
"""simple docstring"""





import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np






class     _UpperCAmelCase:



		def 						UpperCAmelCase							(					self)       ->       List[str]:






							'''simple docstring'''
							torch.manual_seed(0)
							_UpperCamelCase							 =  TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5''')

							torch.manual_seed(0)
							_UpperCamelCase							 =  AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-t5''')

							torch.manual_seed(0)
							_UpperCamelCase							 =  UNetaDConditionModel(
							    sample_size=32				,		layers_per_block=1				,		block_out_channels=[32, 64]				,		down_block_types=[
							        '''ResnetDownsampleBlock2D''',
							        '''SimpleCrossAttnDownBlock2D''',
							    ]				,		mid_block_type='''UNetMidBlock2DSimpleCrossAttn'''				,		up_block_types=['''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D''']				,		in_channels=3				,		out_channels=6				,		cross_attention_dim=32				,		encoder_hid_dim=32				,		attention_head_dim=8				,		addition_embed_type='''text'''				,		addition_embed_type_num_heads=2				,		cross_attention_norm='''group_norm'''				,		resnet_time_scale_shift='''scale_shift'''				,		act_fn='''gelu'''				,		)
							unet.set_attn_processor(AttnAddedKVProcessor())  # For reproducibility tests

							torch.manual_seed(0)
							_UpperCamelCase							 =  DDPMScheduler(
							    num_train_timesteps=10_00				,		beta_schedule='''squaredcos_cap_v2'''				,		beta_start=0.0001				,		beta_end=0.02				,		thresholding=UpperCamelCase_				,		dynamic_thresholding_ratio=0.95				,		sample_max_value=1.0				,		prediction_type='''epsilon'''				,		variance_type='''learned_range'''				,		)

							torch.manual_seed(0)
							_UpperCamelCase							 =  IFWatermarker()

							return {
							    "text_encoder": text_encoder,
							    "tokenizer": tokenizer,
							    "unet": unet,
							    "scheduler": scheduler,
							    "watermarker": watermarker,
							    "safety_checker": None,
							    "feature_extractor": None,
							}



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






							'''simple docstring'''
							torch.manual_seed(0)
							_UpperCamelCase							 =  TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5''')

							torch.manual_seed(0)
							_UpperCamelCase							 =  AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-t5''')

							torch.manual_seed(0)
							_UpperCamelCase							 =  UNetaDConditionModel(
							    sample_size=32				,		layers_per_block=[1, 2]				,		block_out_channels=[32, 64]				,		down_block_types=[
							        '''ResnetDownsampleBlock2D''',
							        '''SimpleCrossAttnDownBlock2D''',
							    ]				,		mid_block_type='''UNetMidBlock2DSimpleCrossAttn'''				,		up_block_types=['''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D''']				,		in_channels=6				,		out_channels=6				,		cross_attention_dim=32				,		encoder_hid_dim=32				,		attention_head_dim=8				,		addition_embed_type='''text'''				,		addition_embed_type_num_heads=2				,		cross_attention_norm='''group_norm'''				,		resnet_time_scale_shift='''scale_shift'''				,		act_fn='''gelu'''				,		class_embed_type='''timestep'''				,		mid_block_scale_factor=1.414				,		time_embedding_act_fn='''gelu'''				,		time_embedding_dim=32				,		)
							unet.set_attn_processor(AttnAddedKVProcessor())  # For reproducibility tests

							torch.manual_seed(0)
							_UpperCamelCase							 =  DDPMScheduler(
							    num_train_timesteps=10_00				,		beta_schedule='''squaredcos_cap_v2'''				,		beta_start=0.0001				,		beta_end=0.02				,		thresholding=UpperCamelCase_				,		dynamic_thresholding_ratio=0.95				,		sample_max_value=1.0				,		prediction_type='''epsilon'''				,		variance_type='''learned_range'''				,		)

							torch.manual_seed(0)
							_UpperCamelCase							 =  DDPMScheduler(
							    num_train_timesteps=10_00				,		beta_schedule='''squaredcos_cap_v2'''				,		beta_start=0.0001				,		beta_end=0.02				,		)

							torch.manual_seed(0)
							_UpperCamelCase							 =  IFWatermarker()

							return {
							    "text_encoder": text_encoder,
							    "tokenizer": tokenizer,
							    "unet": unet,
							    "scheduler": scheduler,
							    "image_noising_scheduler": image_noising_scheduler,
							    "watermarker": watermarker,
							    "safety_checker": None,
							    "feature_extractor": None,
							}



		def 						UpperCAmelCase							(					self)       ->       List[str]:






							'''simple docstring'''
							_UpperCamelCase							 =  self.get_dummy_components()
							_UpperCamelCase							 =  self.pipeline_class(**UpperCamelCase_)
							pipe.to(UpperCamelCase_)
							pipe.set_progress_bar_config(disable=UpperCamelCase_)

							_UpperCamelCase							 =  self.get_dummy_inputs(UpperCamelCase_)

