

import inspect
import os
import re

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
_SCREAMING_SNAKE_CASE  :						Union[str, Any]         =    '''src/transformers'''


# This is to make sure the transformers module imported is the one in the repo.
_SCREAMING_SNAKE_CASE  :						List[Any]         =    direct_transformers_import(PATH_TO_TRANSFORMERS)

_SCREAMING_SNAKE_CASE  :						Optional[int]         =    transformers.models.auto.configuration_auto.CONFIG_MAPPING

_SCREAMING_SNAKE_CASE  :						Tuple         =    {
    # used to compute the property `self.chunk_length`
    '''EncodecConfig''': ['''overlap'''],
    # used as `self.bert_model = BertModel(config, ...)`
    '''DPRConfig''': True,
    # not used in modeling files, but it's an important information
    '''FSMTConfig''': ['''langs'''],
    # used internally in the configuration class file
    '''GPTNeoConfig''': ['''attention_types'''],
    # used internally in the configuration class file
    '''EsmConfig''': ['''is_folding_model'''],
    # used during training (despite we don't have training script for these models yet)
    '''Mask2FormerConfig''': ['''ignore_value'''],
    # `ignore_value` used during training (despite we don't have training script for these models yet)
    # `norm` used in conversion script (despite not using in the modeling file)
    '''OneFormerConfig''': ['''ignore_value''', '''norm'''],
    # used during preprocessing and collation, see `collating_graphormer.py`
    '''GraphormerConfig''': ['''spatial_pos_max'''],
    # used internally in the configuration class file
    '''T5Config''': ['''feed_forward_proj'''],
    # used internally in the configuration class file
    # `tokenizer_class` get default value `T5Tokenizer` intentionally
    '''MT5Config''': ['''feed_forward_proj''', '''tokenizer_class'''],
    '''UMT5Config''': ['''feed_forward_proj''', '''tokenizer_class'''],
    # used internally in the configuration class file
    '''LongT5Config''': ['''feed_forward_proj'''],
    # used internally in the configuration class file
    '''SwitchTransformersConfig''': ['''feed_forward_proj'''],
    # having default values other than `1e-5` - we can't fix them without breaking
    '''BioGptConfig''': ['''layer_norm_eps'''],
    # having default values other than `1e-5` - we can't fix them without breaking
    '''GLPNConfig''': ['''layer_norm_eps'''],
    # having default values other than `1e-5` - we can't fix them without breaking
    '''SegformerConfig''': ['''layer_norm_eps'''],
    # having default values other than `1e-5` - we can't fix them without breaking
    '''CvtConfig''': ['''layer_norm_eps'''],
    # having default values other than `1e-5` - we can't fix them without breaking
    '''PerceiverConfig''': ['''layer_norm_eps'''],
    # used internally to calculate the feature size
    '''InformerConfig''': ['''num_static_real_features''', '''num_time_features'''],
    # used internally to calculate the feature size
    '''TimeSeriesTransformerConfig''': ['''num_static_real_features''', '''num_time_features'''],
    # used internally to calculate the feature size
    '''AutoformerConfig''': ['''num_static_real_features''', '''num_time_features'''],
    # used internally to calculate `mlp_dim`
    '''SamVisionConfig''': ['''mlp_ratio'''],
    # For (head) training, but so far not implemented
    '''ClapAudioConfig''': ['''num_classes'''],
    # Not used, but providing useful information to users
    '''SpeechT5HifiGanConfig''': ['''sampling_rate'''],
}


