

from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional

from packaging import version


if TYPE_CHECKING:
    from ... import PreTrainedTokenizer, TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import is_torch_available, logging


lowerCAmelCase						     =  logging.get_logger(__name__)

lowerCAmelCase						     =  {
    'bigscience/bloom': 'https://huggingface.co/bigscience/bloom/resolve/main/config.json',
    'bigscience/bloom-560m': 'https://huggingface.co/bigscience/bloom-560m/blob/main/config.json',
    'bigscience/bloom-1b1': 'https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json',
    'bigscience/bloom-1b7': 'https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json',
    'bigscience/bloom-3b': 'https://huggingface.co/bigscience/bloom-3b/blob/main/config.json',
    'bigscience/bloom-7b1': 'https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json',
}
class  _A		(  __magic_name__):
  SCREAMING_SNAKE_CASE    :      List[str]       =  """bloom"""
  SCREAMING_SNAKE_CASE    :      Any       =  ["""past_key_values"""]
  SCREAMING_SNAKE_CASE    :      Dict       =  {
      """num_hidden_layers""": """n_layer""",
      """num_attention_heads""": """n_head""",
  }

  def __init__(				self ,					_SCREAMING_SNAKE_CASE=25_0880 ,					_SCREAMING_SNAKE_CASE=64 ,					_SCREAMING_SNAKE_CASE=2 ,					_SCREAMING_SNAKE_CASE=8 ,					_SCREAMING_SNAKE_CASE=1e-5 ,					_SCREAMING_SNAKE_CASE=0.02 ,					_SCREAMING_SNAKE_CASE=True ,					_SCREAMING_SNAKE_CASE=1 ,					_SCREAMING_SNAKE_CASE=2 ,					_SCREAMING_SNAKE_CASE=False ,					_SCREAMING_SNAKE_CASE=0.0 ,					_SCREAMING_SNAKE_CASE=0.0 ,					_SCREAMING_SNAKE_CASE=1 ,					_SCREAMING_SNAKE_CASE=False ,					**_SCREAMING_SNAKE_CASE ,					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Dict           =     vocab_size
        # Backward compatibility with n_embed kwarg
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     kwargs.pop('n_embed' ,					_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     hidden_size if n_embed is None else n_embed
        SCREAMING_SNAKE_CASE_				:  List[str]           =     n_layer
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     n_head
        SCREAMING_SNAKE_CASE_				:  Tuple           =     layer_norm_epsilon
        SCREAMING_SNAKE_CASE_				:  Any           =     initializer_range
        SCREAMING_SNAKE_CASE_				:  Tuple           =     use_cache
        SCREAMING_SNAKE_CASE_				:  List[str]           =     pretraining_tp
        SCREAMING_SNAKE_CASE_				:  Any           =     apply_residual_connection_post_layernorm
        SCREAMING_SNAKE_CASE_				:  Any           =     hidden_dropout
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     attention_dropout

        SCREAMING_SNAKE_CASE_				:  int           =     bos_token_id
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     eos_token_id
        SCREAMING_SNAKE_CASE_				:  Any           =     slow_but_exact

        super().__init__(bos_token_id=_SCREAMING_SNAKE_CASE ,					eos_token_id=_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					)

class  _A		(  __magic_name__):
  SCREAMING_SNAKE_CASE    :      List[Any]       =  version.parse('''1.12''')

  def __init__(				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE = "default" ,					_SCREAMING_SNAKE_CASE = None ,					_SCREAMING_SNAKE_CASE = False ,					):





        """simple docstring"""







        super().__init__(_SCREAMING_SNAKE_CASE ,					task=_SCREAMING_SNAKE_CASE ,					patching_specs=_SCREAMING_SNAKE_CASE ,					use_past=_SCREAMING_SNAKE_CASE					)
        if not getattr(self._config ,					'pad_token_id' ,					_SCREAMING_SNAKE_CASE					):
              # TODO: how to do that better?
              SCREAMING_SNAKE_CASE_				:  List[str]           =     0

