






'''simple docstring'''







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

import black


__lowerCAmelCase       		=					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.
__lowerCAmelCase       		=					'    \"""\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 	SCREAMING_SNAKE_CASE					(					unittest.TestCase       ):






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]					)			->			Union[str, Any]:
							a_		:  Dict											=      tempfile.mkdtemp()
							os.makedirs(os.path.join(self.diffusers_dir  ,						'''schedulers/'''					)					)
							a_		:  List[str]											=      self.diffusers_dir
							shutil.copy(
							    os.path.join(__SCREAMING_SNAKE_CASE  ,						'''src/diffusers/schedulers/scheduling_ddpm.py'''					)  ,						os.path.join(self.diffusers_dir  ,						'''schedulers/scheduling_ddpm.py'''					)  ,						)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]					)			->			Dict:
							a_		:  str											=      '''src/diffusers'''
							shutil.rmtree(self.diffusers_dir					)






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






			def 			SCREAMING_SNAKE_CASE      ( self      :			Tuple					)			->			int:
							a_		:  List[str]											=      check_copies.find_code_in_diffusers('''schedulers.scheduling_ddpm.DDPMSchedulerOutput'''					)
							self.assertEqual(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)







			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[int]					)			->			Optional[Any]:
							# 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'''  ,						__SCREAMING_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'''  ,						__SCREAMING_SNAKE_CASE					)  ,						)

							# Copy consistency with a really long name
							a_		:  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'''  ,						__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)  ,						)

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












'''simple docstring'''







import torch

from transformers import AutoModel





class 	SCREAMING_SNAKE_CASE					(					torch.nn.Module       ):






			def __init__( self      :			Optional[int]  ,						__SCREAMING_SNAKE_CASE      :			int="sayef/fsner-bert-base-uncased"					)			->			str:
							super(__SCREAMING_SNAKE_CASE  ,						self					).__init__()

							a_		:  str											=      AutoModel.from_pretrained(__SCREAMING_SNAKE_CASE  ,						return_dict=__SCREAMING_SNAKE_CASE					)
							a_		:  Optional[int]											=      torch.nn.CosineSimilarity(3  ,						1e-08					)
							a_		:  Dict											=      torch.nn.Softmax(dim=1					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			str  ,						**__SCREAMING_SNAKE_CASE      :			int					)			->			str:
							return self.bert(**__SCREAMING_SNAKE_CASE					).last_hidden_state






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]  ,						__SCREAMING_SNAKE_CASE      :			List[Any]					)			->			Optional[int]:
							return token_embeddings.sum(2  ,						keepdim=__SCREAMING_SNAKE_CASE					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[Any]  ,						__SCREAMING_SNAKE_CASE      :			Optional[int]  ,						__SCREAMING_SNAKE_CASE      :			Union[str, Any]  ,						__SCREAMING_SNAKE_CASE      :			int=1					)			->			Dict:
							return self.softmax(T * self.cos(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)					)







			def 			SCREAMING_SNAKE_CASE      ( self      :			str  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]  ,						__SCREAMING_SNAKE_CASE      :			Optional[int]					)			->			Any:
							a_		:  Dict											=      W_supports['''sizes'''].tolist()
							a_		:  Tuple											=      W_supports['''start_token_id'''].item()
							a_		:  List[Any]											=      W_supports['''end_token_id'''].item()

							del W_supports["sizes"]
							del W_supports["start_token_id"]
							del W_supports["end_token_id"]

							a_		:  int											=      self.BERT(**__SCREAMING_SNAKE_CASE					)
							a_		:  Any											=      self.BERT(**__SCREAMING_SNAKE_CASE					)

							a_		:  Optional[Any]											=      None
							a_		:  Tuple											=      None

							a_		:  List[str]											=      W_supports['''input_ids'''] == start_token_id
							a_		:  Dict											=      W_supports['''input_ids'''] == end_token_id

							for i, size in enumerate(__SCREAMING_SNAKE_CASE					):
											if i == 0:
															a_		:  str											=      0
											else:
															a_		:  str											=      support_sizes[i - 1]

											a_		:  Union[str, Any]											=      S[s : s + size][start_token_masks[s : s + size]]
											a_		:  Tuple											=      S[s : s + size][end_token_masks[s : s + size]]

											a_		:  Tuple											=      torch.matmul(q[i]  ,						s_start.T					).sum(1					).softmax(0					)
											a_		:  Optional[Any]											=      torch.matmul(q[i]  ,						s_end.T					).sum(1					).softmax(0					)

											if p_starts is not None:
															a_		:  Any											=      torch.vstack((p_starts, p_start)					)
															a_		:  Dict											=      torch.vstack((p_ends, p_end)					)
											else:
															a_		:  Optional[int]											=      p_start
															a_		:  List[Any]											=      p_end

							return p_starts, p_ends




