import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class 						__magic_name__    (       _UpperCamelCase	,						unittest.TestCase    ):
 UpperCAmelCase							    =						DDIMPipeline
 UpperCAmelCase							    =						UNCONDITIONAL_IMAGE_GENERATION_PARAMS
 UpperCAmelCase							    =						PipelineTesterMixin.required_optional_params - {
     "num_images_per_prompt",
     "latents",
     "callback",
     "callback_steps",
 }
 UpperCAmelCase							    =						UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
 UpperCAmelCase							    =						False


 def _UpperCamelCase	(						self		: Optional[Any]    )    ->      Union[str, Any]:
   torch.manual_seed(0    )
   UpperCAmelCase					      =  UNetaDModel(
       block_out_channels=(3_2, 6_4)			,						layers_per_block=2			,						sample_size=3_2			,						in_channels=3			,						out_channels=3			,						down_block_types=("DownBlock2D", "AttnDownBlock2D")			,						up_block_types=("AttnUpBlock2D", "UpBlock2D")			,						)
   UpperCAmelCase					      =  DDIMScheduler()
   UpperCAmelCase					      =  {"unet": unet, "scheduler": scheduler}
   return components


 def _UpperCamelCase	(						self		: Optional[int]			,						lowerCAmelCase__		: Union[str, Any]			,						lowerCAmelCase__		: int=0    )    ->      Dict:
   if str(__a    ).startswith("mps"    ):
     UpperCAmelCase					      =  torch.manual_seed(__a    )
   else:
     UpperCAmelCase					      =  torch.Generator(device=__a    ).manual_seed(__a    )
   UpperCAmelCase					      =  {
       "batch_size": 1,
       "generator": generator,
       "num_inference_steps": 2,
       "output_type": "numpy",
   }
   return inputs


 def _UpperCamelCase	(						self		: Optional[int]    )    ->      Union[str, Any]:
   UpperCAmelCase					      =  "cpu"

   UpperCAmelCase					      =  self.get_dummy_components()
   UpperCAmelCase					      =  self.pipeline_class(**__a    )
   pipe.to(__a    )
   pipe.set_progress_bar_config(disable=__a    )

   UpperCAmelCase					      =  self.get_dummy_inputs(__a    )
   UpperCAmelCase					      =  pipe(**__a    ).images
   UpperCAmelCase					      =  image[0, -3:, -3:, -1]

   self.assertEqual(image.shape			,						(1, 3_2, 3_2, 3)    )
   UpperCAmelCase					      =  np.array(
       [1.0_0_0e0_0, 5.7_1_7e-0_1, 4.7_1_7e-0_1, 1.0_0_0e0_0, 0.0_0_0e0_0, 1.0_0_0e0_0, 3.0_0_0e-0_4, 0.0_0_0e0_0, 9.0_0_0e-0_4]    )
   UpperCAmelCase					      =  np.abs(image_slice.flatten() - expected_slice    ).max()
   self.assertLessEqual(__a			,						1e-3    )


 def _UpperCamelCase	(						self		: int    )    ->      str:
   super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3    )


 def _UpperCamelCase	(						self		: Any    )    ->      List[Any]:
   super().test_save_load_local(expected_max_difference=3e-3    )


 def _UpperCamelCase	(						self		: Any    )    ->      Dict:
   super().test_save_load_optional_components(expected_max_difference=3e-3    )


 def _UpperCamelCase	(						self		: int    )    ->      List[str]:
   super().test_inference_batch_single_identical(expected_max_diff=3e-3    )






@slow
@require_torch_gpu
class 						__magic_name__    (       unittest.TestCase    ):


 def _UpperCamelCase	(						self		: List[Any]    )    ->      Tuple:
   UpperCAmelCase					      =  "google/ddpm-cifar10-32"

   UpperCAmelCase					      =  UNetaDModel.from_pretrained(__a    )
   UpperCAmelCase					      =  DDIMScheduler()

   UpperCAmelCase					      =  DDIMPipeline(unet=__a			,						scheduler=__a    )
   ddim.to(__a    )
   ddim.set_progress_bar_config(disable=__a    )

   UpperCAmelCase					      =  torch.manual_seed(0    )
   UpperCAmelCase					      =  ddim(generator=__a			,						eta=0.0			,						output_type="numpy"    ).images

