


import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class   _lowerCamelCase (					UpperCamelCase					,							unittest.TestCase			):




       """simple docstring"""
       snake_case      =    DiTPipeline
       snake_case      =    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
       snake_case      =    PipelineTesterMixin.required_optional_params - {
           "latents",
           "num_images_per_prompt",
           "callback",
           "callback_steps",
       }
       snake_case      =    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
       snake_case      =    False



       def        _snake_case       (      self		)->str:




         '''simple docstring'''





         torch.manual_seed(0		)
         A_      :				Union[str, Any]		     =					TransformeraDModel(
             sample_size=16				,       num_layers=2				,       patch_size=4				,       attention_head_dim=8				,       num_attention_heads=2				,       in_channels=4				,       out_channels=8				,       attention_bias=_SCREAMING_SNAKE_CASE				,       activation_fn='''gelu-approximate'''				,       num_embeds_ada_norm=1000				,       norm_type='''ada_norm_zero'''				,       norm_elementwise_affine=_SCREAMING_SNAKE_CASE				,       )
         A_      :				Tuple		     =					AutoencoderKL()
         A_      :				Optional[Any]		     =					DDIMScheduler()
         A_      :				Union[str, Any]		     =					{'''transformer''': transformer.eval(), '''vae''': vae.eval(), '''scheduler''': scheduler}
         return components



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=0		)->int:




         '''simple docstring'''





         if str(_SCREAMING_SNAKE_CASE		).startswith('''mps'''		):
           A_      :				Optional[int]		     =					torch.manual_seed(_SCREAMING_SNAKE_CASE		)
         else:
           A_      :				List[Any]		     =					torch.Generator(device=_SCREAMING_SNAKE_CASE		).manual_seed(_SCREAMING_SNAKE_CASE		)
         A_      :				int		     =					{
             '''class_labels''': [1],
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''output_type''': '''numpy''',
         }
         return inputs



       def        _snake_case       (      self		)->Dict:




         '''simple docstring'''





         A_      :				Optional[Any]		     =					'''cpu'''

         A_      :				List[Any]		     =					self.get_dummy_components()
         A_      :				List[Any]		     =					self.pipeline_class(**_SCREAMING_SNAKE_CASE		)
         pipe.to(_SCREAMING_SNAKE_CASE		)
         pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

         A_      :				Optional[int]		     =					self.get_dummy_inputs(_SCREAMING_SNAKE_CASE		)
         A_      :				List[Any]		     =					pipe(**_SCREAMING_SNAKE_CASE		).images
         A_      :				Tuple		     =					image[0, -3:, -3:, -1]

         self.assertEqual(image.shape				,       (1, 16, 16, 3)		)
         A_      :				Optional[Any]		     =					np.array([0.2_9_4_6, 0.6_6_0_1, 0.4_3_2_9, 0.3_2_9_6, 0.4_1_4_4, 0.5_3_1_9, 0.7_2_7_3, 0.5_0_1_3, 0.4_4_5_7]		)
         A_      :				List[str]		     =					np.abs(image_slice.flatten() - expected_slice		).max()
         self.assertLessEqual(_SCREAMING_SNAKE_CASE				,       1e-3		)



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




         '''simple docstring'''





         self._test_inference_batch_single_identical(relax_max_difference=_SCREAMING_SNAKE_CASE				,       expected_max_diff=1e-3		)







       @unittest.skipIf(
           torch_device != '''cuda''' or not is_xformers_available()				,       reason='''XFormers attention is only available with CUDA and `xformers` installed'''				,       )
       def        _snake_case       (      self		)->Dict:




         '''simple docstring'''





         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3		)






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




       """simple docstring"""



       def        _snake_case       (      self		)->Any:




         '''simple docstring'''





         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()



       def        _snake_case       (      self		)->str:




         '''simple docstring'''





         A_      :				List[str]		     =					torch.manual_seed(0		)

         A_      :				List[str]		     =					DiTPipeline.from_pretrained('''facebook/DiT-XL-2-256'''		)
         pipe.to('''cuda'''		)

         A_      :				Any		     =					['''vase''', '''umbrella''', '''white shark''', '''white wolf''']
         A_      :				Optional[Any]		     =					pipe.get_label_ids(_SCREAMING_SNAKE_CASE		)

         A_      :				Dict		     =					pipe(_SCREAMING_SNAKE_CASE				,       generator=_SCREAMING_SNAKE_CASE				,       num_inference_steps=40				,       output_type='''np'''		).images

         for word, image in zip(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		):
           A_      :				List[str]		     =					load_numpy(
               F'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy'''		)
           assert np.abs((expected_image - image).max()		) < 1e-2







       def        _snake_case       (      self		)->Tuple:




         '''simple docstring'''





         A_      :				Any		     =					DiTPipeline.from_pretrained('''facebook/DiT-XL-2-512'''		)
         A_      :				str		     =					DPMSolverMultistepScheduler.from_config(pipe.scheduler.config		)
         pipe.to('''cuda'''		)

         A_      :				Optional[Any]		     =					['''vase''', '''umbrella''']
         A_      :				Tuple		     =					pipe.get_label_ids(_SCREAMING_SNAKE_CASE		)

