






import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
		import torch



class   lowerCamelCase				(_snake_case      ):
		_snake_case   :   Any							=  '''char'''
		_snake_case   :   int							=  '''bpe'''
		_snake_case   :   Optional[int]							=  '''wp'''





__UpperCAmelCase     							=						(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)



class   lowerCamelCase				(_snake_case      ):
		_snake_case   :   List[str]							=  ['''image_processor''', '''char_tokenizer''']
		_snake_case   :   List[str]							=  '''ViTImageProcessor'''
		_snake_case   :   int							=  '''MgpstrTokenizer'''



		def __init__(							self    ,		_UpperCamelCase=None    ,		_UpperCamelCase=None    ,		**_UpperCamelCase       )     ->							Any:
							UpperCAmelCase_   :				str						=						None
							if "feature_extractor" in kwargs:
												warnings.warn(
												    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
												    ' instead.'    ,		_lowerCAmelCase    ,		)
												UpperCAmelCase_   :				Tuple						=						kwargs.pop('feature_extractor'       )

							UpperCAmelCase_   :				List[str]						=						image_processor if image_processor is not None else feature_extractor
							if image_processor is None:
												raise ValueError('You need to specify an `image_processor`.'       )
							if tokenizer is None:
												raise ValueError('You need to specify a `tokenizer`.'       )

							UpperCAmelCase_   :				int						=						tokenizer
							UpperCAmelCase_   :				Optional[Any]						=						AutoTokenizer.from_pretrained('gpt2'       )
							UpperCAmelCase_   :				str						=						AutoTokenizer.from_pretrained('bert-base-uncased'       )

							super().__init__(_lowerCAmelCase    ,		_lowerCAmelCase       )



		def __call__(							self    ,		_UpperCamelCase=None    ,		_UpperCamelCase=None    ,		_UpperCamelCase=None    ,		**_UpperCamelCase       )     ->							Any:

							if images is None and text is None:
												raise ValueError('You need to specify either an `images` or `text` input to process.'       )

							if images is not None:
												UpperCAmelCase_   :				Any						=						self.image_processor(_lowerCAmelCase    ,		return_tensors=_lowerCAmelCase    ,		**_lowerCAmelCase       )
							if text is not None:
												UpperCAmelCase_   :				Union[str, Any]						=						self.char_tokenizer(_lowerCAmelCase    ,		return_tensors=_lowerCAmelCase    ,		**_lowerCAmelCase       )

							if text is None:
												return inputs
							elif images is None:
												return encodings
							else:
												UpperCAmelCase_   :				Union[str, Any]						=						encodings['input_ids']
												return inputs



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							Tuple:
							UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_   :				Optional[int]						=						sequences
							UpperCAmelCase_   :				Any						=						char_preds.size(0       )

							UpperCAmelCase_	,  UpperCAmelCase_   :				Optional[Any]						=						self._decode_helper(_lowerCAmelCase    ,		'char'       )
							UpperCAmelCase_	,  UpperCAmelCase_   :				Optional[int]						=						self._decode_helper(_lowerCAmelCase    ,		'bpe'       )
							UpperCAmelCase_	,  UpperCAmelCase_   :				Union[str, Any]						=						self._decode_helper(_lowerCAmelCase    ,		'wp'       )

							UpperCAmelCase_   :				Tuple						=						[]
							UpperCAmelCase_   :				Union[str, Any]						=						[]
							for i in range(_lowerCAmelCase       ):
												UpperCAmelCase_   :				Dict						=						[char_scores[i], bpe_scores[i], wp_scores[i]]
												UpperCAmelCase_   :				Any						=						[char_strs[i], bpe_strs[i], wp_strs[i]]
												UpperCAmelCase_   :				List[Any]						=						scores.index(max(_lowerCAmelCase       )       )
												final_strs.append(strs[max_score_index]       )
												final_scores.append(scores[max_score_index]       )

							UpperCAmelCase_   :				Any						=						{}
							UpperCAmelCase_   :				List[str]						=						final_strs
							UpperCAmelCase_   :				int						=						final_scores
							UpperCAmelCase_   :				Optional[Any]						=						char_strs
							UpperCAmelCase_   :				Any						=						bpe_strs
							UpperCAmelCase_   :				int						=						wp_strs
							return out



