


'''simple docstring'''



import sys

from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

__UpperCAmelCase      :List[Any]			    =       "python tqdm regex requests packaging filelock numpy tokenizers".split()
if sys.version_info < (3, 7):
      pkgs_to_check_at_runtime.append("dataclasses")
if sys.version_info < (3, 8):
      pkgs_to_check_at_runtime.append("importlib_metadata")

for pkg in pkgs_to_check_at_runtime:
      if pkg in deps:
            if pkg == "tokenizers":
                  # must be loaded here, or else tqdm check may fail
                  from .utils import is_tokenizers_available

                  if not is_tokenizers_available():
                        continue  # not required, check version only if installed

            require_version_core(deps[pkg])
      else:
            raise ValueError(f"""can't find {pkg} in {deps.keys()}, check dependency_versions_table.py""")




def      _a     (						_lowercase :     str      , _lowercase :     Optional[int]=None  ):





     '''simple docstring'''






     require_version(deps[pkg]      , _lowercase  )



'''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      a    (		_a     ,  _a     ,  unittest.TestCase	):


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			Dict							       =       StableDiffusionXLImgaImgPipeline
     SCREAMING_SNAKE_CASE	:			Tuple							       =       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
     SCREAMING_SNAKE_CASE	:			Optional[Any]							       =       PipelineTesterMixin.required_optional_params - {"latents"}
     SCREAMING_SNAKE_CASE	:			int							       =       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     SCREAMING_SNAKE_CASE	:			Union[str, Any]							       =       IMAGE_TO_IMAGE_IMAGE_PARAMS
     SCREAMING_SNAKE_CASE	:			Any							       =       IMAGE_TO_IMAGE_IMAGE_PARAMS

     def 			lowerCamelCase__			(    self					:		Any      )						->							Any:
          torch.manual_seed(0      )
          __UpperCAmelCase     :			Optional[Any]  							=      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							,			)
          __UpperCAmelCase     :			int  							=      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     :			Tuple  							=      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      )
          __UpperCAmelCase     :			int  							=      CLIPTextConfig(
              bos_token_id=0							,			eos_token_id=2							,			hidden_size=32							,			intermediate_size=37							,			layer_norm_eps=1E-05							,			num_attention_heads=4							,			num_hidden_layers=5							,			pad_token_id=1							,			vocab_size=1000							,			hidden_act='''gelu'''							,			projection_dim=32							,			)
          __UpperCAmelCase     :			Tuple  							=      CLIPTextModel(snake_case      )
          __UpperCAmelCase     :			Optional[int]  							=      CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							,			local_files_only=snake_case      )

          __UpperCAmelCase     :			Optional[Any]  							=      CLIPTextModelWithProjection(snake_case      )
          __UpperCAmelCase     :			Union[str, Any]  							=      CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							,			local_files_only=snake_case      )

          __UpperCAmelCase     :			Tuple  							=      {
              '''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					:		Optional[int]							,			snake_case					:		List[str]=0      )						->							List[str]:
          __UpperCAmelCase     :			List[str]  							=      floats_tensor((1, 3, 32, 32)							,			rng=random.Random(snake_case      )      ).to(snake_case      )
          __UpperCAmelCase     :			Optional[Any]  							=      image / 2 + 0.5
          if str(snake_case      ).startswith('''mps'''      ):
               __UpperCAmelCase     :			List[str]  							=      torch.manual_seed(snake_case      )
          else:
               __UpperCAmelCase     :			Any  							=      torch.Generator(device=snake_case      ).manual_seed(snake_case      )
          __UpperCAmelCase     :			Optional[int]  							=      {
              '''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					:		Tuple      )						->							Optional[int]:
          __UpperCAmelCase     :			Any  							=      '''cpu'''  # ensure determinism for the device-dependent torch.Generator
          __UpperCAmelCase     :			Optional[Any]  							=      self.get_dummy_components()
          __UpperCAmelCase     :			Any  							=      StableDiffusionXLImgaImgPipeline(**snake_case      )
          __UpperCAmelCase     :			Union[str, Any]  							=      sd_pipe.to(snake_case      )
          sd_pipe.set_progress_bar_config(disable=snake_case      )

