



import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest

class    _A	(     __UpperCAmelCase   ):
       UpperCamelCase__  :   Tuple						 =						(UniPCMultistepScheduler,)
       UpperCamelCase__  :   Optional[Any]						 =						(('''num_inference_steps''', 25),)



       def 		_lowerCamelCase      (		self							:					Optional[Any]					,		**__SCREAMING_SNAKE_CASE							:					Union[str, Any]):


           '''simple docstring'''

           __a							=    {
               '''num_train_timesteps''': 1_000,
               '''beta_start''': 0.00_01,
               '''beta_end''': 0.02,
               '''beta_schedule''': '''linear''',
               '''solver_order''': 2,
               '''solver_type''': '''bh2''',
           }

           config.update(**__SCREAMING_SNAKE_CASE)
           return config



       def 		_lowerCamelCase      (		self							:					str					,		__SCREAMING_SNAKE_CASE							:					List[Any]=0					,		**__SCREAMING_SNAKE_CASE							:					Any):


           '''simple docstring'''

           __a							=    dict(self.forward_default_kwargs)
           __a							=    kwargs.pop('''num_inference_steps'''					,		__SCREAMING_SNAKE_CASE)
           __a							=    self.dummy_sample
           __a							=    0.1 * sample
           __a							=    [residual + 0.2, residual + 0.15, residual + 0.10]

           for scheduler_class in self.scheduler_classes:
               __a							=    self.get_scheduler_config(**__SCREAMING_SNAKE_CASE)
               __a							=    scheduler_class(**__SCREAMING_SNAKE_CASE)
               scheduler.set_timesteps(__SCREAMING_SNAKE_CASE)
               # copy over dummy past residuals
               __a							=    dummy_past_residuals[: scheduler.config.solver_order]

               with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(__SCREAMING_SNAKE_CASE)
                   __a							=    scheduler_class.from_pretrained(__SCREAMING_SNAKE_CASE)
                   new_scheduler.set_timesteps(__SCREAMING_SNAKE_CASE)
                   # copy over dummy past residuals
                   __a							=    dummy_past_residuals[: new_scheduler.config.solver_order]

               __a	,       __a							=    sample, sample
               for t in range(__SCREAMING_SNAKE_CASE					,		time_step + scheduler.config.solver_order + 1):
                   __a							=    scheduler.step(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE).prev_sample
                   __a							=    new_scheduler.step(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE).prev_sample

                   assert torch.sum(torch.abs(output - new_output)) < 1E-5, "Scheduler outputs are not identical"



       def 		_lowerCamelCase      (		self							:					int					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]=0					,		**__SCREAMING_SNAKE_CASE							:					Any):


           '''simple docstring'''

           __a							=    dict(self.forward_default_kwargs)
           __a							=    kwargs.pop('''num_inference_steps'''					,		__SCREAMING_SNAKE_CASE)
           __a							=    self.dummy_sample
           __a							=    0.1 * sample
           __a							=    [residual + 0.2, residual + 0.15, residual + 0.10]

           for scheduler_class in self.scheduler_classes:
               __a							=    self.get_scheduler_config()
               __a							=    scheduler_class(**__SCREAMING_SNAKE_CASE)
               scheduler.set_timesteps(__SCREAMING_SNAKE_CASE)

               # copy over dummy past residuals (must be after setting timesteps)
               __a							=    dummy_past_residuals[: scheduler.config.solver_order]

               with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(__SCREAMING_SNAKE_CASE)
                   __a							=    scheduler_class.from_pretrained(__SCREAMING_SNAKE_CASE)
                   # copy over dummy past residuals
                   new_scheduler.set_timesteps(__SCREAMING_SNAKE_CASE)

                   # copy over dummy past residual (must be after setting timesteps)
                   __a							=    dummy_past_residuals[: new_scheduler.config.solver_order]

               __a							=    scheduler.step(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE).prev_sample
               __a							=    new_scheduler.step(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE).prev_sample

               assert torch.sum(torch.abs(output - new_output)) < 1E-5, "Scheduler outputs are not identical"



       def 		_lowerCamelCase      (		self							:					Any					,		__SCREAMING_SNAKE_CASE							:					Any=None					,		**__SCREAMING_SNAKE_CASE							:					Tuple):


           '''simple docstring'''

           if scheduler is None:
               __a							=    self.scheduler_classes[0]
               __a							=    self.get_scheduler_config(**__SCREAMING_SNAKE_CASE)
               __a							=    scheduler_class(**__SCREAMING_SNAKE_CASE)

           __a							=    self.scheduler_classes[0]
           __a							=    self.get_scheduler_config(**__SCREAMING_SNAKE_CASE)
           __a							=    scheduler_class(**__SCREAMING_SNAKE_CASE)

