






"""simple docstring"""






import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


A_      	=     False
class     __lowerCAmelCase (      unittest.TestCase							):







  '''simple docstring'''


  def        UpperCamelCase__ (   self:		Optional[Any]       ):
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()
  @property
  def        UpperCamelCase__ (   self:		List[str]       ):
     return 12
  @property
  def        UpperCamelCase__ (   self:		Tuple       ):
     return 12
  @property
  def        UpperCamelCase__ (   self:		Union[str, Any]       ):
     return 32
  @property
  def        UpperCamelCase__ (   self:		Optional[Any]       ):
     torch.manual_seed(0       )
     UpperCamelCase_			     =VQModel(
         block_out_channels=[32, 64]					,      in_channels=3					,      out_channels=3					,      down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]					,      up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]					,      latent_channels=3					,      num_vq_embeddings=self.num_embed					,      vq_embed_dim=3					,      )
     return model
  @property
  def        UpperCamelCase__ (   self:		Tuple       ):
     UpperCamelCase_			     =CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       )
     return tokenizer
  @property
  def        UpperCamelCase__ (   self:		int       ):
     torch.manual_seed(0       )
     UpperCamelCase_			     =CLIPTextConfig(
         bos_token_id=0					,      eos_token_id=2					,      hidden_size=self.text_embedder_hidden_size					,      intermediate_size=37					,      layer_norm_eps=1e-0_5					,      num_attention_heads=4					,      num_hidden_layers=5					,      pad_token_id=1					,      vocab_size=1000					,      )
     return CLIPTextModel(UpperCamelCase_       )
  @property
  def        UpperCamelCase__ (   self:		List[Any]       ):
     torch.manual_seed(0       )

     UpperCamelCase_			     =12
     UpperCamelCase_			     =12

     UpperCamelCase_			     ={
         """attention_bias""": True,
         """cross_attention_dim""": 32,
         """attention_head_dim""": height * width,
         """num_attention_heads""": 1,
         """num_vector_embeds""": self.num_embed,
         """num_embeds_ada_norm""": self.num_embeds_ada_norm,
         """norm_num_groups""": 32,
         """sample_size""": width,
         """activation_fn""": """geglu-approximate""",
     }

     UpperCamelCase_			     =TransformeraDModel(**UpperCamelCase_       )
     return model
  def        UpperCamelCase__ (   self:		Any       ):
     UpperCamelCase_			     ="""cpu"""

     UpperCamelCase_			     =self.dummy_vqvae
     UpperCamelCase_			     =self.dummy_text_encoder
     UpperCamelCase_			     =self.dummy_tokenizer
     UpperCamelCase_			     =self.dummy_transformer
     UpperCamelCase_			     =VQDiffusionScheduler(self.num_embed       )
     UpperCamelCase_			     =LearnedClassifierFreeSamplingEmbeddings(learnable=UpperCamelCase_       )

     UpperCamelCase_			     =VQDiffusionPipeline(
         vqvae=UpperCamelCase_					,      text_encoder=UpperCamelCase_					,      tokenizer=UpperCamelCase_					,      transformer=UpperCamelCase_					,      scheduler=UpperCamelCase_					,      learned_classifier_free_sampling_embeddings=UpperCamelCase_					,      )
     UpperCamelCase_			     =pipe.to(UpperCamelCase_       )
     pipe.set_progress_bar_config(disable=UpperCamelCase_       )

     UpperCamelCase_			     ="""teddy bear playing in the pool"""

     UpperCamelCase_			     =torch.Generator(device=UpperCamelCase_       ).manual_seed(0       )
     UpperCamelCase_			     =pipe([prompt]					,      generator=UpperCamelCase_					,      num_inference_steps=2					,      output_type="np"       )
     UpperCamelCase_			     =output.images

     UpperCamelCase_			     =torch.Generator(device=UpperCamelCase_       ).manual_seed(0       )
     UpperCamelCase_			     =pipe(
         [prompt]					,      generator=UpperCamelCase_					,      output_type="np"					,      return_dict=UpperCamelCase_					,      num_inference_steps=2       )[0]

     UpperCamelCase_			     =image[0, -3:, -3:, -1]
     UpperCamelCase_			     =image_from_tuple[0, -3:, -3:, -1]

     assert image.shape == (1, 24, 24, 3)

     UpperCamelCase_			     =np.array([0.6551, 0.6168, 0.5008, 0.5676, 0.5659, 0.4295, 0.6073, 0.5599, 0.4992]       )

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





  def        UpperCamelCase__ (   self:		List[Any]       ):
     UpperCamelCase_			     ="""cpu"""

