'''simple docstring'''

import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionAttendAndExcitePipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import 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 PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


__a: str          =   False



@skip_mps
class 			UpperCAmelCase  (						a__    ,						a__    ,						a__    ,						unittest.TestCase					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    StableDiffusionAttendAndExcitePipeline
       SCREAMING_SNAKE_CASE							=    False
       SCREAMING_SNAKE_CASE							=    TEXT_TO_IMAGE_PARAMS
       SCREAMING_SNAKE_CASE							=    TEXT_TO_IMAGE_BATCH_PARAMS.union({"token_indices"}					)
       SCREAMING_SNAKE_CASE							=    TEXT_TO_IMAGE_IMAGE_PARAMS
       SCREAMING_SNAKE_CASE							=    TEXT_TO_IMAGE_IMAGE_PARAMS

       @classmethod
       def _lowerCAmelCase(						cls )  ->  Any:
        super().setUpClass()
        torch.use_deterministic_algorithms(__lowerCAmelCase )

       @classmethod
       def _lowerCAmelCase(						cls )  ->  Dict:
        super().tearDownClass()
        torch.use_deterministic_algorithms(__lowerCAmelCase )

       def _lowerCAmelCase(						self )  ->  str:
        torch.manual_seed(0 )
        lowercase__				:    Optional[Any]  =					UNetaDConditionModel(
            block_out_channels=(32, 64)	,   layers_per_block=1	,   sample_size=32	,   in_channels=4	,   out_channels=4	,   down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')	,   up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')	,   cross_attention_dim=32	,   attention_head_dim=(2, 4)	,   use_linear_projection=__lowerCAmelCase	,   )
        lowercase__				:    Dict  =					DDIMScheduler(
            beta_start=0.0_0_0_8_5	,   beta_end=0.0_1_2	,   beta_schedule='''scaled_linear'''	,   clip_sample=__lowerCAmelCase	,   set_alpha_to_one=__lowerCAmelCase	,   )
        torch.manual_seed(0 )
        lowercase__				:    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 )
        lowercase__				:    Tuple  =					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=512	,   )
        lowercase__				:    List[Any]  =					CLIPTextModel(__lowerCAmelCase )
        lowercase__				:    Dict  =					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )

        lowercase__				:    str  =					{
            '''unet''': unet,
            '''scheduler''': scheduler,
            '''vae''': vae,
            '''text_encoder''': text_encoder,
            '''tokenizer''': tokenizer,
            '''safety_checker''': None,
            '''feature_extractor''': None,
        }

        return components

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase=0 )  ->  Optional[int]:
        if str(__lowerCAmelCase ).startswith('''mps''' ):
         lowercase__				:    Optional[Any]  =					torch.manual_seed(__lowerCAmelCase )
        else:
         lowercase__				:    Any  =					torch.Generator(device=__lowerCAmelCase ).manual_seed(__lowerCAmelCase )
        lowercase__				:    List[Any]  =					{
            '''prompt''': '''a cat and a frog''',
            '''token_indices''': [2, 5],
            '''generator''': generator,
            '''num_inference_steps''': 1,
            '''guidance_scale''': 6.0,
            '''output_type''': '''numpy''',
            '''max_iter_to_alter''': 2,
            '''thresholds''': {0: 0.7},
        }
        return inputs

       def _lowerCAmelCase(						self )  ->  Dict:
        lowercase__				:    Tuple  =					'''cpu'''

        lowercase__				:    Dict  =					self.get_dummy_components()
        lowercase__				:    str  =					self.pipeline_class(**__lowerCAmelCase )
        pipe.to(__lowerCAmelCase )
        pipe.set_progress_bar_config(disable=__lowerCAmelCase )

        lowercase__				:    int  =					self.get_dummy_inputs(__lowerCAmelCase )
        lowercase__				:    Optional[int]  =					pipe(**__lowerCAmelCase ).images
        lowercase__				:    List[Any]  =					image[0, -3:, -3:, -1]

