


"""simple docstring"""



import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()
class 					__magic_name__		(			_UpperCAmelCase			,						_UpperCAmelCase			,						_UpperCAmelCase			,						unittest.TestCase     ):
       __A			:      Tuple        =   StableUnCLIPPipeline
       __A			:      Dict        =   TEXT_TO_IMAGE_PARAMS
       __A			:      List[Any]        =   TEXT_TO_IMAGE_BATCH_PARAMS
       __A			:      Any        =   TEXT_TO_IMAGE_IMAGE_PARAMS
       __A			:      Optional[int]        =   TEXT_TO_IMAGE_IMAGE_PARAMS

       # TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
       __A			:      Optional[int]        =   False






       def  __snake_case						(   self  :    Optional[Any]				):

              '''simple docstring'''






              lowercase		:Optional[Any]       							=				3_2
              lowercase		:List[Any]       							=				embedder_hidden_size

              # prior components

              torch.manual_seed(0				)
              lowercase		:str       							=				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

              torch.manual_seed(0				)
              lowercase		:Any       							=				CLIPTextModelWithProjection(
                  CLIPTextConfig(
                      bos_token_id=0  ,     eos_token_id=2  ,     hidden_size=A_  ,     projection_dim=A_  ,     intermediate_size=3_7  ,     layer_norm_eps=1e-0_5  ,     num_attention_heads=4  ,     num_hidden_layers=5  ,     pad_token_id=1  ,     vocab_size=1_0_0_0  ,     )				)

              torch.manual_seed(0				)
              lowercase		:str       							=				PriorTransformer(
                  num_attention_heads=2  ,     attention_head_dim=1_2  ,     embedding_dim=A_  ,     num_layers=1  ,     )

              torch.manual_seed(0				)
              lowercase		:List[Any]       							=				DDPMScheduler(
                  variance_type='''fixed_small_log'''  ,     prediction_type='''sample'''  ,     num_train_timesteps=1_0_0_0  ,     clip_sample=A_  ,     clip_sample_range=5.0  ,     beta_schedule='''squaredcos_cap_v2'''  ,     )

              # regular denoising components

              torch.manual_seed(0				)
              lowercase		:Optional[Any]       							=				StableUnCLIPImageNormalizer(embedding_dim=A_				)
              lowercase		:Optional[int]       							=				DDPMScheduler(beta_schedule='''squaredcos_cap_v2'''				)

              torch.manual_seed(0				)
              lowercase		:Optional[int]       							=				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

              torch.manual_seed(0				)
              lowercase		:Dict       							=				CLIPTextModel(
                  CLIPTextConfig(
                      bos_token_id=0  ,     eos_token_id=2  ,     hidden_size=A_  ,     projection_dim=3_2  ,     intermediate_size=3_7  ,     layer_norm_eps=1e-0_5  ,     num_attention_heads=4  ,     num_hidden_layers=5  ,     pad_token_id=1  ,     vocab_size=1_0_0_0  ,     )				)

              torch.manual_seed(0				)
              lowercase		:Dict       							=				UNetaDConditionModel(
                  sample_size=3_2  ,     in_channels=4  ,     out_channels=4  ,     down_block_types=('''CrossAttnDownBlock2D''', '''DownBlock2D''')  ,     up_block_types=('''UpBlock2D''', '''CrossAttnUpBlock2D''')  ,     block_out_channels=(3_2, 6_4)  ,     attention_head_dim=(2, 4)  ,     class_embed_type='''projection'''  ,     projection_class_embeddings_input_dim=embedder_projection_dim * 2  ,     cross_attention_dim=A_  ,     layers_per_block=1  ,     upcast_attention=A_  ,     use_linear_projection=A_  ,     )

              torch.manual_seed(0				)
              lowercase		:Optional[Any]       							=				DDIMScheduler(
                  beta_schedule='''scaled_linear'''  ,     beta_start=0.0_00_85  ,     beta_end=0.0_12  ,     prediction_type='''v_prediction'''  ,     set_alpha_to_one=A_  ,     steps_offset=1  ,     )

              torch.manual_seed(0				)
              lowercase		:int       							=				AutoencoderKL()

              lowercase		:Any       							=				{
                  # prior components
                  '''prior_tokenizer''': prior_tokenizer,
                  '''prior_text_encoder''': prior_text_encoder,
                  '''prior''': prior,
                  '''prior_scheduler''': prior_scheduler,
                  # image noising components
                  '''image_normalizer''': image_normalizer,
                  '''image_noising_scheduler''': image_noising_scheduler,
                  # regular denoising components
                  '''tokenizer''': tokenizer,
                  '''text_encoder''': text_encoder,
                  '''unet''': unet,
                  '''scheduler''': scheduler,
                  '''vae''': vae,
              }

              return components






       def  __snake_case						(   self  :    Tuple  ,     snake_case__  :    int  ,     snake_case__  :    Optional[Any]=0				):

