




'''simple docstring'''


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      A       (						_a       ):
							lowercase_							     =							'char'
							lowercase_							     =							'bpe'
							lowercase_							     =							'wp'



_snake_case : Optional[int]  				=						(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)

class      A       (						_a       ):
							lowercase_							     =							['image_processor', 'char_tokenizer']
							lowercase_							     =							'ViTImageProcessor'
							lowercase_							     =							'MgpstrTokenizer'







							def __init__(    self			:    Dict				,     lowerCAmelCase_			:    Optional[Any]=None				,     lowerCAmelCase_			:    str=None				,     **lowerCAmelCase_			:    List[Any]   )     ->				Optional[Any]:


														"""simple docstring"""


														_a      	=							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_				,     )
																					_a      	=							kwargs.pop('''feature_extractor'''   )

														_a      	=							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`.'''   )

														_a      	=							tokenizer
														_a      	=							AutoTokenizer.from_pretrained('''gpt2'''   )
														_a      	=							AutoTokenizer.from_pretrained('''bert-base-uncased'''   )

														super().__init__(lowerCAmelCase_				,     lowerCAmelCase_   )







							def __call__(    self			:    str				,     lowerCAmelCase_			:    List[Any]=None				,     lowerCAmelCase_			:    Union[str, Any]=None				,     lowerCAmelCase_			:    Any=None				,     **lowerCAmelCase_			:    Tuple   )     ->				Optional[Any]:


														"""simple docstring"""


														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:
																					_a      	=							self.image_processor(lowerCAmelCase_				,     return_tensors=lowerCAmelCase_				,     **lowerCAmelCase_   )
														if text is not None:
																					_a      	=							self.char_tokenizer(lowerCAmelCase_				,     return_tensors=lowerCAmelCase_				,     **lowerCAmelCase_   )

														if text is None:
																					return inputs
														elif images is None:
																					return encodings
														else:
																					_a      	=							encodings['''input_ids''']
																					return inputs







							def   __lowerCAmelCase     (    self			:    Optional[int]				,     lowerCAmelCase_			:    Optional[Any]   )     ->				List[Any]:


														"""simple docstring"""


														_a	,				_a	,				_a      	=							sequences
														_a      	=							char_preds.size(0   )

														_a	,				_a      	=							self._decode_helper(lowerCAmelCase_				,     '''char'''   )
														_a	,				_a      	=							self._decode_helper(lowerCAmelCase_				,     '''bpe'''   )
														_a	,				_a      	=							self._decode_helper(lowerCAmelCase_				,     '''wp'''   )

														_a      	=							[]
														_a      	=							[]
														for i in range(lowerCAmelCase_   ):
																					_a      	=							[char_scores[i], bpe_scores[i], wp_scores[i]]
																					_a      	=							[char_strs[i], bpe_strs[i], wp_strs[i]]
																					_a      	=							scores.index(max(lowerCAmelCase_   )   )
																					final_strs.append(strs[max_score_index]   )
																					final_scores.append(scores[max_score_index]   )

														_a      	=							{}
														_a      	=							final_strs
														_a      	=							final_scores
														_a      	=							char_strs
														_a      	=							bpe_strs
														_a      	=							wp_strs
														return out







							def   __lowerCAmelCase     (    self			:    Optional[int]				,     lowerCAmelCase_			:    Dict				,     lowerCAmelCase_			:    Optional[Any]   )     ->				Dict:


														"""simple docstring"""


														if format == DecodeType.CHARACTER:
																					_a      	=							self.char_decode
																					_a      	=							1
																					_a      	=							'''[s]'''
														elif format == DecodeType.BPE:
																					_a      	=							self.bpe_decode
																					_a      	=							2
																					_a      	=							'''#'''
														elif format == DecodeType.WORDPIECE:
																					_a      	=							self.wp_decode
																					_a      	=							1_02
																					_a      	=							'''[SEP]'''
														else:
																					raise ValueError(F'Format {format} is not supported.'   )

														_a	,				_a      	=							[], []
														_a      	=							pred_logits.size(0   )
														_a      	=							pred_logits.size(1   )
														_a	,				_a      	=							pred_logits.topk(1				,     dim=-1				,     largest=lowerCAmelCase_				,     sorted=lowerCAmelCase_   )
														_a      	=							preds_index.view(-1				,     lowerCAmelCase_   )[:, 1:]
														_a      	=							decoder(lowerCAmelCase_   )
														_a	,				_a      	=							torch.nn.functional.softmax(lowerCAmelCase_				,     dim=2   ).max(dim=2   )
														_a      	=							preds_max_prob[:, 1:]

														for index in range(lowerCAmelCase_   ):
																					_a      	=							preds_str[index].find(lowerCAmelCase_   )
																					_a      	=							preds_str[index][:pred_eos]
																					_a      	=							preds_index[index].cpu().tolist()
																					_a      	=							pred_index.index(lowerCAmelCase_   ) if eos_token in pred_index else -1
																					_a      	=							preds_max_prob[index][: pred_eos_index + 1]
																					_a      	=							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   __lowerCAmelCase     (    self			:    Optional[Any]				,     lowerCAmelCase_			:    Optional[int]   )     ->				Union[str, Any]:


