


'''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				:					Optional[int]    						=			False


@skip_mps
class 			__lowerCamelCase			(	a_    , a_    , a_    , unittest.TestCase	):
    """simple docstring"""
    a													=   StableDiffusionAttendAndExcitePipeline
    a													=   False
    a													=   TEXT_TO_IMAGE_PARAMS
    a													=   TEXT_TO_IMAGE_BATCH_PARAMS.union({"token_indices"}	)
    a													=   TEXT_TO_IMAGE_IMAGE_PARAMS
    a													=   TEXT_TO_IMAGE_IMAGE_PARAMS


    @classmethod
    def        A   (  cls      :							Dict):
           super().setUpClass()
           torch.use_deterministic_algorithms(SCREAMING_SNAKE_CASE)


    @classmethod
    def        A   (  cls      :							Union[str, Any]):
           super().tearDownClass()
           torch.use_deterministic_algorithms(SCREAMING_SNAKE_CASE)


    def        A   (  self      :							str):
           torch.manual_seed(0)
           _A					:     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=SCREAMING_SNAKE_CASE				,							)
           _A					:     List[Any]     = DDIMScheduler(
               beta_start=0.0_0085				,							beta_end=0.012				,							beta_schedule='scaled_linear'				,							clip_sample=SCREAMING_SNAKE_CASE				,							set_alpha_to_one=SCREAMING_SNAKE_CASE				,							)
           torch.manual_seed(0)
           _A					:     int     = 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)
           _A					:     Any     = 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				,							)
           _A					:     str     = CLIPTextModel(SCREAMING_SNAKE_CASE)
           _A					:     Union[str, Any]     = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')

           _A					:     List[str]     = {
               'unet': unet,
               'scheduler': scheduler,
               'vae': vae,
               'text_encoder': text_encoder,
               'tokenizer': tokenizer,
               'safety_checker': None,
               'feature_extractor': None,
           }

           return components


    def        A   (  self      :							Tuple				,							SCREAMING_SNAKE_CASE      :							str				,							SCREAMING_SNAKE_CASE      :							str=0):
           if str(SCREAMING_SNAKE_CASE).startswith('mps'):
                  _A					:     Dict     = torch.manual_seed(SCREAMING_SNAKE_CASE)
           else:
                  _A					:     Union[str, Any]     = torch.Generator(device=SCREAMING_SNAKE_CASE).manual_seed(SCREAMING_SNAKE_CASE)
           _A					:     Tuple     = {
               '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        A   (  self      :							Tuple):
           _A					:     Optional[int]     = 'cpu'

           _A					:     Dict     = self.get_dummy_components()
           _A					:     int     = self.pipeline_class(**SCREAMING_SNAKE_CASE)
           pipe.to(SCREAMING_SNAKE_CASE)
           pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)

           _A					:     Optional[int]     = self.get_dummy_inputs(SCREAMING_SNAKE_CASE)
           _A					:     Union[str, Any]     = pipe(**SCREAMING_SNAKE_CASE).images
           _A					:     List[Any]     = image[0, -3:, -3:, -1]

           self.assertEqual(image.shape				,							(1, 64, 64, 3))
           _A					:     List[Any]     = np.array(
               [0.6390_5364, 0.6289_7307, 0.4859_9017, 0.513_3624, 0.555_0048, 0.4576_9516, 0.5032_6973, 0.502_3139, 0.4538_4496])
           _A					:     str     = np.abs(image_slice.flatten() - expected_slice).max()
           self.assertLessEqual(SCREAMING_SNAKE_CASE				,							1e-3)


    def        A   (  self      :							Any):
           super().test_cpu_offload_forward_pass(expected_max_diff=5e-4)