# TODO (ydshieh): Check the failing cases, try to fix them or move some cases to the above block once we are sure
SPECIAL_CASES_TO_ALLOW.update(
    {
        '''CLIPSegConfig''': True,
        '''DeformableDetrConfig''': True,
        '''DetaConfig''': True,
        '''DinatConfig''': True,
        '''DonutSwinConfig''': True,
        '''EfficientFormerConfig''': True,
        '''FSMTConfig''': True,
        '''JukeboxConfig''': True,
        '''LayoutLMv2Config''': True,
        '''MaskFormerSwinConfig''': True,
        '''MT5Config''': True,
        '''NatConfig''': True,
        '''OneFormerConfig''': True,
        '''PerceiverConfig''': True,
        '''RagConfig''': True,
        '''SpeechT5Config''': True,
        '''SwinConfig''': True,
        '''Swin2SRConfig''': True,
        '''Swinv2Config''': True,
        '''SwitchTransformersConfig''': True,
        '''TableTransformerConfig''': True,
        '''TapasConfig''': True,
        '''TransfoXLConfig''': True,
        '''UniSpeechConfig''': True,
        '''UniSpeechSatConfig''': True,
        '''WavLMConfig''': True,
        '''WhisperConfig''': True,
        # TODO: @Arthur (for `alignment_head` and `alignment_layer`)
        '''JukeboxPriorConfig''': True,
        # TODO: @Younes (for `is_decoder`)
        '''Pix2StructTextConfig''': True,
    }
)
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					False
     for attribute in attributes:
          for modeling_source in source_strings:
               # check if we can find `config.xxx`, `getattr(config, "xxx", ...)` or `getattr(self.config, "xxx", ...)`
               if (
                   F'''config.{attribute}''' in modeling_source
                   or F'''getattr(config, "{attribute}"''' in modeling_source
                   or F'''getattr(self.config, "{attribute}"''' in modeling_source
               ):
                    SCREAMING_SNAKE_CASE__					=					True
               # Deal with multi-line cases
               elif (
                   re.search(
                       RF'''getattr[ \t\v\n\r\f]*\([ \t\v\n\r\f]*(self\.)?config,[ \t\v\n\r\f]*"{attribute}"'''   ,	_A   ,	)
                   is not None
               ):
                    SCREAMING_SNAKE_CASE__					=					True
               # `SequenceSummary` is called with `SequenceSummary(config)`
               elif attribute in [
                   "summary_type",
                   "summary_use_proj",
                   "summary_activation",
                   "summary_last_dropout",
                   "summary_proj_to_labels",
                   "summary_first_dropout",
               ]:
                    if "SequenceSummary" in modeling_source:
                         SCREAMING_SNAKE_CASE__					=					True
               if attribute_used:
                    break
          if attribute_used:
               break

    # common and important attributes, even if they do not always appear in the modeling files
     SCREAMING_SNAKE_CASE__					=					[
         '''bos_index''',
         '''eos_index''',
         '''pad_index''',
         '''unk_index''',
         '''mask_index''',
         '''image_size''',
         '''use_cache''',
         '''out_features''',
         '''out_indices''',
     ]
     SCREAMING_SNAKE_CASE__					=					['''encoder_no_repeat_ngram_size''']

     # Special cases to be allowed
     SCREAMING_SNAKE_CASE__					=					True
     if not attribute_used:
          SCREAMING_SNAKE_CASE__					=					False
          for attribute in attributes:
               # Allow if the default value in the configuration class is different from the one in `PretrainedConfig`
               if attribute in ["is_encoder_decoder"] and default_value is True:
                    SCREAMING_SNAKE_CASE__					=					True
               elif attribute in ["tie_word_embeddings"] and default_value is False:
                    SCREAMING_SNAKE_CASE__					=					True

               # Allow cases without checking the default value in the configuration class
               elif attribute in attributes_to_allow + attributes_used_in_generation:
                    SCREAMING_SNAKE_CASE__					=					True
               elif attribute.endswith('''_token_id'''       ):
                    SCREAMING_SNAKE_CASE__					=					True

               # configuration class specific cases
               if not case_allowed:
                    SCREAMING_SNAKE_CASE__					=					SPECIAL_CASES_TO_ALLOW.get(config_class.__name__   ,	[]       )
                    SCREAMING_SNAKE_CASE__					=					allowed_cases is True or attribute in allowed_cases

     return attribute_used or case_allowed
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					dict(inspect.signature(config_class.__init__       ).parameters       )
     SCREAMING_SNAKE_CASE__					=					[x for x in list(signature.keys()       ) if x not in ['''self''', '''kwargs''']]
     SCREAMING_SNAKE_CASE__					=					[signature[param].default for param in parameter_names]

