





'''simple docstring'''




from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    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_tf_available():
  from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
  import torch

  from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

lowercase__       : Any         =       logging.get_logger(__name__)


@add_end_docstrings(_snake_case				)
class 					SCREAMING_SNAKE_CASE       (_snake_case				):


     def __init__(		self		,      *_UpperCAmelCase		,      **_UpperCAmelCase):

          '''simple docstring'''






          super().__init__(*_lowerCamelCase		,      **_lowerCamelCase)
          requires_backends(self		,      'vision')
          self.check_model_type(
              TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == 'tf' else MODEL_FOR_VISION_2_SEQ_MAPPING)


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase=None		,      _UpperCAmelCase=None		,      _UpperCAmelCase=None):

          '''simple docstring'''






          __A		:					Optional[int]						=			{}
          __A		:					Tuple						=			{}

          if prompt is not None:
               __A		:					str						=			prompt

          if generate_kwargs is not None:
               __A		:					Dict						=			generate_kwargs
          if max_new_tokens is not None:
               if "generate_kwargs" not in forward_kwargs:
                    __A		:					str						=			{}
               if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
                    raise ValueError(
                        '\'max_new_tokens\' is defined twice, once in \'generate_kwargs\' and once as a direct parameter,'
                        ' please use only one')
               __A		:					Union[str, Any]						=			max_new_tokens
          return preprocess_params, forward_kwargs, {}


     def __call__(		self		,      _UpperCAmelCase		,      **_UpperCAmelCase):

          '''simple docstring'''






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


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase=None):

          '''simple docstring'''






          __A		:					Any						=			load_image(_lowerCamelCase)

          if prompt is not None:
               if not isinstance(_lowerCamelCase		,      _lowerCamelCase):
                    raise ValueError(
                        F'Received an invalid text input, got - {type(_lowerCamelCase)} - but expected a single string. '
                        'Note also that one single text can be provided for conditional image to text generation.')

               __A		:					Optional[int]						=			self.model.config.model_type

               if model_type == "git":
                    __A		:					Tuple						=			self.image_processor(images=_lowerCamelCase		,      return_tensors=self.framework)
                    __A		:					Tuple						=			self.tokenizer(text=_lowerCamelCase		,      add_special_tokens=_lowerCamelCase).input_ids
                    __A		:					Any						=			[self.tokenizer.cls_token_id] + input_ids
                    __A		:					Tuple						=			torch.tensor(_lowerCamelCase).unsqueeze(0)
                    model_inputs.update({'input_ids': input_ids})

               elif model_type == "pix2struct":
                    __A		:					int						=			self.image_processor(images=_lowerCamelCase		,      header_text=_lowerCamelCase		,      return_tensors=self.framework)

               elif model_type != "vision-encoder-decoder":
                    # vision-encoder-decoder does not support conditional generation
                    __A		:					List[Any]						=			self.image_processor(images=_lowerCamelCase		,      return_tensors=self.framework)
                    __A		:					Optional[Any]						=			self.tokenizer(_lowerCamelCase		,      return_tensors=self.framework)
                    model_inputs.update(_lowerCamelCase)

               else:
                    raise ValueError(F'Model type {model_type} does not support conditional text generation')

          else:
               __A		:					Any						=			self.image_processor(images=_lowerCamelCase		,      return_tensors=self.framework)

          if self.model.config.model_type == "git" and prompt is None:
               __A		:					Union[str, Any]						=			None

          return model_inputs


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase=None):

          '''simple docstring'''






          if (
              "input_ids" in model_inputs
              and isinstance(model_inputs['input_ids']		,      _lowerCamelCase)
              and all(x is None for x in model_inputs['input_ids'])
          ):
               __A		:					str						=			None

          if generate_kwargs is None:
               __A		:					Union[str, Any]						=			{}
          # FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
          #  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
          #  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
          #  in the `_prepare_model_inputs` method.
          __A		:					Optional[Any]						=			model_inputs.pop(self.model.main_input_name)
          __A		:					List[str]						=			self.model.generate(_lowerCamelCase		,      **_lowerCamelCase		,      **_lowerCamelCase)
          return model_outputs


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					List[Any]						=			[]
          for output_ids in model_outputs:
               __A		:					Optional[Any]						=			{
                   '''generated_text''': self.tokenizer.decode(
                       _lowerCamelCase		,      skip_special_tokens=_lowerCamelCase		,      )
               }
               records.append(_lowerCamelCase)
          return records



