



'''simple docstring'''





import gc
import random
import unittest

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

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

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class     UpperCAmelCase_       (       _a		,		_a		,		unittest.TestCase	):
    """simple docstring"""







    lowercase					=		StableDiffusionXLImgaImgPipeline
    lowercase					=		TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
    lowercase					=		PipelineTesterMixin.required_optional_params - {"latents"}
    lowercase					=		TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    lowercase					=		IMAGE_TO_IMAGE_IMAGE_PARAMS
    lowercase					=		IMAGE_TO_IMAGE_IMAGE_PARAMS




    def lowerCamelCase       (    self					:     List[Any]						):
          torch.manual_seed(0						)
          snake_case__  :    List[str]    						=      UNetaDConditionModel(
              block_out_channels=(32, 64) ,     layers_per_block=2 ,     sample_size=32 ,     in_channels=4 ,     out_channels=4 ,     down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") ,     up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") ,     attention_head_dim=(2, 4) ,     use_linear_projection=snake_case_ ,     addition_embed_type="""text_time""" ,     addition_time_embed_dim=8 ,     transformer_layers_per_block=(1, 2) ,     projection_class_embeddings_input_dim=80 ,     cross_attention_dim=64 ,     )
          snake_case__  :    Optional[int]    						=      EulerDiscreteScheduler(
              beta_start=0.00085 ,     beta_end=0.012 ,     steps_offset=1 ,     beta_schedule="""scaled_linear""" ,     timestep_spacing="""leading""" ,     )
          torch.manual_seed(0						)
          snake_case__  :    Dict    						=      AutoencoderKL(
              block_out_channels=[32, 64] ,     in_channels=3 ,     out_channels=3 ,     down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] ,     up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] ,     latent_channels=4 ,     sample_size=128 ,     )
          torch.manual_seed(0						)
          snake_case__  :    int    						=      CLIPTextConfig(
              bos_token_id=0 ,     eos_token_id=2 ,     hidden_size=32 ,     intermediate_size=37 ,     layer_norm_eps=1E-0_5 ,     num_attention_heads=4 ,     num_hidden_layers=5 ,     pad_token_id=1 ,     vocab_size=1_000 ,     hidden_act="""gelu""" ,     projection_dim=32 ,     )
          snake_case__  :    Optional[Any]    						=      CLIPTextModel(snake_case_						)
          snake_case__  :    Optional[int]    						=      CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" ,     local_files_only=snake_case_						)

          snake_case__  :    Dict    						=      CLIPTextModelWithProjection(snake_case_						)
          snake_case__  :    List[Any]    						=      CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" ,     local_files_only=snake_case_						)

          snake_case__  :    List[str]    						=      {
              """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 lowerCamelCase       (    self					:     Dict ,     snake_case_					:     str ,     snake_case_					:     Tuple=0						):
          snake_case__  :    Optional[int]    						=      floats_tensor((1, 3, 32, 32) ,     rng=random.Random(snake_case_						)						).to(snake_case_						)
          snake_case__  :    Union[str, Any]    						=      image / 2 + 0.5
          if str(snake_case_						).startswith("""mps"""						):
                snake_case__  :    List[str]    						=      torch.manual_seed(snake_case_						)
          else:
                snake_case__  :    List[Any]    						=      torch.Generator(device=snake_case_						).manual_seed(snake_case_						)
          snake_case__  :    Optional[Any]    						=      {
              """prompt""": """A painting of a squirrel eating a burger""",
              """image""": image,
              """generator""": generator,
              """num_inference_steps""": 2,
              """guidance_scale""": 5.0,
              """output_type""": """numpy""",
              """strength""": 0.75,
          }
          return inputs




    def lowerCamelCase       (    self					:     Dict						):
          snake_case__  :    List[Any]    						=      """cpu"""  # ensure determinism for the device-dependent torch.Generator
          snake_case__  :    Optional[Any]    						=      self.get_dummy_components()
          snake_case__  :    Any    						=      StableDiffusionXLImgaImgPipeline(**snake_case_						)
          snake_case__  :    Optional[int]    						=      sd_pipe.to(snake_case_						)
          sd_pipe.set_progress_bar_config(disable=snake_case_						)

          snake_case__  :    Optional[Any]    						=      self.get_dummy_inputs(snake_case_						)
          snake_case__  :    Any    						=      sd_pipe(**snake_case_						).images
          snake_case__  :    Optional[Any]    						=      image[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)

          snake_case__  :    Dict    						=      np.array([0.4656, 0.4840, 0.4439, 0.6698, 0.5574, 0.4524, 0.5799, 0.5943, 0.5165]						)

