



"""simple docstring"""






import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest






class        __snake_case       (	_lowercase):
  snake_case__				:   Optional[Any]  			=						(CMStochasticIterativeScheduler,)
  snake_case__				:   Tuple  			=						1_0




  def 						SCREAMING_SNAKE_CASE				( self     :						str						,					**__lowerCAmelCase     :						List[str]		):






         """simple docstring"""






         _lowerCamelCase						:				List[str]    			= {
             '''num_train_timesteps''': 2_0_1,
             '''sigma_min''': 0.0_02,
             '''sigma_max''': 80.0,
         }

         config.update(**__lowerCAmelCase		)
         return config




  def 						SCREAMING_SNAKE_CASE				( self     :						List[Any]		):






         """simple docstring"""






         _lowerCamelCase						:				Optional[int]    			= 1_0

         _lowerCamelCase						:				List[str]    			= self.get_scheduler_config()
         _lowerCamelCase						:				Union[str, Any]    			= self.scheduler_classes[0](**__lowerCAmelCase		)

         scheduler.set_timesteps(__lowerCAmelCase		)

         _lowerCamelCase						:				Optional[Any]    			= scheduler.timesteps[0]
         _lowerCamelCase						:				str    			= scheduler.timesteps[1]

         _lowerCamelCase						:				List[Any]    			= self.dummy_sample
         _lowerCamelCase						:				str    			= 0.1 * sample

         _lowerCamelCase						:				str    			= scheduler.step(__lowerCAmelCase						,					__lowerCAmelCase						,					__lowerCAmelCase		).prev_sample
         _lowerCamelCase						:				Tuple    			= scheduler.step(__lowerCAmelCase						,					__lowerCAmelCase						,					__lowerCAmelCase		).prev_sample

         self.assertEqual(output_a.shape						,					sample.shape		)
         self.assertEqual(output_a.shape						,					output_a.shape		)




  def 						SCREAMING_SNAKE_CASE				( self     :						List[str]		):






         """simple docstring"""






         for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
                self.check_over_configs(num_train_timesteps=__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE				( self     :						Optional[Any]		):






         """simple docstring"""






         for clip_denoised in [True, False]:
                self.check_over_configs(clip_denoised=__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE				( self     :						Union[str, Any]		):






         """simple docstring"""






         _lowerCamelCase						:				List[Any]    			= self.scheduler_classes[0]
         _lowerCamelCase						:				Optional[Any]    			= self.get_scheduler_config()
         _lowerCamelCase						:				int    			= scheduler_class(**__lowerCAmelCase		)

         _lowerCamelCase						:				str    			= 1
         scheduler.set_timesteps(__lowerCAmelCase		)
         _lowerCamelCase						:				Union[str, Any]    			= scheduler.timesteps

         _lowerCamelCase						:				Tuple    			= torch.manual_seed(0		)

         _lowerCamelCase						:				int    			= self.dummy_model()
         _lowerCamelCase						:				Dict    			= self.dummy_sample_deter * scheduler.init_noise_sigma

         for i, t in enumerate(__lowerCAmelCase		):
                # 1. scale model input
                _lowerCamelCase						:				Dict    			= scheduler.scale_model_input(__lowerCAmelCase						,					__lowerCAmelCase		)

                # 2. predict noise residual
                _lowerCamelCase						:				Optional[int]    			= model(__lowerCAmelCase						,					__lowerCAmelCase		)

                # 3. predict previous sample x_t-1
                _lowerCamelCase						:				Any    			= scheduler.step(__lowerCAmelCase						,					__lowerCAmelCase						,					__lowerCAmelCase						,					generator=__lowerCAmelCase		).prev_sample

                _lowerCamelCase						:				List[str]    			= pred_prev_sample

         _lowerCamelCase						:				int    			= torch.sum(torch.abs(__lowerCAmelCase		)		)
         _lowerCamelCase						:				str    			= torch.mean(torch.abs(__lowerCAmelCase		)		)

         assert abs(result_sum.item() - 1_92.76_14		) < 1E-2
         assert abs(result_mean.item() - 0.25_10		) < 1E-3




  def 						SCREAMING_SNAKE_CASE				( self     :						Union[str, Any]		):






         """simple docstring"""






         _lowerCamelCase						:				Optional[int]    			= self.scheduler_classes[0]
         _lowerCamelCase						:				List[str]    			= self.get_scheduler_config()
         _lowerCamelCase						:				str    			= scheduler_class(**__lowerCAmelCase		)

         _lowerCamelCase						:				int    			= [1_0_6, 0]
         scheduler.set_timesteps(timesteps=__lowerCAmelCase		)
         _lowerCamelCase						:				str    			= scheduler.timesteps

         _lowerCamelCase						:				str    			= torch.manual_seed(0		)

         _lowerCamelCase						:				Optional[int]    			= self.dummy_model()
         _lowerCamelCase						:				List[str]    			= self.dummy_sample_deter * scheduler.init_noise_sigma

         for t in timesteps:
                # 1. scale model input
                _lowerCamelCase						:				Optional[Any]    			= scheduler.scale_model_input(__lowerCAmelCase						,					__lowerCAmelCase		)

                # 2. predict noise residual
                _lowerCamelCase						:				Optional[Any]    			= model(__lowerCAmelCase						,					__lowerCAmelCase		)

                # 3. predict previous sample x_t-1
                _lowerCamelCase						:				Union[str, Any]    			= scheduler.step(__lowerCAmelCase						,					__lowerCAmelCase						,					__lowerCAmelCase						,					generator=__lowerCAmelCase		).prev_sample