          __UpperCAmelCase     :			Optional[int]  							=      self.get_dummy_inputs(snake_case      )
          __UpperCAmelCase     :			int  							=      sd_pipe(**snake_case      ).images
          __UpperCAmelCase     :			List[str]  							=      image[0, -3:, -3:, -1]

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

          __UpperCAmelCase     :			Any  							=      np.array([0.4_656, 0.4_840, 0.4_439, 0.6_698, 0.5_574, 0.4_524, 0.5_799, 0.5_943, 0.5_165]      )

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

     def 			lowerCamelCase__			(    self					:		Any      )						->							int:
          super().test_attention_slicing_forward_pass(expected_max_diff=3E-3      )

     def 			lowerCamelCase__			(    self					:		Optional[Any]      )						->							Optional[int]:
          super().test_inference_batch_single_identical(expected_max_diff=3E-3      )

     def 			lowerCamelCase__			(    self					:		Optional[int]      )						->							List[Any]:
          pass






     def 			lowerCamelCase__			(    self					:		Optional[int]      )						->							int:
          __UpperCAmelCase     :			Union[str, Any]  							=      self.get_dummy_components()
          __UpperCAmelCase     :			List[Any]  							=      StableDiffusionXLImgaImgPipeline(**snake_case      )
          __UpperCAmelCase     :			Tuple  							=      sd_pipe.to(snake_case      )
          __UpperCAmelCase     :			Tuple  							=      sd_pipe.to(snake_case      )
          sd_pipe.set_progress_bar_config(disable=snake_case      )

          # forward without prompt embeds
          __UpperCAmelCase     :			Tuple  							=      self.get_dummy_inputs(snake_case      )
          __UpperCAmelCase     :			Optional[Any]  							=      3 * ['''this is a negative prompt''']
          __UpperCAmelCase     :			Optional[int]  							=      negative_prompt
          __UpperCAmelCase     :			Tuple  							=      3 * [inputs['''prompt''']]

          __UpperCAmelCase     :			Optional[Any]  							=      sd_pipe(**snake_case      )
          __UpperCAmelCase     :			int  							=      output.images[0, -3:, -3:, -1]

          # forward with prompt embeds
          __UpperCAmelCase     :			Union[str, Any]  							=      self.get_dummy_inputs(snake_case      )
          __UpperCAmelCase     :			Tuple  							=      3 * ['''this is a negative prompt''']
          __UpperCAmelCase     :			str  							=      3 * [inputs.pop('''prompt'''      )]

          (
              (
              __UpperCAmelCase
          )							,       (
              __UpperCAmelCase
          )							,       (
              __UpperCAmelCase
          )							,       (
              __UpperCAmelCase
          )							,       
          )     :			Any  							=      sd_pipe.encode_prompt(snake_case							,			negative_prompt=snake_case      )

          __UpperCAmelCase     :			Dict  							=      sd_pipe(
              **snake_case							,			prompt_embeds=snake_case							,			negative_prompt_embeds=snake_case							,			pooled_prompt_embeds=snake_case							,			negative_pooled_prompt_embeds=snake_case							,			)
          __UpperCAmelCase     :			List[str]  							=      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      a    (		unittest.TestCase	):


     """simple docstring"""






     def 			lowerCamelCase__			(    self					:		Union[str, Any]      )						->							Optional[Any]:
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()

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

          __UpperCAmelCase     :			str  							=      self.get_inputs(snake_case      )
          __UpperCAmelCase     :			Dict  							=      pipe(**snake_case      ).images
          __UpperCAmelCase     :			List[str]  							=      image[0, -3:, -3:, -1].flatten()

          assert image.shape == (1, 512, 512, 3)
          __UpperCAmelCase     :			Tuple  							=      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