from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin





@dataclass
class 					A							(    UpperCAmelCase_					):
   __UpperCAmelCase			:			torch.FloatTensor





class 					A							(    UpperCAmelCase_		,    UpperCAmelCase_					):


   @register_to_config
   def __init__(self					:						Any	,  __UpperCAmelCase					:						int = 1_6	,  __UpperCAmelCase					:						int = 8_8	,  __UpperCAmelCase					:						Optional[int] = None	,  __UpperCAmelCase					:						Optional[int] = None	,  __UpperCAmelCase					:						int = 1	,  __UpperCAmelCase					:						float = 0.0	,  __UpperCAmelCase					:						int = 3_2	,  __UpperCAmelCase					:						Optional[int] = None	,  __UpperCAmelCase					:						bool = False	,  __UpperCAmelCase					:						Optional[int] = None	,  __UpperCAmelCase					:						str = "geglu"	,  __UpperCAmelCase					:						bool = True	,  __UpperCAmelCase					:						bool = True	,  )						->    Dict:
         """simple docstring"""



         super().__init__()
         UpperCAmelCase__								=  num_attention_heads
         UpperCAmelCase__								=  attention_head_dim
         UpperCAmelCase__								=  num_attention_heads * attention_head_dim

         UpperCAmelCase__								=  in_channels

         UpperCAmelCase__								=  torch.nn.GroupNorm(num_groups=__UpperCAmelCase	,  num_channels=__UpperCAmelCase	,  eps=1E-6	,  affine=__UpperCAmelCase		)
         UpperCAmelCase__								=  nn.Linear(__UpperCAmelCase	,  __UpperCAmelCase		)

         # 3. Define transformers blocks
         UpperCAmelCase__								=  nn.ModuleList(
             [
                 BasicTransformerBlock(
                     __UpperCAmelCase	,  __UpperCAmelCase	,  __UpperCAmelCase	,  dropout=__UpperCAmelCase	,  cross_attention_dim=__UpperCAmelCase	,  activation_fn=__UpperCAmelCase	,  attention_bias=__UpperCAmelCase	,  double_self_attention=__UpperCAmelCase	,  norm_elementwise_affine=__UpperCAmelCase	,  )
                 for d in range(__UpperCAmelCase		)
             ]		)

         UpperCAmelCase__								=  nn.Linear(__UpperCAmelCase	,  __UpperCAmelCase		)





   def       lowercase_				(self					:						List[Any]	,  __UpperCAmelCase					:						Optional[int]	,  __UpperCAmelCase					:						Optional[Any]=None	,  __UpperCAmelCase					:						Optional[int]=None	,  __UpperCAmelCase					:						str=None	,  __UpperCAmelCase					:						Dict=1	,  __UpperCAmelCase					:						Dict=None	,  __UpperCAmelCase					:						bool = True	,  )						->    Dict:
         """simple docstring"""



         UpperCAmelCase__		,				UpperCAmelCase__		,				UpperCAmelCase__		,				UpperCAmelCase__								=  hidden_states.shape
         UpperCAmelCase__								=  batch_frames // num_frames

         UpperCAmelCase__								=  hidden_states

         UpperCAmelCase__								=  hidden_states[None, :].reshape(__UpperCAmelCase	,  __UpperCAmelCase	,  __UpperCAmelCase	,  __UpperCAmelCase	,  __UpperCAmelCase		)
         UpperCAmelCase__								=  hidden_states.permute(0	,  2	,  1	,  3	,  4		)

         UpperCAmelCase__								=  self.norm(__UpperCAmelCase		)
         UpperCAmelCase__								=  hidden_states.permute(0	,  3	,  4	,  2	,  1		).reshape(batch_size * height * width	,  __UpperCAmelCase	,  __UpperCAmelCase		)

         UpperCAmelCase__								=  self.proj_in(__UpperCAmelCase		)

         # 2. Blocks
         for block in self.transformer_blocks:
               UpperCAmelCase__								=  block(
                   __UpperCAmelCase	,  encoder_hidden_states=__UpperCAmelCase	,  timestep=__UpperCAmelCase	,  cross_attention_kwargs=__UpperCAmelCase	,  class_labels=__UpperCAmelCase	,  )

         # 3. Output
         UpperCAmelCase__								=  self.proj_out(__UpperCAmelCase		)
         UpperCAmelCase__								=  (
             hidden_states[None, None, :]
             .reshape(__UpperCAmelCase	,  __UpperCAmelCase	,  __UpperCAmelCase	,  __UpperCAmelCase	,  __UpperCAmelCase		)
             .permute(0	,  3	,  4	,  1	,  2		)
             .contiguous()
         )
         UpperCAmelCase__								=  hidden_states.reshape(__UpperCAmelCase	,  __UpperCAmelCase	,  __UpperCAmelCase	,  __UpperCAmelCase		)

         UpperCAmelCase__								=  hidden_states + residual

         if not return_dict:
               return (output,)

         return TransformerTemporalModelOutput(sample=__UpperCAmelCase		)




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

UpperCamelCase__    							= logging.get_logger(__name__)


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





@add_end_docstrings(UpperCAmelCase_					)
class 					A							(    UpperCAmelCase_					):


   def __init__(self					:						Any	,  *__UpperCAmelCase					:						List[str]	,  **__UpperCAmelCase					:						Any		)						->    Union[str, Any]:
         """simple docstring"""



         super().__init__(*__UpperCAmelCase	,  **__UpperCAmelCase		)

         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       lowercase_				(self					:						Optional[Any]	,  **__UpperCAmelCase					:						Dict		)						->    Any:
         """simple docstring"""



         UpperCAmelCase__								=  {}
         if "threshold" in kwargs:
               UpperCAmelCase__								=  kwargs["threshold"]
         return {}, {}, postprocess_kwargs


   def __call__(self					:						Optional[int]	,  *__UpperCAmelCase					:						List[Any]	,  **__UpperCAmelCase					:						Dict		)						->    Union[Predictions, List[Prediction]]:
         """simple docstring"""



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


   def       lowercase_				(self					:						Optional[int]	,  __UpperCAmelCase					:						Any		)						->    Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  load_image(__UpperCAmelCase		)
         UpperCAmelCase__								=  torch.IntTensor([[image.height, image.width]]		)
         UpperCAmelCase__								=  self.image_processor(images=[image]	,  return_tensors="pt"		)
         if self.tokenizer is not None:
               UpperCAmelCase__								=  self.tokenizer(text=inputs["words"]	,  boxes=inputs["boxes"]	,  return_tensors="pt"		)
         UpperCAmelCase__								=  target_size
         return inputs


   def       lowercase_				(self					:						List[str]	,  __UpperCAmelCase					:						str		)						->    Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  model_inputs.pop("target_size"		)
         UpperCAmelCase__								=  self.model(**__UpperCAmelCase		)
         UpperCAmelCase__								=  outputs.__class__({"target_size": target_size, **outputs}		)
         if self.tokenizer is not None:
               UpperCAmelCase__								=  model_inputs["bbox"]
         return model_outputs


   def       lowercase_				(self					:						List[Any]	,  __UpperCAmelCase					:						Any	,  __UpperCAmelCase					:						Dict=0.9		)						->    List[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  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.
               UpperCAmelCase__		,				UpperCAmelCase__								=  target_size[0].tolist()

               def unnormalize(__UpperCAmelCase					:						List[str]		):
                     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),
                             ]		)		)

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

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

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

         return annotation





   def       lowercase_				(self					:						List[str]	,  __UpperCAmelCase					:						"torch.Tensor"		)						->    Dict[str, int]:
         """simple docstring"""



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



