

import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class     __magic_name__     (							_snake_case						,    _snake_case						,    unittest.TestCase     ):
 UpperCAmelCase				     =    StableDiffusionXLPipeline
 UpperCAmelCase				     =    TEXT_TO_IMAGE_PARAMS
 UpperCAmelCase				     =    TEXT_TO_IMAGE_BATCH_PARAMS
 UpperCAmelCase				     =    TEXT_TO_IMAGE_IMAGE_PARAMS
 UpperCAmelCase				     =    TEXT_TO_IMAGE_IMAGE_PARAMS


 def _UpperCamelCase						(    self		:			Tuple     )     ->   List[str]:
   torch.manual_seed(0     )
   UpperCAmelCase          =	UNetaDConditionModel(
       block_out_channels=(3_2, 6_4)      ,    layers_per_block=2      ,    sample_size=3_2      ,    in_channels=4      ,    out_channels=4      ,    down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")      ,    up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")      ,    attention_head_dim=(2, 4)      ,    use_linear_projection=lowerCAmelCase__      ,    addition_embed_type="text_time"      ,    addition_time_embed_dim=8      ,    transformer_layers_per_block=(1, 2)      ,    projection_class_embeddings_input_dim=8_0      ,    cross_attention_dim=6_4      ,    )
   UpperCAmelCase          =	EulerDiscreteScheduler(
       beta_start=0.00_085      ,    beta_end=0.012      ,    steps_offset=1      ,    beta_schedule="scaled_linear"      ,    timestep_spacing="leading"      ,    )
   torch.manual_seed(0     )
   UpperCAmelCase          =	AutoencoderKL(
       block_out_channels=[3_2, 6_4]      ,    in_channels=3      ,    out_channels=3      ,    down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]      ,    up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]      ,    latent_channels=4      ,    sample_size=1_2_8      ,    )
   torch.manual_seed(0     )
   UpperCAmelCase          =	CLIPTextConfig(
       bos_token_id=0      ,    eos_token_id=2      ,    hidden_size=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      ,    hidden_act="gelu"      ,    projection_dim=3_2      ,    )
   UpperCAmelCase          =	CLIPTextModel(lowerCAmelCase__     )
   UpperCAmelCase          =	CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"      ,    local_files_only=lowerCAmelCase__     )

   UpperCAmelCase          =	CLIPTextModelWithProjection(lowerCAmelCase__     )
   UpperCAmelCase          =	CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"      ,    local_files_only=lowerCAmelCase__     )

   UpperCAmelCase          =	{
       "unet": unet,
       "scheduler": scheduler,
       "vae": vae,
       "text_encoder": text_encoder,
       "tokenizer": tokenizer,
       "text_encoder_2": text_encoder_a,
       "tokenizer_2": tokenizer_a,
       # "safety_checker": None,
       # "feature_extractor": None,
   }
   return components


 def _UpperCamelCase						(    self		:			Optional[Any]      ,    lowerCAmelCase__		:			Any      ,    lowerCAmelCase__		:			Optional[Any]=0     )     ->   List[Any]:
   if str(lowerCAmelCase__     ).startswith("mps"     ):
     UpperCAmelCase          =	torch.manual_seed(lowerCAmelCase__     )
   else:
     UpperCAmelCase          =	torch.Generator(device=lowerCAmelCase__     ).manual_seed(lowerCAmelCase__     )
   UpperCAmelCase          =	{
       "prompt": "A painting of a squirrel eating a burger",
       "generator": generator,
       "num_inference_steps": 2,
       "guidance_scale": 5.0,
       "output_type": "numpy",
   }
   return inputs


 def _UpperCamelCase						(    self		:			Any     )     ->   List[Any]:
   UpperCAmelCase          =	"cpu"  # ensure determinism for the device-dependent torch.Generator
   UpperCAmelCase          =	self.get_dummy_components()
   UpperCAmelCase          =	StableDiffusionXLPipeline(**lowerCAmelCase__     )
   UpperCAmelCase          =	sd_pipe.to(lowerCAmelCase__     )
   sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__     )

   UpperCAmelCase          =	self.get_dummy_inputs(lowerCAmelCase__     )
   UpperCAmelCase          =	sd_pipe(**lowerCAmelCase__     ).images
   UpperCAmelCase          =	image[0, -3:, -3:, -1]

   assert image.shape == (1, 6_4, 6_4, 3)
   UpperCAmelCase          =	np.array([0.5_873, 0.6_128, 0.4_797, 0.5_122, 0.5_674, 0.4_639, 0.5_227, 0.5_149, 0.4_747]     )

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


 def _UpperCamelCase						(    self		:			Union[str, Any]     )     ->   List[str]:
   UpperCAmelCase          =	self.get_dummy_components()
   UpperCAmelCase          =	StableDiffusionXLPipeline(**lowerCAmelCase__     )
   UpperCAmelCase          =	sd_pipe.to(lowerCAmelCase__     )
   UpperCAmelCase          =	sd_pipe.to(lowerCAmelCase__     )
   sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__     )

