"""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

UpperCAmelCase__     						=  logging.get_logger(__name__)


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


@add_end_docstrings(__lowercase      )
class 				a (						__lowercase      ):


    def __init__(    self		:     List[Any]      , *__lowerCAmelCase		:     str      , **__lowerCAmelCase		:     List[Any]    ):
      super().__init__(*snake_case_      , **snake_case_    )

      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		:     List[str]      , **__lowerCAmelCase		:     Dict    ):
      _UpperCAmelCase         =					{}
      if "threshold" in kwargs:
        _UpperCAmelCase         =					kwargs['''threshold''']
      return {}, {}, postprocess_kwargs


    def __call__(    self		:     Union[str, Any]      , *__lowerCAmelCase		:     Tuple      , **__lowerCAmelCase		:     Dict    ):
      return super().__call__(*snake_case_      , **snake_case_    )


    def    lowerCAmelCase_     (    self		:     Dict      , __lowerCAmelCase		:     List[str]    ):
      _UpperCAmelCase         =					load_image(snake_case_    )
      _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    lowerCAmelCase_     (    self		:     int      , __lowerCAmelCase		:     List[str]    ):
      _UpperCAmelCase         =					model_inputs.pop("""target_size"""    )
      _UpperCAmelCase         =					self.model(**snake_case_    )
      _UpperCAmelCase         =					outputs.__class__({"""target_size""": target_size, **outputs}    )
      if self.tokenizer is not None:
        _UpperCAmelCase         =					model_inputs['''bbox''']
      return model_outputs


    def    lowerCAmelCase_     (    self		:     Any      , __lowerCAmelCase		:     int      , __lowerCAmelCase		:     Tuple=0.9    ):
      _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         =					target_size[0].tolist()

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

        _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(snake_case_    ) for bbox in model_outputs['''bbox'''].squeeze(0    )]
        _UpperCAmelCase         =					['''score''', '''label''', '''box''']
        _UpperCAmelCase         =					[dict(zip(snake_case_      , snake_case_    )    ) for vals in zip(scores.tolist()      , snake_case_      , snake_case_    ) if vals[0] > threshold]
      else:
        # This is a regular ForObjectDetectionModel
        _UpperCAmelCase         =					self.image_processor.post_process_object_detection(snake_case_      , snake_case_      , snake_case_    )
        _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(snake_case_    ) for box in boxes]

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

      return annotation


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


import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
      import jax
      import jax.numpy as jnp
      from flax.jax_utils import replicate
      from flax.training.common_utils import shard



@slow
@require_flax
class 		UpperCamelCase__  (       unittest.TestCase    ):
   def       lowerCAmelCase			(self :					Optional[Any]   ):
    # clean up the VRAM after each test
    super().tearDown()
    gc.collect()
   def       lowerCAmelCase			(self :					int   ):
    __a				,       __a :  Optional[Any]						=  FlaxStableDiffusionPipeline.from_pretrained(
        '''stabilityai/stable-diffusion-2'''					,				revision='''bf16'''					,				dtype=jnp.bfloataa					,				)

    __a :  Any						=  '''A painting of a squirrel eating a burger'''
    __a :  Dict						=  jax.device_count()
    __a :  Optional[int]						=  num_samples * [prompt]
    __a :  Optional[Any]						=  sd_pipe.prepare_inputs(snake_case_   )

    __a :  Optional[Any]						=  replicate(snake_case_   )
    __a :  Optional[int]						=  shard(snake_case_   )

    __a :  int						=  jax.random.PRNGKey(0   )
    __a :  str						=  jax.random.split(snake_case_					,				jax.device_count()   )

    __a :  int						=  sd_pipe(snake_case_					,				snake_case_					,				snake_case_					,				num_inference_steps=2_5					,				jit=snake_case_   )[0]
    assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

    __a :  Optional[int]						=  images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]   )
    __a :  List[Any]						=  images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

    __a :  Dict						=  jnp.asarray(jax.device_get(image_slice.flatten()   )   )
    __a :  str						=  jnp.array([0.4238, 0.4414, 0.4395, 0.4453, 0.4629, 0.4590, 0.4531, 0.4_5508, 0.4512]   )
    print(f"output_slice: {output_slice}"   )
    assert jnp.abs(output_slice - expected_slice   ).max() < 1E-2







   def       lowerCAmelCase			(self :					Tuple   ):
    __a :  Optional[Any]						=  '''stabilityai/stable-diffusion-2'''
    __a				,       __a :  Dict						=  FlaxDPMSolverMultistepScheduler.from_pretrained(snake_case_					,				subfolder='''scheduler'''   )
    __a				,       __a :  List[Any]						=  FlaxStableDiffusionPipeline.from_pretrained(
        snake_case_					,				scheduler=snake_case_					,				revision='''bf16'''					,				dtype=jnp.bfloataa					,				)
    __a :  Union[str, Any]						=  scheduler_params

    __a :  List[Any]						=  '''A painting of a squirrel eating a burger'''
    __a :  Any						=  jax.device_count()
    __a :  Any						=  num_samples * [prompt]
    __a :  List[Any]						=  sd_pipe.prepare_inputs(snake_case_   )

    __a :  Tuple						=  replicate(snake_case_   )
    __a :  Dict						=  shard(snake_case_   )

    __a :  Dict						=  jax.random.PRNGKey(0   )
    __a :  Dict						=  jax.random.split(snake_case_					,				jax.device_count()   )

    __a :  str						=  sd_pipe(snake_case_					,				snake_case_					,				snake_case_					,				num_inference_steps=2_5					,				jit=snake_case_   )[0]
    assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

    __a :  List[Any]						=  images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]   )
    __a :  Any						=  images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

    __a :  List[Any]						=  jnp.asarray(jax.device_get(image_slice.flatten()   )   )
    __a :  List[Any]						=  jnp.array([0.4336, 0.4_2969, 0.4453, 0.4199, 0.4297, 0.4531, 0.4434, 0.4434, 0.4297]   )
    print(f"output_slice: {output_slice}"   )
    assert jnp.abs(output_slice - expected_slice   ).max() < 1E-2