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




    def lowerCamelCase       (    self					:     Union[str, Any]						):
          super().test_attention_slicing_forward_pass(expected_max_diff=3E-3						)




    def lowerCamelCase       (    self					:     Any						):
          super().test_inference_batch_single_identical(expected_max_diff=3E-3						)




    def lowerCamelCase       (    self					:     Any						):
          pass




    def lowerCamelCase       (    self					:     List[str]						):
          snake_case__  :    Union[str, Any]    						=      self.get_dummy_components()
          snake_case__  :    str    						=      StableDiffusionXLImgaImgPipeline(**snake_case_						)
          snake_case__  :    Optional[int]    						=      sd_pipe.to(snake_case_						)
          snake_case__  :    Optional[Any]    						=      sd_pipe.to(snake_case_						)
          sd_pipe.set_progress_bar_config(disable=snake_case_						)

          # forward without prompt embeds
          snake_case__  :    Union[str, Any]    						=      self.get_dummy_inputs(snake_case_						)
          snake_case__  :    List[Any]    						=      3 * ["""this is a negative prompt"""]
          snake_case__  :    Union[str, Any]    						=      negative_prompt
          snake_case__  :    Optional[Any]    						=      3 * [inputs["""prompt"""]]

          snake_case__  :    str    						=      sd_pipe(**snake_case_						)
          snake_case__  :    str    						=      output.images[0, -3:, -3:, -1]

          # forward with prompt embeds
          snake_case__  :    Union[str, Any]    						=      self.get_dummy_inputs(snake_case_						)
          snake_case__  :    Any    						=      3 * ["""this is a negative prompt"""]
          snake_case__  :    Any    						=      3 * [inputs.pop("""prompt"""						)]

          (
              (
              snake_case__
          )						,				(
              snake_case__
          )						,				(
              snake_case__
          )						,				(
              snake_case__
          )						,				
          )  :    Union[str, Any]    						=      sd_pipe.encode_prompt(snake_case_ ,     negative_prompt=snake_case_						)

          snake_case__  :    List[str]    						=      sd_pipe(
              **snake_case_ ,     prompt_embeds=snake_case_ ,     negative_prompt_embeds=snake_case_ ,     pooled_prompt_embeds=snake_case_ ,     negative_pooled_prompt_embeds=snake_case_ ,     )
          snake_case__  :    Optional[int]    						=      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







@slow
@require_torch_gpu
class     UpperCAmelCase_       (       unittest.TestCase	):
    """simple docstring"""







    def lowerCamelCase       (    self					:     Union[str, Any]						):
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()




    def lowerCamelCase       (    self					:     Optional[Any] ,     snake_case_					:     Optional[int] ,     snake_case_					:     Optional[int]="cpu" ,     snake_case_					:     Dict=torch.floataa ,     snake_case_					:     Optional[int]=0						):
          snake_case__  :    Tuple    						=      torch.Generator(device=snake_case_						).manual_seed(snake_case_						)
          snake_case__  :    List[Any]    						=      np.random.RandomState(snake_case_						).standard_normal((1, 4, 64, 64)						)
          snake_case__  :    str    						=      torch.from_numpy(snake_case_						).to(device=snake_case_ ,     dtype=snake_case_						)
          snake_case__  :    List[Any]    						=      {
              """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 lowerCamelCase       (    self					:     List[Any]						):
          snake_case__  :    Optional[Any]    						=      DiffusionPipeline.from_pretrained("""stabilityai/stable-diffusion-2-base"""						)
          pipe.to(snake_case_						)
          pipe.set_progress_bar_config(disable=snake_case_						)

          snake_case__  :    Optional[Any]    						=      self.get_inputs(snake_case_						)
          snake_case__  :    List[str]    						=      pipe(**snake_case_						).images
          snake_case__  :    Dict    						=      image[0, -3:, -3:, -1].flatten()

          assert image.shape == (1, 512, 512, 3)
          snake_case__  :    int    						=      np.array([0.49493, 0.47896, 0.40798, 0.54214, 0.53212, 0.48202, 0.47656, 0.46329, 0.48506]						)
          assert np.abs(image_slice - expected_slice						).max() < 7E-3





'''simple docstring'''





from typing import TYPE_CHECKING

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


__a          =		{
    "configuration_bloom": ["BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP", "BloomConfig", "BloomOnnxConfig"],
}
try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __a          =		["BloomTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __a          =		[
           "BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST",
           "BloomForCausalLM",
           "BloomModel",
           "BloomPreTrainedModel",
           "BloomForSequenceClassification",
           "BloomForTokenClassification",
           "BloomForQuestionAnswering",
       ]

if TYPE_CHECKING:
       from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_bloom_fast import BloomTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_bloom import (
                  BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  BloomForCausalLM,
                  BloomForQuestionAnswering,
                  BloomForSequenceClassification,
                  BloomForTokenClassification,
                  BloomModel,
                  BloomPreTrainedModel,
              )

else:
       import sys

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