           __a							=    10
           __a							=    self.dummy_model()
           __a							=    self.dummy_sample_deter
           scheduler.set_timesteps(__SCREAMING_SNAKE_CASE)

           for i, t in enumerate(scheduler.timesteps):
               __a							=    model(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)
               __a							=    scheduler.step(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE).prev_sample

           return sample



       def 		_lowerCamelCase      (		self							:					int):


           '''simple docstring'''

           __a							=    dict(self.forward_default_kwargs)

           __a							=    kwargs.pop('''num_inference_steps'''					,		__SCREAMING_SNAKE_CASE)

           for scheduler_class in self.scheduler_classes:
               __a							=    self.get_scheduler_config()
               __a							=    scheduler_class(**__SCREAMING_SNAKE_CASE)

               __a							=    self.dummy_sample
               __a							=    0.1 * sample

               if num_inference_steps is not None and hasattr(__SCREAMING_SNAKE_CASE					,		'''set_timesteps'''):
                   scheduler.set_timesteps(__SCREAMING_SNAKE_CASE)
               elif num_inference_steps is not None and not hasattr(__SCREAMING_SNAKE_CASE					,		'''set_timesteps'''):
                   __a							=    num_inference_steps

               # copy over dummy past residuals (must be done after set_timesteps)
               __a							=    [residual + 0.2, residual + 0.15, residual + 0.10]
               __a							=    dummy_past_residuals[: scheduler.config.solver_order]

               __a							=    scheduler.timesteps[5]
               __a							=    scheduler.timesteps[6]

               __a							=    scheduler.step(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE).prev_sample
               __a							=    scheduler.step(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE).prev_sample

               self.assertEqual(output_a.shape					,		sample.shape)
               self.assertEqual(output_a.shape					,		output_a.shape)



       def 		_lowerCamelCase      (		self							:					Dict):


           '''simple docstring'''

           __a							=    UniPCMultistepScheduler(**self.get_scheduler_config())
           __a							=    self.full_loop(scheduler=__SCREAMING_SNAKE_CASE)
           __a							=    torch.mean(torch.abs(__SCREAMING_SNAKE_CASE))

           assert abs(result_mean.item() - 0.24_64) < 1E-3

           __a							=    DPMSolverSinglestepScheduler.from_config(scheduler.config)
           __a							=    DEISMultistepScheduler.from_config(scheduler.config)
           __a							=    DPMSolverMultistepScheduler.from_config(scheduler.config)
           __a							=    UniPCMultistepScheduler.from_config(scheduler.config)

           __a							=    self.full_loop(scheduler=__SCREAMING_SNAKE_CASE)
           __a							=    torch.mean(torch.abs(__SCREAMING_SNAKE_CASE))

           assert abs(result_mean.item() - 0.24_64) < 1E-3



       def 		_lowerCamelCase      (		self							:					Any):


           '''simple docstring'''

           for timesteps in [25, 50, 100, 999, 1_000]:
               self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					List[Any]):


           '''simple docstring'''

           self.check_over_configs(thresholding=__SCREAMING_SNAKE_CASE)
           for order in [1, 2, 3]:
               for solver_type in ["bh1", "bh2"]:
                   for threshold in [0.5, 1.0, 2.0]:
                       for prediction_type in ["epsilon", "sample"]:
                           self.check_over_configs(
                               thresholding=__SCREAMING_SNAKE_CASE					,		prediction_type=__SCREAMING_SNAKE_CASE					,		sample_max_value=__SCREAMING_SNAKE_CASE					,		solver_order=__SCREAMING_SNAKE_CASE					,		solver_type=__SCREAMING_SNAKE_CASE					,		)



       def 		_lowerCamelCase      (		self							:					Dict):


           '''simple docstring'''

           for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(prediction_type=__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					int):


           '''simple docstring'''

           for solver_type in ["bh1", "bh2"]:
               for order in [1, 2, 3]:
                   for prediction_type in ["epsilon", "sample"]:
                       self.check_over_configs(
                           solver_order=__SCREAMING_SNAKE_CASE					,		solver_type=__SCREAMING_SNAKE_CASE					,		prediction_type=__SCREAMING_SNAKE_CASE					,		)
                       __a							=    self.full_loop(
                           solver_order=__SCREAMING_SNAKE_CASE					,		solver_type=__SCREAMING_SNAKE_CASE					,		prediction_type=__SCREAMING_SNAKE_CASE					,		)
                       assert not torch.isnan(__SCREAMING_SNAKE_CASE).any(), "Samples have nan numbers"



       def 		_lowerCamelCase      (		self							:					int):


           '''simple docstring'''

           self.check_over_configs(lower_order_final=__SCREAMING_SNAKE_CASE)
           self.check_over_configs(lower_order_final=__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Union[str, Any]):


