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 		_lowercase						(					_lowerCamelCase						,      _lowerCamelCase						,      unittest.TestCase       ):
 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 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]			)   ->						Union[str, Any]:

  """simple docstring"""





  torch.manual_seed(0			)
  UpperCamelCase_					:						Any =					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=a_   ,      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_					:						List[str] =					EulerDiscreteScheduler(
      beta_start=0.00085   ,      beta_end=0.012   ,      steps_offset=1   ,      beta_schedule='scaled_linear'   ,      timestep_spacing='leading'   ,      )
  torch.manual_seed(0			)
  UpperCamelCase_					:						Union[str, Any] =					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_					:						List[Any] =					CLIPTextConfig(
      bos_token_id=0   ,      eos_token_id=2   ,      hidden_size=3_2   ,      intermediate_size=3_7   ,      layer_norm_eps=1e-05   ,      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_					:						Union[str, Any] =					CLIPTextModel(a_			)
  UpperCamelCase_					:						str =					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'   ,      local_files_only=a_			)

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

  UpperCamelCase_					:						Optional[int] =					{
      """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 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[Any]   ,      snake_case	:							Dict   ,      snake_case	:							Optional[int]=0			)   ->						int:

  """simple docstring"""





  UpperCamelCase_					:						str =					floats_tensor((1, 3, 3_2, 3_2)   ,      rng=random.Random(a_			)			).to(a_			)
  UpperCamelCase_					:						Dict =					image / 2 + 0.5
  if str(a_			).startswith('mps'			):
   UpperCamelCase_					:						List[Any] =					torch.manual_seed(a_			)
  else:
   UpperCamelCase_					:						Optional[Any] =					torch.Generator(device=a_			).manual_seed(a_			)
  UpperCamelCase_					:						List[str] =					{
      """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 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]			)   ->						Union[str, Any]:

  """simple docstring"""





  UpperCamelCase_					:						int =					"""cpu"""  # ensure determinism for the device-dependent torch.Generator
  UpperCamelCase_					:						Optional[int] =					self.get_dummy_components()
  UpperCamelCase_					:						str =					StableDiffusionXLImgaImgPipeline(**a_			)
  UpperCamelCase_					:						Any =					sd_pipe.to(a_			)
  sd_pipe.set_progress_bar_config(disable=a_			)

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

  assert image.shape == (1, 3_2, 3_2, 3)

  UpperCamelCase_					:						List[str] =					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 						SCREAMING_SNAKE_CASE__   ( self	:							int			)   ->						Optional[int]:

  """simple docstring"""





  super().test_attention_slicing_forward_pass(expected_max_diff=3e-3			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Dict			)   ->						int:

  """simple docstring"""





  super().test_inference_batch_single_identical(expected_max_diff=3e-3			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any			)   ->						Tuple:

  """simple docstring"""





  pass


 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any			)   ->						int:

  """simple docstring"""





  UpperCamelCase_					:						str =					self.get_dummy_components()
  UpperCamelCase_					:						Tuple =					StableDiffusionXLImgaImgPipeline(**a_			)
  UpperCamelCase_					:						Any =					sd_pipe.to(a_			)
  UpperCamelCase_					:						Optional[int] =					sd_pipe.to(a_			)
  sd_pipe.set_progress_bar_config(disable=a_			)

  # forward without prompt embeds
  UpperCamelCase_					:						Any =					self.get_dummy_inputs(a_			)
  UpperCamelCase_					:						List[str] =					3 * ["""this is a negative prompt"""]
  UpperCamelCase_					:						str =					negative_prompt
  UpperCamelCase_					:						str =					3 * [inputs["""prompt"""]]

  UpperCamelCase_					:						Any =					sd_pipe(**a_			)
  UpperCamelCase_					:						int =					output.images[0, -3:, -3:, -1]

  # forward with prompt embeds
  UpperCamelCase_					:						Dict =					self.get_dummy_inputs(a_			)
  UpperCamelCase_					:						int =					3 * ["""this is a negative prompt"""]
  UpperCamelCase_					:						Tuple =					3 * [inputs.pop('prompt'			)]

  (
      UpperCamelCase_
  )					:						List[str] =					sd_pipe.encode_prompt(a_   ,      negative_prompt=a_			)

  UpperCamelCase_					:						Any =					sd_pipe(
      **a_   ,      prompt_embeds=a_   ,      negative_prompt_embeds=a_   ,      pooled_prompt_embeds=a_   ,      negative_pooled_prompt_embeds=a_   ,      )
  UpperCamelCase_					:						Union[str, Any] =					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 		_lowercase						(					unittest.TestCase       ):

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Tuple			)   ->						Union[str, Any]:

  """simple docstring"""





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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any   ,      snake_case	:							Optional[int]   ,      snake_case	:							Optional[Any]="cpu"   ,      snake_case	:							List[str]=torch.floataa   ,      snake_case	:							Optional[Any]=0			)   ->						List[Any]:

  """simple docstring"""





  UpperCamelCase_					:						Optional[int] =					torch.Generator(device=a_			).manual_seed(a_			)
  UpperCamelCase_					:						List[Any] =					np.random.RandomState(a_			).standard_normal((1, 4, 6_4, 6_4)			)
  UpperCamelCase_					:						List[Any] =					torch.from_numpy(a_			).to(device=a_   ,      dtype=a_			)
  UpperCamelCase_					:						Optional[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 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[int]			)   ->						Optional[int]:

  """simple docstring"""





  UpperCamelCase_					:						Tuple =					DiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-base'			)
  pipe.to(a_			)
  pipe.set_progress_bar_config(disable=a_			)

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

  assert image.shape == (1, 5_1_2, 5_1_2, 3)
  UpperCamelCase_					:						Dict =					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_torch_available


A_							: Optional[int]						 =	{"""configuration_ibert""": ["""IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """IBertConfig""", """IBertOnnxConfig"""]}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_							: str						 =	[
       """IBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """IBertForMaskedLM""",
       """IBertForMultipleChoice""",
       """IBertForQuestionAnswering""",
       """IBertForSequenceClassification""",
       """IBertForTokenClassification""",
       """IBertModel""",
       """IBertPreTrainedModel""",
   ]

if TYPE_CHECKING:
   from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_ibert import (
          IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          IBertForMaskedLM,
          IBertForMultipleChoice,
          IBertForQuestionAnswering,
          IBertForSequenceClassification,
          IBertForTokenClassification,
          IBertModel,
          IBertPreTrainedModel,
      )

else:
   import sys

   A_							: str						 =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