							_UpperCamelCase							 =  inputs['''prompt''']
							_UpperCamelCase							 =  inputs['''generator''']
							_UpperCamelCase							 =  inputs['''num_inference_steps''']
							_UpperCamelCase							 =  inputs['''output_type''']

							if "image" in inputs:
												_UpperCamelCase							 =  inputs['''image''']
							else:
												_UpperCamelCase							 =  None

							if "mask_image" in inputs:
												_UpperCamelCase							 =  inputs['''mask_image''']
							else:
												_UpperCamelCase							 =  None

							if "original_image" in inputs:
												_UpperCamelCase							 =  inputs['''original_image''']
							else:
												_UpperCamelCase							 =  None

							_UpperCamelCase							 =  pipe.encode_prompt(UpperCamelCase_)

							# inputs with prompt converted to embeddings
							_UpperCamelCase							 =  {
							    '''prompt_embeds''': prompt_embeds,
							    '''negative_prompt_embeds''': negative_prompt_embeds,
							    '''generator''': generator,
							    '''num_inference_steps''': num_inference_steps,
							    '''output_type''': output_type,
							}

							if image is not None:
												_UpperCamelCase							 =  image

							if mask_image is not None:
												_UpperCamelCase							 =  mask_image

							if original_image is not None:
												_UpperCamelCase							 =  original_image

							# set all optional components to None
							for optional_component in pipe._optional_components:
												setattr(UpperCamelCase_				,		UpperCamelCase_				,		UpperCamelCase_)

							_UpperCamelCase							 =  pipe(**UpperCamelCase_)[0]

							with tempfile.TemporaryDirectory() as tmpdir:
												pipe.save_pretrained(UpperCamelCase_)
												_UpperCamelCase							 =  self.pipeline_class.from_pretrained(UpperCamelCase_)
												pipe_loaded.to(UpperCamelCase_)
												pipe_loaded.set_progress_bar_config(disable=UpperCamelCase_)

							pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor())  # For reproducibility tests

							for optional_component in pipe._optional_components:
												self.assertTrue(
												    getattr(UpperCamelCase_				,		UpperCamelCase_) is None				,		F'''`{optional_component}` did not stay set to None after loading.'''				,		)

							_UpperCamelCase							 =  self.get_dummy_inputs(UpperCamelCase_)

							_UpperCamelCase							 =  inputs['''generator''']
							_UpperCamelCase							 =  inputs['''num_inference_steps''']
							_UpperCamelCase							 =  inputs['''output_type''']

							# inputs with prompt converted to embeddings
							_UpperCamelCase							 =  {
							    '''prompt_embeds''': prompt_embeds,
							    '''negative_prompt_embeds''': negative_prompt_embeds,
							    '''generator''': generator,
							    '''num_inference_steps''': num_inference_steps,
							    '''output_type''': output_type,
							}

							if image is not None:
												_UpperCamelCase							 =  image

							if mask_image is not None:
												_UpperCamelCase							 =  mask_image

							if original_image is not None:
												_UpperCamelCase							 =  original_image

							_UpperCamelCase							 =  pipe_loaded(**UpperCamelCase_)[0]

							_UpperCamelCase							 =  np.abs(to_np(UpperCamelCase_) - to_np(UpperCamelCase_)).max()
							self.assertLess(UpperCamelCase_				,		1e-4)




		def 						UpperCAmelCase							(					self)       ->       List[str]:






							'''simple docstring'''
							_UpperCamelCase							 =  self.get_dummy_components()
							_UpperCamelCase							 =  self.pipeline_class(**UpperCamelCase_)
							pipe.to(UpperCamelCase_)
							pipe.set_progress_bar_config(disable=UpperCamelCase_)

							_UpperCamelCase							 =  self.get_dummy_inputs(UpperCamelCase_)
							_UpperCamelCase							 =  pipe(**UpperCamelCase_)[0]

							with tempfile.TemporaryDirectory() as tmpdir:
												pipe.save_pretrained(UpperCamelCase_)
												_UpperCamelCase							 =  self.pipeline_class.from_pretrained(UpperCamelCase_)
												pipe_loaded.to(UpperCamelCase_)
												pipe_loaded.set_progress_bar_config(disable=UpperCamelCase_)

							pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor())  # For reproducibility tests

							_UpperCamelCase							 =  self.get_dummy_inputs(UpperCamelCase_)
							_UpperCamelCase							 =  pipe_loaded(**UpperCamelCase_)[0]

							_UpperCamelCase							 =  np.abs(to_np(UpperCamelCase_) - to_np(UpperCamelCase_)).max()
							self.assertLess(UpperCamelCase_				,		1e-4)










"""simple docstring"""





import mpmath  # for roots of unity
import numpy as np



class 						snake_case_:






     def __init__(		self     :    str    , UpperCamelCase_     :    int=None    , UpperCamelCase_     :    List[str]=None ):
           # Input as list
           lowerCAmelCase    :		str				=      list(poly_a or [0] )[:]
           lowerCAmelCase    :		Any				=      list(poly_b or [0] )[:]

