





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

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin



@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class    A__  (			__snake_case     ):
      _UpperCAmelCase      :torch.FloatTensor
      _UpperCAmelCase      :Optional[torch.FloatTensor]		       =  None



def 						A_							(	_lowerCAmelCase				,  _lowerCAmelCase=0.999				,  _lowerCAmelCase="cosine"				,  )		->					str:
 if alpha_transform_type == "cosine":

  def alpha_bar_fn(_lowerCAmelCase	):
   return math.cos((t + 0.008) / 1.008 * math.pi / 2	) ** 2

 elif alpha_transform_type == "exp":

  def alpha_bar_fn(_lowerCAmelCase	):
   return math.exp(t * -12.0	)

 else:
  raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}"""	)

 UpperCamelCase      :   Any			      =  []
 for i in range(_lowerCAmelCase	):
  UpperCamelCase      :   str			      =  i / num_diffusion_timesteps
  UpperCamelCase      :   List[Any]			      =  (i + 1) / num_diffusion_timesteps
  betas.append(min(1 - alpha_bar_fn(_lowerCAmelCase	) / alpha_bar_fn(_lowerCAmelCase	)				,  _lowerCAmelCase	)	)
 return torch.tensor(_lowerCAmelCase				,  dtype=torch.floataa	)



class    A__  (			__snake_case			,  __snake_case     ):





      @register_to_config
      def __init__(					self			,				A_ = 1000			,				A_ = "fixed_small_log"			,				A_ = True			,				A_ = 1.0			,				A_ = "epsilon"			,				A_ = "squaredcos_cap_v2"			,				):
       '''simple docstring'''




       if beta_schedule != "squaredcos_cap_v2":
        raise ValueError("UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'"  )

       UpperCamelCase      :   str			      =  betas_for_alpha_bar(A_  )

       UpperCamelCase      :   Optional[Any]			      =  1.0 - self.betas
       UpperCamelCase      :   List[str]			      =  torch.cumprod(self.alphas			,				dim=0  )
       UpperCamelCase      :   List[str]			      =  torch.tensor(1.0  )

       # standard deviation of the initial noise distribution
       UpperCamelCase      :   str			      =  1.0

       # setable values
       UpperCamelCase      :   Optional[int]			      =  None
       UpperCamelCase      :   Any			      =  torch.from_numpy(np.arange(0			,				A_  )[::-1].copy()  )

       UpperCamelCase      :   List[str]			      =  variance_type





      def 			__UpperCamelCase(					self			,				A_			,				A_ = None  ):
       '''simple docstring'''




       return sample





      def 			__UpperCamelCase(					self			,				A_			,				A_ = None  ):
       '''simple docstring'''




       UpperCamelCase      :   Union[str, Any]			      =  num_inference_steps
       UpperCamelCase      :   str			      =  (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
       UpperCamelCase      :   Any			      =  (np.arange(0			,				A_  ) * step_ratio).round()[::-1].copy().astype(np.intaa  )
       UpperCamelCase      :   Any			      =  torch.from_numpy(A_  ).to(A_  )





      def 			__UpperCamelCase(					self			,				A_			,				A_=None			,				A_=None			,				A_=None  ):
       '''simple docstring'''




       if prev_timestep is None:
        UpperCamelCase      :   int			      =  t - 1

       UpperCamelCase      :   List[Any]			      =  self.alphas_cumprod[t]
       UpperCamelCase      :   Dict			      =  self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
       UpperCamelCase      :   Optional[Any]			      =  1 - alpha_prod_t
       UpperCamelCase      :   List[str]			      =  1 - alpha_prod_t_prev

       if prev_timestep == t - 1:
        UpperCamelCase      :   str			      =  self.betas[t]
       else:
        UpperCamelCase      :   Optional[int]			      =  1 - alpha_prod_t / alpha_prod_t_prev

       # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
       # and sample from it to get previous sample
       # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
       UpperCamelCase      :   str			      =  beta_prod_t_prev / beta_prod_t * beta

       if variance_type is None:
        UpperCamelCase      :   Tuple			      =  self.config.variance_type

       # hacks - were probably added for training stability
       if variance_type == "fixed_small_log":
        UpperCamelCase      :   Optional[int]			      =  torch.log(torch.clamp(A_			,				min=1e-20  )  )
        UpperCamelCase      :   Tuple			      =  torch.exp(0.5 * variance  )
       elif variance_type == "learned_range":
        # NOTE difference with DDPM scheduler
        UpperCamelCase      :   List[Any]			      =  variance.log()
        UpperCamelCase      :   str			      =  beta.log()

        UpperCamelCase      :   List[Any]			      =  (predicted_variance + 1) / 2
        UpperCamelCase      :   Optional[Any]			      =  frac * max_log + (1 - frac) * min_log

       return variance





      def 			__UpperCamelCase(					self			,				A_			,				A_			,				A_			,				A_ = None			,				A_=None			,				A_ = True			,				):
       '''simple docstring'''




