




"""simple docstring"""



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

import black


SCREAMING_SNAKE_CASE				=       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.
SCREAMING_SNAKE_CASE				=       "    \"\"\"\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 	UpperCAmelCase_  (						unittest.TestCase							):




    def 							__magic_name__							(    self						:       List[Any]      )      ->							Dict:




     '''simple docstring'''

     A__       =				tempfile.mkdtemp()
     os.makedirs(os.path.join(self.diffusers_dir	, "schedulers/"      )      )
     A__       =				self.diffusers_dir
     shutil.copy(
         os.path.join(UpperCAmelCase__	, "src/diffusers/schedulers/scheduling_ddpm.py"      )	, os.path.join(self.diffusers_dir	, "schedulers/scheduling_ddpm.py"      )	, )




    def 							__magic_name__							(    self						:       int      )      ->							Dict:




     '''simple docstring'''

     A__       =				"""src/diffusers"""
     shutil.rmtree(self.diffusers_dir      )




    def 							__magic_name__							(    self						:       Any	, snake_case_						:       Optional[int]	, snake_case_						:       Optional[int]	, snake_case_						:       Optional[int]	, snake_case_						:       Union[str, Any]=None      )      ->							Dict:




     '''simple docstring'''

     A__       =				comment + F"""\nclass {class_name}(nn.Module):\n""" + class_code
     if overwrite_result is not None:
      A__       =				comment + F"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
     A__       =				black.Mode(target_versions={black.TargetVersion.PYaa}	, line_length=119      )
     A__       =				black.format_str(UpperCAmelCase__	, mode=UpperCAmelCase__      )
     A__       =				os.path.join(self.diffusers_dir	, "new_code.py"      )
     with open(UpperCAmelCase__	, "w"	, newline="\n"      ) as f:
      f.write(UpperCAmelCase__      )
     if overwrite_result is None:
      self.assertTrue(len(check_copies.is_copy_consistent(UpperCAmelCase__      )      ) == 0      )
     else:
      check_copies.is_copy_consistent(f.name	, overwrite=UpperCAmelCase__      )
      with open(UpperCAmelCase__	, "r"      ) as f:
       self.assertTrue(f.read()	, UpperCAmelCase__      )




    def 							__magic_name__							(    self						:       Any      )      ->							Tuple:




     '''simple docstring'''

     A__       =				check_copies.find_code_in_diffusers("schedulers.scheduling_ddpm.DDPMSchedulerOutput"      )
     self.assertEqual(UpperCAmelCase__	, UpperCAmelCase__      )




    def 							__magic_name__							(    self						:       int      )      ->							Optional[int]:




     '''simple docstring'''

     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"	, UpperCAmelCase__	, )

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

     # Copy consistency with a really long name
     A__       =				"""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"	, UpperCAmelCase__	, UpperCAmelCase__      )	, )

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













"""simple docstring"""




from typing import TYPE_CHECKING

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


_snake_case          =   {
    'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
    'tokenization_transfo_xl': ['TransfoXLCorpus', 'TransfoXLTokenizer'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _snake_case          =   [
     'TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
     'AdaptiveEmbedding',
     'TransfoXLForSequenceClassification',
     'TransfoXLLMHeadModel',
     'TransfoXLModel',
     'TransfoXLPreTrainedModel',
     'load_tf_weights_in_transfo_xl',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _snake_case          =   [
     'TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFAdaptiveEmbedding',
     'TFTransfoXLForSequenceClassification',
     'TFTransfoXLLMHeadModel',
     'TFTransfoXLMainLayer',
     'TFTransfoXLModel',
     'TFTransfoXLPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
 from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_transfo_xl import (
      TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
      AdaptiveEmbedding,
      TransfoXLForSequenceClassification,
      TransfoXLLMHeadModel,
      TransfoXLModel,
      TransfoXLPreTrainedModel,
      load_tf_weights_in_transfo_xl,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_transfo_xl import (
      TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFAdaptiveEmbedding,
      TFTransfoXLForSequenceClassification,
      TFTransfoXLLMHeadModel,
      TFTransfoXLMainLayer,
      TFTransfoXLModel,
      TFTransfoXLPreTrainedModel,
  )

else:
 import sys

 _snake_case          =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

