'''simple docstring'''




import gc
import unittest

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

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, 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


enable_full_determinism()




class     a__      (   UpperCAmelCase__							,   UpperCAmelCase__							,   UpperCAmelCase__							,   unittest.TestCase ):
     lowerCamelCase  :     Tuple							=AltDiffusionPipeline
     lowerCamelCase  :     List[str]							=TEXT_TO_IMAGE_PARAMS
     lowerCamelCase  :     Any							=TEXT_TO_IMAGE_BATCH_PARAMS
     lowerCamelCase  :     Union[str, Any]							=TEXT_TO_IMAGE_IMAGE_PARAMS
     lowerCamelCase  :     Optional[Any]							=TEXT_TO_IMAGE_IMAGE_PARAMS






     def    SCREAMING_SNAKE_CASE__   ( self		:		List[str]    ):





         """simple docstring"""



         torch.manual_seed(0    )
         __lowerCamelCase		=  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''')  ,				cross_attention_dim=32  ,				)
         __lowerCamelCase		=  DDIMScheduler(
             beta_start=0.0_00_85  ,				beta_end=0.0_12  ,				beta_schedule='''scaled_linear'''  ,				clip_sample=a  ,				set_alpha_to_one=a  ,				)
         torch.manual_seed(0    )
         __lowerCamelCase		=  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  ,				)

         # TODO: address the non-deterministic text encoder (fails for save-load tests)
         # torch.manual_seed(0)
         # text_encoder_config = RobertaSeriesConfig(
         #     hidden_size=32,
         #     project_dim=32,
         #     intermediate_size=37,
         #     layer_norm_eps=1e-05,
         #     num_attention_heads=4,
         #     num_hidden_layers=5,
         #     vocab_size=5002,
         # )
         # text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

         torch.manual_seed(0    )
         __lowerCamelCase		=  CLIPTextConfig(
             bos_token_id=0  ,				eos_token_id=2  ,				hidden_size=32  ,				projection_dim=32  ,				intermediate_size=37  ,				layer_norm_eps=1e-0_5  ,				num_attention_heads=4  ,				num_hidden_layers=5  ,				pad_token_id=1  ,				vocab_size=50_02  ,				)
         __lowerCamelCase		=  CLIPTextModel(a    )

         __lowerCamelCase		=  XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta'''    )
         __lowerCamelCase		=  77

         __lowerCamelCase		=  {
             '''unet''': unet,
             '''scheduler''': scheduler,
             '''vae''': vae,
             '''text_encoder''': text_encoder,
             '''tokenizer''': tokenizer,
             '''safety_checker''': None,
             '''feature_extractor''': None,
         }
         return components






     def    SCREAMING_SNAKE_CASE__   ( self		:		List[str]  ,				a		:		List[Any]  ,				a		:		Union[str, Any]=0    ):





         """simple docstring"""



         if str(a    ).startswith('''mps'''    ):
             __lowerCamelCase		=  torch.manual_seed(a    )
         else:
             __lowerCamelCase		=  torch.Generator(device=a    ).manual_seed(a    )
         __lowerCamelCase		=  {
             '''prompt''': '''A painting of a squirrel eating a burger''',
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''guidance_scale''': 6.0,
             '''output_type''': '''numpy''',
         }
         return inputs






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





         """simple docstring"""



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






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





         """simple docstring"""



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






     def    SCREAMING_SNAKE_CASE__   ( self		:		Dict    ):





         """simple docstring"""



         __lowerCamelCase		=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator

         __lowerCamelCase		=  self.get_dummy_components()
         torch.manual_seed(0    )
         __lowerCamelCase		=  RobertaSeriesConfig(
             hidden_size=32  ,				project_dim=32  ,				intermediate_size=37  ,				layer_norm_eps=1e-0_5  ,				num_attention_heads=4  ,				num_hidden_layers=5  ,				vocab_size=50_02  ,				)
         # TODO: remove after fixing the non-deterministic text encoder
         __lowerCamelCase		=  RobertaSeriesModelWithTransformation(a    )
         __lowerCamelCase		=  text_encoder

         __lowerCamelCase		=  AltDiffusionPipeline(**a    )
         __lowerCamelCase		=  alt_pipe.to(a    )
         alt_pipe.set_progress_bar_config(disable=a    )

         __lowerCamelCase		=  self.get_dummy_inputs(a    )
         __lowerCamelCase		=  '''A photo of an astronaut'''
         __lowerCamelCase		=  alt_pipe(**a    )
         __lowerCamelCase		=  output.images
         __lowerCamelCase		=  image[0, -3:, -3:, -1]

         assert image.shape == (1, 64, 64, 3)
         __lowerCamelCase		=  np.array(
             [0.5_74_81_62, 0.60_44_71_45, 0.48_82_12_17, 0.50_10_06_36, 0.5_43_11_85, 0.45_76_36_83, 0.49_65_76_96, 0.48_13_27_33, 0.47_57_30_93]    )

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






     def    SCREAMING_SNAKE_CASE__   ( self		:		Optional[int]    ):