   # forward without prompt embeds
   UpperCAmelCase          =	self.get_dummy_inputs(lowerCAmelCase__     )
   UpperCAmelCase          =	3 * ["this is a negative prompt"]
   UpperCAmelCase          =	negative_prompt
   UpperCAmelCase          =	3 * [inputs["prompt"]]

   UpperCAmelCase          =	sd_pipe(**lowerCAmelCase__     )
   UpperCAmelCase          =	output.images[0, -3:, -3:, -1]

   # forward with prompt embeds
   UpperCAmelCase          =	self.get_dummy_inputs(lowerCAmelCase__     )
   UpperCAmelCase          =	3 * ["this is a negative prompt"]
   UpperCAmelCase          =	3 * [inputs.pop("prompt"     )]

   (
       (
       UpperCAmelCase
   )   ,						(
       UpperCAmelCase
   )   ,						(
       UpperCAmelCase
   )   ,						(
       UpperCAmelCase
   )   ,						
   )          =	sd_pipe.encode_prompt(lowerCAmelCase__      ,    negative_prompt=lowerCAmelCase__     )

   UpperCAmelCase          =	sd_pipe(
       **lowerCAmelCase__      ,    prompt_embeds=lowerCAmelCase__      ,    negative_prompt_embeds=lowerCAmelCase__      ,    pooled_prompt_embeds=lowerCAmelCase__      ,    negative_pooled_prompt_embeds=lowerCAmelCase__      ,    )
   UpperCAmelCase          =	output.images[0, -3:, -3:, -1]

   # make sure that it's equal
   assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()     ).max() < 1e-4


 def _UpperCamelCase						(    self		:			List[Any]     )     ->   Union[str, Any]:
   super().test_attention_slicing_forward_pass(expected_max_diff=3e-3     )


 def _UpperCamelCase						(    self		:			Optional[Any]     )     ->   List[str]:
   super().test_inference_batch_single_identical(expected_max_diff=3e-3     )






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


 def _UpperCamelCase						(    self		:			Optional[int]     )     ->   str:
   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()


 def _UpperCamelCase						(    self		:			List[Any]      ,    lowerCAmelCase__		:			int      ,    lowerCAmelCase__		:			List[Any]="cpu"      ,    lowerCAmelCase__		:			Dict=torch.floataa      ,    lowerCAmelCase__		:			Tuple=0     )     ->   Any:
   UpperCAmelCase          =	torch.Generator(device=lowerCAmelCase__     ).manual_seed(lowerCAmelCase__     )
   UpperCAmelCase          =	np.random.RandomState(lowerCAmelCase__     ).standard_normal((1, 4, 6_4, 6_4)     )
   UpperCAmelCase          =	torch.from_numpy(lowerCAmelCase__     ).to(device=lowerCAmelCase__      ,    dtype=lowerCAmelCase__     )
   UpperCAmelCase          =	{
       "prompt": "a photograph of an astronaut riding a horse",
       "latents": latents,
       "generator": generator,
       "num_inference_steps": 3,
       "guidance_scale": 7.5,
       "output_type": "numpy",
   }
   return inputs


 def _UpperCamelCase						(    self		:			Optional[Any]     )     ->   int:
   UpperCAmelCase          =	DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-base"     )
   pipe.to(lowerCAmelCase__     )
   pipe.set_progress_bar_config(disable=lowerCAmelCase__     )

   UpperCAmelCase          =	self.get_inputs(lowerCAmelCase__     )
   UpperCAmelCase          =	pipe(**lowerCAmelCase__     ).images
   UpperCAmelCase          =	image[0, -3:, -3:, -1].flatten()

   assert image.shape == (1, 5_1_2, 5_1_2, 3)
   UpperCAmelCase          =	np.array([0.49_493, 0.47_896, 0.40_798, 0.54_214, 0.53_212, 0.48_202, 0.47_656, 0.46_329, 0.48_506]     )
   assert np.abs(image_slice - expected_slice     ).max() < 7e-3






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


lowerCAmelCase__							=			{
    "configuration_xlm_prophetnet": ["XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMProphetNetConfig"],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowerCAmelCase__							=			["XLMProphetNetTokenizer"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowerCAmelCase__							=			[
          "XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST",
          "XLMProphetNetDecoder",
          "XLMProphetNetEncoder",
          "XLMProphetNetForCausalLM",
          "XLMProphetNetForConditionalGeneration",
          "XLMProphetNetModel",
          "XLMProphetNetPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_xlm_prophetnet import (
                XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                XLMProphetNetDecoder,
                XLMProphetNetEncoder,
                XLMProphetNetForCausalLM,
                XLMProphetNetForConditionalGeneration,
                XLMProphetNetModel,
                XLMProphetNetPreTrainedModel,
            )

else:
      import sys

      lowerCAmelCase__							=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