     # If `attribute_map` exists, an attribute can have different names to be used in the modeling files, and as long
     # as one variant is used, the test should pass
     SCREAMING_SNAKE_CASE__					=					{}
     if len(config_class.attribute_map       ) > 0:
          SCREAMING_SNAKE_CASE__					=					{v: k for k, v in config_class.attribute_map.items()}

     # Get the path to modeling source files
     SCREAMING_SNAKE_CASE__					=					inspect.getsourcefile(_A       )
     SCREAMING_SNAKE_CASE__					=					os.path.dirname(_A       )
     # Let's check against all frameworks: as long as one framework uses an attribute, we are good.
     SCREAMING_SNAKE_CASE__					=					[os.path.join(_A   ,	_A       ) for fn in os.listdir(_A       ) if fn.startswith('''modeling_'''       )]

     # Get the source code strings
     SCREAMING_SNAKE_CASE__					=					[]
     for path in modeling_paths:
          if os.path.isfile(_A       ):
               with open(_A       ) as fp:
                    modeling_sources.append(fp.read()       )

     SCREAMING_SNAKE_CASE__					=					[]
     for config_param, default_value in zip(_A   ,	_A       ):
          # `attributes` here is all the variant names for `config_param`
          SCREAMING_SNAKE_CASE__					=					[config_param]
          # some configuration classes have non-empty `attribute_map`, and both names could be used in the
          # corresponding modeling files. As long as one of them appears, it is fine.
          if config_param in reversed_attribute_map:
               attributes.append(reversed_attribute_map[config_param]       )

          if not check_attribute_being_used(_A   ,	_A   ,	_A   ,	_A       ):
               unused_attributes.append(attributes[0]       )

     return sorted(_A       )
def        UpperCAmelCase_     (		):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					{}
     for _config_class in list(CONFIG_MAPPING.values()       ):
          # Skip deprecated models
          if "models.deprecated" in _config_class.__module__:
               continue
          # Some config classes are not in `CONFIG_MAPPING` (e.g. `CLIPVisionConfig`, `Blip2VisionConfig`, etc.)
          SCREAMING_SNAKE_CASE__					=					[
              cls
              for name, cls in inspect.getmembers(
                  inspect.getmodule(_config_class       )   ,	lambda _A       : inspect.isclass(_A       )
                  and issubclass(_A   ,	_A       )
                  and inspect.getmodule(_A       ) == inspect.getmodule(_config_class       )   ,	)
          ]
          for config_class in config_classes_in_module:
               SCREAMING_SNAKE_CASE__					=					check_config_attributes_being_used(_A       )
               if len(_A       ) > 0:
                    SCREAMING_SNAKE_CASE__					=					unused_attributes

     if len(_A       ) > 0:
          SCREAMING_SNAKE_CASE__					=					'''The following configuration classes contain unused attributes in the corresponding modeling files:\n'''
          for name, attributes in configs_with_unused_attributes.items():
               error += F'''{name}: {attributes}\n'''

          raise ValueError(_A       )


if __name__ == "__main__":
       check_config_attributes()






import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionPanoramaPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






@skip_mps
class  UpperCAmelCase__	( A__		,  A__		,  unittest.TestCase ):






  """simple docstring"""


  a     =      StableDiffusionPanoramaPipeline
  a     =      TEXT_TO_IMAGE_PARAMS
  a     =      TEXT_TO_IMAGE_BATCH_PARAMS
  a     =      TEXT_TO_IMAGE_IMAGE_PARAMS
  a     =      TEXT_TO_IMAGE_IMAGE_PARAMS






  def    lowercase_	(							self		:	Union[str, Any]      )	->							List[str]:
       torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__					=					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			,     )
       SCREAMING_SNAKE_CASE__					=					DDIMScheduler()
       torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__					=					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			,     )
       torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__					=					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			,     )
       SCREAMING_SNAKE_CASE__					=					CLIPTextModel(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      )