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


    def        A   (  self      :							List[Any]):
           self._test_inference_batch_single_identical(batch_size=2				,							expected_max_diff=7e-4)


    def        A   (  self      :							int):
           super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3)


    def        A   (  self      :							Union[str, Any]):
           super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5e-4)


    def        A   (  self      :							Optional[int]):
           super().test_save_load_local(expected_max_difference=5e-4)


    def        A   (  self      :							int):
           super().test_save_load_optional_components(expected_max_difference=4e-4)




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


    @classmethod
    def        A   (  cls      :							Dict):
           super().setUpClass()
           torch.use_deterministic_algorithms(SCREAMING_SNAKE_CASE)


    @classmethod
    def        A   (  cls      :							Optional[Any]):
           super().tearDownClass()
           torch.use_deterministic_algorithms(SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Any):
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()


    def        A   (  self      :							int):
           _A					:     Tuple     = torch.manual_seed(51)

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

           _A					:     Optional[Any]     = 'a painting of an elephant with glasses'
           _A					:     Any     = [5, 7]

           _A					:     Tuple     = pipe(
               prompt=SCREAMING_SNAKE_CASE				,							token_indices=SCREAMING_SNAKE_CASE				,							guidance_scale=7.5				,							generator=SCREAMING_SNAKE_CASE				,							num_inference_steps=5				,							max_iter_to_alter=5				,							output_type='numpy'				,							).images[0]

           _A					:     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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A				:					List[str]    						=			{
    '''configuration_electra''': ['''ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ElectraConfig''', '''ElectraOnnxConfig'''],
    '''tokenization_electra''': ['''ElectraTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					int    						=			['''ElectraTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					List[str]    						=			[
       '''ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''ElectraForCausalLM''',
       '''ElectraForMaskedLM''',
       '''ElectraForMultipleChoice''',
       '''ElectraForPreTraining''',
       '''ElectraForQuestionAnswering''',
       '''ElectraForSequenceClassification''',
       '''ElectraForTokenClassification''',
       '''ElectraModel''',
       '''ElectraPreTrainedModel''',
       '''load_tf_weights_in_electra''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					Dict    						=			[
       '''TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFElectraForMaskedLM''',
       '''TFElectraForMultipleChoice''',
       '''TFElectraForPreTraining''',
       '''TFElectraForQuestionAnswering''',
       '''TFElectraForSequenceClassification''',
       '''TFElectraForTokenClassification''',
       '''TFElectraModel''',
       '''TFElectraPreTrainedModel''',
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					List[Any]    						=			[
       '''FlaxElectraForCausalLM''',
       '''FlaxElectraForMaskedLM''',
       '''FlaxElectraForMultipleChoice''',
       '''FlaxElectraForPreTraining''',
       '''FlaxElectraForQuestionAnswering''',
       '''FlaxElectraForSequenceClassification''',
       '''FlaxElectraForTokenClassification''',
       '''FlaxElectraModel''',
       '''FlaxElectraPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
   from .tokenization_electra import ElectraTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_electra_fast import ElectraTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_electra import (
          ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
          ElectraForCausalLM,
          ElectraForMaskedLM,
          ElectraForMultipleChoice,
          ElectraForPreTraining,
          ElectraForQuestionAnswering,
          ElectraForSequenceClassification,
          ElectraForTokenClassification,
          ElectraModel,
          ElectraPreTrainedModel,
          load_tf_weights_in_electra,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_electra import (
          TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFElectraForMaskedLM,
          TFElectraForMultipleChoice,
          TFElectraForPreTraining,
          TFElectraForQuestionAnswering,
          TFElectraForSequenceClassification,
          TFElectraForTokenClassification,
          TFElectraModel,
          TFElectraPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_electra import (
          FlaxElectraForCausalLM,
          FlaxElectraForMaskedLM,
          FlaxElectraForMultipleChoice,
          FlaxElectraForPreTraining,
          FlaxElectraForQuestionAnswering,
          FlaxElectraForSequenceClassification,
          FlaxElectraForTokenClassification,
          FlaxElectraModel,
          FlaxElectraPreTrainedModel,
      )

else:
   import sys

   A				:					Tuple    						=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






