from typing import TYPE_CHECKING

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


_lowercase							:      Optional[int]      							={
    '''configuration_ernie''': ['''ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ErnieConfig''', '''ErnieOnnxConfig'''],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _lowercase							:      Dict      							=[
        '''ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''ErnieForCausalLM''',
        '''ErnieForMaskedLM''',
        '''ErnieForMultipleChoice''',
        '''ErnieForNextSentencePrediction''',
        '''ErnieForPreTraining''',
        '''ErnieForQuestionAnswering''',
        '''ErnieForSequenceClassification''',
        '''ErnieForTokenClassification''',
        '''ErnieModel''',
        '''ErniePreTrainedModel''',
    ]

if TYPE_CHECKING:
    from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_ernie import (
            ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
            ErnieForCausalLM,
            ErnieForMaskedLM,
            ErnieForMultipleChoice,
            ErnieForNextSentencePrediction,
            ErnieForPreTraining,
            ErnieForQuestionAnswering,
            ErnieForSequenceClassification,
            ErnieForTokenClassification,
            ErnieModel,
            ErniePreTrainedModel,
        )

else:
    import sys

    _lowercase							:      Any      							=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)







import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_   ,						unittest.TestCase			):
  '''simple docstring'''


  lowercase		:     Optional[int]            =				DDIMPipeline
  lowercase		:     int            =				UNCONDITIONAL_IMAGE_GENERATION_PARAMS
  lowercase		:     Optional[Any]            =				PipelineTesterMixin.required_optional_params - {
      "num_images_per_prompt",
      "latents",
      "callback",
      "callback_steps",
  }
  lowercase		:     Optional[Any]            =				UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
  lowercase		:     Union[str, Any]            =				False







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]     )    ->				Optional[int]:
         torch.manual_seed(0     )
         A						:     str         =UNetaDModel(
             block_out_channels=(32, 64)      ,	layers_per_block=2      ,	sample_size=32      ,	in_channels=3      ,	out_channels=3      ,	down_block_types=('DownBlock2D', 'AttnDownBlock2D')      ,	up_block_types=('AttnUpBlock2D', 'UpBlock2D')      ,	)
         A						:     Optional[int]         =DDIMScheduler()
         A						:     Optional[Any]         ={'unet': unet, 'scheduler': scheduler}
         return components







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Union[str, Any]      ,	SCREAMING_SNAKE_CASE__    :					Any      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=0     )    ->				Any:
         if str(SCREAMING_SNAKE_CASE__     ).startswith('mps'     ):
                A						:     List[Any]         =torch.manual_seed(SCREAMING_SNAKE_CASE__     )
         else:
                A						:     Union[str, Any]         =torch.Generator(device=SCREAMING_SNAKE_CASE__     ).manual_seed(SCREAMING_SNAKE_CASE__     )
         A						:     Optional[int]         ={
             'batch_size': 1,
             'generator': generator,
             'num_inference_steps': 2,
             'output_type': 'numpy',
         }
         return inputs







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Dict     )    ->				List[Any]:
         A						:     Union[str, Any]         ='cpu'

         A						:     Tuple         =self.get_dummy_components()
         A						:     Union[str, Any]         =self.pipeline_class(**SCREAMING_SNAKE_CASE__     )
         pipe.to(SCREAMING_SNAKE_CASE__     )
         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__     )

         A						:     str         =self.get_dummy_inputs(SCREAMING_SNAKE_CASE__     )
         A						:     str         =pipe(**SCREAMING_SNAKE_CASE__     ).images
         A						:     Optional[Any]         =image[0, -3:, -3:, -1]

         self.assertEqual(image.shape      ,	(1, 32, 32, 3)     )
         A						:     Optional[Any]         =np.array(
             [1.000e00, 5.717e-01, 4.717e-01, 1.000e00, 0.000e00, 1.000e00, 3.000e-04, 0.000e00, 9.000e-04]     )
         A						:     str         =np.abs(image_slice.flatten() - expected_slice     ).max()
         self.assertLessEqual(SCREAMING_SNAKE_CASE__      ,	1e-3     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					int     )    ->				Dict:
         super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]     )    ->				List[Any]:
         super().test_save_load_local(expected_max_difference=3e-3     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]     )    ->				Tuple:
         super().test_save_load_optional_components(expected_max_difference=3e-3     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Dict     )    ->				Tuple:
         super().test_inference_batch_single_identical(expected_max_diff=3e-3     )







@slow
@require_torch_gpu
class  SCREAMING_SNAKE_CASE_ (					unittest.TestCase			):
  '''simple docstring'''


  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]     )    ->				Dict:
         A						:     Any         ='google/ddpm-cifar10-32'

         A						:     Optional[int]         =UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE__     )
         A						:     Tuple         =DDIMScheduler()

         A						:     int         =DDIMPipeline(unet=SCREAMING_SNAKE_CASE__      ,	scheduler=SCREAMING_SNAKE_CASE__     )
         ddim.to(SCREAMING_SNAKE_CASE__     )
         ddim.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__     )

         A						:     Dict         =torch.manual_seed(0     )
         A						:     Optional[Any]         =ddim(generator=SCREAMING_SNAKE_CASE__      ,	eta=0.0      ,	output_type='numpy'     ).images

         A						:     str         =image[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         A						:     Tuple         =np.array([0.1_7_2_3, 0.1_6_1_7, 0.1_6_0_0, 0.1_6_2_6, 0.1_4_9_7, 0.1_5_1_3, 0.1_5_0_5, 0.1_4_4_2, 0.1_4_5_3]     )

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







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]     )    ->				int:
         A						:     Optional[int]         ='google/ddpm-ema-bedroom-256'

         A						:     str         =UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE__     )
         A						:     str         =DDIMScheduler.from_pretrained(SCREAMING_SNAKE_CASE__     )

         A						:     Tuple         =DDIMPipeline(unet=SCREAMING_SNAKE_CASE__      ,	scheduler=SCREAMING_SNAKE_CASE__     )
         ddpm.to(SCREAMING_SNAKE_CASE__     )
         ddpm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__     )

         A						:     Any         =torch.manual_seed(0     )
         A						:     Optional[int]         =ddpm(generator=SCREAMING_SNAKE_CASE__      ,	output_type='numpy'     ).images

         A						:     List[Any]         =image[0, -3:, -3:, -1]

         assert image.shape == (1, 2_56, 2_56, 3)
         A						:     Optional[int]         =np.array([0.0_0_6_0, 0.0_2_0_1, 0.0_3_4_4, 0.0_0_2_4, 0.0_0_1_8, 0.0_0_0_2, 0.0_0_2_2, 0.0_0_0_0, 0.0_0_6_9]     )

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