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






  def    lowercase_	(							self		:	Union[str, Any]			,     __lowerCamelCase		:	Union[str, Any]			,     __lowerCamelCase		:	List[str]=0      )	->							Any:
       SCREAMING_SNAKE_CASE__					=					torch.manual_seed(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					{
           '''prompt''': '''a photo of the dolomites''',
           '''generator''': generator,
           # Setting height and width to None to prevent OOMs on CPU.
           '''height''': None,
           '''width''': None,
           '''num_inference_steps''': 1,
           '''guidance_scale''': 6.0,
           '''output_type''': '''numpy''',
       }
       return inputs






  def    lowercase_	(							self		:	Optional[int]      )	->							List[str]:
       SCREAMING_SNAKE_CASE__					=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       SCREAMING_SNAKE_CASE__					=					self.get_dummy_components()
       SCREAMING_SNAKE_CASE__					=					StableDiffusionPanoramaPipeline(**__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					sd_pipe.to(__lowerCamelCase      )
       sd_pipe.set_progress_bar_config(disable=__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					self.get_dummy_inputs(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					sd_pipe(**__lowerCamelCase      ).images
       SCREAMING_SNAKE_CASE__					=					image[0, -3:, -3:, -1]
       assert image.shape == (1, 64, 64, 3)

       SCREAMING_SNAKE_CASE__					=					np.array([0.6186, 0.5374, 0.4915, 0.4135, 0.4114, 0.4563, 0.5128, 0.4977, 0.4757]      )

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






  def    lowercase_	(							self		:	Any      )	->							Optional[Any]:
       super().test_inference_batch_consistent(batch_sizes=[1, 2]      )






  def    lowercase_	(							self		:	List[Any]      )	->							List[Any]:
       super().test_inference_batch_single_identical(batch_size=2			,     expected_max_diff=3.25e-3      )






  def    lowercase_	(							self		:	Union[str, Any]      )	->							str:
       SCREAMING_SNAKE_CASE__					=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       SCREAMING_SNAKE_CASE__					=					self.get_dummy_components()
       SCREAMING_SNAKE_CASE__					=					StableDiffusionPanoramaPipeline(**__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					sd_pipe.to(__lowerCamelCase      )
       sd_pipe.set_progress_bar_config(disable=__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					self.get_dummy_inputs(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					'''french fries'''
       SCREAMING_SNAKE_CASE__					=					sd_pipe(**__lowerCamelCase			,     negative_prompt=__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					output.images
       SCREAMING_SNAKE_CASE__					=					image[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)

       SCREAMING_SNAKE_CASE__					=					np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757]      )

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






  def    lowercase_	(							self		:	Tuple      )	->							Dict:
       SCREAMING_SNAKE_CASE__					=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       SCREAMING_SNAKE_CASE__					=					self.get_dummy_components()
       SCREAMING_SNAKE_CASE__					=					StableDiffusionPanoramaPipeline(**__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					sd_pipe.to(__lowerCamelCase      )
       sd_pipe.set_progress_bar_config(disable=__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					self.get_dummy_inputs(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					sd_pipe(**__lowerCamelCase			,     view_batch_size=2      )
       SCREAMING_SNAKE_CASE__					=					output.images
       SCREAMING_SNAKE_CASE__					=					image[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)

       SCREAMING_SNAKE_CASE__					=					np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757]      )

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






  def    lowercase_	(							self		:	Optional[Any]      )	->							Any:
       SCREAMING_SNAKE_CASE__					=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       SCREAMING_SNAKE_CASE__					=					self.get_dummy_components()
       SCREAMING_SNAKE_CASE__					=					EulerAncestralDiscreteScheduler(
           beta_start=0.00085			,     beta_end=0.012			,     beta_schedule='''scaled_linear'''      )
       SCREAMING_SNAKE_CASE__					=					StableDiffusionPanoramaPipeline(**__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					sd_pipe.to(__lowerCamelCase      )
       sd_pipe.set_progress_bar_config(disable=__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					self.get_dummy_inputs(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					sd_pipe(**__lowerCamelCase      ).images
       SCREAMING_SNAKE_CASE__					=					image[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)

