




from collections import OrderedDict
from typing import Mapping

from packaging import version

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


UpperCamelCase								=	logging.get_logger(__name__)

UpperCamelCase								=	{
    'facebook/data2vec-vision-base-ft': (
        'https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json'
    ),
}





class 					__lowerCamelCase			(       UpperCamelCase__       ):




    """simple docstring"""

    snake_case__   = "data2vec-vision"


    def __init__(			self	:	Union[str, Any]  ,      SCREAMING_SNAKE_CASE__	:	Dict=768  ,      SCREAMING_SNAKE_CASE__	:	Optional[int]=12  ,      SCREAMING_SNAKE_CASE__	:	int=12  ,      SCREAMING_SNAKE_CASE__	:	Any=3_072  ,      SCREAMING_SNAKE_CASE__	:	List[str]="gelu"  ,      SCREAMING_SNAKE_CASE__	:	Optional[int]=0.0  ,      SCREAMING_SNAKE_CASE__	:	str=0.0  ,      SCREAMING_SNAKE_CASE__	:	List[str]=0.02  ,      SCREAMING_SNAKE_CASE__	:	List[Any]=1e-1_2  ,      SCREAMING_SNAKE_CASE__	:	Tuple=224  ,      SCREAMING_SNAKE_CASE__	:	Optional[Any]=16  ,      SCREAMING_SNAKE_CASE__	:	Any=3  ,      SCREAMING_SNAKE_CASE__	:	Any=False  ,      SCREAMING_SNAKE_CASE__	:	List[Any]=False  ,      SCREAMING_SNAKE_CASE__	:	str=False  ,      SCREAMING_SNAKE_CASE__	:	int=False  ,      SCREAMING_SNAKE_CASE__	:	Optional[int]=0.1  ,      SCREAMING_SNAKE_CASE__	:	Tuple=0.1  ,      SCREAMING_SNAKE_CASE__	:	str=True  ,      SCREAMING_SNAKE_CASE__	:	Optional[Any]=[3, 5, 7, 11]  ,      SCREAMING_SNAKE_CASE__	:	Optional[int]=[1, 2, 3, 6]  ,      SCREAMING_SNAKE_CASE__	:	Dict=True  ,      SCREAMING_SNAKE_CASE__	:	Optional[int]=0.4  ,      SCREAMING_SNAKE_CASE__	:	Dict=256  ,      SCREAMING_SNAKE_CASE__	:	List[Any]=1  ,      SCREAMING_SNAKE_CASE__	:	str=False  ,      SCREAMING_SNAKE_CASE__	:	Union[str, Any]=255  ,      **SCREAMING_SNAKE_CASE__	:	Union[str, Any]  ,      ) ->			List[str]:
         super().__init__(**SCREAMING_SNAKE_CASE__  )

         lowerCAmelCase__            =					hidden_size
         lowerCAmelCase__            =					num_hidden_layers
         lowerCAmelCase__            =					num_attention_heads
         lowerCAmelCase__            =					intermediate_size
         lowerCAmelCase__            =					hidden_act
         lowerCAmelCase__            =					hidden_dropout_prob
         lowerCAmelCase__            =					attention_probs_dropout_prob
         lowerCAmelCase__            =					initializer_range
         lowerCAmelCase__            =					layer_norm_eps

         lowerCAmelCase__            =					image_size
         lowerCAmelCase__            =					patch_size
         lowerCAmelCase__            =					num_channels
         lowerCAmelCase__            =					use_mask_token
         lowerCAmelCase__            =					use_absolute_position_embeddings
         lowerCAmelCase__            =					use_relative_position_bias
         lowerCAmelCase__            =					use_shared_relative_position_bias
         lowerCAmelCase__            =					layer_scale_init_value
         lowerCAmelCase__            =					drop_path_rate
         lowerCAmelCase__            =					use_mean_pooling
         # decode head attributes (semantic segmentation)
         lowerCAmelCase__            =					out_indices
         lowerCAmelCase__            =					pool_scales
         # auxiliary head attributes (semantic segmentation)
         lowerCAmelCase__            =					use_auxiliary_head
         lowerCAmelCase__            =					auxiliary_loss_weight
         lowerCAmelCase__            =					auxiliary_channels
         lowerCAmelCase__            =					auxiliary_num_convs
         lowerCAmelCase__            =					auxiliary_concat_input
         lowerCAmelCase__            =					semantic_loss_ignore_index






class 					__lowerCamelCase			(       UpperCamelCase__       ):




    """simple docstring"""

    snake_case__   = version.parse("1.11"       )


    @property
    def   a (			self	:	Optional[Any]  ) ->			Mapping[str, Mapping[int, str]]:
         return OrderedDict(
             [
                 ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
             ]  )