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_roberta import RobertaTokenizer


snake_case							:       List[Any]										=	logging.get_logger(__name__)

snake_case							:       Optional[int]										=	{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case							:       Union[str, Any]										=	{
    '''vocab_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/vocab.json''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/vocab.json''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/vocab.json''',
        '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json''',
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'''
        ),
    },
    '''merges_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/merges.txt''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/merges.txt''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/merges.txt''',
        '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt''',
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/tokenizer.json''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/tokenizer.json''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json''',
        '''roberta-base-openai-detector''': (
            '''https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json'''
        ),
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json'''
        ),
    },
}

snake_case							:       Optional[int]										=	{
    '''roberta-base''': 5_12,
    '''roberta-large''': 5_12,
    '''roberta-large-mnli''': 5_12,
    '''distilroberta-base''': 5_12,
    '''roberta-base-openai-detector''': 5_12,
    '''roberta-large-openai-detector''': 5_12,
}







class 						_snake_case    ( _snake_case				):
 SCREAMING_SNAKE_CASE__ =			VOCAB_FILES_NAMES
 SCREAMING_SNAKE_CASE__ =			PRETRAINED_VOCAB_FILES_MAP
 SCREAMING_SNAKE_CASE__ =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 SCREAMING_SNAKE_CASE__ =			['input_ids', 'attention_mask']
 SCREAMING_SNAKE_CASE__ =			RobertaTokenizer




 def __init__(						self   ,    _lowerCamelCase=None   ,    _lowerCamelCase=None   ,    _lowerCamelCase=None   ,    _lowerCamelCase="replace"   ,    _lowerCamelCase="<s>"   ,    _lowerCamelCase="</s>"   ,    _lowerCamelCase="</s>"   ,    _lowerCamelCase="<s>"   ,    _lowerCamelCase="<unk>"   ,    _lowerCamelCase="<pad>"   ,    _lowerCamelCase="<mask>"   ,    _lowerCamelCase=False   ,    _lowerCamelCase=True   ,    **_lowerCamelCase   ,    ):
       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   ,    )

       a		:List[Any]									=			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
       if pre_tok_state.get('''add_prefix_space'''   ,    _lowerCamelCase       ) != add_prefix_space:
             a		:Optional[int]									=			getattr(_lowerCamelCase   ,    pre_tok_state.pop('''type'''       )       )
             a		:Union[str, Any]									=			add_prefix_space
             a		:Dict									=			pre_tok_class(**_lowerCamelCase       )

       a		:Union[str, Any]									=			add_prefix_space

       a		:List[Any]									=			'''post_processor'''
       a		:List[str]									=			getattr(self.backend_tokenizer   ,    _lowerCamelCase   ,    _lowerCamelCase       )
       if tokenizer_component_instance:
             a		:Optional[Any]									=			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:
                   a		:Optional[int]									=			tuple(state['''sep''']       )
             if "cls" in state:
                   a		:Union[str, Any]									=			tuple(state['''cls''']       )

             a		:Optional[int]									=			False

             if state.get('''add_prefix_space'''   ,    _lowerCamelCase       ) != add_prefix_space:
                   a		:int									=			add_prefix_space
                   a		:Union[str, Any]									=			True

             if state.get('''trim_offsets'''   ,    _lowerCamelCase       ) != trim_offsets:
                   a		:int									=			trim_offsets
                   a		:List[str]									=			True

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




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       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   ,    _lowerCamelCase       ):
       a		:Union[str, Any]									=			AddedToken(_lowerCamelCase   ,    lstrip=_lowerCamelCase   ,    rstrip=_lowerCamelCase       ) if isinstance(_lowerCamelCase   ,    _lowerCamelCase       ) else value
       a		:int									=			value




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    *_lowerCamelCase   ,    **_lowerCamelCase       ):
       a		:Union[str, Any]									=			kwargs.get('''is_split_into_words'''   ,    _lowerCamelCase       )
       assert self.add_prefix_space or not is_split_into_words, (
           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   ,    *_lowerCamelCase   ,    **_lowerCamelCase       ):
       a		:Any									=			kwargs.get('''is_split_into_words'''   ,    _lowerCamelCase       )

       assert self.add_prefix_space or not is_split_into_words, (
           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   ,    _lowerCamelCase   ,    _lowerCamelCase = None       ):
       a		:Any									=			self._tokenizer.model.save(_lowerCamelCase   ,    name=_lowerCamelCase       )
       return tuple(_lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase=None       ):
       a		: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   ,    _lowerCamelCase   ,    _lowerCamelCase = None       ):
       a		:Dict									=			[self.sep_token_id]
       a		:int									=			[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]





