


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   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""
       snake_case      =    42
       snake_case      =    None




def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE=0.9_9_9 ,			SCREAMING_SNAKE_CASE="cosine" ,			):
  if alpha_transform_type == "cosine":

    def alpha_bar_fn(SCREAMING_SNAKE_CASE       ):
      return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2       ) ** 2

  elif alpha_transform_type == "exp":

    def alpha_bar_fn(SCREAMING_SNAKE_CASE       ):
      return math.exp(t * -1_2.0       )

  else:
    raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}'''       )

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







class   _lowerCamelCase (					UpperCamelCase					,							UpperCamelCase			):




       """simple docstring"""



       @register_to_config
       def __init__(      self				,       _SCREAMING_SNAKE_CASE = 1000				,       _SCREAMING_SNAKE_CASE = "fixed_small_log"				,       _SCREAMING_SNAKE_CASE = True				,       _SCREAMING_SNAKE_CASE = 1.0				,       _SCREAMING_SNAKE_CASE = "epsilon"				,       _SCREAMING_SNAKE_CASE = "squaredcos_cap_v2"				,       )->Union[str, Any]:




         '''simple docstring'''





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

         A_      :				List[str]		     =					betas_for_alpha_bar(_SCREAMING_SNAKE_CASE		)

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

         # standard deviation of the initial noise distribution
         A_      :				Any		     =					1.0

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

         A_      :				Optional[Any]		     =					variance_type



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE = None		)->torch.FloatTensor:




         '''simple docstring'''





         return sample



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE = None		)->Union[str, Any]:




         '''simple docstring'''





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



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=None				,       _SCREAMING_SNAKE_CASE=None				,       _SCREAMING_SNAKE_CASE=None		)->List[str]:




         '''simple docstring'''





         if prev_timestep is None:
           A_      :				Optional[Any]		     =					t - 1

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

         if prev_timestep == t - 1:
           A_      :				Optional[int]		     =					self.betas[t]
         else:
           A_      :				Any		     =					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
         A_      :				Any		     =					beta_prod_t_prev / beta_prod_t * beta

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

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

           A_      :				int		     =					(predicted_variance + 1) / 2
           A_      :				Any		     =					frac * max_log + (1 - frac) * min_log

         return variance



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE=None				,       _SCREAMING_SNAKE_CASE = True				,       )->Union[UnCLIPSchedulerOutput, Tuple]:




         '''simple docstring'''





         A_      :				Union[str, Any]		     =					timestep

         if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
           A_     ,       A_      :				Optional[Any]		     =					torch.split(_SCREAMING_SNAKE_CASE				,       sample.shape[1]				,       dim=1		)
         else:
           A_      :				Tuple		     =					None

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

         A_      :				List[str]		     =					self.alphas_cumprod[t]
         A_      :				List[str]		     =					self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
         A_      :				Any		     =					1 - alpha_prod_t
         A_      :				Union[str, Any]		     =					1 - alpha_prod_t_prev

         if prev_timestep == t - 1:
           A_      :				List[Any]		     =					self.betas[t]
           A_      :				List[str]		     =					self.alphas[t]
         else:
           A_      :				Union[str, Any]		     =					1 - alpha_prod_t / alpha_prod_t_prev
           A_      :				Tuple		     =					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":
           A_      :				int		     =					(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
         elif self.config.prediction_type == "sample":
           A_      :				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:
           A_      :				Optional[Any]		     =					torch.clamp(
               _SCREAMING_SNAKE_CASE				,       -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
         A_      :				Any		     =					(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
         A_      :				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
         A_      :				Any		     =					pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

         # 6. Add noise
         A_      :				int		     =					0
         if t > 0:
           A_      :				Dict		     =					randn_tensor(
               model_output.shape				,       dtype=model_output.dtype				,       generator=_SCREAMING_SNAKE_CASE				,       device=model_output.device		)

           A_      :				List[Any]		     =					self._get_variance(
               _SCREAMING_SNAKE_CASE				,       predicted_variance=_SCREAMING_SNAKE_CASE				,       prev_timestep=_SCREAMING_SNAKE_CASE				,       )

           if self.variance_type == "fixed_small_log":
             A_      :				List[Any]		     =					variance
           elif self.variance_type == "learned_range":
             A_      :				Dict		     =					(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.'''		)

           A_      :				Union[str, Any]		     =					variance * variance_noise

         A_      :				List[str]		     =					pred_prev_sample + variance

         if not return_dict:
           return (pred_prev_sample,)

         return UnCLIPSchedulerOutput(prev_sample=_SCREAMING_SNAKE_CASE				,       pred_original_sample=_SCREAMING_SNAKE_CASE		)







       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       )->torch.FloatTensor:




         '''simple docstring'''





         A_      :				List[str]		     =					self.alphas_cumprod.to(device=original_samples.device				,       dtype=original_samples.dtype		)
         A_      :				List[str]		     =					timesteps.to(original_samples.device		)