     UpperCamelCase_			     =self.dummy_vqvae
     UpperCamelCase_			     =self.dummy_text_encoder
     UpperCamelCase_			     =self.dummy_tokenizer
     UpperCamelCase_			     =self.dummy_transformer
     UpperCamelCase_			     =VQDiffusionScheduler(self.num_embed       )
     UpperCamelCase_			     =LearnedClassifierFreeSamplingEmbeddings(
         learnable=UpperCamelCase_					,      hidden_size=self.text_embedder_hidden_size					,      length=tokenizer.model_max_length       )

     UpperCamelCase_			     =VQDiffusionPipeline(
         vqvae=UpperCamelCase_					,      text_encoder=UpperCamelCase_					,      tokenizer=UpperCamelCase_					,      transformer=UpperCamelCase_					,      scheduler=UpperCamelCase_					,      learned_classifier_free_sampling_embeddings=UpperCamelCase_					,      )
     UpperCamelCase_			     =pipe.to(UpperCamelCase_       )
     pipe.set_progress_bar_config(disable=UpperCamelCase_       )

     UpperCamelCase_			     ="""teddy bear playing in the pool"""

     UpperCamelCase_			     =torch.Generator(device=UpperCamelCase_       ).manual_seed(0       )
     UpperCamelCase_			     =pipe([prompt]					,      generator=UpperCamelCase_					,      num_inference_steps=2					,      output_type="np"       )
     UpperCamelCase_			     =output.images

     UpperCamelCase_			     =torch.Generator(device=UpperCamelCase_       ).manual_seed(0       )
     UpperCamelCase_			     =pipe(
         [prompt]					,      generator=UpperCamelCase_					,      output_type="np"					,      return_dict=UpperCamelCase_					,      num_inference_steps=2       )[0]

     UpperCamelCase_			     =image[0, -3:, -3:, -1]
     UpperCamelCase_			     =image_from_tuple[0, -3:, -3:, -1]

     assert image.shape == (1, 24, 24, 3)

     UpperCamelCase_			     =np.array([0.6693, 0.6075, 0.4959, 0.5701, 0.5583, 0.4333, 0.6171, 0.5684, 0.4988]       )

     assert np.abs(image_slice.flatten() - expected_slice       ).max() < 2.0
     assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1e-2




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







  '''simple docstring'''


  def        UpperCamelCase__ (   self:		List[str]       ):
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()





  def        UpperCamelCase__ (   self:		Union[str, Any]       ):
     UpperCamelCase_			     =load_numpy(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
         "/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy"       )

     UpperCamelCase_			     =VQDiffusionPipeline.from_pretrained("microsoft/vq-diffusion-ithq"       )
     UpperCamelCase_			     =pipeline.to(UpperCamelCase_       )
     pipeline.set_progress_bar_config(disable=UpperCamelCase_       )

     # requires GPU generator for gumbel softmax
     # don't use GPU generator in tests though
     UpperCamelCase_			     =torch.Generator(device=UpperCamelCase_       ).manual_seed(0       )
     UpperCamelCase_			     =pipeline(
         "teddy bear playing in the pool"					,      num_images_per_prompt=1					,      generator=UpperCamelCase_					,      output_type="np"					,      )

     UpperCamelCase_			     =output.images[0]

     assert image.shape == (256, 256, 3)
     assert np.abs(expected_image - image       ).max() < 2.0




"""simple docstring"""
import mpmath  # for roots of unity
import numpy as np




class 	lowercase__				:





   def __init__(							self      ,   SCREAMING_SNAKE_CASE=None      ,   SCREAMING_SNAKE_CASE=None)  ->  Tuple:
      # Input as list
      _lowerCamelCase	:					Any   =		list(poly_a or [0])[:]
      _lowerCamelCase	:					Optional[Any]   =		list(poly_b or [0])[:]

      # Remove leading zero coefficients
      while self.polyA[-1] == 0:
         self.polyA.pop()
      _lowerCamelCase	:					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	:					List[Any]   =		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	:					Optional[Any]   =		complex(mpmath.root(x=1      ,   n=self.c_max_length      ,   k=1))

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





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

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





   def 		UpperCamelCase_  (							self)  ->  str:
      _lowerCamelCase	:					Optional[Any]   =		self.__dft("""A""")
      _lowerCamelCase	:					List[str]   =		self.__dft("""B""")
      _lowerCamelCase	:					List[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	:					List[str]   =		2
      while next_ncol <= self.c_max_length:
         _lowerCamelCase	:					Any   =		[[] for i in range(SCREAMING_SNAKE_CASE)]
         _lowerCamelCase	:					List[Any]   =		self.root ** (next_ncol // 2)
         _lowerCamelCase	:					str   =		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[Any]   =		[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)  ->  Any:
      _lowerCamelCase	:					Dict   =		"""A = """ + """ + """.join(
          F'{coef}*x^{i}' for coef, i in enumerate(self.polyA[: self.len_A]))
      _lowerCamelCase	:					List[Any]   =		"""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()

