
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

_A   =			logging.get_logger(__name__)


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







@add_end_docstrings(A_  )
class 					lowerCamelCase		(  A_  ):





     def __init__(self				:   List[str]    ,   *_A				:   Dict    ,   **_A				:   Optional[int]       )						->						List[str]:
         super().__init__(*_A    ,   **_A       )

         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 		UpperCAmelCase(self				:   int    ,   **_A				:   Dict       )						->						Tuple:
         snake_case								= {}
         if "threshold" in kwargs:
             snake_case								= kwargs["threshold"]
         return {}, {}, postprocess_kwargs





     def __call__(self				:   str    ,   *_A				:   Tuple    ,   **_A				:   Optional[Any]       )						->						Union[Predictions, List[Prediction]]:
         return super().__call__(*_A    ,   **_A       )





     def 		UpperCAmelCase(self				:   Dict    ,   _A				:   int       )						->						int:
         snake_case								= load_image(_A       )
         snake_case								= torch.IntTensor([[image.height, image.width]]       )
         snake_case								= self.image_processor(images=[image]    ,   return_tensors="pt"       )
         if self.tokenizer is not None:
             snake_case								= self.tokenizer(text=inputs["words"]    ,   boxes=inputs["boxes"]    ,   return_tensors="pt"       )
         snake_case								= target_size
         return inputs





     def 		UpperCAmelCase(self				:   str    ,   _A				:   str       )						->						Union[str, Any]:
         snake_case								= model_inputs.pop("target_size"       )
         snake_case								= self.model(**_A       )
         snake_case								= outputs.__class__({"target_size": target_size, **outputs}       )
         if self.tokenizer is not None:
             snake_case								= model_inputs["bbox"]
         return model_outputs





     def 		UpperCAmelCase(self				:   Optional[int]    ,   _A				:   List[str]    ,   _A				:   Optional[int]=0.9       )						->						Union[str, Any]:
         snake_case								= 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.
             snake_case					,							snake_case								= target_size[0].tolist()

             def unnormalize(_A				:   Optional[int]       ):
                 return self._get_bounding_box(
                     torch.Tensor(
                         [
                             (width * bbox[0] / 1_0_0_0),
                             (height * bbox[1] / 1_0_0_0),
                             (width * bbox[2] / 1_0_0_0),
                             (height * bbox[3] / 1_0_0_0),
                         ]       )       )

             snake_case					,							snake_case								= model_outputs["logits"].squeeze(0       ).softmax(dim=-1       ).max(dim=-1       )
             snake_case								= [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
             snake_case								= [unnormalize(_A       ) for bbox in model_outputs["bbox"].squeeze(0       )]
             snake_case								= ["score", "label", "box"]
             snake_case								= [dict(zip(_A    ,   _A       )       ) for vals in zip(scores.tolist()    ,   _A    ,   _A       ) if vals[0] > threshold]
         else:
             # This is a regular ForObjectDetectionModel
             snake_case								= self.image_processor.post_process_object_detection(_A    ,   _A    ,   _A       )
             snake_case								= raw_annotations[0]
             snake_case								= raw_annotation["scores"]
             snake_case								= raw_annotation["labels"]
             snake_case								= raw_annotation["boxes"]

             snake_case								= scores.tolist()
             snake_case								= [self.model.config.idalabel[label.item()] for label in labels]
             snake_case								= [self._get_bounding_box(_A       ) for box in boxes]

             # {"scores": [...], ...} --> [{"score":x, ...}, ...]
             snake_case								= ["score", "label", "box"]
             snake_case								= [
                 dict(zip(_A    ,   _A       )       )
                 for vals in zip(raw_annotation["scores"]    ,   raw_annotation["labels"]    ,   raw_annotation["boxes"]       )
             ]

         return annotation





     def 		UpperCAmelCase(self				:   Optional[Any]    ,   _A				:   "torch.Tensor"       )						->						Dict[str, int]:
         if self.framework != "pt":
             raise ValueError("The ObjectDetectionPipeline is only available in PyTorch."       )
         snake_case					,							snake_case					,							snake_case					,							snake_case								= box.int().tolist()
         snake_case								= {
             "xmin": xmin,
             "ymin": ymin,
             "xmax": xmax,
             "ymax": ymax,
         }
         return bbox



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer


_A   =			logging.get_logger(__name__)

_A   =			{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

_A   =			{
    "vocab_file": {"mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/vocab.txt"},
    "tokenizer_file": {
        "mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/tokenizer.json"
    },
}

_A   =			{"mobilebert-uncased": 5_12}


_A   =			{}







class 					lowerCamelCase		(  A_  ):
     UpperCAmelCase__    :							Tuple									=		VOCAB_FILES_NAMES
     UpperCAmelCase__    :							Any									=		PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase__    :							Tuple									=		PRETRAINED_INIT_CONFIGURATION
     UpperCAmelCase__    :							Tuple									=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase__    :							Optional[Any]									=		MobileBertTokenizer





     def __init__(self				:   Any    ,   _A				:   str=None    ,   _A				:   str=None    ,   _A				:   Union[str, Any]=True    ,   _A				:   Optional[Any]="[UNK]"    ,   _A				:   int="[SEP]"    ,   _A				:   Dict="[PAD]"    ,   _A				:   int="[CLS]"    ,   _A				:   Union[str, Any]="[MASK]"    ,   _A				:   Any=True    ,   _A				:   Dict=None    ,   **_A				:   List[str]    ,   )						->						List[str]:
         super().__init__(
             _A    ,   tokenizer_file=_A    ,   do_lower_case=_A    ,   unk_token=_A    ,   sep_token=_A    ,   pad_token=_A    ,   cls_token=_A    ,   mask_token=_A    ,   tokenize_chinese_chars=_A    ,   strip_accents=_A    ,   **_A    ,   )

         snake_case								= json.loads(self.backend_tokenizer.normalizer.__getstate__()       )
         if (
             normalizer_state.get("lowercase"    ,   _A       ) != do_lower_case
             or normalizer_state.get("strip_accents"    ,   _A       ) != strip_accents
             or normalizer_state.get("handle_chinese_chars"    ,   _A       ) != tokenize_chinese_chars
         ):
             snake_case								= getattr(_A    ,   normalizer_state.pop("type"       )       )
             snake_case								= do_lower_case
             snake_case								= strip_accents
             snake_case								= tokenize_chinese_chars
             snake_case								= normalizer_class(**_A       )

         snake_case								= do_lower_case





     def 		UpperCAmelCase(self				:   List[str]    ,   _A				:   Union[str, Any]    ,   _A				:   Dict=None       )						->						Optional[Any]:
         snake_case								= [self.cls_token_id] + token_ids_a + [self.sep_token_id]

         if token_ids_a:
             output += token_ids_a + [self.sep_token_id]

         return output





     def 		UpperCAmelCase(self				:   Union[str, Any]    ,   _A				:   List[int]    ,   _A				:   Optional[List[int]] = None       )						->						List[int]:
         snake_case								= [self.sep_token_id]
         snake_case								= [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       ) * [0] + len(token_ids_a + sep       ) * [1]





     def 		UpperCAmelCase(self				:   int    ,   _A				:   str    ,   _A				:   Optional[str] = None       )						->						Tuple[str]:
         snake_case								= self._tokenizer.model.save(_A    ,   name=_A       )
         return tuple(_A       )

