





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


_lowerCAmelCase								=  False

class  _UpperCAmelCase				(		unittest.TestCase  ):
      def 		_lowerCamelCase   (		self							):
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()
      @property
      def 		_lowerCamelCase   (		self							):
         return 12
      @property
      def 		_lowerCamelCase   (		self							):
         return 12
      @property
      def 		_lowerCamelCase   (		self							):
         return 32
      @property
      def 		_lowerCamelCase   (		self							):
         torch.manual_seed(0							)
         A_  :					Tuple         =						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 		_lowerCamelCase   (		self							):
         A_  :					Dict         =						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)
         return tokenizer
      @property
      def 		_lowerCamelCase   (		self							):
         torch.manual_seed(0							)
         A_  :					Any         =						CLIPTextConfig(
             bos_token_id=0   ,							eos_token_id=2   ,							hidden_size=self.text_embedder_hidden_size   ,							intermediate_size=37   ,							layer_norm_eps=1E-05   ,							num_attention_heads=4   ,							num_hidden_layers=5   ,							pad_token_id=1   ,							vocab_size=1000   ,							)
         return CLIPTextModel(__UpperCAmelCase							)
      @property
      def 		_lowerCamelCase   (		self							):
         torch.manual_seed(0							)

         A_  :					Dict         =						12
         A_  :					Tuple         =						12

         A_  :					Optional[int]         =						{
             """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""",
         }

         A_  :					List[Any]         =						TransformeraDModel(**__UpperCAmelCase							)
         return model
      def 		_lowerCamelCase   (		self							):
         A_  :					Dict         =						"""cpu"""

         A_  :					Any         =						self.dummy_vqvae
         A_  :					List[Any]         =						self.dummy_text_encoder
         A_  :					Optional[Any]         =						self.dummy_tokenizer
         A_  :					Any         =						self.dummy_transformer
         A_  :					Tuple         =						VQDiffusionScheduler(self.num_embed							)
         A_  :					str         =						LearnedClassifierFreeSamplingEmbeddings(learnable=__UpperCAmelCase							)

         A_  :					Optional[Any]         =						VQDiffusionPipeline(
             vqvae=__UpperCAmelCase   ,							text_encoder=__UpperCAmelCase   ,							tokenizer=__UpperCAmelCase   ,							transformer=__UpperCAmelCase   ,							scheduler=__UpperCAmelCase   ,							learned_classifier_free_sampling_embeddings=__UpperCAmelCase   ,							)
         A_  :					Tuple         =						pipe.to(__UpperCAmelCase							)
         pipe.set_progress_bar_config(disable=__UpperCAmelCase							)

         A_  :					Optional[Any]         =						"""teddy bear playing in the pool"""

         A_  :					str         =						torch.Generator(device=__UpperCAmelCase							).manual_seed(0							)
         A_  :					Union[str, Any]         =						pipe([prompt]   ,							generator=__UpperCAmelCase   ,							num_inference_steps=2   ,							output_type="""np"""							)
         A_  :					str         =						output.images

         A_  :					Union[str, Any]         =						torch.Generator(device=__UpperCAmelCase							).manual_seed(0							)
         A_  :					Optional[Any]         =						pipe(
             [prompt]   ,							generator=__UpperCAmelCase   ,							output_type="""np"""   ,							return_dict=__UpperCAmelCase   ,							num_inference_steps=2							)[0]

         A_  :					str         =						image[0, -3:, -3:, -1]
         A_  :					Dict         =						image_from_tuple[0, -3:, -3:, -1]

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

         A_  :					int         =						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 		_lowerCamelCase   (		self							):
         A_  :					Union[str, Any]         =						"""cpu"""

         A_  :					Optional[int]         =						self.dummy_vqvae
         A_  :					Optional[Any]         =						self.dummy_text_encoder
         A_  :					Any         =						self.dummy_tokenizer
         A_  :					Optional[int]         =						self.dummy_transformer
         A_  :					List[Any]         =						VQDiffusionScheduler(self.num_embed							)
         A_  :					Dict         =						LearnedClassifierFreeSamplingEmbeddings(
             learnable=__UpperCAmelCase   ,							hidden_size=self.text_embedder_hidden_size   ,							length=tokenizer.model_max_length							)

         A_  :					List[Any]         =						VQDiffusionPipeline(
             vqvae=__UpperCAmelCase   ,							text_encoder=__UpperCAmelCase   ,							tokenizer=__UpperCAmelCase   ,							transformer=__UpperCAmelCase   ,							scheduler=__UpperCAmelCase   ,							learned_classifier_free_sampling_embeddings=__UpperCAmelCase   ,							)
         A_  :					Tuple         =						pipe.to(__UpperCAmelCase							)
         pipe.set_progress_bar_config(disable=__UpperCAmelCase							)

         A_  :					Optional[int]         =						"""teddy bear playing in the pool"""

         A_  :					int         =						torch.Generator(device=__UpperCAmelCase							).manual_seed(0							)
         A_  :					Any         =						pipe([prompt]   ,							generator=__UpperCAmelCase   ,							num_inference_steps=2   ,							output_type="""np"""							)
         A_  :					Dict         =						output.images

         A_  :					Dict         =						torch.Generator(device=__UpperCAmelCase							).manual_seed(0							)
         A_  :					List[str]         =						pipe(
             [prompt]   ,							generator=__UpperCAmelCase   ,							output_type="""np"""   ,							return_dict=__UpperCAmelCase   ,							num_inference_steps=2							)[0]

         A_  :					Optional[int]         =						image[0, -3:, -3:, -1]
         A_  :					Tuple         =						image_from_tuple[0, -3:, -3:, -1]

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

         A_  :					Tuple         =						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  _UpperCAmelCase				(		unittest.TestCase  ):
      def 		_lowerCamelCase   (		self							):
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()






      def 		_lowerCamelCase   (		self							):
         A_  :					Optional[Any]         =						load_numpy(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy"""							)

         A_  :					Any         =						VQDiffusionPipeline.from_pretrained("""microsoft/vq-diffusion-ithq"""							)
         A_  :					Dict         =						pipeline.to(__UpperCAmelCase							)
         pipeline.set_progress_bar_config(disable=__UpperCAmelCase							)

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

         A_  :					List[str]         =						output.images[0]

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














from typing import Any

import numpy as np
def 		_a       ( a					:np.ndarray					)				->     bool:
 return np.array_equal(a      ,				matrix.conjugate().T					)
def 		_a       ( a					:np.ndarray      ,				a					:np.ndarray					)				->     Any:
 a						    = v.conjugate().T
 a						    = v_star.dot(a					)
 assert isinstance(a      ,				np.ndarray					)
 return (v_star_dot.dot(a					)) / (v_star.dot(a					))
def 		_a       ( )				->     None:
 a						    = np.array([[2, 2 + 1j, 4], [2 - 1j, 3, 1j], [4, -1j, 1]]					)
 a						    = np.array([[1], [2], [3]]					)
 assert is_hermitian(a					), F"""{a} is not hermitian."""
 print(rayleigh_quotient(a      ,				a					)					)

 a						    = np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]]					)
 assert is_hermitian(a					), F"""{a} is not hermitian."""
 assert rayleigh_quotient(a      ,				a					) == float(3					)


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     tests()


