

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


lowerCAmelCase_			      =      logging.get_logger(__name__)

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





class       snake_case_  (						__A   ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE	:  Union[str, Any]		     =							"data2vec-vision"







			def __init__(	self					:							int				,  _UpperCamelCase					:							int=7_6_8				,  _UpperCamelCase					:							str=1_2				,  _UpperCamelCase					:							str=1_2				,  _UpperCamelCase					:							int=3_0_7_2				,  _UpperCamelCase					:							List[Any]="gelu"				,  _UpperCamelCase					:							List[str]=0.0				,  _UpperCamelCase					:							str=0.0				,  _UpperCamelCase					:							List[str]=0.02				,  _UpperCamelCase					:							Optional[int]=1e-12				,  _UpperCamelCase					:							Optional[int]=2_2_4				,  _UpperCamelCase					:							str=1_6				,  _UpperCamelCase					:							Optional[int]=3				,  _UpperCamelCase					:							Optional[int]=False				,  _UpperCamelCase					:							Optional[Any]=False				,  _UpperCamelCase					:							List[Any]=False				,  _UpperCamelCase					:							List[Any]=False				,  _UpperCamelCase					:							Union[str, Any]=0.1				,  _UpperCamelCase					:							List[str]=0.1				,  _UpperCamelCase					:							Tuple=True				,  _UpperCamelCase					:							Optional[int]=[3, 5, 7, 1_1]				,  _UpperCamelCase					:							Dict=[1, 2, 3, 6]				,  _UpperCamelCase					:							Optional[int]=True				,  _UpperCamelCase					:							Dict=0.4				,  _UpperCamelCase					:							Any=2_5_6				,  _UpperCamelCase					:							List[str]=1				,  _UpperCamelCase					:							Dict=False				,  _UpperCamelCase					:							Union[str, Any]=2_5_5				,  **_UpperCamelCase					:							Dict				,  ) ->List[Any]:
									super().__init__(**_UpperCamelCase				)

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

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






class       snake_case_  (						__A   ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE	:  Optional[Any]		     =							version.parse("1.11"   )







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







			@property
			def 							snake_case__(	self					:							Tuple				) ->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