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

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

         A_      :				str		     =					sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
         return noisy_samples








import inspect
import unittest

from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch
     from torch import nn

     from transformers import RegNetForImageClassification, RegNetModel
     from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import AutoImageProcessor






class   _lowerCamelCase :




       """simple docstring"""



       def __init__(      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=3				,       _SCREAMING_SNAKE_CASE=32				,       _SCREAMING_SNAKE_CASE=3				,       _SCREAMING_SNAKE_CASE=10				,       _SCREAMING_SNAKE_CASE=[10, 20, 30, 40]				,       _SCREAMING_SNAKE_CASE=[1, 1, 2, 1]				,       _SCREAMING_SNAKE_CASE=True				,       _SCREAMING_SNAKE_CASE=True				,       _SCREAMING_SNAKE_CASE="relu"				,       _SCREAMING_SNAKE_CASE=3				,       _SCREAMING_SNAKE_CASE=None				,       )->List[str]:




         '''simple docstring'''





         A_      :				str		     =					parent
         A_      :				int		     =					batch_size
         A_      :				List[str]		     =					image_size
         A_      :				Dict		     =					num_channels
         A_      :				Tuple		     =					embeddings_size
         A_      :				Union[str, Any]		     =					hidden_sizes
         A_      :				Dict		     =					depths
         A_      :				str		     =					is_training
         A_      :				Union[str, Any]		     =					use_labels
         A_      :				Union[str, Any]		     =					hidden_act
         A_      :				Optional[Any]		     =					num_labels
         A_      :				Tuple		     =					scope
         A_      :				Optional[int]		     =					len(_SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self		)->Optional[Any]:




         '''simple docstring'''





         A_      :				List[str]		     =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

         A_      :				str		     =					None
         if self.use_labels:
           A_      :				Union[str, Any]		     =					ids_tensor([self.batch_size]				,       self.num_labels		)

         A_      :				Optional[Any]		     =					self.get_config()

         return config, pixel_values, labels



       def        _snake_case       (      self		)->Union[str, Any]:




         '''simple docstring'''





         return RegNetConfig(
             num_channels=self.num_channels				,       embeddings_size=self.embeddings_size				,       hidden_sizes=self.hidden_sizes				,       depths=self.depths				,       hidden_act=self.hidden_act				,       num_labels=self.num_labels				,       )



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)->Union[str, Any]:




         '''simple docstring'''





         A_      :				Dict		     =					RegNetModel(config=_SCREAMING_SNAKE_CASE		)
         model.to(_SCREAMING_SNAKE_CASE		)
         model.eval()
         A_      :				Any		     =					model(_SCREAMING_SNAKE_CASE		)
         # expected last hidden states: B, C, H // 32, W // 32
         self.parent.assertEqual(
             result.last_hidden_state.shape				,       (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)				,       )



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)->Union[str, Any]:




         '''simple docstring'''





         A_      :				Union[str, Any]		     =					self.num_labels
         A_      :				Dict		     =					RegNetForImageClassification(_SCREAMING_SNAKE_CASE		)
         model.to(_SCREAMING_SNAKE_CASE		)
         model.eval()
         A_      :				int		     =					model(_SCREAMING_SNAKE_CASE				,       labels=_SCREAMING_SNAKE_CASE		)
         self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.num_labels)		)







       def        _snake_case       (      self		)->Union[str, Any]:




         '''simple docstring'''





         A_      :				Tuple		     =					self.prepare_config_and_inputs()
         A_     ,       A_     ,       A_      :				str		     =					config_and_inputs
         A_      :				Any		     =					{'''pixel_values''': pixel_values}
         return config, inputs_dict






@require_torch
class   _lowerCamelCase (					UpperCamelCase					,							UpperCamelCase					,							unittest.TestCase			):




       """simple docstring"""
       snake_case      =    (RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
       snake_case      =    (
           {"feature-extraction": RegNetModel, "image-classification": RegNetForImageClassification}
           if is_torch_available()
           else {}
       )

       snake_case      =    False
       snake_case      =    False
       snake_case      =    False
       snake_case      =    False



       def        _snake_case       (      self		)->Union[str, Any]:




         '''simple docstring'''





         A_      :				Union[str, Any]		     =					RegNetModelTester(self		)
         A_      :				Union[str, Any]		     =					ConfigTester(self				,       config_class=_SCREAMING_SNAKE_CASE				,       has_text_modality=_SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self		)->Dict:




         '''simple docstring'''





         self.create_and_test_config_common_properties()
         self.config_tester.create_and_test_config_to_json_string()
         self.config_tester.create_and_test_config_to_json_file()
         self.config_tester.create_and_test_config_from_and_save_pretrained()
         self.config_tester.create_and_test_config_with_num_labels()
         self.config_tester.check_config_can_be_init_without_params()
         self.config_tester.check_config_arguments_init()



       def        _snake_case       (      self		)->Tuple:




         '''simple docstring'''





         return



       @unittest.skip(reason='''RegNet does not use inputs_embeds'''		)
       def        _snake_case       (      self		)->Dict:




         '''simple docstring'''





         pass



       @unittest.skip(reason='''RegNet does not support input and output embeddings'''		)
       def        _snake_case       (      self		)->str:




         '''simple docstring'''





         pass



       def        _snake_case       (      self		)->List[Any]:




         '''simple docstring'''





         A_     ,       A_      :				Optional[Any]		     =					self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
           A_      :				str		     =					model_class(_SCREAMING_SNAKE_CASE		)
           A_      :				Union[str, Any]		     =					inspect.signature(model.forward		)
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           A_      :				Any		     =					[*signature.parameters.keys()]

           A_      :				Any		     =					['''pixel_values''']
           self.assertListEqual(arg_names[:1]				,       _SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self		)->Any:




         '''simple docstring'''





         A_      :				Dict		     =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self		)->Optional[Any]:




         '''simple docstring'''





         A_     ,       A_      :				int		     =					self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
           A_      :				Union[str, Any]		     =					model_class(config=_SCREAMING_SNAKE_CASE		)
           for name, module in model.named_modules():
             if isinstance(_SCREAMING_SNAKE_CASE				,       (nn.BatchNormad, nn.GroupNorm)		):
               self.assertTrue(
                   torch.all(module.weight == 1		)				,       msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''				,       )
               self.assertTrue(
                   torch.all(module.bias == 0		)				,       msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''				,       )



       def        _snake_case       (      self		)->List[Any]:




         '''simple docstring'''





         def check_hidden_states_output(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		):
           A_      :				str		     =					model_class(_SCREAMING_SNAKE_CASE		)
           model.to(_SCREAMING_SNAKE_CASE		)
           model.eval()

           with torch.no_grad():
             A_      :				Tuple		     =					model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)		)

           A_      :				Union[str, Any]		     =					outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

           A_      :				Optional[int]		     =					self.model_tester.num_stages
           self.assertEqual(len(_SCREAMING_SNAKE_CASE		)				,       expected_num_stages + 1		)

           # RegNet's feature maps are of shape (batch_size, num_channels, height, width)
           self.assertListEqual(
               list(hidden_states[0].shape[-2:]		)				,       [self.model_tester.image_size // 2, self.model_tester.image_size // 2]				,       )

         A_     ,       A_      :				Tuple		     =					self.model_tester.prepare_config_and_inputs_for_common()
         A_      :				int		     =					['''basic''', '''bottleneck''']
         for model_class in self.all_model_classes:
           for layer_type in layers_type:
             A_      :				int		     =					layer_type
             A_      :				List[Any]		     =					True
             check_hidden_states_output(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             A_      :				str		     =					True

             check_hidden_states_output(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self		)->Dict:




         '''simple docstring'''





         A_      :				List[str]		     =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE		)







       @slow
       def        _snake_case       (      self		)->str:




         '''simple docstring'''





         for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           A_      :				Dict		     =					RegNetModel.from_pretrained(_SCREAMING_SNAKE_CASE		)
           self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)




def   _SCREAMING_SNAKE_CASE	( ):
  A_      :				int		     =					Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
  return image







@require_torch
@require_vision
class   _lowerCamelCase (					unittest.TestCase			):




       """simple docstring"""



       @cached_property
       def        _snake_case       (      self		)->List[str]:




         '''simple docstring'''





         return (
             AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)
             if is_vision_available()
             else None
         )







       @slow
       def        _snake_case       (      self		)->Tuple:




         '''simple docstring'''





         A_      :				List[Any]		     =					RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(_SCREAMING_SNAKE_CASE		)

         A_      :				Optional[Any]		     =					self.default_image_processor
         A_      :				Any		     =					prepare_img()
         A_      :				Optional[Any]		     =					image_processor(images=_SCREAMING_SNAKE_CASE				,       return_tensors='''pt'''		).to(_SCREAMING_SNAKE_CASE		)

         # forward pass
         with torch.no_grad():
           A_      :				Union[str, Any]		     =					model(**_SCREAMING_SNAKE_CASE		)

         # verify the logits
         A_      :				Union[str, Any]		     =					torch.Size((1, 1000)		)
         self.assertEqual(outputs.logits.shape				,       _SCREAMING_SNAKE_CASE		)

         A_      :				Optional[int]		     =					torch.tensor([-0.4_1_8_0, -1.5_0_5_1, -3.4_8_3_6]		).to(_SCREAMING_SNAKE_CASE		)

         self.assertTrue(torch.allclose(outputs.logits[0, :3]				,       _SCREAMING_SNAKE_CASE				,       atol=1e-4		)		)