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Any           =     OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}					)
        if self.use_past:
              # BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
              self.fill_with_past_key_values_(_SCREAMING_SNAKE_CASE ,					direction='inputs' ,					inverted_values_shape=_SCREAMING_SNAKE_CASE					)
              SCREAMING_SNAKE_CASE_				:  Dict           =     {0: 'batch', 1: 'past_sequence + sequence'}
        else:
              SCREAMING_SNAKE_CASE_				:  Tuple           =     {0: 'batch', 1: 'sequence'}

        return common_inputs

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return self._config.n_layer

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return self._config.n_head

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return 1e-3

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE = -1 ,					_SCREAMING_SNAKE_CASE = -1 ,					_SCREAMING_SNAKE_CASE = False ,					_SCREAMING_SNAKE_CASE = None ,					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  str           =     super(_SCREAMING_SNAKE_CASE ,					self					).generate_dummy_inputs(
            _SCREAMING_SNAKE_CASE ,					batch_size=_SCREAMING_SNAKE_CASE ,					seq_length=_SCREAMING_SNAKE_CASE ,					is_pair=_SCREAMING_SNAKE_CASE ,					framework=_SCREAMING_SNAKE_CASE					)

        # We need to order the input in the way they appears in the forward()
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     OrderedDict({'input_ids': common_inputs['input_ids']}					)

        # Need to add the past_keys
        if self.use_past:
              if not is_torch_available():
                    raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'					)
              else:
                    import torch

                    SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  int           =     common_inputs['input_ids'].shape
                    # Not using the same length for past_key_values
                    SCREAMING_SNAKE_CASE_				:  List[Any]           =     seqlen + 2
                    SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     self._config.hidden_size // self.num_attention_heads
                    SCREAMING_SNAKE_CASE_				:  Optional[int]           =     (
                        batch * self.num_attention_heads,
                        head_dim,
                        past_key_values_length,
                    )
                    SCREAMING_SNAKE_CASE_				:  Optional[int]           =     (
                        batch * self.num_attention_heads,
                        past_key_values_length,
                        head_dim,
                    )
                    SCREAMING_SNAKE_CASE_				:  Any           =     [
                        (torch.zeros(_SCREAMING_SNAKE_CASE					), torch.zeros(_SCREAMING_SNAKE_CASE					)) for _ in range(self.num_layers					)
                    ]

        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     common_inputs['attention_mask']
        if self.use_past:
              SCREAMING_SNAKE_CASE_				:  Dict           =     ordered_inputs['attention_mask'].dtype
              SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     torch.cat(
                  [ordered_inputs['attention_mask'], torch.ones(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					dtype=_SCREAMING_SNAKE_CASE					)] ,					dim=1					)

        return ordered_inputs



  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return 13









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

lowerCAmelCase     :   Optional[int]						     =  logging.get_logger(__name__)


lowerCAmelCase     :   Optional[int]						     =  Dict[str, Any]
lowerCAmelCase     :   Any						     =  List[Prediction]
@add_end_docstrings(__magic_name__)
class  _A		(  __magic_name__):

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





        """simple docstring"""







        super().__init__(*_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					)

        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 ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Any           =     {}
        if "threshold" in kwargs:
              SCREAMING_SNAKE_CASE_				:  List[str]           =     kwargs['threshold']
        return {}, {}, postprocess_kwargs

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





        """simple docstring"""







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

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







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

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







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

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE=0.9					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  List[Any]           =     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.
              SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  Any           =     target_size[0].tolist()

              def unnormalize(_SCREAMING_SNAKE_CASE					):
                    return self._get_bounding_box(
                        torch.Tensor(
                            [
                                (width * bbox[0] / 1000),
                                (height * bbox[1] / 1000),
                                (width * bbox[2] / 1000),
                                (height * bbox[3] / 1000),
                            ]					)					)

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

              SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     scores.tolist()
              SCREAMING_SNAKE_CASE_				:  Any           =     [self.model.config.idalabel[label.item()] for label in labels]
              SCREAMING_SNAKE_CASE_				:  Optional[int]           =     [self._get_bounding_box(_SCREAMING_SNAKE_CASE					) for box in boxes]

              # {"scores": [...], ...} --> [{"score":x, ...}, ...]
              SCREAMING_SNAKE_CASE_				:  str           =     ['score', 'label', 'box']
              SCREAMING_SNAKE_CASE_				:  Optional[int]           =     [
                  dict(zip(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)					)
                  for vals in zip(raw_annotation['scores'] ,					raw_annotation['labels'] ,					raw_annotation['boxes']					)
              ]

        return annotation



  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







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