           # Remove leading zero coefficients
           while self.polyA[-1] == 0:
                 self.polyA.pop()
           lowerCAmelCase    :		Optional[int]				=      len(self.polyA )

           while self.polyB[-1] == 0:
                 self.polyB.pop()
           lowerCAmelCase    :		Union[str, Any]				=      len(self.polyB )

           # Add 0 to make lengths equal a power of 2
           lowerCAmelCase    :		str				=      int(
               2 ** np.ceil(np.loga(len(self.polyA ) + len(self.polyB ) - 1 ) ) )

           while len(self.polyA ) < self.c_max_length:
                 self.polyA.append(0 )
           while len(self.polyB ) < self.c_max_length:
                 self.polyB.append(0 )
           # A complex root used for the fourier transform
           lowerCAmelCase    :		int				=      complex(mpmath.root(x=1    , n=self.c_max_length    , k=1 ) )

           # The product
           lowerCAmelCase    :		int				=      self.__multiply()






     def    lowerCamelCase__   (		self     :    List[str]    , UpperCamelCase_     :    str ):
           lowerCAmelCase    :		Optional[Any]				=      [[x] for x in self.polyA] if which == '''A''' else [[x] for x in self.polyB]
           # Corner case
           if len(UpperCamelCase_ ) <= 1:
                 return dft[0]
           #
           lowerCAmelCase    :		Tuple				=      self.c_max_length // 2
           while next_ncol > 0:
                 lowerCAmelCase    :		Dict				=      [[] for i in range(UpperCamelCase_ )]
                 lowerCAmelCase    :		List[Any]				=      self.root**next_ncol

                 # First half of next step
                 lowerCAmelCase    :		Dict				=      1
                 for j in range(self.c_max_length // (next_ncol * 2) ):
                       for i in range(UpperCamelCase_ ):
                             new_dft[i].append(dft[i][j] + current_root * dft[i + next_ncol][j] )
                       current_root *= root
                 # Second half of next step
                 lowerCAmelCase    :		int				=      1
                 for j in range(self.c_max_length // (next_ncol * 2) ):
                       for i in range(UpperCamelCase_ ):
                             new_dft[i].append(dft[i][j] - current_root * dft[i + next_ncol][j] )
                       current_root *= root
                 # Update
                 lowerCAmelCase    :		Optional[Any]				=      new_dft
                 lowerCAmelCase    :		Union[str, Any]				=      next_ncol // 2
           return dft[0]






     def    lowerCamelCase__   (		self     :    List[Any] ):
           lowerCAmelCase    :		Optional[Any]				=      self.__dft('''A''' )
           lowerCAmelCase    :		Optional[int]				=      self.__dft('''B''' )
           lowerCAmelCase    :		Any				=      [[dft_a[i] * dft_b[i] for i in range(self.c_max_length )]]
           del dft_a
           del dft_b

           # Corner Case
           if len(inverce_c[0] ) <= 1:
                 return inverce_c[0]
           # Inverse DFT
           lowerCAmelCase    :		str				=      2
           while next_ncol <= self.c_max_length:
                 lowerCAmelCase    :		Union[str, Any]				=      [[] for i in range(UpperCamelCase_ )]
                 lowerCAmelCase    :		Optional[Any]				=      self.root ** (next_ncol // 2)
                 lowerCAmelCase    :		Tuple				=      1
                 # First half of next step
                 for j in range(self.c_max_length // next_ncol ):
                       for i in range(next_ncol // 2 ):
                             # Even positions
                             new_inverse_c[i].append(
                                 (
                                     inverce_c[i][j]
                                     + inverce_c[i][j + self.c_max_length // next_ncol]
                                 )
                                 / 2 )
                             # Odd positions
                             new_inverse_c[i + next_ncol // 2].append(
                                 (
                                     inverce_c[i][j]
                                     - inverce_c[i][j + self.c_max_length // next_ncol]
                                 )
                                 / (2 * current_root) )
                       current_root *= root
                 # Update
                 lowerCAmelCase    :		Any				=      new_inverse_c
                 next_ncol *= 2
           # Unpack
           lowerCAmelCase    :		Optional[int]				=      [round(x[0].real    , 8 ) + round(x[0].imag    , 8 ) * 1j for x in inverce_c]

           # Remove leading 0's
           while inverce_c[-1] == 0:
                 inverce_c.pop()
           return inverce_c






     def __str__(		self     :    int ):
           lowerCAmelCase    :		int				=      '''A = ''' + ''' + '''.join(
               F'''{coef}*x^{i}''' for coef, i in enumerate(self.polyA[: self.len_A] ) )
           lowerCAmelCase    :		str				=      '''B = ''' + ''' + '''.join(
               F'''{coef}*x^{i}''' for coef, i in enumerate(self.polyB[: self.len_B] ) )
           lowerCAmelCase    :		int				=      '''A*B = ''' + ''' + '''.join(
               F'''{coef}*x^{i}''' for coef, i in enumerate(self.product ) )

           return F'''{a}\n{b}\n{c}'''


# Unit tests
if __name__ == "__main__":
 import doctest

 doctest.testmod()


