     dim=-1				,     largest=lowerCAmelCase_				,     sorted=lowerCAmelCase_   )
														_a      	=							preds_index.view(-1				,     lowerCAmelCase_   )[:, 1:]
														_a      	=							decoder(lowerCAmelCase_   )
														_a	,				_a      	=							torch.nn.functional.softmax(lowerCAmelCase_				,     dim=2   ).max(dim=2   )
														_a      	=							preds_max_prob[:, 1:]

														for index in range(lowerCAmelCase_   ):
																					_a      	=							preds_str[index].find(lowerCAmelCase_   )
																					_a      	=							preds_str[index][:pred_eos]
																					_a      	=							preds_index[index].cpu().tolist()
																					_a      	=							pred_index.index(lowerCAmelCase_   ) if eos_token in pred_index else -1
																					_a      	=							preds_max_prob[index][: pred_eos_index + 1]
																					_a      	=							pred_max_prob.cumprod(dim=0   )[-1] if pred_max_prob.nelement() != 0 else 0.0
																					dec_strs.append(lowerCAmelCase_   )
																					conf_scores.append(lowerCAmelCase_   )

														return dec_strs, conf_scores







							def   __lowerCAmelCase     (    self			:    Optional[Any]				,     lowerCAmelCase_			:    Optional[int]   )     ->				Union[str, Any]:


														"""simple docstring"""


														_a      	=							[seq.replace(''' '''				,     ''''''   ) for seq in self.char_tokenizer.batch_decode(lowerCAmelCase_   )]
														return decode_strs







							def   __lowerCAmelCase     (    self			:    List[Any]				,     lowerCAmelCase_			:    Optional[Any]   )     ->				Tuple:


														"""simple docstring"""


														return self.bpe_tokenizer.batch_decode(lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    List[str]				,     lowerCAmelCase_			:    Dict   )     ->				Optional[Any]:


														"""simple docstring"""


														_a      	=							[seq.replace(''' '''				,     ''''''   ) for seq in self.wp_tokenizer.batch_decode(lowerCAmelCase_   )]
														return decode_strs



from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
						from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


_snake_case      :							Dict     	=    logging.get_logger(__name__)

_snake_case      :							List[str]     	=    {
    "microsoft/deberta-v2-xlarge": "https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xxlarge": "https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"
    ),
    "microsoft/deberta-v2-xxlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"
    ),
}





class   UpperCamelCase_      (      __a		):







						'''simple docstring'''



						UpperCamelCase      :  Optional[Any]				   =					'''deberta-v2'''





						def __init__(			self							:int		,    lowerCAmelCase__							:Union[str, Any]=128100		,    lowerCAmelCase__							:Optional[Any]=1536		,    lowerCAmelCase__							:Tuple=24		,    lowerCAmelCase__							:List[Any]=24		,    lowerCAmelCase__							:Union[str, Any]=6144		,    lowerCAmelCase__							:List[str]="gelu"		,    lowerCAmelCase__							:Union[str, Any]=0.1		,    lowerCAmelCase__							:Optional[Any]=0.1		,    lowerCAmelCase__							:List[str]=512		,    lowerCAmelCase__							:Optional[int]=0		,    lowerCAmelCase__							:Optional[int]=0.02		,    lowerCAmelCase__							:int=1E-7		,    lowerCAmelCase__							:Optional[int]=False		,    lowerCAmelCase__							:List[Any]=-1		,    lowerCAmelCase__							:str=0		,    lowerCAmelCase__							:str=True		,    lowerCAmelCase__							:Optional[Any]=None		,    lowerCAmelCase__							:List[Any]=0		,    lowerCAmelCase__							:Union[str, Any]="gelu"		,    **lowerCAmelCase__							:str		,    )  ->Optional[Any]:
										super().__init__(**lowerCAmelCase__     )

										lowercase         =      hidden_size
										lowercase         =      num_hidden_layers
										lowercase         =      num_attention_heads
										lowercase         =      intermediate_size
										lowercase         =      hidden_act
										lowercase         =      hidden_dropout_prob
										lowercase         =      attention_probs_dropout_prob
										lowercase         =      max_position_embeddings
										lowercase         =      type_vocab_size
										lowercase         =      initializer_range
										lowercase         =      relative_attention
										lowercase         =      max_relative_positions
										lowercase         =      pad_token_id
										lowercase         =      position_biased_input

										# Backwards compatibility
										if type(lowerCAmelCase__     ) == str:
														lowercase         =      [x.strip() for x in pos_att_type.lower().split("|"     )]

										lowercase         =      pos_att_type
										lowercase         =      vocab_size
										lowercase         =      layer_norm_eps

										lowercase         =      kwargs.get("pooler_hidden_size"		,    lowerCAmelCase__     )
										lowercase         =      pooler_dropout
										lowercase         =      pooler_hidden_act





class   UpperCamelCase_      (      __a		):







						'''simple docstring'''



						@property
						def        SCREAMING_SNAKE_CASE(			self							:str     )  ->Mapping[str, Mapping[int, str]]:
										if self.task == "multiple-choice":
														lowercase         =      {0: "batch", 1: "choice", 2: "sequence"}
										else:
														lowercase         =      {0: "batch", 1: "sequence"}
										if self._config.type_vocab_size > 0:
														return OrderedDict(
														    [("input_ids", dynamic_axis), ("attention_mask", dynamic_axis), ("token_type_ids", dynamic_axis)]     )
										else:
														return OrderedDict([("input_ids", dynamic_axis), ("attention_mask", dynamic_axis)]     )





						@property
						def        SCREAMING_SNAKE_CASE(			self							:List[str]     )  ->int:
										return 12





						def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]		,    lowerCAmelCase__							:Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]		,    lowerCAmelCase__							:int = -1		,    lowerCAmelCase__							:int = -1		,    lowerCAmelCase__							:int = -1		,    lowerCAmelCase__							:bool = False		,    lowerCAmelCase__							:Optional["TensorType"] = None		,    lowerCAmelCase__							:int = 3		,    lowerCAmelCase__							:int = 40		,    lowerCAmelCase__							:int = 40		,    lowerCAmelCase__							:"PreTrainedTokenizerBase" = None		,    )  ->Mapping[str, Any]:
										lowercase         =      super().generate_dummy_inputs(preprocessor=lowerCAmelCase__		,    framework=lowerCAmelCase__     )
										if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
														del dummy_inputs["token_type_ids"]
										return dummy_inputs