           '''simple docstring'''

           for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
               self.check_over_forward(num_inference_steps=__SCREAMING_SNAKE_CASE					,		time_step=0)



       def 		_lowerCamelCase      (		self							:					Optional[Any]):


           '''simple docstring'''

           __a							=    self.full_loop()
           __a							=    torch.mean(torch.abs(__SCREAMING_SNAKE_CASE))

           assert abs(result_mean.item() - 0.24_64) < 1E-3



       def 		_lowerCamelCase      (		self							:					Optional[int]):


           '''simple docstring'''

           __a							=    self.full_loop(prediction_type='''v_prediction''')
           __a							=    torch.mean(torch.abs(__SCREAMING_SNAKE_CASE))

           assert abs(result_mean.item() - 0.10_14) < 1E-3



       def 		_lowerCamelCase      (		self							:					Optional[int]):


           '''simple docstring'''

           __a							=    self.scheduler_classes[0]
           __a							=    self.get_scheduler_config(thresholding=__SCREAMING_SNAKE_CASE					,		dynamic_thresholding_ratio=0)
           __a							=    scheduler_class(**__SCREAMING_SNAKE_CASE)

           __a							=    10
           __a							=    self.dummy_model()
           __a							=    self.dummy_sample_deter.half()
           scheduler.set_timesteps(__SCREAMING_SNAKE_CASE)

           for i, t in enumerate(scheduler.timesteps):
               __a							=    model(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)
               __a							=    scheduler.step(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE).prev_sample

           assert sample.dtype == torch.floataa





       def 		_lowerCamelCase      (		self							:					List[Any]					,		**__SCREAMING_SNAKE_CASE							:					int):


           '''simple docstring'''

           for scheduler_class in self.scheduler_classes:
               __a							=    self.get_scheduler_config(**__SCREAMING_SNAKE_CASE)
               __a							=    scheduler_class(**__SCREAMING_SNAKE_CASE)

               scheduler.set_timesteps(scheduler.config.num_train_timesteps)
               assert len(scheduler.timesteps.unique()) == scheduler.num_inference_steps








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

__snake_case						:Tuple						=					logging.get_logger(__name__)

@add_end_docstrings(__UpperCAmelCase   )
class    _A	(     __UpperCAmelCase   ):



       def __init__(		self							:					Optional[int]					,		**__SCREAMING_SNAKE_CASE							:					Optional[Any]):


           '''simple docstring'''

           super().__init__(**__SCREAMING_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(__SCREAMING_SNAKE_CASE)



       def __call__(		self							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					Union[str, "Image.Image", List[Dict[str, Any]]]					,		__SCREAMING_SNAKE_CASE							:					Union[str, List[str]] = None					,		**__SCREAMING_SNAKE_CASE							:					Union[str, Any]					,		):


           '''simple docstring'''

           if "text_queries" in kwargs:
               __a							=    kwargs.pop('''text_queries''')

           if isinstance(__SCREAMING_SNAKE_CASE					,		(str, Image.Image)):
               __a							=    {'''image''': image, '''candidate_labels''': candidate_labels}
           else:
               __a							=    image
           __a							=    super().__call__(__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE)
           return results



       def 		_lowerCamelCase      (		self							:					str					,		**__SCREAMING_SNAKE_CASE							:					Union[str, Any]):


           '''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							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					Tuple):


           '''simple docstring'''

           __a							=    load_image(inputs['''image'''])
           __a							=    inputs['''candidate_labels''']
           if isinstance(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE):
               __a							=    candidate_labels.split(''',''')

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



       def 		_lowerCamelCase      (		self							:					int					,		__SCREAMING_SNAKE_CASE							:					Tuple):


           '''simple docstring'''

           __a							=    model_inputs.pop('''target_size''')
           __a							=    model_inputs.pop('''candidate_label''')
           __a							=    model_inputs.pop('''is_last''')

           __a							=    self.model(**__SCREAMING_SNAKE_CASE)

           __a							=    {'''target_size''': target_size, '''candidate_label''': candidate_label, '''is_last''': is_last, **outputs}
           return model_outputs



       def 		_lowerCamelCase      (		self							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					Tuple=0.1					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]=None):


           '''simple docstring'''

           __a							=    []
           for model_output in model_outputs:
               __a							=    model_output['''candidate_label''']
               __a							=    BaseModelOutput(__SCREAMING_SNAKE_CASE)
               __a							=    self.image_processor.post_process_object_detection(
                   outputs=__SCREAMING_SNAKE_CASE					,		threshold=__SCREAMING_SNAKE_CASE					,		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(__SCREAMING_SNAKE_CASE)

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

           return results





       def 		_lowerCamelCase      (		self							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					"torch.Tensor"):


           '''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