       SCREAMING_SNAKE_CASE__					=					np.array([0.4024, 0.6510, 0.4901, 0.5378, 0.5813, 0.5622, 0.4795, 0.4467, 0.4952]      )

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







  def    lowercase_	(							self		:	Optional[int]      )	->							List[str]:
       SCREAMING_SNAKE_CASE__					=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       SCREAMING_SNAKE_CASE__					=					self.get_dummy_components()
       SCREAMING_SNAKE_CASE__					=					PNDMScheduler(
           beta_start=0.00085			,     beta_end=0.012			,     beta_schedule='''scaled_linear'''			,     skip_prk_steps=__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					StableDiffusionPanoramaPipeline(**__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					sd_pipe.to(__lowerCamelCase      )
       sd_pipe.set_progress_bar_config(disable=__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					self.get_dummy_inputs(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					sd_pipe(**__lowerCamelCase      ).images
       SCREAMING_SNAKE_CASE__					=					image[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)

       SCREAMING_SNAKE_CASE__					=					np.array([0.6391, 0.6291, 0.4861, 0.5134, 0.5552, 0.4578, 0.5032, 0.5023, 0.4539]      )

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






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






  """simple docstring"""


  def    lowercase_	(							self		:	Tuple      )	->							str:
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






  def    lowercase_	(							self		:	Any			,     __lowerCamelCase		:	Dict=0      )	->							Dict:
       SCREAMING_SNAKE_CASE__					=					torch.manual_seed(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					{
           '''prompt''': '''a photo of the dolomites''',
           '''generator''': generator,
           '''num_inference_steps''': 3,
           '''guidance_scale''': 7.5,
           '''output_type''': '''numpy''',
       }
       return inputs






  def    lowercase_	(							self		:	Union[str, Any]      )	->							List[Any]:
       SCREAMING_SNAKE_CASE__					=					'''stabilityai/stable-diffusion-2-base'''
       SCREAMING_SNAKE_CASE__					=					DDIMScheduler.from_pretrained(__lowerCamelCase			,     subfolder='''scheduler'''      )
       SCREAMING_SNAKE_CASE__					=					StableDiffusionPanoramaPipeline.from_pretrained(__lowerCamelCase			,     scheduler=__lowerCamelCase			,     safety_checker=__lowerCamelCase      )
       pipe.to(__lowerCamelCase      )
       pipe.set_progress_bar_config(disable=__lowerCamelCase      )
       pipe.enable_attention_slicing()

       SCREAMING_SNAKE_CASE__					=					self.get_inputs()
       SCREAMING_SNAKE_CASE__					=					pipe(**__lowerCamelCase      ).images
       SCREAMING_SNAKE_CASE__					=					image[0, -3:, -3:, -1].flatten()

       assert image.shape == (1, 512, 2048, 3)

       SCREAMING_SNAKE_CASE__					=					np.array(
           [
               0.36968392,
               0.27025372,
               0.32446766,
               0.28379387,
               0.36363274,
               0.30733347,
               0.27100027,
               0.27054125,
               0.25536096,
           ]      )

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






  def    lowercase_	(							self		:	Optional[Any]      )	->							Dict:
       SCREAMING_SNAKE_CASE__					=					StableDiffusionPanoramaPipeline.from_pretrained(
           '''stabilityai/stable-diffusion-2-base'''			,     safety_checker=__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					LMSDiscreteScheduler.from_config(pipe.scheduler.config      )
       pipe.to(__lowerCamelCase      )
       pipe.set_progress_bar_config(disable=__lowerCamelCase      )
       pipe.enable_attention_slicing()

       SCREAMING_SNAKE_CASE__					=					self.get_inputs()
       SCREAMING_SNAKE_CASE__					=					pipe(**__lowerCamelCase      ).images
       SCREAMING_SNAKE_CASE__					=					image[0, -3:, -3:, -1].flatten()

       assert image.shape == (1, 512, 2048, 3)

