





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       __A							(   unittest.TestCase	):




      """simple docstring"""







      def 					__snake_case      (		self):



             """simple docstring"""





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


      @property
      def 					__snake_case      (		self):



             """simple docstring"""





             return 12


      @property
      def 					__snake_case      (		self):



             """simple docstring"""





             return 12


      @property
      def 					__snake_case      (		self):



             """simple docstring"""





             return 32


      @property
      def 					__snake_case      (		self):



             """simple docstring"""





             torch.manual_seed(0)
             _lowerCamelCase				:  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 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  List[str]						=		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')
             return tokenizer


      @property
      def 					__snake_case      (		self):



             """simple docstring"""





             torch.manual_seed(0)
             _lowerCamelCase				:  Tuple						=		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(a__)


      @property
      def 					__snake_case      (		self):



             """simple docstring"""





             torch.manual_seed(0)

             _lowerCamelCase				:  int						=		12
             _lowerCamelCase				:  Optional[int]						=		12

             _lowerCamelCase				:  List[str]						=		{
                 '''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''',
             }

             _lowerCamelCase				:  Optional[Any]						=		TransformeraDModel(**a__)
             return model


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Any						=		'''cpu'''

             _lowerCamelCase				:  Dict						=		self.dummy_vqvae
             _lowerCamelCase				:  Optional[int]						=		self.dummy_text_encoder
             _lowerCamelCase				:  str						=		self.dummy_tokenizer
             _lowerCamelCase				:  Dict						=		self.dummy_transformer
             _lowerCamelCase				:  List[str]						=		VQDiffusionScheduler(self.num_embed)
             _lowerCamelCase				:  Optional[int]						=		LearnedClassifierFreeSamplingEmbeddings(learnable=a__)

             _lowerCamelCase				:  Any						=		VQDiffusionPipeline(
                 vqvae=a__   ,				text_encoder=a__   ,				tokenizer=a__   ,				transformer=a__   ,				scheduler=a__   ,				learned_classifier_free_sampling_embeddings=a__   ,				)
             _lowerCamelCase				:  Tuple						=		pipe.to(a__)
             pipe.set_progress_bar_config(disable=a__)

             _lowerCamelCase				:  Any						=		'''teddy bear playing in the pool'''

             _lowerCamelCase				:  str						=		torch.Generator(device=a__).manual_seed(0)
             _lowerCamelCase				:  Tuple						=		pipe([prompt]   ,				generator=a__   ,				num_inference_steps=2   ,				output_type='''np''')
             _lowerCamelCase				:  List[Any]						=		output.images

             _lowerCamelCase				:  Optional[int]						=		torch.Generator(device=a__).manual_seed(0)
             _lowerCamelCase				:  Optional[int]						=		pipe(
                 [prompt]   ,				generator=a__   ,				output_type='''np'''   ,				return_dict=a__   ,				num_inference_steps=2)[0]

             _lowerCamelCase				:  List[Any]						=		image[0, -3:, -3:, -1]
             _lowerCamelCase				:  List[Any]						=		image_from_tuple[0, -3:, -3:, -1]

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

             _lowerCamelCase				:  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 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Tuple						=		'''cpu'''

             _lowerCamelCase				:  Optional[int]						=		self.dummy_vqvae
             _lowerCamelCase				:  Union[str, Any]						=		self.dummy_text_encoder
             _lowerCamelCase				:  Union[str, Any]						=		self.dummy_tokenizer
             _lowerCamelCase				:  Dict						=		self.dummy_transformer
             _lowerCamelCase				:  int						=		VQDiffusionScheduler(self.num_embed)
             _lowerCamelCase				:  Optional[int]						=		LearnedClassifierFreeSamplingEmbeddings(
                 learnable=a__   ,				hidden_size=self.text_embedder_hidden_size   ,				length=tokenizer.model_max_length)

             _lowerCamelCase				:  Any						=		VQDiffusionPipeline(
                 vqvae=a__   ,				text_encoder=a__   ,				tokenizer=a__   ,				transformer=a__   ,				scheduler=a__   ,				learned_classifier_free_sampling_embeddings=a__   ,				)
             _lowerCamelCase				:  int						=		pipe.to(a__)
             pipe.set_progress_bar_config(disable=a__)

             _lowerCamelCase				:  Optional[Any]						=		'''teddy bear playing in the pool'''

             _lowerCamelCase				:  Dict						=		torch.Generator(device=a__).manual_seed(0)
             _lowerCamelCase				:  int						=		pipe([prompt]   ,				generator=a__   ,				num_inference_steps=2   ,				output_type='''np''')
             _lowerCamelCase				:  Tuple						=		output.images

             _lowerCamelCase				:  List[Any]						=		torch.Generator(device=a__).manual_seed(0)
             _lowerCamelCase				:  str						=		pipe(
                 [prompt]   ,				generator=a__   ,				output_type='''np'''   ,				return_dict=a__   ,				num_inference_steps=2)[0]

             _lowerCamelCase				:  Optional[int]						=		image[0, -3:, -3:, -1]
             _lowerCamelCase				:  Union[str, Any]						=		image_from_tuple[0, -3:, -3:, -1]

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

             _lowerCamelCase				:  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       __A							(   unittest.TestCase	):




      """simple docstring"""







      def 					__snake_case      (		self):



             """simple docstring"""





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



      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Any						=		load_numpy(
                 '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                 '''/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy''')

             _lowerCamelCase				:  Optional[Any]						=		VQDiffusionPipeline.from_pretrained('''microsoft/vq-diffusion-ithq''')
             _lowerCamelCase				:  Union[str, Any]						=		pipeline.to(a__)
             pipeline.set_progress_bar_config(disable=a__)

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

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

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








from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCamelCase      							=   logging.get_logger(__name__)

_lowerCamelCase      							=   {
    'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      UpperCAmelCase__							=   """vit_msn"""


      def __init__(		self   ,				a__=768   ,				a__=12   ,				a__=12   ,				a__=3072   ,				a__="gelu"   ,				a__=0.0   ,				a__=0.0   ,				a__=0.02   ,				a__=1e-06   ,				a__=224   ,				a__=16   ,				a__=3   ,				a__=True   ,				**a__   ,				):



             """simple docstring"""





             super().__init__(**a__)

             _lowerCamelCase				:  Tuple						=		hidden_size
             _lowerCamelCase				:  str						=		num_hidden_layers
             _lowerCamelCase				:  int						=		num_attention_heads
             _lowerCamelCase				:  Optional[int]						=		intermediate_size
             _lowerCamelCase				:  Union[str, Any]						=		hidden_act
             _lowerCamelCase				:  str						=		hidden_dropout_prob
             _lowerCamelCase				:  List[Any]						=		attention_probs_dropout_prob
             _lowerCamelCase				:  Optional[int]						=		initializer_range
             _lowerCamelCase				:  Optional[Any]						=		layer_norm_eps
             _lowerCamelCase				:  Dict						=		image_size
             _lowerCamelCase				:  Tuple						=		patch_size
             _lowerCamelCase				:  List[str]						=		num_channels
             _lowerCamelCase				:  Optional[int]						=		qkv_bias

