






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

import black


a_							=    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.
a_							=    '''    \"""
    Output class for the scheduler\'s step function output.

    Args:
        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
            denoising loop.
        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
            The predicted denoised sample (x_{0}) based on the model output from the current timestep.
            `pred_original_sample` can be used to preview progress or for guidance.
    \"""

    prev_sample: torch.FloatTensor
    pred_original_sample: Optional[torch.FloatTensor] = None
'''


class      lowercase__     (			unittest.TestCase	):

	def 		UpperCAmelCase  (							self       )->					Optional[Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						tempfile.mkdtemp()
							os.makedirs(os.path.join(self.diffusers_dir		,    "schedulers/"       )       )
							lowerCAmelCase__       =						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 		UpperCAmelCase  (							self       )->					Union[str, Any]:







							'''simple docstring'''





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

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase=None       )->					Optional[int]:







							'''simple docstring'''





							lowerCAmelCase__       =						comment + F"\nclass {class_name}(nn.Module):\n" + class_code
							if overwrite_result is not None:
													lowerCAmelCase__       =						comment + F"\nclass {class_name}(nn.Module):\n" + overwrite_result
							lowerCAmelCase__       =						black.Mode(target_versions={black.TargetVersion.PYaa}		,    line_length=119       )
							lowerCAmelCase__       =						black.format_str(__UpperCAmelCase		,    mode=__UpperCAmelCase       )
							lowerCAmelCase__       =						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 		UpperCAmelCase  (							self       )->					List[Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						check_copies.find_code_in_diffusers("schedulers.scheduling_ddpm.DDPMSchedulerOutput"       )
							self.assertEqual(__UpperCAmelCase		,    __UpperCAmelCase       )


	def 		UpperCAmelCase  (							self       )->					List[str]:







							'''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
							lowerCAmelCase__       =						"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       )		,    )














from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


a_							=    {'''configuration_plbart''': ['''PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PLBartConfig''']}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_							=    ['''PLBartTokenizer''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_							=    [
	    '''PLBART_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''PLBartForCausalLM''',
	    '''PLBartForConditionalGeneration''',
	    '''PLBartForSequenceClassification''',
	    '''PLBartModel''',
	    '''PLBartPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_plbart import PLBartTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_plbart import (
		    PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
		    PLBartForCausalLM,
		    PLBartForConditionalGeneration,
		    PLBartForSequenceClassification,
		    PLBartModel,
		    PLBartPreTrainedModel,
		)


else:
	import sys

	a_							=    _LazyModule(__name__, globals()['''__file__'''], _import_structure)






