import json
from typing import TYPE_CHECKING, 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_blenderbot import BlenderbotTokenizer


if TYPE_CHECKING:
 from transformers.pipelines.conversational import Conversation

lowercase__ 	=     logging.get_logger(__name__)


lowercase__ 	=     {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

lowercase__ 	=     {
    'vocab_file': {'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'},
    'merges_file': {'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'},
    'tokenizer_config_file': {
        'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'
    },
}

lowercase__ 	=     {'facebook/blenderbot-3B': 128}




class        __snake_case	(					__lowerCamelCase    ):
      a__				=		VOCAB_FILES_NAMES
      a__				=		PRETRAINED_VOCAB_FILES_MAP
      a__				=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      a__				=		["""input_ids""", """attention_mask"""]
      a__				=		BlenderbotTokenizer



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





        '''simple docstring'''





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

        a__:					str      							=     json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
        if pre_tok_state.get('add_prefix_space'   ,      __lowercase) != add_prefix_space:
          a__:					Tuple      							=     getattr(__lowercase   ,      pre_tok_state.pop('type'))
          a__:					Tuple      							=     add_prefix_space
          a__:					Any      							=     pre_tok_class(**__lowercase)

        a__:					Optional[int]      							=     add_prefix_space

        a__:					Tuple      							=     '''post_processor'''
        a__:					Optional[int]      							=     getattr(self.backend_tokenizer   ,      __lowercase   ,      __lowercase)
        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__:					List[Any]      							=     tuple(state['sep'])
          if "cls" in state:
            a__:					Optional[Any]      							=     tuple(state['cls'])

          a__:					Optional[Any]      							=     False

          if state.get('add_prefix_space'   ,      __lowercase) != add_prefix_space:
            a__:					Any      							=     add_prefix_space
            a__:					Optional[Any]      							=     True

          if state.get('trim_offsets'   ,      __lowercase) != trim_offsets:
            a__:					str      							=     trim_offsets
            a__:					int      							=     True

          if changes_to_apply:
            a__:					Tuple      							=     getattr(__lowercase   ,      state.pop('type'))
            a__:					Dict      							=     component_class(**__lowercase)
            setattr(self.backend_tokenizer   ,      __lowercase   ,      __lowercase)



      @property
      # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
      def   lowerCamelCase_       (						self)     ->      List[str]:





        '''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   lowerCamelCase_       (						self   ,      lowercase)     ->      Optional[int]:





        '''simple docstring'''





        a__:					int      							=     AddedToken(__lowercase   ,      lstrip=__lowercase   ,      rstrip=__lowercase) if isinstance(__lowercase   ,      __lowercase) else value
        a__:					List[Any]      							=     value



      def   lowerCamelCase_       (						self   ,      *lowercase   ,      **lowercase)     ->      Optional[Any]:





        '''simple docstring'''





        a__:					Dict      							=     kwargs.get('is_split_into_words'   ,      __lowercase)
        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(*__lowercase   ,      **__lowercase)



      def   lowerCamelCase_       (						self   ,      *lowercase   ,      **lowercase)     ->      int:





        '''simple docstring'''





        a__:					Dict      							=     kwargs.get('is_split_into_words'   ,      __lowercase)

        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(*__lowercase   ,      **__lowercase)



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase = None)     ->      List[Any]:





        '''simple docstring'''





        a__:					Dict      							=     self._tokenizer.model.save(__lowercase   ,      name=__lowercase)
        return tuple(__lowercase)



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase = None)     ->      List[Any]:





        '''simple docstring'''





        a__:					str      							=     [self.sep_token_id]
        a__:					Union[str, Any]      							=     [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]



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase = None)     ->      Dict:





        '''simple docstring'''





        return token_ids_a + [self.eos_token_id]



      def   lowerCamelCase_       (						self   ,      lowercase)     ->      List[str]:





        '''simple docstring'''





        a__:					Tuple      							=     []
        for is_user, text in conversation.iter_texts():
          if is_user:
            # We need to space prefix as it's being done within blenderbot
            inputs.append(' ' + text)
          else:
            # Generated responses should contain them already.
            inputs.append(__lowercase)

        a__:					Union[str, Any]      							=     '''  '''.join(__lowercase)
        a__:					Union[str, Any]      							=     self.encode(__lowercase)
        if len(__lowercase) > self.model_max_length:
          a__:					List[Any]      							=     input_ids[-self.model_max_length :]
          logger.warning(f'Trimmed input from conversation as it was longer than {self.model_max_length} tokens.')
        return input_ids



"""simple docstring"""
from typing import Any, Dict, List, Union

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 ..image_utils import load_image


if is_torch_available():
 import torch

 from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

lowercase__ 	=     logging.get_logger(__name__)


lowercase__ 	=     Dict[str, Any]
lowercase__ 	=     List[Prediction]




