






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

import black


SCREAMING_SNAKE_CASE__   :      Tuple				=		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__   :      str				=		"""    \"\"\"
    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     lowerCamelCase_					(   unittest.TestCase			):







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]   =   tempfile.mkdtemp()
								os.makedirs(os.path.join(self.diffusers_dir							,       '''schedulers/'''  )  )
								__magic_name__		:Optional[int]   =   self.diffusers_dir
								shutil.copy(
								    os.path.join(__lowerCAmelCase							,       '''src/diffusers/schedulers/scheduling_ddpm.py'''  )							,       os.path.join(self.diffusers_dir							,       '''schedulers/scheduling_ddpm.py'''  )							,       )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Optional[int]   =   '''src/diffusers'''
								shutil.rmtree(self.diffusers_dir  )







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase=None  ):
								"""simple docstring"""
								__magic_name__		:Tuple   =   comment + F'''\nclass {class_name}(nn.Module):\n''' + class_code
								if overwrite_result is not None:
												__magic_name__		:List[str]   =   comment + F'''\nclass {class_name}(nn.Module):\n''' + overwrite_result
								__magic_name__		:List[Any]   =   black.Mode(target_versions={black.TargetVersion.PYaa}							,       line_length=1_1_9  )
								__magic_name__		:Dict   =   black.format_str(__lowerCAmelCase							,       mode=__lowerCAmelCase  )
								__magic_name__		:int   =   os.path.join(self.diffusers_dir							,       '''new_code.py'''  )
								with open(__lowerCAmelCase							,       '''w'''							,       newline='''\n'''  ) as f:
												f.write(__lowerCAmelCase  )
								if overwrite_result is None:
												self.assertTrue(len(check_copies.is_copy_consistent(__lowerCAmelCase  )  ) == 0  )
								else:
												check_copies.is_copy_consistent(f.name							,       overwrite=__lowerCAmelCase  )
												with open(__lowerCAmelCase							,       '''r'''  ) as f:
																self.assertTrue(f.read()							,       __lowerCAmelCase  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Tuple   =   check_copies.find_code_in_diffusers('''schedulers.scheduling_ddpm.DDPMSchedulerOutput'''  )
								self.assertEqual(__lowerCAmelCase							,       __lowerCAmelCase  )







				def       A						(    self  ):
								"""simple docstring"""
								# Base copy consistency
								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'''							,       __lowerCAmelCase							,       )

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

								# Copy consistency with a really long name
								__magic_name__		:List[Any]   =   '''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'''							,       __lowerCAmelCase							,       __lowerCAmelCase  )							,       )

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









from ...configuration_utils import PretrainedConfig
from ...utils import logging


a__												=						logging.get_logger(__name__)

a__												=						{
    '''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/config.json''',
    '''funnel-transformer/small-base''': '''https://huggingface.co/funnel-transformer/small-base/resolve/main/config.json''',
    '''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/config.json''',
    '''funnel-transformer/medium-base''': '''https://huggingface.co/funnel-transformer/medium-base/resolve/main/config.json''',
    '''funnel-transformer/intermediate''': (
        '''https://huggingface.co/funnel-transformer/intermediate/resolve/main/config.json'''
    ),
    '''funnel-transformer/intermediate-base''': (
        '''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/config.json'''
    ),
    '''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/config.json''',
    '''funnel-transformer/large-base''': '''https://huggingface.co/funnel-transformer/large-base/resolve/main/config.json''',
    '''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/config.json''',
    '''funnel-transformer/xlarge-base''': '''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/config.json''',
}

class 							UpperCAmelCase_						(     __lowercase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		Optional[int]             =       "funnel"
							UpperCAmelCase__			:		Tuple             =       {
							    "hidden_size": "d_model",
							    "num_attention_heads": "n_head",
							}


							def __init__(     self			,				_a=3_0_5_2_2			,				_a=[4, 4, 4]			,				_a=None			,				_a=2			,				_a=7_6_8			,				_a=1_2			,				_a=6_4			,				_a=3_0_7_2			,				_a="gelu_new"			,				_a=0.1			,				_a=0.1			,				_a=0.0			,				_a=0.1			,				_a=None			,				_a=1e-9			,				_a="mean"			,				_a="relative_shift"			,				_a=True			,				_a=True			,				_a=True			,				**_a			,				)       ->       List[Any]:
												_a      :       Optional[int]   =						vocab_size
												_a      :       Dict   =						block_sizes
												_a      :       Optional[int]   =						[1] * len(_a			) if block_repeats is None else block_repeats
												assert len(_a			) == len(
												    self.block_repeats			), "`block_sizes` and `block_repeats` should have the same length."
												_a      :       int   =						num_decoder_layers
												_a      :       List[str]   =						d_model
												_a      :       Optional[Any]   =						n_head
												_a      :       Tuple   =						d_head
												_a      :       Dict   =						d_inner
												_a      :       List[str]   =						hidden_act
												_a      :       int   =						hidden_dropout
												_a      :       Union[str, Any]   =						attention_dropout
												_a      :       Tuple   =						activation_dropout
												_a      :       Optional[Any]   =						initializer_range
												_a      :       Dict   =						initializer_std
												_a      :       Union[str, Any]   =						layer_norm_eps
												assert pooling_type in [
												    "mean",
												    "max",
												], F"""Got {pooling_type} for `pooling_type` but only 'mean' and 'max' are supported."""
												_a      :       Any   =						pooling_type
												assert attention_type in [
												    "relative_shift",
												    "factorized",
												], F"""Got {attention_type} for `attention_type` but only 'relative_shift' and 'factorized' are supported."""
												_a      :       Optional[Any]   =						attention_type
												_a      :       int   =						separate_cls
												_a      :       Tuple   =						truncate_seq
												_a      :       List[Any]   =						pool_q_only

												super().__init__(**_a			)


							@property
							def 							__lowercase							(     self			)       ->       Tuple:
												return sum(self.block_sizes			)


							@num_hidden_layers.setter
							def 							__lowercase							(     self			,				_a			)       ->       List[str]:
												raise NotImplementedError(
												    '''This model does not support the setting of `num_hidden_layers`. Please set `block_sizes`.'''			)


							@property
							def 							__lowercase							(     self			)       ->       Optional[int]:
												return len(self.block_sizes			)




							@num_blocks.setter
							def 							__lowercase							(     self			,				_a			)       ->       Dict:
												raise NotImplementedError('''This model does not support the setting of `num_blocks`. Please set `block_sizes`.'''			)