         A_      :				str		     =					torch.manual_seed(0		)
         A_      :				Optional[Any]		     =					pipe(_SCREAMING_SNAKE_CASE				,       generator=_SCREAMING_SNAKE_CASE				,       num_inference_steps=25				,       output_type='''np'''		).images

         for word, image in zip(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		):
           A_      :				str		     =					load_numpy(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
               F'''/dit/{word}_512.npy'''		)

           assert np.abs((expected_image - image).max()		) < 1e-1








def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  A_      :				List[Any]		     =					current_set.copy()
  for row_index, row in enumerate(SCREAMING_SNAKE_CASE       ):
    A_      :				List[str]		     =					row[0]
    for column_index, column in enumerate(SCREAMING_SNAKE_CASE       ):
      if magnitude == 0:
        A_      :				Union[str, Any]		     =					column
        continue
      A_      :				Dict		     =					column / magnitude
    # Subtract to cancel term
  A_      :				Union[str, Any]		     =					current_set[0]
  A_      :				Tuple		     =					[first_row]
  A_      :				int		     =					current_set[1::]
  for row in current_set:
    A_      :				Tuple		     =					[]
    # If first term is 0, it is already in form we want, so we preserve it
    if row[0] == 0:
      final_set.append(SCREAMING_SNAKE_CASE       )
      continue
    for column_index in range(len(SCREAMING_SNAKE_CASE       )       ):
      temp_row.append(first_row[column_index] - row[column_index]       )
    final_set.append(SCREAMING_SNAKE_CASE       )
  # Create next recursion iteration set
  if len(final_set[0]       ) != 3:
    A_      :				Optional[Any]		     =					final_set[0]
    A_      :				Any		     =					[]
    A_      :				str		     =					[]
    for row in final_set[1::]:
      current_first_column.append(row[0]       )
      next_iteration.append(row[1::]       )
    A_      :				Optional[Any]		     =					simplify(SCREAMING_SNAKE_CASE       )
    for i in range(len(SCREAMING_SNAKE_CASE       )       ):
      resultant[i].insert(0 ,			current_first_column[i]       )
    resultant.insert(0 ,			SCREAMING_SNAKE_CASE       )
    A_      :				List[Any]		     =					resultant
  return final_set


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  if len(SCREAMING_SNAKE_CASE       ) == 0:
    raise IndexError('''solve_simultaneous() requires n lists of length n+1'''       )
  A_      :				str		     =					len(SCREAMING_SNAKE_CASE       ) + 1
  if any(len(SCREAMING_SNAKE_CASE       ) != _length for item in equations       ):
    raise IndexError('''solve_simultaneous() requires n lists of length n+1'''       )
  for row in equations:
    if any(not isinstance(SCREAMING_SNAKE_CASE ,			(int, float)       ) for column in row       ):
      raise ValueError('''solve_simultaneous() requires lists of integers'''       )
  if len(SCREAMING_SNAKE_CASE       ) == 1:
    return [equations[0][-1] / equations[0][0]]
  A_      :				Dict		     =					equations.copy()
  if any(0 in row for row in data_set       ):
    A_      :				Tuple		     =					data_set.copy()
    A_      :				Optional[Any]		     =					[]
    for row_index, row in enumerate(SCREAMING_SNAKE_CASE       ):
      if 0 not in row:
        A_      :				str		     =					data_set.pop(SCREAMING_SNAKE_CASE       )
        break
    if not full_row:
      raise ValueError('''solve_simultaneous() requires at least 1 full equation'''       )
    data_set.insert(0 ,			SCREAMING_SNAKE_CASE       )
  A_      :				int		     =					data_set.copy()
  A_      :				Dict		     =					simplify(SCREAMING_SNAKE_CASE       )
  A_      :				Dict		     =					simplified[::-1]
  A_      :				list		     =					[]
  for row in simplified:
    A_      :				Union[str, Any]		     =					row[-1]
    if not solutions:
      if row[-2] == 0:
        solutions.append(0       )
        continue
      solutions.append(current_solution / row[-2]       )
      continue
    A_      :				Optional[Any]		     =					row.copy()[: len(SCREAMING_SNAKE_CASE       ) - 1 :]
    while temp_row[0] == 0:
      temp_row.pop(0       )
    if len(SCREAMING_SNAKE_CASE       ) == 0:
      solutions.append(0       )
      continue
    A_      :				int		     =					temp_row[1::]
    A_      :				int		     =					temp_row[::-1]
    for column_index, column in enumerate(SCREAMING_SNAKE_CASE       ):
      current_solution -= column * solutions[column_index]
    solutions.append(SCREAMING_SNAKE_CASE       )
  A_      :				Union[str, Any]		     =					[]
  for item in solutions:
    final.append(float(round(SCREAMING_SNAKE_CASE ,			5       )       )       )
  return final[::-1]


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     UpperCamelCase								=	[
         [2, 1, 1, 1, 1, 4],
         [1, 2, 1, 1, 1, 5],
         [1, 1, 2, 1, 1, 6],
         [1, 1, 1, 2, 1, 7],
         [1, 1, 1, 1, 2, 8],
     ]
     print(solve_simultaneous(eq))
     print(solve_simultaneous([[4, 2]]))




