


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(_UpperCAmelCase)
class 					lowerCAmelCase_ (       _UpperCAmelCase):




						def __init__(	self						:   int   ,     *__A						:   Union[str, Any]   ,     **__A						:   List[Any]						)      ->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  _snake_case					(	self						:   Dict   ,     **__A						:   Optional[Any]						)      ->List[str]:






									"""simple docstring"""






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




						def __call__(	self						:   Any   ,     *__A						:   Optional[Any]   ,     **__A						:   Union[str, Any]						)      ->List[Any]:






									"""simple docstring"""






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




						def  _snake_case					(	self						:   str   ,     __A						:   str						)      ->Union[str, Any]:






									"""simple docstring"""






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




						def  _snake_case					(	self						:   Union[str, Any]   ,     __A						:   Optional[int]						)      ->Dict:






									"""simple docstring"""






									a__ :Dict						  =    model_inputs.pop("target_size"						)
									a__ :int						  =    self.model(**lowercase__						)
									a__ :Dict						  =    outputs.__class__({"target_size": target_size, **outputs}						)
									if self.tokenizer is not None:
												a__ :str						  =    model_inputs["bbox"]
									return model_outputs




						def  _snake_case					(	self						:   Dict   ,     __A						:   Optional[int]   ,     __A						:   List[Any]=0.9						)      ->Optional[int]:






									"""simple docstring"""






									a__ :Tuple						  =    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__ :Union[str, Any]						  =    target_size[0].tolist()

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

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

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

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

									return annotation






						def  _snake_case					(	self						:   int   ,     __A						:   str						)      ->Tuple:






									"""simple docstring"""






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






import functools
import operator

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


snake_case__			 =						logging.get_logger(__name__)

snake_case__			 =						{
    '''asapp/sew-tiny-100k''': '''https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json''',
    # See all SEW models at https://huggingface.co/models?filter=sew
}


class 					lowerCAmelCase_ (       _a):
						lowerCamelCase_         =     'sew'




						def __init__(	self						:   Any   ,     __A						:   str=32   ,     __A						:   Dict=768   ,     __A						:   int=12   ,     __A						:   Dict=12   ,     __A						:   Dict=3072   ,     __A						:   int=2   ,     __A						:   Union[str, Any]="gelu"   ,     __A						:   Union[str, Any]=0.1   ,     __A						:   Optional[Any]=0.1   ,     __A						:   Union[str, Any]=0.1   ,     __A						:   str=0.0   ,     __A						:   Union[str, Any]=0.1   ,     __A						:   Optional[Any]=0.1   ,     __A						:   Tuple=0.02   ,     __A						:   Any=1E-5   ,     __A						:   Optional[Any]="group"   ,     __A						:   str="gelu"   ,     __A						:   Dict=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)   ,     __A						:   Tuple=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)   ,     __A						:   int=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)   ,     __A						:   List[str]=False   ,     __A						:   Tuple=128   ,     __A						:   Tuple=16   ,     __A						:   Optional[int]=True   ,     __A						:   Union[str, Any]=0.05   ,     __A						:   List[str]=10   ,     __A						:   Optional[int]=2   ,     __A						:   List[Any]=0.0   ,     __A						:   Optional[Any]=10   ,     __A						:   Tuple=0   ,     __A						:   Tuple="mean"   ,     __A						:   Any=False   ,     __A						:   str=False   ,     __A						:   Dict=256   ,     __A						:   Union[str, Any]=0   ,     __A						:   Optional[int]=1   ,     __A						:   Optional[Any]=2   ,     **__A						:   Tuple   ,     )      ->Tuple:






									"""simple docstring"""






									super().__init__(**__A   ,     pad_token_id=__A   ,     bos_token_id=__A   ,     eos_token_id=__A						)
									a__ :List[Any]						  =    hidden_size
									a__ :List[Any]						  =    feat_extract_norm
									a__ :List[str]						  =    feat_extract_activation
									a__ :Any						  =    list(__A						)
									a__ :Dict						  =    list(__A						)
									a__ :Optional[int]						  =    list(__A						)
									a__ :Any						  =    conv_bias
									a__ :List[str]						  =    num_conv_pos_embeddings
									a__ :str						  =    num_conv_pos_embedding_groups
									a__ :Optional[int]						  =    len(self.conv_dim						)
									a__ :List[Any]						  =    num_hidden_layers
									a__ :str						  =    intermediate_size
									a__ :Dict						  =    squeeze_factor
									a__ :List[Any]						  =    hidden_act
									a__ :Optional[Any]						  =    num_attention_heads
									a__ :Tuple						  =    hidden_dropout
									a__ :Tuple						  =    attention_dropout
									a__ :List[Any]						  =    activation_dropout
									a__ :str						  =    feat_proj_dropout
									a__ :Any						  =    final_dropout
									a__ :Dict						  =    layerdrop
									a__ :List[str]						  =    layer_norm_eps
									a__ :Tuple						  =    initializer_range
									a__ :Dict						  =    vocab_size

									if (
									    (len(self.conv_stride						) != self.num_feat_extract_layers)
									    or (len(self.conv_kernel						) != self.num_feat_extract_layers)
									    or (len(self.conv_dim						) != self.num_feat_extract_layers)
									):
												raise ValueError(
												    "Configuration for convolutional layers is incorrect."
												    "It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"
												    F'''but is `len(config.conv_dim) = {len(self.conv_dim						)}`, `len(config.conv_stride)'''
												    F'''= {len(self.conv_stride						)}`, `len(config.conv_kernel) = {len(self.conv_kernel						)}`.'''						)

									# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
									a__ :int						  =    apply_spec_augment
									a__ :Optional[Any]						  =    mask_time_prob
									a__ :List[Any]						  =    mask_time_length
									a__ :Any						  =    mask_time_min_masks
									a__ :Any						  =    mask_feature_prob
									a__ :Dict						  =    mask_feature_length
									a__ :Union[str, Any]						  =    mask_feature_min_masks

									# ctc loss
									a__ :Dict						  =    ctc_loss_reduction
									a__ :Any						  =    ctc_zero_infinity

									# sequence classification
									a__ :Optional[int]						  =    use_weighted_layer_sum
									a__ :Tuple						  =    classifier_proj_size






						@property
						def  _snake_case					(	self						:   Dict						)      ->Union[str, Any]:






									"""simple docstring"""






									return functools.reduce(operator.mul   ,     self.conv_stride   ,     1						)