    @property
    def   a (			self	:	Optional[Any]  ) ->			float:
         return 1e-4






'''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				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       A						:				Dict        =						'''char'''
       A						:				Any        =						'''bpe'''
       A						:				Dict        =						'''wp'''

UpperCamelCase_    	=				(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       A						:				List[Any]        =						['''image_processor''', '''char_tokenizer''']
       A						:				int        =						'''ViTImageProcessor'''
       A						:				List[str]        =						'''MgpstrTokenizer'''







       def __init__(      self,							A=None,							A=None,							**A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						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   :      str    					=						kwargs.pop('feature_extractor'  )

             SCREAMING_SNAKE_CASE   :      Optional[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`.'  )

             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						tokenizer
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						AutoTokenizer.from_pretrained('gpt2'  )
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						AutoTokenizer.from_pretrained('bert-base-uncased'  )

             super().__init__(A,							A  )







       def __call__(      self,							A=None,							A=None,							A=None,							**A  ):







             '''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:
                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						self.image_processor(A,							return_tensors=A,							**A  )
             if text is not None:
                   SCREAMING_SNAKE_CASE   :      int    					=						self.char_tokenizer(A,							return_tensors=A,							**A  )

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







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      int    					=						sequences
             SCREAMING_SNAKE_CASE   :      List[str]    					=						char_preds.size(0  )

             SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Dict    					=						self._decode_helper(A,							'char'  )
             SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      int    					=						self._decode_helper(A,							'bpe'  )
             SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						self._decode_helper(A,							'wp'  )

             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						[]
             SCREAMING_SNAKE_CASE   :      Tuple    					=						[]
             for i in range(A  ):
                   SCREAMING_SNAKE_CASE   :      str    					=						[char_scores[i], bpe_scores[i], wp_scores[i]]
                   SCREAMING_SNAKE_CASE   :      Dict    					=						[char_strs[i], bpe_strs[i], wp_strs[i]]
                   SCREAMING_SNAKE_CASE   :      List[str]    					=						scores.index(max(A  )  )
                   final_strs.append(strs[max_score_index]  )
                   final_scores.append(scores[max_score_index]  )

             SCREAMING_SNAKE_CASE   :      List[Any]    					=						{}
             SCREAMING_SNAKE_CASE   :      int    					=						final_strs
             SCREAMING_SNAKE_CASE   :      Any    					=						final_scores
             SCREAMING_SNAKE_CASE   :      Dict    					=						char_strs
             SCREAMING_SNAKE_CASE   :      Any    					=						bpe_strs
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						wp_strs
             return out







       def        UpperCamelCase_							(      self,							A,							A  ):







             '''simple docstring'''







             if format == DecodeType.CHARACTER:
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						self.char_decode
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						1
                   SCREAMING_SNAKE_CASE   :      str    					=						'[s]'
             elif format == DecodeType.BPE:
                   SCREAMING_SNAKE_CASE   :      str    					=						self.bpe_decode
                   SCREAMING_SNAKE_CASE   :      str    					=						2
                   SCREAMING_SNAKE_CASE   :      List[str]    					=						'#'
             elif format == DecodeType.WORDPIECE:
                   SCREAMING_SNAKE_CASE   :      Any    					=						self.wp_decode
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						102
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						'[SEP]'
             else:
                   raise ValueError(F"Format {format} is not supported."  )

             SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						[], []
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						pred_logits.size(0  )
             SCREAMING_SNAKE_CASE   :      Any    					=						pred_logits.size(1  )
             SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      List[Any]    					=						pred_logits.topk(1,							dim=-1,							largest=A,							sorted=A  )
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						preds_index.view(-1,							A  )[:, 1:]
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						decoder(A  )
             SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Optional[int]    					=						torch.nn.functional.softmax(A,							dim=2  ).max(dim=2  )
             SCREAMING_SNAKE_CASE   :      Dict    					=						preds_max_prob[:, 1:]

             for index in range(A  ):
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						preds_str[index].find(A  )
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						preds_str[index][:pred_eos]
                   SCREAMING_SNAKE_CASE   :      Dict    					=						preds_index[index].cpu().tolist()
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						pred_index.index(A  ) if eos_token in pred_index else -1
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						preds_max_prob[index][: pred_eos_index + 1]
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						pred_max_prob.cumprod(dim=0  )[-1] if pred_max_prob.nelement() != 0 else 0.0
                   dec_strs.append(A  )
                   conf_scores.append(A  )

             return dec_strs, conf_scores







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						[seq.replace(' ',							''  ) for seq in self.char_tokenizer.batch_decode(A  )]
             return decode_strs







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             return self.bpe_tokenizer.batch_decode(A  )







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[str]    					=						[seq.replace(' ',							''  ) for seq in self.wp_tokenizer.batch_decode(A  )]
             return decode_strs