       UpperCamelCase      :   Optional[Any]			      =  timestep

       if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
        UpperCamelCase      ,       UpperCamelCase      :   List[str]			      =  torch.split(A_			,				sample.shape[1]			,				dim=1  )
       else:
        UpperCamelCase      :   Union[str, Any]			      =  None

       # 1. compute alphas, betas
       if prev_timestep is None:
        UpperCamelCase      :   List[Any]			      =  t - 1

       UpperCamelCase      :   Optional[int]			      =  self.alphas_cumprod[t]
       UpperCamelCase      :   List[Any]			      =  self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
       UpperCamelCase      :   str			      =  1 - alpha_prod_t
       UpperCamelCase      :   Tuple			      =  1 - alpha_prod_t_prev

       if prev_timestep == t - 1:
        UpperCamelCase      :   Dict			      =  self.betas[t]
        UpperCamelCase      :   Tuple			      =  self.alphas[t]
       else:
        UpperCamelCase      :   Dict			      =  1 - alpha_prod_t / alpha_prod_t_prev
        UpperCamelCase      :   Dict			      =  1 - beta

       # 2. compute predicted original sample from predicted noise also called
       # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
       if self.config.prediction_type == "epsilon":
        UpperCamelCase      :   Optional[int]			      =  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
       elif self.config.prediction_type == "sample":
        UpperCamelCase      :   List[Any]			      =  model_output
       else:
        raise ValueError(
            F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"""
            " for the UnCLIPScheduler."  )

       # 3. Clip "predicted x_0"
       if self.config.clip_sample:
        UpperCamelCase      :   List[str]			      =  torch.clamp(
            A_			,				-self.config.clip_sample_range			,				self.config.clip_sample_range  )

       # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
       # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
       UpperCamelCase      :   Tuple			      =  (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
       UpperCamelCase      :   Optional[Any]			      =  alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

       # 5. Compute predicted previous sample µ_t
       # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
       UpperCamelCase      :   Dict			      =  pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

       # 6. Add noise
       UpperCamelCase      :   Any			      =  0
       if t > 0:
        UpperCamelCase      :   List[Any]			      =  randn_tensor(
            model_output.shape			,				dtype=model_output.dtype			,				generator=A_			,				device=model_output.device  )

        UpperCamelCase      :   Union[str, Any]			      =  self._get_variance(
            A_			,				predicted_variance=A_			,				prev_timestep=A_			,				)

        if self.variance_type == "fixed_small_log":
         UpperCamelCase      :   int			      =  variance
        elif self.variance_type == "learned_range":
         UpperCamelCase      :   int			      =  (0.5 * variance).exp()
        else:
         raise ValueError(
             F"""variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"""
             " for the UnCLIPScheduler."  )

        UpperCamelCase      :   int			      =  variance * variance_noise

       UpperCamelCase      :   Union[str, Any]			      =  pred_prev_sample + variance

       if not return_dict:
        return (pred_prev_sample,)

       return UnCLIPSchedulerOutput(prev_sample=A_			,				pred_original_sample=A_  )





      def 			__UpperCamelCase(					self			,				A_			,				A_			,				A_			,				):
       '''simple docstring'''




       UpperCamelCase      :   Dict			      =  self.alphas_cumprod.to(device=original_samples.device			,				dtype=original_samples.dtype  )
       UpperCamelCase      :   Optional[int]			      =  timesteps.to(original_samples.device  )

       UpperCamelCase      :   Optional[Any]			      =  alphas_cumprod[timesteps] ** 0.5
       UpperCamelCase      :   Dict			      =  sqrt_alpha_prod.flatten()
       while len(sqrt_alpha_prod.shape  ) < len(original_samples.shape  ):
        UpperCamelCase      :   List[Any]			      =  sqrt_alpha_prod.unsqueeze(-1  )

       UpperCamelCase      :   int			      =  (1 - alphas_cumprod[timesteps]) ** 0.5
       UpperCamelCase      :   Optional[Any]			      =  sqrt_one_minus_alpha_prod.flatten()
       while len(sqrt_one_minus_alpha_prod.shape  ) < len(original_samples.shape  ):
        UpperCamelCase      :   List[str]			      =  sqrt_one_minus_alpha_prod.unsqueeze(-1  )

       UpperCamelCase      :   Dict			      =  sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
       return noisy_samples







import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
    from PIL import Image
else:



    class    A__  :





          @staticmethod
          def 			__UpperCamelCase(					*A_			,				**A_  ):
           '''simple docstring'''




           pass



def 						A_							(	_lowerCAmelCase	)		->					str:
 UpperCamelCase      :   Tuple			      =  hashlib.mda(image.tobytes()	)
 return m.hexdigest()[:10]



def 						A_							(	_lowerCAmelCase	)		->					Dict:
 UpperCamelCase      :   Dict			      =  np.array(_lowerCAmelCase	)
 UpperCamelCase      :   Union[str, Any]			      =  npimg.shape
 return {"hash": hashimage(_lowerCAmelCase	), "shape": shape}