														"""simple docstring"""


														_a      	=							[seq.replace(''' '''				,     ''''''   ) for seq in self.char_tokenizer.batch_decode(lowerCAmelCase_   )]
														return decode_strs







							def   __lowerCAmelCase     (    self			:    List[Any]				,     lowerCAmelCase_			:    Optional[Any]   )     ->				Tuple:


														"""simple docstring"""


														return self.bpe_tokenizer.batch_decode(lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    List[str]				,     lowerCAmelCase_			:    Dict   )     ->				Optional[Any]:


														"""simple docstring"""


														_a      	=							[seq.replace(''' '''				,     ''''''   ) for seq in self.wp_tokenizer.batch_decode(lowerCAmelCase_   )]
														return decode_strs







'''simple docstring'''


import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class      A       (						_a       ):
							lowercase_							     =							(DDPMParallelScheduler,)







							def   __lowerCAmelCase     (    self			:    Optional[Any]				,     **lowerCAmelCase_			:    Optional[int]   )     ->				List[Any]:


														"""simple docstring"""


														_a      	=							{
														    '''num_train_timesteps''': 10_00,
														    '''beta_start''': 0.0_0_0_1,
														    '''beta_end''': 0.0_2,
														    '''beta_schedule''': '''linear''',
														    '''variance_type''': '''fixed_small''',
														    '''clip_sample''': True,
														}

														config.update(**lowerCAmelCase_   )
														return config







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				Any:


														"""simple docstring"""


														for timesteps in [1, 5, 1_00, 10_00]:
																					self.check_over_configs(num_train_timesteps=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Optional[Any]   )     ->				List[Any]:


														"""simple docstring"""


														for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]				,     [0.0_0_2, 0.0_2, 0.2, 2]   ):
																					self.check_over_configs(beta_start=lowerCAmelCase_				,     beta_end=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    List[str]   )     ->				List[Any]:


														"""simple docstring"""


														for schedule in ["linear", "squaredcos_cap_v2"]:
																					self.check_over_configs(beta_schedule=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    int   )     ->				Optional[Any]:


														"""simple docstring"""


														for variance in ["fixed_small", "fixed_large", "other"]:
																					self.check_over_configs(variance_type=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Any   )     ->				List[Any]:


														"""simple docstring"""


														for clip_sample in [True, False]:
																					self.check_over_configs(clip_sample=lowerCAmelCase_   )







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


														"""simple docstring"""


														self.check_over_configs(thresholding=lowerCAmelCase_   )
														for threshold in [0.5, 1.0, 2.0]:
																					for prediction_type in ["epsilon", "sample", "v_prediction"]:
																												self.check_over_configs(
																												    thresholding=lowerCAmelCase_				,     prediction_type=lowerCAmelCase_				,     sample_max_value=lowerCAmelCase_				,     )







							def   __lowerCAmelCase     (    self			:    Tuple   )     ->				str:


														"""simple docstring"""


														for prediction_type in ["epsilon", "sample", "v_prediction"]:
																					self.check_over_configs(prediction_type=lowerCAmelCase_   )







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


														"""simple docstring"""


														for t in [0, 5_00, 9_99]:
																					self.check_over_forward(time_step=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    str   )     ->				Optional[int]:


														"""simple docstring"""


														_a      	=							self.scheduler_classes[0]
														_a      	=							self.get_scheduler_config()
														_a      	=							scheduler_class(**lowerCAmelCase_   )

														assert torch.sum(torch.abs(scheduler._get_variance(0   ) - 0.0   )   ) < 1e-5
														assert torch.sum(torch.abs(scheduler._get_variance(4_87   ) - 0.0_0_9_7_9   )   ) < 1e-5
														assert torch.sum(torch.abs(scheduler._get_variance(9_99   ) - 0.0_2   )   ) < 1e-5







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				str:


														"""simple docstring"""


														_a      	=							self.scheduler_classes[0]
														_a      	=							self.get_scheduler_config()
														_a      	=							scheduler_class(**lowerCAmelCase_   )

														_a      	=							len(lowerCAmelCase_   )

														_a      	=							self.dummy_model()
														_a      	=							self.dummy_sample_deter
														_a      	=							self.dummy_sample_deter + 0.1
														_a      	=							self.dummy_sample_deter - 0.1

														_a      	=							samplea.shape[0]
														_a      	=							torch.stack([samplea, samplea, samplea]				,     dim=0   )
														_a      	=							torch.arange(lowerCAmelCase_   )[0:3, None].repeat(1				,     lowerCAmelCase_   )

														_a      	=							model(samples.flatten(0				,     1   )				,     timesteps.flatten(0				,     1   )   )
														_a      	=							scheduler.batch_step_no_noise(lowerCAmelCase_				,     timesteps.flatten(0				,     1   )				,     samples.flatten(0				,     1   )   )

