




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, ChunkPipeline


if is_vision_available():
   from PIL import Image

   from ..image_utils import load_image

if is_torch_available():
   import torch

   from transformers.modeling_outputs import BaseModelOutput

   from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

lowercase__				:      Any	   = logging.get_logger(__name__)




@add_end_docstrings(UpperCamelCase__				)
class       a__	( UpperCamelCase__				):







     def __init__(     self  ,		**A			)  ->   Optional[int]:



          '''simple docstring'''



          super().__init__(**A			)

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

          requires_backends(self  ,		"vision"			)
          self.check_model_type(A			)







     def __call__(     self  ,		A  ,		A = None  ,		**A  ,		)  ->   Dict:



          '''simple docstring'''



          if "text_queries" in kwargs:
               a     =				kwargs.pop("text_queries"			)

          if isinstance(A  ,		(str, Image.Image)			):
               a     =				{"image": image, "candidate_labels": candidate_labels}
          else:
               a     =				image
          a     =				super().__call__(A  ,		**A			)
          return results







     def       lowerCAmelCase_    (     self  ,		**A			)  ->   Optional[int]:



          '''simple docstring'''



          a     =				{}
          if "threshold" in kwargs:
               a     =				kwargs["threshold"]
          if "top_k" in kwargs:
               a     =				kwargs["top_k"]
          return {}, {}, postprocess_params







     def       lowerCAmelCase_    (     self  ,		A			)  ->   str:



          '''simple docstring'''



          a     =				load_image(inputs["image"]			)
          a     =				inputs["candidate_labels"]
          if isinstance(A  ,		A			):
               a     =				candidate_labels.split(","			)

          a     =				torch.tensor([[image.height, image.width]]  ,		dtype=torch.intaa			)
          for i, candidate_label in enumerate(A			):
               a     =				self.tokenizer(A  ,		return_tensors=self.framework			)
               a     =				self.image_processor(A  ,		return_tensors=self.framework			)
               yield {
                   "is_last": i == len(A			) - 1,
                   "target_size": target_size,
                   "candidate_label": candidate_label,
                   **text_inputs,
                   **image_features,
               }







     def       lowerCAmelCase_    (     self  ,		A			)  ->   Tuple:



          '''simple docstring'''



          a     =				model_inputs.pop("target_size"			)
          a     =				model_inputs.pop("candidate_label"			)
          a     =				model_inputs.pop("is_last"			)

          a     =				self.model(**A			)

          a     =				{"target_size": target_size, "candidate_label": candidate_label, "is_last": is_last, **outputs}
          return model_outputs







     def       lowerCAmelCase_    (     self  ,		A  ,		A=0.1  ,		A=None			)  ->   Tuple:



          '''simple docstring'''



          a     =				[]
          for model_output in model_outputs:
               a     =				model_output["candidate_label"]
               a     =				BaseModelOutput(A			)
               a     =				self.image_processor.post_process_object_detection(
                   outputs=A  ,		threshold=A  ,		target_sizes=model_output["target_size"]			)[0]

               for index in outputs["scores"].nonzero():
                    a     =				outputs["scores"][index].item()
                    a     =				self._get_bounding_box(outputs["boxes"][index][0]			)

                    a     =				{"score": score, "label": label, "box": box}
                    results.append(A			)

          a     =				sorted(A  ,		key=lambda A			: x["score"]  ,		reverse=A			)
          if top_k:
               a     =				results[:top_k]

          return results







     def       lowerCAmelCase_    (     self  ,		A			)  ->   Dict[str, int]:



          '''simple docstring'''



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







import functools
import operator

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


lowercase__				:      Any	   = logging.get_logger(__name__)

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




class       a__	( UpperCamelCase__				):
     a		:      Optional[Any]		 =     """sew-d"""







     def __init__(     self  ,		A=32  ,		A=768  ,		A=12  ,		A=12  ,		A=3072  ,		A=2  ,		A=512  ,		A=256  ,		A=True  ,		A=True  ,		A=("p2c", "c2p")  ,		A="layer_norm"  ,		A="gelu_python"  ,		A=0.1  ,		A=0.1  ,		A=0.1  ,		A=0.0  ,		A=0.1  ,		A=0.0_2  ,		A=1e-7  ,		A=1e-5  ,		A="group"  ,		A="gelu"  ,		A=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)  ,		A=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)  ,		A=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)  ,		A=False  ,		A=128  ,		A=16  ,		A=True  ,		A=0.0_5  ,		A=10  ,		A=2  ,		A=0.0  ,		A=10  ,		A=0  ,		A="mean"  ,		A=False  ,		A=False  ,		A=256  ,		A=0  ,		A=1  ,		A=2  ,		**A  ,		)  ->   Dict:



          '''simple docstring'''



          super().__init__(**A  ,		pad_token_id=A  ,		bos_token_id=A  ,		eos_token_id=A			)
          a     =				hidden_size
          a     =				feat_extract_norm
          a     =				feat_extract_activation
          a     =				list(A			)
          a     =				list(A			)
          a     =				list(A			)
          a     =				conv_bias
          a     =				num_conv_pos_embeddings
          a     =				num_conv_pos_embedding_groups
          a     =				len(self.conv_dim			)
          a     =				num_hidden_layers
          a     =				intermediate_size
          a     =				squeeze_factor
          a     =				max_position_embeddings
          a     =				position_buckets
          a     =				share_att_key
          a     =				relative_attention
          a     =				norm_rel_ebd
          a     =				list(A			)
          a     =				hidden_act
          a     =				num_attention_heads
          a     =				hidden_dropout
          a     =				attention_dropout
          a     =				activation_dropout
          a     =				feat_proj_dropout
          a     =				final_dropout
          a     =				layer_norm_eps
          a     =				feature_layer_norm_eps
          a     =				initializer_range
          a     =				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     =				apply_spec_augment
          a     =				mask_time_prob
          a     =				mask_time_length
          a     =				mask_time_min_masks
          a     =				mask_feature_prob
          a     =				mask_feature_length
          a     =				mask_feature_min_masks

          # ctc loss
          a     =				ctc_loss_reduction
          a     =				ctc_zero_infinity

          # sequence classification
          a     =				use_weighted_layer_sum
          a     =				classifier_proj_size







     @property
     def       lowerCAmelCase_    (     self			)  ->   List[str]:



          '''simple docstring'''



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