@is_pipeline_test
@require_vision
@require_torch
class    A__  (			unittest.TestCase     ):
      _UpperCAmelCase      :Any		       =  dict(
          (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()     ) if MODEL_FOR_MASK_GENERATION_MAPPING else [])     )
      _UpperCAmelCase      :str		       =  dict(
          (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()     ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])     )





      def 			__UpperCamelCase(					self			,				A_			,				A_			,				A_  ):
       '''simple docstring'''




       UpperCamelCase      :   Optional[int]			      =  MaskGenerationPipeline(model=A_			,				image_processor=A_  )
       return image_segmenter, [
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
       ]





      def 			__UpperCamelCase(					self			,				A_			,				A_  ):
       '''simple docstring'''




       pass





      @require_tf
      @unittest.skip("Image segmentation not implemented in TF"  )
      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       pass





      @slow
      @require_torch
      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   List[Any]			      =  pipeline("mask-generation"			,				model="facebook/sam-vit-huge"  )

       UpperCamelCase      :   Tuple			      =  image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg"			,				points_per_batch=256  )

       # Shortening by hashing
       UpperCamelCase      :   str			      =  []
       for i, o in enumerate(outputs["masks"]  ):
        new_outupt += [{"mask": mask_to_test_readable(A_  ), "scores": outputs["scores"][i]}]

       # fmt: off
       self.assertEqual(
           nested_simplify(A_			,				decimals=4  )			,				[
               {"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.04_44},
               {"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.0_21},
               {"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.01_67},
               {"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.01_32},
               {"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.00_53},
               {"mask": {"hash": "e2d0b7a0b7", "shape": (480, 640)}, "scores": 0.99_67},
               {"mask": {"hash": "453c7844bd", "shape": (480, 640)}, "scores": 0.9_93},
               {"mask": {"hash": "3d44f2926d", "shape": (480, 640)}, "scores": 0.99_09},
               {"mask": {"hash": "64033ddc3f", "shape": (480, 640)}, "scores": 0.98_79},
               {"mask": {"hash": "801064ff79", "shape": (480, 640)}, "scores": 0.98_34},
               {"mask": {"hash": "6172f276ef", "shape": (480, 640)}, "scores": 0.97_16},
               {"mask": {"hash": "b49e60e084", "shape": (480, 640)}, "scores": 0.96_12},
               {"mask": {"hash": "a811e775fd", "shape": (480, 640)}, "scores": 0.95_99},
               {"mask": {"hash": "a6a8ebcf4b", "shape": (480, 640)}, "scores": 0.95_52},
               {"mask": {"hash": "9d8257e080", "shape": (480, 640)}, "scores": 0.95_32},
               {"mask": {"hash": "32de6454a8", "shape": (480, 640)}, "scores": 0.95_16},
               {"mask": {"hash": "af3d4af2c8", "shape": (480, 640)}, "scores": 0.94_99},
               {"mask": {"hash": "3c6db475fb", "shape": (480, 640)}, "scores": 0.94_83},
               {"mask": {"hash": "c290813fb9", "shape": (480, 640)}, "scores": 0.94_64},
               {"mask": {"hash": "b6f0b8f606", "shape": (480, 640)}, "scores": 0.9_43},
               {"mask": {"hash": "92ce16bfdf", "shape": (480, 640)}, "scores": 0.9_43},
               {"mask": {"hash": "c749b25868", "shape": (480, 640)}, "scores": 0.94_08},
               {"mask": {"hash": "efb6cab859", "shape": (480, 640)}, "scores": 0.93_35},
               {"mask": {"hash": "1ff2eafb30", "shape": (480, 640)}, "scores": 0.93_26},
               {"mask": {"hash": "788b798e24", "shape": (480, 640)}, "scores": 0.92_62},
               {"mask": {"hash": "abea804f0e", "shape": (480, 640)}, "scores": 0.89_99},
               {"mask": {"hash": "7b9e8ddb73", "shape": (480, 640)}, "scores": 0.89_86},
               {"mask": {"hash": "cd24047c8a", "shape": (480, 640)}, "scores": 0.89_84},
               {"mask": {"hash": "6943e6bcbd", "shape": (480, 640)}, "scores": 0.88_73},
               {"mask": {"hash": "b5f47c9191", "shape": (480, 640)}, "scores": 0.88_71}
           ]			,				)
       # fmt: on





      @require_torch
      @slow
      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   Dict			      =  "facebook/sam-vit-huge"
       UpperCamelCase      :   str			      =  pipeline("mask-generation"			,				model=A_  )

       UpperCamelCase      :   str			      =  image_segmenter(
           "http://images.cocodataset.org/val2017/000000039769.jpg"			,				pred_iou_thresh=1			,				points_per_batch=256  )

       # Shortening by hashing
       UpperCamelCase      :   Tuple			      =  []
       for i, o in enumerate(outputs["masks"]  ):
        new_outupt += [{"mask": mask_to_test_readable(A_  ), "scores": outputs["scores"][i]}]

       self.assertEqual(
           nested_simplify(A_			,				decimals=4  )			,				[
               {"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.04_44},
               {"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.02_10},
               {"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.01_67},
               {"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.01_32},
               {"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.00_53},
           ]			,				)
