from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block




@dataclass
class        __snake_case	(					__lowerCAmelCase    ):
      a__				=		42







class        __snake_case	(					__lowerCAmelCase	,				__lowerCAmelCase    ):



      @register_to_config
      def __init__(						self   ,      lowercase = 6_55_36   ,      lowercase = None   ,      lowercase = 2   ,      lowercase = 2   ,      lowercase = 0   ,      lowercase = "fourier"   ,      lowercase = True   ,      lowercase = False   ,      lowercase = 0.0   ,      lowercase = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")   ,      lowercase = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")   ,      lowercase = "UNetMidBlock1D"   ,      lowercase = None   ,      lowercase = (32, 32, 64)   ,      lowercase = None   ,      lowercase = 8   ,      lowercase = 1   ,      lowercase = False   ,      )     ->      List[Any]:





        '''simple docstring'''





        super().__init__()
        a__:					int      							=     sample_size

        # time
        if time_embedding_type == "fourier":
          a__:					Dict      							=     GaussianFourierProjection(
              embedding_size=8   ,      set_W_to_weight=lowercase   ,      log=lowercase   ,      flip_sin_to_cos=lowercase)
          a__:					List[Any]      							=     2 * block_out_channels[0]
        elif time_embedding_type == "positional":
          a__:					Union[str, Any]      							=     Timesteps(
              block_out_channels[0]   ,      flip_sin_to_cos=lowercase   ,      downscale_freq_shift=lowercase)
          a__:					List[Any]      							=     block_out_channels[0]

        if use_timestep_embedding:
          a__:					List[Any]      							=     block_out_channels[0] * 4
          a__:					List[Any]      							=     TimestepEmbedding(
              in_channels=lowercase   ,      time_embed_dim=lowercase   ,      act_fn=lowercase   ,      out_dim=block_out_channels[0]   ,      )

        a__:					Optional[int]      							=     nn.ModuleList([])
        a__:					Union[str, Any]      							=     None
        a__:					List[Any]      							=     nn.ModuleList([])
        a__:					Dict      							=     None

        # down
        a__:					Tuple      							=     in_channels
        for i, down_block_type in enumerate(lowercase):
          a__:					Optional[Any]      							=     output_channel
          a__:					Optional[int]      							=     block_out_channels[i]

          if i == 0:
            input_channel += extra_in_channels

          a__:					Optional[int]      							=     i == len(lowercase) - 1

          a__:					Any      							=     get_down_block(
              lowercase   ,      num_layers=lowercase   ,      in_channels=lowercase   ,      out_channels=lowercase   ,      temb_channels=block_out_channels[0]   ,      add_downsample=not is_final_block or downsample_each_block   ,      )
          self.down_blocks.append(lowercase)

        # mid
        a__:					int      							=     get_mid_block(
            lowercase   ,      in_channels=block_out_channels[-1]   ,      mid_channels=block_out_channels[-1]   ,      out_channels=block_out_channels[-1]   ,      embed_dim=block_out_channels[0]   ,      num_layers=lowercase   ,      add_downsample=lowercase   ,      )

        # up
        a__:					Tuple      							=     list(reversed(lowercase))
        a__:					Optional[int]      							=     reversed_block_out_channels[0]
        if out_block_type is None:
          a__:					List[Any]      							=     out_channels
        else:
          a__:					Optional[Any]      							=     block_out_channels[0]

        for i, up_block_type in enumerate(lowercase):
          a__:					str      							=     output_channel
          a__:					Union[str, Any]      							=     (
              reversed_block_out_channels[i + 1] if i < len(lowercase) - 1 else final_upsample_channels
          )

          a__:					Any      							=     i == len(lowercase) - 1

          a__:					Tuple      							=     get_up_block(
              lowercase   ,      num_layers=lowercase   ,      in_channels=lowercase   ,      out_channels=lowercase   ,      temb_channels=block_out_channels[0]   ,      add_upsample=not is_final_block   ,      )
          self.up_blocks.append(lowercase)
          a__:					Tuple      							=     output_channel

        # out
        a__:					Union[str, Any]      							=     norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4   ,      32)
        a__:					Union[str, Any]      							=     get_out_block(
            out_block_type=lowercase   ,      num_groups_out=lowercase   ,      embed_dim=block_out_channels[0]   ,      out_channels=lowercase   ,      act_fn=lowercase   ,      fc_dim=block_out_channels[-1] // 4   ,      )



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase   ,      lowercase = True   ,      )     ->      Union[UNetaDOutput, Tuple]:





        '''simple docstring'''





        a__:					Optional[int]      							=     timestep
        if not torch.is_tensor(lowercase):
          a__:					Any      							=     torch.tensor([timesteps]   ,      dtype=torch.long   ,      device=sample.device)
        elif torch.is_tensor(lowercase) and len(timesteps.shape) == 0:
          a__:					Tuple      							=     timesteps[None].to(sample.device)

        a__:					Union[str, Any]      							=     self.time_proj(lowercase)
        if self.config.use_timestep_embedding:
          a__:					Union[str, Any]      							=     self.time_mlp(lowercase)
        else:
          a__:					Dict      							=     timestep_embed[..., None]
          a__:					Any      							=     timestep_embed.repeat([1, 1, sample.shape[2]]).to(sample.dtype)
          a__:					int      							=     timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:]))

        # 2. down
        a__:					Tuple      							=     ()
        for downsample_block in self.down_blocks:
          a__:					Tuple      							=     downsample_block(hidden_states=lowercase   ,      temb=lowercase)
          down_block_res_samples += res_samples

        # 3. mid
        if self.mid_block:
          a__:					str      							=     self.mid_block(lowercase   ,      lowercase)

        # 4. up
        for i, upsample_block in enumerate(self.up_blocks):
          a__:					List[Any]      							=     down_block_res_samples[-1:]
          a__:					Optional[Any]      							=     down_block_res_samples[:-1]
          a__:					List[Any]      							=     upsample_block(lowercase   ,      res_hidden_states_tuple=lowercase   ,      temb=lowercase)

        # 5. post-process
        if self.out_block:
          a__:					Union[str, Any]      							=     self.out_block(lowercase   ,      lowercase)

        if not return_dict:
          return (sample,)

        return UNetaDOutput(sample=lowercase)



"""simple docstring"""
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

lowercase__ 	=     logging.get_logger(__name__)


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




@add_end_docstrings(__lowerCAmelCase    )
class        __snake_case	(					__lowerCAmelCase    ):



      def __init__(						self   ,      *lowercase   ,      **lowercase)     ->      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   lowerCamelCase_       (						self   ,      **lowercase)     ->      int:





        '''simple docstring'''





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



      def __call__(						self   ,      *lowercase   ,      **lowercase)     ->      Union[Predictions, List[Prediction]]:





        '''simple docstring'''





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



      def   lowerCamelCase_       (						self   ,      lowercase)     ->      List[Any]:





        '''simple docstring'''





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



      def   lowerCamelCase_       (						self   ,      lowercase)     ->      int:





        '''simple docstring'''





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



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase=0.9)     ->      Optional[Any]:





        '''simple docstring'''





        a__:					int      							=     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__							,   a__:					str      							=     target_size[0].tolist()

          def unnormalize(lowercase):
            return self._get_bounding_box(
                torch.Tensor(
                    [
                        (width * bbox[0] / 10_00),
                        (height * bbox[1] / 10_00),
                        (width * bbox[2] / 10_00),
                        (height * bbox[3] / 10_00),
                    ]))

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

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

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

        return annotation



      def   lowerCamelCase_       (						self   ,      lowercase)     ->      Dict[str, int]:





        '''simple docstring'''





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