														_a      	=							torch.sum(torch.abs(lowerCAmelCase_   )   )
														_a      	=							torch.mean(torch.abs(lowerCAmelCase_   )   )

														assert abs(result_sum.item() - 1_1_5_3.1_8_3_3   ) < 1e-2
														assert abs(result_mean.item() - 0.5_0_0_5   ) < 1e-3







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


														"""simple docstring"""


														_a      	=							self.scheduler_classes[0]
														_a      	=							self.get_scheduler_config()
														_a      	=							scheduler_class(**lowerCAmelCase_   )

														_a      	=							len(lowerCAmelCase_   )

														_a      	=							self.dummy_model()
														_a      	=							self.dummy_sample_deter
														_a      	=							torch.manual_seed(0   )

														for t in reversed(range(lowerCAmelCase_   )   ):
																					# 1. predict noise residual
																					_a      	=							model(lowerCAmelCase_				,     lowerCAmelCase_   )

																					# 2. predict previous mean of sample x_t-1
																					_a      	=							scheduler.step(lowerCAmelCase_				,     lowerCAmelCase_				,     lowerCAmelCase_				,     generator=lowerCAmelCase_   ).prev_sample

																					_a      	=							pred_prev_sample

														_a      	=							torch.sum(torch.abs(lowerCAmelCase_   )   )
														_a      	=							torch.mean(torch.abs(lowerCAmelCase_   )   )

														assert abs(result_sum.item() - 2_5_8.9_6_0_6   ) < 1e-2
														assert abs(result_mean.item() - 0.3_3_7_2   ) < 1e-3







							def   __lowerCAmelCase     (    self			:    Optional[Any]   )     ->				List[Any]:


														"""simple docstring"""


														_a      	=							self.scheduler_classes[0]
														_a      	=							self.get_scheduler_config(prediction_type='''v_prediction'''   )
														_a      	=							scheduler_class(**lowerCAmelCase_   )

														_a      	=							len(lowerCAmelCase_   )

														_a      	=							self.dummy_model()
														_a      	=							self.dummy_sample_deter
														_a      	=							torch.manual_seed(0   )

														for t in reversed(range(lowerCAmelCase_   )   ):
																					# 1. predict noise residual
																					_a      	=							model(lowerCAmelCase_				,     lowerCAmelCase_   )

																					# 2. predict previous mean of sample x_t-1
																					_a      	=							scheduler.step(lowerCAmelCase_				,     lowerCAmelCase_				,     lowerCAmelCase_				,     generator=lowerCAmelCase_   ).prev_sample

																					_a      	=							pred_prev_sample

														_a      	=							torch.sum(torch.abs(lowerCAmelCase_   )   )
														_a      	=							torch.mean(torch.abs(lowerCAmelCase_   )   )

														assert abs(result_sum.item() - 2_0_2.0_2_9_6   ) < 1e-2
														assert abs(result_mean.item() - 0.2_6_3_1   ) < 1e-3







							def   __lowerCAmelCase     (    self			:    int   )     ->				Dict:


														"""simple docstring"""


														_a      	=							self.scheduler_classes[0]
														_a      	=							self.get_scheduler_config()
														_a      	=							scheduler_class(**lowerCAmelCase_   )

														_a      	=							[1_00, 87, 50, 1, 0]

														scheduler.set_timesteps(timesteps=lowerCAmelCase_   )

														_a      	=							scheduler.timesteps

														for i, timestep in enumerate(lowerCAmelCase_   ):
																					if i == len(lowerCAmelCase_   ) - 1:
																												_a      	=							-1
																					else:
																												_a      	=							timesteps[i + 1]

																					_a      	=							scheduler.previous_timestep(lowerCAmelCase_   )
																					_a      	=							prev_t.item()

																					self.assertEqual(lowerCAmelCase_				,     lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				List[Any]:


														"""simple docstring"""


														_a      	=							self.scheduler_classes[0]
														_a      	=							self.get_scheduler_config()
														_a      	=							scheduler_class(**lowerCAmelCase_   )

														_a      	=							[1_00, 87, 50, 51, 0]

														with self.assertRaises(lowerCAmelCase_				,     msg='''`custom_timesteps` must be in descending order.'''   ):
																					scheduler.set_timesteps(timesteps=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    List[Any]   )     ->				Optional[Any]:


														"""simple docstring"""


														_a      	=							self.scheduler_classes[0]
														_a      	=							self.get_scheduler_config()
														_a      	=							scheduler_class(**lowerCAmelCase_   )

														_a      	=							[1_00, 87, 50, 1, 0]
														_a      	=							len(lowerCAmelCase_   )

														with self.assertRaises(lowerCAmelCase_				,     msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''   ):
																					scheduler.set_timesteps(num_inference_steps=lowerCAmelCase_				,     timesteps=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				Any:


														"""simple docstring"""


														_a      	=							self.scheduler_classes[0]
														_a      	=							self.get_scheduler_config()
														_a      	=							scheduler_class(**lowerCAmelCase_   )

														_a      	=							[scheduler.config.num_train_timesteps]

														with self.assertRaises(
														    lowerCAmelCase_				,     msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''				,     ):
																					scheduler.set_timesteps(timesteps=lowerCAmelCase_   )