        self.assertEqual(image.shape	,   (1, 64, 64, 3) )
        lowercase__				:    Dict  =					np.array(
            [0.6_3_9_0_5_3_6_4, 0.6_2_8_9_7_3_0_7, 0.4_8_5_9_9_0_1_7, 0.5_1_3_3_6_2_4, 0.5_5_5_0_0_4_8, 0.4_5_7_6_9_5_1_6, 0.5_0_3_2_6_9_7_3, 0.5_0_2_3_1_3_9, 0.4_5_3_8_4_4_9_6] )
        lowercase__				:    Optional[int]  =					np.abs(image_slice.flatten() - expected_slice ).max()
        self.assertLessEqual(__lowerCAmelCase	,   1E-3 )

       def _lowerCAmelCase(						self )  ->  int:
        super().test_cpu_offload_forward_pass(expected_max_diff=5E-4 )

       def _lowerCAmelCase(						self )  ->  Any:
        # NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
        self._test_inference_batch_consistent(batch_sizes=[1, 2] )

       def _lowerCAmelCase(						self )  ->  Tuple:
        self._test_inference_batch_single_identical(batch_size=2	,   expected_max_diff=7E-4 )

       def _lowerCAmelCase(						self )  ->  str:
        super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3 )

       def _lowerCAmelCase(						self )  ->  Optional[int]:
        super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5E-4 )

       def _lowerCAmelCase(						self )  ->  int:
        super().test_save_load_local(expected_max_difference=5E-4 )


       def _lowerCAmelCase(						self )  ->  Tuple:
        super().test_save_load_optional_components(expected_max_difference=4E-4 )



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





       '''simple docstring'''



       @classmethod
       def _lowerCAmelCase(						cls )  ->  Optional[int]:
        super().setUpClass()
        torch.use_deterministic_algorithms(__lowerCAmelCase )

       @classmethod
       def _lowerCAmelCase(						cls )  ->  Union[str, Any]:
        super().tearDownClass()
        torch.use_deterministic_algorithms(__lowerCAmelCase )

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


       def _lowerCAmelCase(						self )  ->  Union[str, Any]:
        lowercase__				:    Any  =					torch.manual_seed(51 )

        lowercase__				:    Optional[Any]  =					StableDiffusionAttendAndExcitePipeline.from_pretrained(
            '''CompVis/stable-diffusion-v1-4'''	,   safety_checker=__lowerCAmelCase	,   torch_dtype=torch.floataa )
        pipe.to('''cuda''' )

        lowercase__				:    str  =					'''a painting of an elephant with glasses'''
        lowercase__				:    Dict  =					[5, 7]

        lowercase__				:    str  =					pipe(
            prompt=__lowerCAmelCase	,   token_indices=__lowerCAmelCase	,   guidance_scale=7.5	,   generator=__lowerCAmelCase	,   num_inference_steps=5	,   max_iter_to_alter=5	,   output_type='''numpy'''	,   ).images[0]

        lowercase__				:    str  =					load_numpy(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy''' )
        assert np.abs((expected_image - image).max() ) < 5E-1


'''simple docstring'''

from typing import TYPE_CHECKING

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


__a: Dict          =   {
    """configuration_ctrl""": ["""CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CTRLConfig"""],
    """tokenization_ctrl""": ["""CTRLTokenizer"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a: int          =   [
      """CTRL_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """CTRLForSequenceClassification""",
      """CTRLLMHeadModel""",
      """CTRLModel""",
      """CTRLPreTrainedModel""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a: int          =   [
      """TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFCTRLForSequenceClassification""",
      """TFCTRLLMHeadModel""",
      """TFCTRLModel""",
      """TFCTRLPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
  from .tokenization_ctrl import CTRLTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_ctrl import (
        CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
        CTRLForSequenceClassification,
        CTRLLMHeadModel,
        CTRLModel,
        CTRLPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_ctrl import (
        TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFCTRLForSequenceClassification,
        TFCTRLLMHeadModel,
        TFCTRLModel,
        TFCTRLPreTrainedModel,
    )

else:
  import sys

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