         """simple docstring"""



         __lowerCamelCase		=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator

         __lowerCamelCase		=  self.get_dummy_components()
         __lowerCamelCase		=  PNDMScheduler(skip_prk_steps=a    )
         torch.manual_seed(0    )
         __lowerCamelCase		=  RobertaSeriesConfig(
             hidden_size=32  ,				project_dim=32  ,				intermediate_size=37  ,				layer_norm_eps=1e-0_5  ,				num_attention_heads=4  ,				num_hidden_layers=5  ,				vocab_size=50_02  ,				)
         # TODO: remove after fixing the non-deterministic text encoder
         __lowerCamelCase		=  RobertaSeriesModelWithTransformation(a    )
         __lowerCamelCase		=  text_encoder
         __lowerCamelCase		=  AltDiffusionPipeline(**a    )
         __lowerCamelCase		=  alt_pipe.to(a    )
         alt_pipe.set_progress_bar_config(disable=a    )

         __lowerCamelCase		=  self.get_dummy_inputs(a    )
         __lowerCamelCase		=  alt_pipe(**a    )
         __lowerCamelCase		=  output.images
         __lowerCamelCase		=  image[0, -3:, -3:, -1]

         assert image.shape == (1, 64, 64, 3)
         __lowerCamelCase		=  np.array(
             [0.51_60_50_93, 0.5_70_72_41, 0.47_36_55_07, 0.50_57_88_86, 0.5_63_38_77, 0.4_64_25_03, 0.5_18_20_81, 0.48_76_34_84, 0.49_08_42_37]    )

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




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






     def    SCREAMING_SNAKE_CASE__   ( self		:		List[str]    ):





         """simple docstring"""



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






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





         """simple docstring"""



         __lowerCamelCase		=  AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion'''  ,				safety_checker=a    )
         __lowerCamelCase		=  alt_pipe.to(a    )
         alt_pipe.set_progress_bar_config(disable=a    )

         __lowerCamelCase		=  '''A painting of a squirrel eating a burger'''
         __lowerCamelCase		=  torch.manual_seed(0    )
         __lowerCamelCase		=  alt_pipe([prompt]  ,				generator=a  ,				guidance_scale=6.0  ,				num_inference_steps=20  ,				output_type='''np'''    )

         __lowerCamelCase		=  output.images

         __lowerCamelCase		=  image[0, -3:, -3:, -1]

         assert image.shape == (1, 5_12, 5_12, 3)
         __lowerCamelCase		=  np.array([0.10_10, 0.08_00, 0.07_94, 0.08_85, 0.08_43, 0.07_62, 0.07_69, 0.07_29, 0.05_86]    )

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






     def    SCREAMING_SNAKE_CASE__   ( self		:		List[Any]    ):





         """simple docstring"""



         __lowerCamelCase		=  DDIMScheduler.from_pretrained('''BAAI/AltDiffusion'''  ,				subfolder='''scheduler'''    )

         __lowerCamelCase		=  AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion'''  ,				scheduler=a  ,				safety_checker=a    )
         __lowerCamelCase		=  alt_pipe.to(a    )
         alt_pipe.set_progress_bar_config(disable=a    )

         __lowerCamelCase		=  '''A painting of a squirrel eating a burger'''
         __lowerCamelCase		=  torch.manual_seed(0    )

         __lowerCamelCase		=  alt_pipe([prompt]  ,				generator=a  ,				num_inference_steps=2  ,				output_type='''numpy'''    )
         __lowerCamelCase		=  output.images

         __lowerCamelCase		=  image[0, -3:, -3:, -1]

         assert image.shape == (1, 5_12, 5_12, 3)
         __lowerCamelCase		=  np.array([0.40_19, 0.40_52, 0.38_10, 0.41_19, 0.39_16, 0.39_82, 0.46_51, 0.41_95, 0.53_23]    )

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





"""simple docstring"""
from typing import TYPE_CHECKING

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


A:   Optional[Any]								=		{
    "configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
    "tokenization_tapas": ["TapasTokenizer"],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   Optional[int]								=		[
          "TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TapasForMaskedLM",
          "TapasForQuestionAnswering",
          "TapasForSequenceClassification",
          "TapasModel",
          "TapasPreTrainedModel",
          "load_tf_weights_in_tapas",
      ]
try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   List[str]								=		[
          "TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFTapasForMaskedLM",
          "TFTapasForQuestionAnswering",
          "TFTapasForSequenceClassification",
          "TFTapasModel",
          "TFTapasPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
      from .tokenization_tapas import TapasTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tapas import (
                TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
                TapasForMaskedLM,
                TapasForQuestionAnswering,
                TapasForSequenceClassification,
                TapasModel,
                TapasPreTrainedModel,
                load_tf_weights_in_tapas,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_tapas import (
                TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFTapasForMaskedLM,
                TFTapasForQuestionAnswering,
                TFTapasForSequenceClassification,
                TFTapasModel,
                TFTapasPreTrainedModel,
            )


else:
      import sys

      A:   Optional[Any]								=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
