




import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional

import torch

from ..utils import add_start_docstrings, logging


lowerCamelCase       :	List[Any]									=     logging.get_logger(__name__)


lowerCamelCase       :	Any									=     r"\n    Args:\n        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):\n            Indices of input sequence tokens in the vocabulary.\n\n            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n            [`PreTrainedTokenizer.__call__`] for details.\n\n            [What are input IDs?](../glossary#input-ids)\n        scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):\n            Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax\n            or scores for each vocabulary token after SoftMax.\n        kwargs (`Dict[str, Any]`, *optional*):\n            Additional stopping criteria specific kwargs.\n\n    Return:\n        `bool`. `False` indicates we should continue, `True` indicates we should stop.\n\n"







class 						A(		UpperCamelCase     ):



 '''simple docstring'''


 @add_start_docstrings(A_						)
 def __call__(       self      :						Any		,							A_      :						torch.LongTensor		,							A_      :						torch.FloatTensor		,							**A_      :						str						)     ->			bool:

       """simple docstring"""






       raise NotImplementedError('StoppingCriteria needs to be subclassed'						)







class 						A(		UpperCamelCase     ):



 '''simple docstring'''


 def __init__(       self      :						List[str]		,							A_      :						int		,							A_      :						Optional[int] = None						)     ->			Optional[Any]:

       """simple docstring"""






       lowerCamelCase_	  =					max_length
       lowerCamelCase_	  =					max_position_embeddings





 @add_start_docstrings(A_						)
 def __call__(       self      :						List[str]		,							A_      :						torch.LongTensor		,							A_      :						torch.FloatTensor		,							**A_      :						Optional[int]						)     ->			bool:

       """simple docstring"""






       lowerCamelCase_	  =					input_ids.shape[-1]
       lowerCamelCase_	  =					cur_len >= self.max_length
       if self.max_position_embeddings is not None and not is_done and cur_len >= self.max_position_embeddings:
             logger.warning_once(
                 'This is a friendly reminder - the current text generation call will exceed the model\'s predefined '
                 f"""maximum length ({self.max_position_embeddings}). Depending on the model, you may observe """
                 'exceptions, performance degradation, or nothing at all.'						)
       return is_done







class 						A(		UpperCamelCase     ):



 '''simple docstring'''


 def __init__(       self      :						List[str]		,							A_      :						int		,							A_      :						int						)     ->			Optional[Any]:

       """simple docstring"""






       warnings.warn(
           'The class `MaxNewTokensCriteria` is deprecated. '
           f"""Please use `MaxLengthCriteria(max_length={start_length + max_new_tokens})` """
           'with `max_length = start_length + max_new_tokens` instead.'		,							A_		,							)
       lowerCamelCase_	  =					start_length
       lowerCamelCase_	  =					max_new_tokens
       lowerCamelCase_	  =					start_length + max_new_tokens





 @add_start_docstrings(A_						)
 def __call__(       self      :						Dict		,							A_      :						torch.LongTensor		,							A_      :						torch.FloatTensor		,							**A_      :						Optional[int]						)     ->			bool:

       """simple docstring"""






       return input_ids.shape[-1] >= self.max_length







class 						A(		UpperCamelCase     ):



 '''simple docstring'''


 def __init__(       self      :						Dict		,							A_      :						float		,							A_      :						Optional[float] = None						)     ->			Any:

       """simple docstring"""






       lowerCamelCase_	  =					max_time
       lowerCamelCase_	  =					time.time() if initial_timestamp is None else initial_timestamp





 @add_start_docstrings(A_						)
 def __call__(       self      :						Union[str, Any]		,							A_      :						torch.LongTensor		,							A_      :						torch.FloatTensor		,							**A_      :						Tuple						)     ->			bool:

       """simple docstring"""






       return time.time() - self.initial_timestamp > self.max_time







class 						A(		UpperCamelCase     ):



 '''simple docstring'''


 @add_start_docstrings(A_						)
 def __call__(       self      :						Optional[int]		,							A_      :						torch.LongTensor		,							A_      :						torch.FloatTensor		,							**A_      :						Optional[int]						)     ->			bool:

       """simple docstring"""






       return any(criteria(A_		,							A_						) for criteria in self						)





 @property
 def        a__				(       self      :						str						)     ->			Optional[int]:

       """simple docstring"""






       for stopping_criterium in self:
             if isinstance(A_		,							A_						):
                   return stopping_criterium.max_length
             elif isinstance(A_		,							A_						):
                   return stopping_criterium.max_length
       return None

def     _SCREAMING_SNAKE_CASE    (							lowercase			: StoppingCriteriaList			,							lowercase			: int  ):


      '''simple docstring'''



      lowerCamelCase_	  =					stopping_criteria.max_length
      lowerCamelCase_	  =					deepcopy(lowercase  )
      if stopping_max_length is not None and stopping_max_length != max_length:
            warnings.warn('You set different `max_length` for stopping criteria and `max_length` parameter'			,							lowercase  )
      elif stopping_max_length is None:
            new_stopping_criteria.append(MaxLengthCriteria(max_length=lowercase  )  )
      return new_stopping_criteria









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

import black


lowerCamelCase       :	List[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.
lowerCamelCase       :	Tuple									=     "    \"\"\"\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        a__				(       self      :						Union[str, Any]						)     ->			List[str]:

       """simple docstring"""






       lowerCamelCase_	  =					tempfile.mkdtemp()
       os.makedirs(os.path.join(self.diffusers_dir		,							'schedulers/'						)						)
       lowerCamelCase_	  =					self.diffusers_dir
       shutil.copy(
           os.path.join(A_		,							'src/diffusers/schedulers/scheduling_ddpm.py'						)		,							os.path.join(self.diffusers_dir		,							'schedulers/scheduling_ddpm.py'						)		,							)

 def        a__				(       self      :						Tuple						)     ->			List[str]:

       """simple docstring"""






       lowerCamelCase_	  =					'src/diffusers'
       shutil.rmtree(self.diffusers_dir						)

 def        a__				(       self      :						str		,							A_      :						Optional[Any]		,							A_      :						Optional[int]		,							A_      :						str		,							A_      :						Optional[Any]=None						)     ->			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(A_		,							mode=A_						)
       lowerCamelCase_	  =					os.path.join(self.diffusers_dir		,							'new_code.py'						)
       with open(A_		,							'w'		,							newline='\n'						) as f:
             f.write(A_						)
       if overwrite_result is None:
             self.assertTrue(len(check_copies.is_copy_consistent(A_						)						) == 0						)
       else:
             check_copies.is_copy_consistent(f.name		,							overwrite=A_						)
             with open(A_		,							'r'						) as f:
                   self.assertTrue(f.read()		,							A_						)

 def        a__				(       self      :						Optional[int]						)     ->			Dict:

       """simple docstring"""






       lowerCamelCase_	  =					check_copies.find_code_in_diffusers('schedulers.scheduling_ddpm.DDPMSchedulerOutput'						)
       self.assertEqual(A_		,							A_						)





 def        a__				(       self      :						Any						)     ->			Dict:

       """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'		,							A_		,							)

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

       # 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'		,							A_		,							A_						)		,							)

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