       SCREAMING_SNAKE_CASE__					=					np.array(
           [
               [
                   0.0,
                   0.0,
                   0.0,
                   0.0,
                   0.0,
                   0.0,
                   0.0,
                   0.0,
                   0.0,
               ]
           ]      )

       assert np.abs(expected_slice - image_slice      ).max() < 1e-3






  def    lowercase_	(							self		:	List[str]      )	->							List[str]:
       SCREAMING_SNAKE_CASE__					=					0

       def callback_fn(__lowerCamelCase		:	int			,     __lowerCamelCase		:	int			,     __lowerCamelCase		:	torch.FloatTensor      ) -> None:
            SCREAMING_SNAKE_CASE__					=					True
            nonlocal number_of_steps
            number_of_steps += 1
            if step == 1:
                 SCREAMING_SNAKE_CASE__					=					latents.detach().cpu().numpy()
                 assert latents.shape == (1, 4, 64, 256)
                 SCREAMING_SNAKE_CASE__					=					latents[0, -3:, -3:, -1]

                 SCREAMING_SNAKE_CASE__					=					np.array(
                     [
                         0.18681869,
                         0.33907816,
                         0.5361276,
                         0.14432865,
                         -0.02856611,
                         -0.73941123,
                         0.23397987,
                         0.47322682,
                         -0.37823164,
                     ]      )
                 assert np.abs(latents_slice.flatten() - expected_slice      ).max() < 5e-2
            elif step == 2:
                 SCREAMING_SNAKE_CASE__					=					latents.detach().cpu().numpy()
                 assert latents.shape == (1, 4, 64, 256)
                 SCREAMING_SNAKE_CASE__					=					latents[0, -3:, -3:, -1]

                 SCREAMING_SNAKE_CASE__					=					np.array(
                     [
                         0.18539645,
                         0.33987248,
                         0.5378559,
                         0.14437142,
                         -0.02455261,
                         -0.7338317,
                         0.23990755,
                         0.47356272,
                         -0.3786505,
                     ]      )

                 assert np.abs(latents_slice.flatten() - expected_slice      ).max() < 5e-2

       SCREAMING_SNAKE_CASE__					=					False

       SCREAMING_SNAKE_CASE__					=					'''stabilityai/stable-diffusion-2-base'''
       SCREAMING_SNAKE_CASE__					=					DDIMScheduler.from_pretrained(__lowerCamelCase			,     subfolder='''scheduler'''      )
       SCREAMING_SNAKE_CASE__					=					StableDiffusionPanoramaPipeline.from_pretrained(__lowerCamelCase			,     scheduler=__lowerCamelCase			,     safety_checker=__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					pipe.to(__lowerCamelCase      )
       pipe.set_progress_bar_config(disable=__lowerCamelCase      )
       pipe.enable_attention_slicing()

       SCREAMING_SNAKE_CASE__					=					self.get_inputs()
       pipe(**__lowerCamelCase			,     callback=__lowerCamelCase			,     callback_steps=1      )
       assert callback_fn.has_been_called
       assert number_of_steps == 3







  def    lowercase_	(							self		:	Tuple      )	->							Tuple:
       torch.cuda.empty_cache()
       torch.cuda.reset_max_memory_allocated()
       torch.cuda.reset_peak_memory_stats()

       SCREAMING_SNAKE_CASE__					=					'''stabilityai/stable-diffusion-2-base'''
       SCREAMING_SNAKE_CASE__					=					DDIMScheduler.from_pretrained(__lowerCamelCase			,     subfolder='''scheduler'''      )
       SCREAMING_SNAKE_CASE__					=					StableDiffusionPanoramaPipeline.from_pretrained(__lowerCamelCase			,     scheduler=__lowerCamelCase			,     safety_checker=__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					pipe.to(__lowerCamelCase      )
       pipe.set_progress_bar_config(disable=__lowerCamelCase      )
       pipe.enable_attention_slicing(1      )
       pipe.enable_sequential_cpu_offload()

       SCREAMING_SNAKE_CASE__					=					self.get_inputs()
       SCREAMING_SNAKE_CASE__					=					pipe(**__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					torch.cuda.max_memory_allocated()
       # make sure that less than 5.2 GB is allocated
       assert mem_bytes < 5.5 * 10**9


