


import os
import re
import shutil
import sys
import tempfile
import unittest

import black


UpperCamelCase					:       Optional[Any]			    =     os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
UpperCamelCase					:       Union[str, Any]			    =     '    \"""\n    Output class for the scheduler\'s step function output.\n\n    Args:\n        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n            denoising loop.\n        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n            `pred_original_sample` can be used to preview progress or for guidance.\n    \"""\n\n    prev_sample: torch.FloatTensor\n    pred_original_sample: Optional[torch.FloatTensor] = None\n'



class A__     ( unittest.TestCase  ):



  """simple docstring"""



  def    _UpperCamelCase( self			:  str       ):
        a__	:  List[str]     =   tempfile.mkdtemp()
        os.makedirs(os.path.join(self.diffusers_dir				,				"schedulers/"       )       )
        a__	:  Tuple     =   self.diffusers_dir
        shutil.copy(
            os.path.join(__snake_case				,				"src/diffusers/schedulers/scheduling_ddpm.py"       )				,				os.path.join(self.diffusers_dir				,				"schedulers/scheduling_ddpm.py"       )				,				)

  def    _UpperCamelCase( self			:  int       ):
        a__	:  Optional[Any]     =   "src/diffusers"
        shutil.rmtree(self.diffusers_dir       )

  def    _UpperCamelCase( self			:  List[Any]				,				lowerCamelCase__			:  Any				,				lowerCamelCase__			:  int				,				lowerCamelCase__			:  Tuple				,				lowerCamelCase__			:  Optional[int]=None       ):
        a__	:  int     =   comment + f'''\nclass {class_name}(nn.Module):\n''' + class_code
        if overwrite_result is not None:
              a__	:  Union[str, Any]     =   comment + f'''\nclass {class_name}(nn.Module):\n''' + overwrite_result
        a__	:  Optional[Any]     =   black.Mode(target_versions={black.TargetVersion.PYaa}				,				line_length=119       )
        a__	:  Tuple     =   black.format_str(__snake_case				,				mode=__snake_case       )
        a__	:  Optional[int]     =   os.path.join(self.diffusers_dir				,				"new_code.py"       )
        with open(__snake_case				,				"w"				,				newline="\n"       ) as f:
              f.write(__snake_case       )
        if overwrite_result is None:
              self.assertTrue(len(check_copies.is_copy_consistent(__snake_case       )       ) == 0       )
        else:
              check_copies.is_copy_consistent(f.name				,				overwrite=__snake_case       )
              with open(__snake_case				,				"r"       ) as f:
                    self.assertTrue(f.read()				,				__snake_case       )

  def    _UpperCamelCase( self			:  List[Any]       ):
        a__	:  Union[str, Any]     =   check_copies.find_code_in_diffusers("schedulers.scheduling_ddpm.DDPMSchedulerOutput"       )
        self.assertEqual(__snake_case				,				__snake_case       )

  def    _UpperCamelCase( self			:  Dict       ):
        self.check_copy_consistency(
            "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput"				,				"DDPMSchedulerOutput"				,				REFERENCE_CODE + "\n"				,				)

        # With no empty line at the end
        self.check_copy_consistency(
            "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput"				,				"DDPMSchedulerOutput"				,				__snake_case				,				)

        # Copy consistency with rename
        self.check_copy_consistency(
            "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test"				,				"TestSchedulerOutput"				,				re.sub("DDPM"				,				"Test"				,				__snake_case       )				,				)

        # Copy consistency with a really long name
        a__	:  int     =   "TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"
        self.check_copy_consistency(
            f'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}'''				,				f'''{long_class_name}SchedulerOutput'''				,				re.sub("Bert"				,				__snake_case				,				__snake_case       )				,				)

        # Copy consistency with overwrite
        self.check_copy_consistency(
            "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test"				,				"TestSchedulerOutput"				,				__snake_case				,				overwrite_result=re.sub("DDPM"				,				"Test"				,				__snake_case       )				,				)








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a__      :     str         =		{
    'configuration_roformer': ['ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoFormerConfig', 'RoFormerOnnxConfig'],
    'tokenization_roformer': ['RoFormerTokenizer'],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      a__      :     List[Any]         =		['RoFormerTokenizerFast']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      a__      :     List[str]         =		[
          'ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'RoFormerForCausalLM',
          'RoFormerForMaskedLM',
          'RoFormerForMultipleChoice',
          'RoFormerForQuestionAnswering',
          'RoFormerForSequenceClassification',
          'RoFormerForTokenClassification',
          'RoFormerLayer',
          'RoFormerModel',
          'RoFormerPreTrainedModel',
          'load_tf_weights_in_roformer',
      ]


try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      a__      :     List[Any]         =		[
          'TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFRoFormerForCausalLM',
          'TFRoFormerForMaskedLM',
          'TFRoFormerForMultipleChoice',
          'TFRoFormerForQuestionAnswering',
          'TFRoFormerForSequenceClassification',
          'TFRoFormerForTokenClassification',
          'TFRoFormerLayer',
          'TFRoFormerModel',
          'TFRoFormerPreTrainedModel',
      ]


try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      a__      :     Dict         =		[
          'FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'FlaxRoFormerForMaskedLM',
          'FlaxRoFormerForMultipleChoice',
          'FlaxRoFormerForQuestionAnswering',
          'FlaxRoFormerForSequenceClassification',
          'FlaxRoFormerForTokenClassification',
          'FlaxRoFormerModel',
          'FlaxRoFormerPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
      from .tokenization_roformer import RoFormerTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_roformer_fast import RoFormerTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roformer import (
                ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                RoFormerForCausalLM,
                RoFormerForMaskedLM,
                RoFormerForMultipleChoice,
                RoFormerForQuestionAnswering,
                RoFormerForSequenceClassification,
                RoFormerForTokenClassification,
                RoFormerLayer,
                RoFormerModel,
                RoFormerPreTrainedModel,
                load_tf_weights_in_roformer,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roformer import (
                TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRoFormerForCausalLM,
                TFRoFormerForMaskedLM,
                TFRoFormerForMultipleChoice,
                TFRoFormerForQuestionAnswering,
                TFRoFormerForSequenceClassification,
                TFRoFormerForTokenClassification,
                TFRoFormerLayer,
                TFRoFormerModel,
                TFRoFormerPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roformer import (
                FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                FlaxRoFormerForMaskedLM,
                FlaxRoFormerForMultipleChoice,
                FlaxRoFormerForQuestionAnswering,
                FlaxRoFormerForSequenceClassification,
                FlaxRoFormerForTokenClassification,
                FlaxRoFormerModel,
                FlaxRoFormerPreTrainedModel,
            )


else:
      import sys

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



