

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

snake_case_        =			logging.get_logger(__name__)


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


@add_end_docstrings(__snake_case					)
class 				SCREAMING_SNAKE_CASE__ (__snake_case					):





    def __init__(				self						,       *a						,       **a):
         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 				snake_case_    (				self						,       **a):
         lowercase__   :       Union[str, Any]				=	{}
         if "threshold" in kwargs:
              lowercase__   :       str				=	kwargs['threshold']
         return {}, {}, postprocess_kwargs





    def __call__(				self						,       *a						,       **a):
         return super().__call__(*a						,       **a)





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





    def 				snake_case_    (				self						,       a):
         lowercase__   :       Dict				=	model_inputs.pop('target_size')
         lowercase__   :       str				=	self.model(**a)
         lowercase__   :       Optional[Any]				=	outputs.__class__({'target_size': target_size, **outputs})
         if self.tokenizer is not None:
              lowercase__   :       Any				=	model_inputs['bbox']
         return model_outputs





    def 				snake_case_    (				self						,       a						,       a=0.9):
         lowercase__   :       str				=	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.
              lowercase__				,			lowercase__   :       Any				=	target_size[0].tolist()

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

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

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

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

         return annotation





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




from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case_        =			logging.get_logger(__name__)

snake_case_        =			{
    '''facebook/s2t-wav2vec2-large-en-de''': (
        '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json'''
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}


class 				SCREAMING_SNAKE_CASE__ (__snake_case					):
    __lowerCamelCase						:	List[Any]														=					"""speech_to_text_2"""
    __lowerCamelCase						:	str														=					["""past_key_values"""]
    __lowerCamelCase						:	List[Any]														=					{"""num_attention_heads""": """decoder_attention_heads""", """hidden_size""": """d_model"""}





    def __init__(				self						,       a=1_0000						,       a=6						,       a=2048						,       a=4						,       a=0.0						,       a=True						,       a="relu"						,       a=256						,       a=0.1						,       a=0.0						,       a=0.0						,       a=0.02						,       a=2						,       a=True						,       a=1						,       a=0						,       a=2						,       a=1024						,       **a						,       ):
         lowercase__   :       Optional[int]				=	vocab_size
         lowercase__   :       List[str]				=	d_model
         lowercase__   :       int				=	decoder_ffn_dim
         lowercase__   :       Optional[Any]				=	decoder_layers
         lowercase__   :       int				=	decoder_attention_heads
         lowercase__   :       Dict				=	dropout
         lowercase__   :       Optional[int]				=	attention_dropout
         lowercase__   :       Optional[int]				=	activation_dropout
         lowercase__   :       Optional[int]				=	activation_function
         lowercase__   :       Dict				=	init_std
         lowercase__   :       List[Any]				=	decoder_layerdrop
         lowercase__   :       int				=	use_cache
         lowercase__   :       Any				=	decoder_layers
         lowercase__   :       Optional[int]				=	scale_embedding  # scale factor will be sqrt(d_model) if True
         lowercase__   :       Tuple				=	max_target_positions

         super().__init__(
             pad_token_id=a						,       bos_token_id=a						,       eos_token_id=a						,       decoder_start_token_id=a						,       **a						,       )