@add_end_docstrings(__lowerCAmelCase    )
class        __snake_case	(					__lowerCAmelCase    ):



      def __init__(						self   ,      *lowercase   ,      **lowercase)     ->      Dict:





        '''simple docstring'''





        super().__init__(*lowercase   ,      **lowercase)

        if self.framework == "tf":
          raise ValueError(f'The {self.__class__} is only available in PyTorch.')

        requires_backends(self   ,      'vision')
        self.check_model_type(
            dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()))



      def   lowerCamelCase_       (						self   ,      **lowercase)     ->      int:





        '''simple docstring'''





        a__:					Optional[Any]      							=     {}
        if "threshold" in kwargs:
          a__:					Dict      							=     kwargs['threshold']
        return {}, {}, postprocess_kwargs



      def __call__(						self   ,      *lowercase   ,      **lowercase)     ->      Union[Predictions, List[Prediction]]:





        '''simple docstring'''





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



      def   lowerCamelCase_       (						self   ,      lowercase)     ->      List[Any]:





        '''simple docstring'''





        a__:					Optional[Any]      							=     load_image(lowercase)
        a__:					List[Any]      							=     torch.IntTensor([[image.height, image.width]])
        a__:					Any      							=     self.image_processor(images=[image]   ,      return_tensors='pt')
        if self.tokenizer is not None:
          a__:					Any      							=     self.tokenizer(text=inputs['words']   ,      boxes=inputs['boxes']   ,      return_tensors='pt')
        a__:					List[str]      							=     target_size
        return inputs



      def   lowerCamelCase_       (						self   ,      lowercase)     ->      int:





        '''simple docstring'''





        a__:					Any      							=     model_inputs.pop('target_size')
        a__:					Union[str, Any]      							=     self.model(**lowercase)
        a__:					List[str]      							=     outputs.__class__({'target_size': target_size, **outputs})
        if self.tokenizer is not None:
          a__:					Union[str, Any]      							=     model_inputs['bbox']
        return model_outputs



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase=0.9)     ->      Optional[Any]:





        '''simple docstring'''





        a__:					int      							=     model_outputs['target_size']
        if self.tokenizer is not None:
          # This is a LayoutLMForTokenClassification variant.
          # The OCR got the boxes and the model classified the words.
          a__							,   a__:					str      							=     target_size[0].tolist()

          def unnormalize(lowercase):
            return self._get_bounding_box(
                torch.Tensor(
                    [
                        (width * bbox[0] / 10_00),
                        (height * bbox[1] / 10_00),
                        (width * bbox[2] / 10_00),
                        (height * bbox[3] / 10_00),
                    ]))

          a__							,   a__:					Optional[Any]      							=     model_outputs['logits'].squeeze(0).softmax(dim=-1).max(dim=-1)
          a__:					str      							=     [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
          a__:					Union[str, Any]      							=     [unnormalize(lowercase) for bbox in model_outputs['bbox'].squeeze(0)]
          a__:					Dict      							=     ['score', 'label', 'box']
          a__:					Any      							=     [dict(zip(lowercase   ,      lowercase)) for vals in zip(scores.tolist()   ,      lowercase   ,      lowercase) if vals[0] > threshold]
        else:
          # This is a regular ForObjectDetectionModel
          a__:					List[str]      							=     self.image_processor.post_process_object_detection(lowercase   ,      lowercase   ,      lowercase)
          a__:					Tuple      							=     raw_annotations[0]
          a__:					List[str]      							=     raw_annotation['scores']
          a__:					int      							=     raw_annotation['labels']
          a__:					int      							=     raw_annotation['boxes']

          a__:					List[Any]      							=     scores.tolist()
          a__:					Any      							=     [self.model.config.idalabel[label.item()] for label in labels]
          a__:					Dict      							=     [self._get_bounding_box(lowercase) for box in boxes]

          # {"scores": [...], ...} --> [{"score":x, ...}, ...]
          a__:					Optional[Any]      							=     ['score', 'label', 'box']
          a__:					List[Any]      							=     [
              dict(zip(lowercase   ,      lowercase))
              for vals in zip(raw_annotation['scores']   ,      raw_annotation['labels']   ,      raw_annotation['boxes'])
          ]

        return annotation



      def   lowerCamelCase_       (						self   ,      lowercase)     ->      Dict[str, int]:





        '''simple docstring'''





        if self.framework != "pt":
          raise ValueError('The ObjectDetectionPipeline is only available in PyTorch.')
        a__							,   a__							,   a__							,   a__:					List[Any]      							=     box.int().tolist()
        a__:					Optional[int]      							=     {
            'xmin': xmin,
            'ymin': ymin,
            'xmax': xmax,
            'ymax': ymax,
        }
        return bbox