   UpperCAmelCase					      =  image[0, -3:, -3:, -1]

   assert image.shape == (1, 3_2, 3_2, 3)
   UpperCAmelCase					      =  np.array([0.1_723, 0.1_617, 0.1_600, 0.1_626, 0.1_497, 0.1_513, 0.1_505, 0.1_442, 0.1_453]    )

   assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2


 def _UpperCamelCase	(						self		: Tuple    )    ->      List[Any]:
   UpperCAmelCase					      =  "google/ddpm-ema-bedroom-256"

   UpperCAmelCase					      =  UNetaDModel.from_pretrained(__a    )
   UpperCAmelCase					      =  DDIMScheduler.from_pretrained(__a    )

   UpperCAmelCase					      =  DDIMPipeline(unet=__a			,						scheduler=__a    )
   ddpm.to(__a    )
   ddpm.set_progress_bar_config(disable=__a    )

   UpperCAmelCase					      =  torch.manual_seed(0    )
   UpperCAmelCase					      =  ddpm(generator=__a			,						output_type="numpy"    ).images

   UpperCAmelCase					      =  image[0, -3:, -3:, -1]

   assert image.shape == (1, 2_5_6, 2_5_6, 3)
   UpperCAmelCase					      =  np.array([0.0_060, 0.0_201, 0.0_344, 0.0_024, 0.0_018, 0.0_002, 0.0_022, 0.0_000, 0.0_069]    )

   assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2






import numpy

# List of input, output pairs
lowerCAmelCase__       = (
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
lowerCAmelCase__       = (((515, 22, 13), 555), ((61, 35, 49), 150))
lowerCAmelCase__       = [2, 4, 1, 5]
lowerCAmelCase__       = len(train_data)
lowerCAmelCase__       = 0.0_0_9
def      _lowerCAmelCase(	__A   ,       __A="train"			):
  return calculate_hypothesis_value(__A   ,       __A			) - output(
      __A   ,       __A			)
def      _lowerCAmelCase(	__A			):
  UpperCAmelCase					      =  0
  for i in range(len(__A			) - 1			):
    hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
  hyp_val += parameter_vector[0]
  return hyp_val
def      _lowerCAmelCase(	__A   ,       __A			):
  if data_set == "train":
    return train_data[example_no][1]
  elif data_set == "test":
    return test_data[example_no][1]
  return None
def      _lowerCAmelCase(	__A   ,       __A			):
  if data_set == "train":
    return _hypothesis_value(train_data[example_no][0]			)
  elif data_set == "test":
    return _hypothesis_value(test_data[example_no][0]			)
  return None
def      _lowerCAmelCase(	__A   ,       __A=m			):
  UpperCAmelCase					      =  0
  for i in range(__A			):
    if index == -1:
      summation_value += _error(__A			)
    else:
      summation_value += _error(__A			) * train_data[i][0][index]
  return summation_value
def      _lowerCAmelCase(	__A			):
  UpperCAmelCase					      =  summation_of_cost_derivative(__A   ,       __A			) / m
  return cost_derivative_value
def      _lowerCAmelCase(	):
  global parameter_vector
  # Tune these values to set a tolerance value for predicted output
  UpperCAmelCase					      =  0.000002
  UpperCAmelCase					      =  0
  UpperCAmelCase					      =  0
  while True:
    j += 1
    UpperCAmelCase					      =  [0, 0, 0, 0]
    for i in range(0   ,       len(__A			)			):
      UpperCAmelCase					      =  get_cost_derivative(i - 1			)
      UpperCAmelCase					      =  (
          parameter_vector[i] - LEARNING_RATE * cost_derivative
      )
    if numpy.allclose(
        __A   ,       __A   ,       atol=__A   ,       rtol=__A   ,       ):
      break
    UpperCAmelCase					      =  temp_parameter_vector
  print(("Number of iterations:", j)			)
def      _lowerCAmelCase(	):
  for i in range(len(__A			)			):
    print(("Actual output value:", output(__A   ,       "test"			))			)
    print(("Hypothesis output:", calculate_hypothesis_value(__A   ,       "test"			))			)


if __name__ == "__main__":
      run_gradient_descent()
      print("\nTesting gradient descent for a linear hypothesis function.\n")
      test_gradient_descent()