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase       )     ->							Union[str, Any]:

							if format == DecodeType.CHARACTER:
												UpperCAmelCase_   :				Optional[Any]						=						self.char_decode
												UpperCAmelCase_   :				Any						=						1
												UpperCAmelCase_   :				Any						=						'[s]'
							elif format == DecodeType.BPE:
												UpperCAmelCase_   :				Any						=						self.bpe_decode
												UpperCAmelCase_   :				Optional[int]						=						2
												UpperCAmelCase_   :				Optional[int]						=						'#'
							elif format == DecodeType.WORDPIECE:
												UpperCAmelCase_   :				Optional[int]						=						self.wp_decode
												UpperCAmelCase_   :				Union[str, Any]						=						1_0_2
												UpperCAmelCase_   :				List[Any]						=						'[SEP]'
							else:
												raise ValueError(f"Format {format} is not supported."       )

							UpperCAmelCase_	,  UpperCAmelCase_   :				str						=						[], []
							UpperCAmelCase_   :				List[str]						=						pred_logits.size(0       )
							UpperCAmelCase_   :				Dict						=						pred_logits.size(1       )
							UpperCAmelCase_	,  UpperCAmelCase_   :				str						=						pred_logits.topk(1    ,		dim=-1    ,		largest=_lowerCAmelCase    ,		sorted=_lowerCAmelCase       )
							UpperCAmelCase_   :				Optional[int]						=						preds_index.view(-1    ,		_lowerCAmelCase       )[:, 1:]
							UpperCAmelCase_   :				Union[str, Any]						=						decoder(_lowerCAmelCase       )
							UpperCAmelCase_	,  UpperCAmelCase_   :				Tuple						=						torch.nn.functional.softmax(_lowerCAmelCase    ,		dim=2       ).max(dim=2       )
							UpperCAmelCase_   :				Tuple						=						preds_max_prob[:, 1:]

							for index in range(_lowerCAmelCase       ):
												UpperCAmelCase_   :				int						=						preds_str[index].find(_lowerCAmelCase       )
												UpperCAmelCase_   :				int						=						preds_str[index][:pred_eos]
												UpperCAmelCase_   :				int						=						preds_index[index].cpu().tolist()
												UpperCAmelCase_   :				Optional[Any]						=						pred_index.index(_lowerCAmelCase       ) if eos_token in pred_index else -1
												UpperCAmelCase_   :				Optional[int]						=						preds_max_prob[index][: pred_eos_index + 1]
												UpperCAmelCase_   :				str						=						pred_max_prob.cumprod(dim=0       )[-1] if pred_max_prob.nelement() != 0 else 0.0
												dec_strs.append(_lowerCAmelCase       )
												conf_scores.append(_lowerCAmelCase       )

							return dec_strs, conf_scores



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							Dict:
							UpperCAmelCase_   :				Tuple						=						[seq.replace(' '    ,		''       ) for seq in self.char_tokenizer.batch_decode(_lowerCAmelCase       )]
							return decode_strs



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							str:
							return self.bpe_tokenizer.batch_decode(_lowerCAmelCase       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							List[str]:
							UpperCAmelCase_   :				int						=						[seq.replace(' '    ,		''       ) for seq in self.wp_tokenizer.batch_decode(_lowerCAmelCase       )]
							return decode_strs














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

import black


__UpperCAmelCase     							=						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.
__UpperCAmelCase     							=						'    \"""\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   lowerCamelCase				(unittest.TestCase      ):







		'''simple docstring'''



		def  __UpperCAmelCase      (							self       )     ->							Tuple:
							UpperCAmelCase_   :				str						=						tempfile.mkdtemp()
							os.makedirs(os.path.join(self.diffusers_dir    ,		'schedulers/'       )       )
							UpperCAmelCase_   :				int						=						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       )     ->							Optional[int]:
							UpperCAmelCase_   :				str						=						'src/diffusers'
							shutil.rmtree(self.diffusers_dir       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase=None       )     ->							Optional[Any]:
							UpperCAmelCase_   :				Dict						=						comment + f"\nclass {class_name}(nn.Module):\n" + class_code
							if overwrite_result is not None:
												UpperCAmelCase_   :				Optional[Any]						=						comment + f"\nclass {class_name}(nn.Module):\n" + overwrite_result
							UpperCAmelCase_   :				Any						=						black.Mode(target_versions={black.TargetVersion.PYaa}    ,		line_length=1_1_9       )
							UpperCAmelCase_   :				Optional[Any]						=						black.format_str(_UpperCamelCase    ,		mode=_UpperCamelCase       )
							UpperCAmelCase_   :				Any						=						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[str]:
							UpperCAmelCase_   :				List[Any]						=						check_copies.find_code_in_diffusers('schedulers.scheduling_ddpm.DDPMSchedulerOutput'       )
							self.assertEqual(_UpperCamelCase    ,		_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							str:
							# 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'    ,		_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
							UpperCAmelCase_   :				Optional[int]						=						'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       )    ,		)






