



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__:

   '''simple docstring'''




   def 			UpperCAmelCase		(						self  :			Tuple)	->				Dict:




         """simple docstring"""

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

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

         torch.manual_seed(0)
         lowercase__     =  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)
         lowercase__     =  DDPMScheduler(
             num_train_timesteps=10_00			,				beta_schedule='squaredcos_cap_v2'			,				beta_start=0.00_01			,				beta_end=0.02			,				thresholding=lowerCAmelCase			,				dynamic_thresholding_ratio=0.95			,				sample_max_value=1.0			,				prediction_type='epsilon'			,				variance_type='learned_range'			,				)

         torch.manual_seed(0)
         lowercase__     =  IFWatermarker()

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


   def 			UpperCAmelCase		(						self  :			int)	->				str:




         """simple docstring"""

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

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

         torch.manual_seed(0)
         lowercase__     =  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=1.4_14			,				time_embedding_act_fn='gelu'			,				time_embedding_dim=32			,				)
         unet.set_attn_processor(AttnAddedKVProcessor())  # For reproducibility tests

         torch.manual_seed(0)
         lowercase__     =  DDPMScheduler(
             num_train_timesteps=10_00			,				beta_schedule='squaredcos_cap_v2'			,				beta_start=0.00_01			,				beta_end=0.02			,				thresholding=lowerCAmelCase			,				dynamic_thresholding_ratio=0.95			,				sample_max_value=1.0			,				prediction_type='epsilon'			,				variance_type='learned_range'			,				)

         torch.manual_seed(0)
         lowercase__     =  DDPMScheduler(
             num_train_timesteps=10_00			,				beta_schedule='squaredcos_cap_v2'			,				beta_start=0.00_01			,				beta_end=0.02			,				)

         torch.manual_seed(0)
         lowercase__     =  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[Any])	->				Optional[Any]:




         """simple docstring"""

         lowercase__     =  self.get_dummy_components()
         lowercase__     =  self.pipeline_class(**lowerCAmelCase)
         pipe.to(lowerCAmelCase)
         pipe.set_progress_bar_config(disable=lowerCAmelCase)

         lowercase__     =  self.get_dummy_inputs(lowerCAmelCase)

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

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

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

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

         lowercase__,	lowercase__     =  pipe.encode_prompt(lowerCAmelCase)

         # inputs with prompt converted to embeddings
         lowercase__     =  {
             '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:
               lowercase__     =  image

         if mask_image is not None:
               lowercase__     =  mask_image

         if original_image is not None:
               lowercase__     =  original_image

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

         lowercase__     =  pipe(**lowerCAmelCase)[0]

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

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

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

         lowercase__     =  self.get_dummy_inputs(lowerCAmelCase)

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

         # inputs with prompt converted to embeddings
         lowercase__     =  {
             '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:
               lowercase__     =  image

         if mask_image is not None:
               lowercase__     =  mask_image

         if original_image is not None:
               lowercase__     =  original_image

         lowercase__     =  pipe_loaded(**lowerCAmelCase)[0]

         lowercase__     =  np.abs(to_np(lowerCAmelCase) - to_np(lowerCAmelCase)).max()
         self.assertLess(lowerCAmelCase			,				1E-4)



   def 			UpperCAmelCase		(						self  :			Dict)	->				str:




         """simple docstring"""

         lowercase__     =  self.get_dummy_components()
         lowercase__     =  self.pipeline_class(**lowerCAmelCase)
         pipe.to(lowerCAmelCase)
         pipe.set_progress_bar_config(disable=lowerCAmelCase)

         lowercase__     =  self.get_dummy_inputs(lowerCAmelCase)
         lowercase__     =  pipe(**lowerCAmelCase)[0]

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

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

         lowercase__     =  self.get_dummy_inputs(lowerCAmelCase)
         lowercase__     =  pipe_loaded(**lowerCAmelCase)[0]

         lowercase__     =  np.abs(to_np(lowerCAmelCase) - to_np(lowerCAmelCase)).max()
         self.assertLess(lowerCAmelCase			,				1E-4)





from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors

def   _lowerCAmelCase   (						A__	):
      lowercase__     =  prime_factors(A__	)
      if is_square_free(A__	):
            return -1 if len(A__	) % 2 else 1
      return 0


if __name__ == "__main__":
       import doctest

       doctest.testmod()