              '''simple docstring'''






              if str(A_				).startswith('''mps'''				):
                     lowercase		:Any       							=				torch.manual_seed(A_				)
              else:
                     lowercase		:Any       							=				torch.Generator(device=A_				).manual_seed(A_				)
              lowercase		:List[str]       							=				{
                  '''prompt''': '''A painting of a squirrel eating a burger''',
                  '''generator''': generator,
                  '''num_inference_steps''': 2,
                  '''prior_num_inference_steps''': 2,
                  '''output_type''': '''numpy''',
              }
              return inputs






       def  __snake_case						(   self  :    List[Any]				):

              '''simple docstring'''






              lowercase		:str       							=				torch_device == '''cpu'''

              self._test_attention_slicing_forward_pass(test_max_difference=A_				)







       def  __snake_case						(   self  :    Any				):

              '''simple docstring'''






              lowercase		:Dict       							=				torch_device in ['''cpu''', '''mps''']

              self._test_inference_batch_single_identical(test_max_difference=A_				)







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






       def  __snake_case						(   self  :    List[str]				):

              '''simple docstring'''






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






       def  __snake_case						(   self  :    Dict				):

              '''simple docstring'''






              lowercase		:Any       							=				load_numpy(
                  '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy'''				)

              lowercase		:List[Any]       							=				StableUnCLIPPipeline.from_pretrained('''fusing/stable-unclip-2-1-l'''  ,     torch_dtype=torch.floataa				)
              pipe.to(A_				)
              pipe.set_progress_bar_config(disable=A_				)
              # stable unclip will oom when integration tests are run on a V100,
              # so turn on memory savings
              pipe.enable_attention_slicing()
              pipe.enable_sequential_cpu_offload()

              lowercase		:List[Any]       							=				torch.Generator(device='''cpu'''				).manual_seed(0				)
              lowercase		:List[str]       							=				pipe('''anime turle'''  ,     generator=A_  ,     output_type='''np'''				)

              lowercase		:Tuple       							=				output.images[0]

              assert image.shape == (7_6_8, 7_6_8, 3)

              assert_mean_pixel_difference(A_  ,     A_				)







       def  __snake_case						(   self  :    Tuple				):

              '''simple docstring'''






              torch.cuda.empty_cache()
              torch.cuda.reset_max_memory_allocated()
              torch.cuda.reset_peak_memory_stats()

              lowercase		:int       							=				StableUnCLIPPipeline.from_pretrained('''fusing/stable-unclip-2-1-l'''  ,     torch_dtype=torch.floataa				)
              lowercase		:int       							=				pipe.to(A_				)
              pipe.set_progress_bar_config(disable=A_				)
              pipe.enable_attention_slicing()
              pipe.enable_sequential_cpu_offload()

              lowercase		:str       							=				pipe(
                  '''anime turtle'''  ,     prior_num_inference_steps=2  ,     num_inference_steps=2  ,     output_type='''np'''  ,     )

              lowercase		:int       							=				torch.cuda.max_memory_allocated()
              # make sure that less than 7 GB is allocated
              assert mem_bytes < 7 * 1_0**9










"""simple docstring"""



from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase      		=			logging.get_logger(__name__)

UpperCAmelCase      		=			{
    '''facebook/deit-base-distilled-patch16-224''': (
        '''https://huggingface.co/facebook/deit-base-patch16-224/resolve/main/config.json'''
    ),
    # See all DeiT models at https://huggingface.co/models?filter=deit
}
class 					__magic_name__		(			__UpperCAmelCase     ):
       __A			:      Tuple        =   "deit"






       def __init__(   self  :    List[str]  ,     snake_case__  :    int=7_6_8  ,     snake_case__  :    List[Any]=1_2  ,     snake_case__  :    Dict=1_2  ,     snake_case__  :    List[Any]=3_0_7_2  ,     snake_case__  :    Optional[int]="gelu"  ,     snake_case__  :    Dict=0.0  ,     snake_case__  :    str=0.0  ,     snake_case__  :    List[str]=0.02  ,     snake_case__  :    Optional[int]=1e-1_2  ,     snake_case__  :    Any=2_2_4  ,     snake_case__  :    Optional[int]=1_6  ,     snake_case__  :    int=3  ,     snake_case__  :    str=True  ,     snake_case__  :    Optional[int]=1_6  ,     **snake_case__  :    List[Any]  ,     ):

              '''simple docstring'''






              super().__init__(**snake_case__				)

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







class 					__magic_name__		(			__UpperCAmelCase     ):
       __A			:      Optional[int]        =   version.parse("1.11"     )






       @property
       def  __snake_case						(   self  :    int				):

              '''simple docstring'''






              return OrderedDict(
                  [
                      ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
                  ]				)







       @property
       def  __snake_case						(   self  :    Optional[int]				):

              '''simple docstring'''






              return 1e-4

