



"""simple docstring"""






import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


lowerCAmelCase__        =				logging.get_logger(__name__)


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

# See all BART models at https://huggingface.co/models?filter=bart
lowerCAmelCase__        =				{
    '''vocab_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/vocab.json''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/vocab.json''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/merges.txt''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/merges.txt''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json''',
    },
}

lowerCAmelCase__        =				{
    '''facebook/bart-base''': 1024,
    '''facebook/bart-large''': 1024,
    '''facebook/bart-large-mnli''': 1024,
    '''facebook/bart-large-cnn''': 1024,
    '''facebook/bart-large-xsum''': 1024,
    '''yjernite/bart_eli5''': 1024,
}






class        __snake_case       (	_lowercase):
  snake_case__				:   Any  			=						VOCAB_FILES_NAMES
  snake_case__				:   Optional[int]  			=						PRETRAINED_VOCAB_FILES_MAP
  snake_case__				:   List[Any]  			=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  snake_case__				:   Optional[int]  			=						["input_ids", "attention_mask"]
  snake_case__				:   Any  			=						BartTokenizer




  def __init__( self     :						int						,					__lowerCAmelCase     :						Dict=None						,					__lowerCAmelCase     :						int=None						,					__lowerCAmelCase     :						Dict=None						,					__lowerCAmelCase     :						List[Any]="replace"						,					__lowerCAmelCase     :						Any="<s>"						,					__lowerCAmelCase     :						Optional[int]="</s>"						,					__lowerCAmelCase     :						str="</s>"						,					__lowerCAmelCase     :						Dict="<s>"						,					__lowerCAmelCase     :						Union[str, Any]="<unk>"						,					__lowerCAmelCase     :						Any="<pad>"						,					__lowerCAmelCase     :						Optional[Any]="<mask>"						,					__lowerCAmelCase     :						Optional[Any]=False						,					__lowerCAmelCase     :						Dict=True						,					**__lowerCAmelCase     :						Optional[Any]						,					):






         """simple docstring"""






         super().__init__(
             __lowerCAmelCase						,					__lowerCAmelCase						,					tokenizer_file=__lowerCAmelCase						,					errors=__lowerCAmelCase						,					bos_token=__lowerCAmelCase						,					eos_token=__lowerCAmelCase						,					sep_token=__lowerCAmelCase						,					cls_token=__lowerCAmelCase						,					unk_token=__lowerCAmelCase						,					pad_token=__lowerCAmelCase						,					mask_token=__lowerCAmelCase						,					add_prefix_space=__lowerCAmelCase						,					trim_offsets=__lowerCAmelCase						,					**__lowerCAmelCase						,					)

         _lowerCamelCase						:				Tuple    			= json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
         if pre_tok_state.get('''add_prefix_space'''						,					__lowerCAmelCase		) != add_prefix_space:
                _lowerCamelCase						:				Dict    			= getattr(__lowerCAmelCase						,					pre_tok_state.pop('''type'''		)		)
                _lowerCamelCase						:				Any    			= add_prefix_space
                _lowerCamelCase						:				int    			= pre_tok_class(**__lowerCAmelCase		)

         _lowerCamelCase						:				Optional[int]    			= add_prefix_space

         # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
         _lowerCamelCase						:				List[str]    			= '''post_processor'''
         _lowerCamelCase						:				List[str]    			= getattr(self.backend_tokenizer						,					__lowerCAmelCase						,					__lowerCAmelCase		)
         if tokenizer_component_instance:
                _lowerCamelCase						:				int    			= json.loads(tokenizer_component_instance.__getstate__()		)

                # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
                if "sep" in state:
                       _lowerCamelCase						:				Tuple    			= tuple(state['''sep''']		)
                if "cls" in state:
                       _lowerCamelCase						:				int    			= tuple(state['''cls''']		)

                _lowerCamelCase						:				Union[str, Any]    			= False

                if state.get('''add_prefix_space'''						,					__lowerCAmelCase		) != add_prefix_space:
                       _lowerCamelCase						:				Dict    			= add_prefix_space
                       _lowerCamelCase						:				Optional[Any]    			= True

                if state.get('''trim_offsets'''						,					__lowerCAmelCase		) != trim_offsets:
                       _lowerCamelCase						:				Any    			= trim_offsets
                       _lowerCamelCase						:				str    			= True

                if changes_to_apply:
                       _lowerCamelCase						:				List[str]    			= getattr(__lowerCAmelCase						,					state.pop('''type'''		)		)
                       _lowerCamelCase						:				str    			= component_class(**__lowerCAmelCase		)
                       setattr(self.backend_tokenizer						,					__lowerCAmelCase						,					__lowerCAmelCase		)




  @property
  def 						SCREAMING_SNAKE_CASE				( self     :						Optional[int]		):






         """simple docstring"""






         if self._mask_token is None:
                if self.verbose:
                       logger.error('''Using mask_token, but it is not set yet.'''		)
                return None
         return str(self._mask_token		)




  @mask_token.setter
  def 						SCREAMING_SNAKE_CASE				( self     :						Any						,					__lowerCAmelCase     :						int		):






         """simple docstring"""






         _lowerCamelCase						:				Tuple    			= AddedToken(__lowerCAmelCase						,					lstrip=__lowerCAmelCase						,					rstrip=__lowerCAmelCase		) if isinstance(__lowerCAmelCase						,					__lowerCAmelCase		) else value
         _lowerCamelCase						:				str    			= value




  def 						SCREAMING_SNAKE_CASE				( self     :						int						,					*__lowerCAmelCase     :						Optional[Any]						,					**__lowerCAmelCase     :						List[Any]		):






         """simple docstring"""






         _lowerCamelCase						:				Dict    			= kwargs.get('''is_split_into_words'''						,					__lowerCAmelCase		)

         if is_split_into_words and not self.add_prefix_space:
                raise ValueError(
                    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
                    '''to use it with pretokenized inputs.'''		)

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




  def 						SCREAMING_SNAKE_CASE				( self     :						Any						,					*__lowerCAmelCase     :						Tuple						,					**__lowerCAmelCase     :						List[Any]		):






         """simple docstring"""






         _lowerCamelCase						:				Any    			= kwargs.get('''is_split_into_words'''						,					__lowerCAmelCase		)

         if is_split_into_words and not self.add_prefix_space:
                raise ValueError(
                    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
                    '''to use it with pretokenized inputs.'''		)

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




  def 						SCREAMING_SNAKE_CASE				( self     :						List[Any]						,					__lowerCAmelCase     :						str						,					__lowerCAmelCase     :						Optional[str] = None		):






         """simple docstring"""






         _lowerCamelCase						:				Tuple    			= self._tokenizer.model.save(__lowerCAmelCase						,					name=__lowerCAmelCase		)
         return tuple(__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE				( self     :						Optional[Any]						,					__lowerCAmelCase     :						Dict						,					__lowerCAmelCase     :						List[str]=None		):






         """simple docstring"""






         _lowerCamelCase						:				Union[str, Any]    			= [self.bos_token_id] + token_ids_a + [self.eos_token_id]
         if token_ids_a is None:
                return output

         return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]




  def 						SCREAMING_SNAKE_CASE				( self     :						int						,					__lowerCAmelCase     :						List[int]						,					__lowerCAmelCase     :						Optional[List[int]] = None		):






         """simple docstring"""






         _lowerCamelCase						:				List[str]    			= [self.sep_token_id]
         _lowerCamelCase						:				Tuple    			= [self.cls_token_id]

         if token_ids_a is None:
                return len(cls + token_ids_a + sep		) * [0]
         return len(cls + token_ids_a + sep + sep + token_ids_a + sep		) * [0]




"""simple docstring"""




from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
					from PIL import Image

					from ..image_utils import load_image

if is_torch_available():
					import torch

					from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

UpperCAmelCase__      :    List[str]    							= logging.get_logger(__name__)



@add_end_docstrings(a__  )
class 	lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				def __init__(self   ,       *SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )					->		Tuple:



											"""simple docstring"""




											super().__init__(*SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )
											requires_backends(self   ,       """vision"""    )
											self.check_model_type(SCREAMING_SNAKE_CASE__    )



				def __call__(self   ,       SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )					->		List[str]:



											"""simple docstring"""




											return super().__call__(SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self   ,       **SCREAMING_SNAKE_CASE__    )					->		Any:



											"""simple docstring"""




											return {}, {}, {}



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[Any]           =   load_image(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Any           =   image.size
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   self.image_processor(images=SCREAMING_SNAKE_CASE__   ,       return_tensors=self.framework    )
											return model_inputs



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Tuple           =   self.model(**SCREAMING_SNAKE_CASE__    )
											return model_outputs



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[Any]           =   model_outputs.predicted_depth
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   torch.nn.functional.interpolate(
											    predicted_depth.unsqueeze(1    )   ,       size=self.image_size[::-1]   ,       mode="""bicubic"""   ,       align_corners=SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   prediction.squeeze().cpu().numpy()
											SCREAMING_SNAKE_CASE__						:       Any           =   (output * 2_55 / np.max(SCREAMING_SNAKE_CASE__    )).astype("""uint8"""    )
											SCREAMING_SNAKE_CASE__						:       List[str]           =   Image.fromarray(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       str           =   {}
											SCREAMING_SNAKE_CASE__						:       Any           =   predicted_depth
											SCREAMING_SNAKE_CASE__						:       Dict           =   depth
											return output_dict