                _lowerCamelCase						:				List[str]    			= pred_prev_sample

         _lowerCamelCase						:				Optional[int]    			= torch.sum(torch.abs(__lowerCAmelCase		)		)
         _lowerCamelCase						:				Tuple    			= torch.mean(torch.abs(__lowerCAmelCase		)		)

         assert abs(result_sum.item() - 3_47.63_57		) < 1E-2
         assert abs(result_mean.item() - 0.45_27		) < 1E-3




  def 						SCREAMING_SNAKE_CASE				( self     :						Dict		):






         """simple docstring"""






         _lowerCamelCase						:				Union[str, Any]    			= self.scheduler_classes[0]
         _lowerCamelCase						:				str    			= self.get_scheduler_config()
         _lowerCamelCase						:				List[Any]    			= scheduler_class(**__lowerCAmelCase		)

         _lowerCamelCase						:				Union[str, Any]    			= [3_9, 3_0, 1_2, 1_5, 0]

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




  def 						SCREAMING_SNAKE_CASE				( self     :						List[Any]		):






         """simple docstring"""






         _lowerCamelCase						:				int    			= self.scheduler_classes[0]
         _lowerCamelCase						:				Any    			= self.get_scheduler_config()
         _lowerCamelCase						:				List[Any]    			= scheduler_class(**__lowerCAmelCase		)

         _lowerCamelCase						:				List[Any]    			= [3_9, 3_0, 1_2, 1, 0]
         _lowerCamelCase						:				Optional[Any]    			= len(__lowerCAmelCase		)

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




  def 						SCREAMING_SNAKE_CASE				( self     :						Optional[Any]		):






         """simple docstring"""






         _lowerCamelCase						:				List[Any]    			= self.scheduler_classes[0]
         _lowerCamelCase						:				Dict    			= self.get_scheduler_config()
         _lowerCamelCase						:				Dict    			= scheduler_class(**__lowerCAmelCase		)

         _lowerCamelCase						:				Dict    			= [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		)










import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


lowercase  							=					logging.get_logger(__name__)

lowercase  							=					{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

lowercase  							=					{
    "vocab_file": {
        "squeezebert/squeezebert-uncased": (
            "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt"
        ),
        "squeezebert/squeezebert-mnli": "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt",
        "squeezebert/squeezebert-mnli-headless": (
            "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "squeezebert/squeezebert-uncased": (
            "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json"
        ),
        "squeezebert/squeezebert-mnli": (
            "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json"
        ),
        "squeezebert/squeezebert-mnli-headless": (
            "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json"
        ),
    },
}

lowercase  							=					{
    "squeezebert/squeezebert-uncased": 512,
    "squeezebert/squeezebert-mnli": 512,
    "squeezebert/squeezebert-mnli-headless": 512,
}


lowercase  							=					{
    "squeezebert/squeezebert-uncased": {"do_lower_case": True},
    "squeezebert/squeezebert-mnli": {"do_lower_case": True},
    "squeezebert/squeezebert-mnli-headless": {"do_lower_case": True},
}




class  UpperCamelCase_      (			snake_case_				):



	'''simple docstring'''




	lowerCAmelCase													= VOCAB_FILES_NAMES
	lowerCAmelCase													= PRETRAINED_VOCAB_FILES_MAP
	lowerCAmelCase													= PRETRAINED_INIT_CONFIGURATION
	lowerCAmelCase													= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCAmelCase													= SqueezeBertTokenizer




	def __init__(       self       ,			a=None       ,			a=None       ,			a=True       ,			a="[UNK]"       ,			a="[SEP]"       ,			a="[PAD]"       ,			a="[CLS]"       ,			a="[MASK]"       ,			a=True       ,			a=None       ,			**a       ,			)       ->    Tuple:
								super().__init__(
								    a       ,			tokenizer_file=a       ,			do_lower_case=a       ,			unk_token=a       ,			sep_token=a       ,			pad_token=a       ,			cls_token=a       ,			mask_token=a       ,			tokenize_chinese_chars=a       ,			strip_accents=a       ,			**a       ,			)

								snake_case_				=							json.loads(self.backend_tokenizer.normalizer.__getstate__()					)
								if (
								    normalizer_state.get('lowercase'       ,			a					) != do_lower_case
								    or normalizer_state.get('strip_accents'       ,			a					) != strip_accents
								    or normalizer_state.get('handle_chinese_chars'       ,			a					) != tokenize_chinese_chars
								):
															snake_case_				=							getattr(a       ,			normalizer_state.pop('type'					)					)
															snake_case_				=							do_lower_case
															snake_case_				=							strip_accents
															snake_case_				=							tokenize_chinese_chars
															snake_case_				=							normalizer_class(**a					)

								snake_case_				=							do_lower_case




	def 							_UpperCamelCase			(       self       ,			a       ,			a=None					)       ->    Tuple:
								snake_case_				=							[self.cls_token_id] + token_ids_a + [self.sep_token_id]

								if token_ids_a:
															output += token_ids_a + [self.sep_token_id]

								return output




	def 							_UpperCamelCase			(       self       ,			a       ,			a = None					)       ->    List[int]:
								snake_case_				=							[self.sep_token_id]
								snake_case_				=							[self.cls_token_id]
								if token_ids_a is None:
															return len(cls + token_ids_a + sep					) * [0]
								return len(cls + token_ids_a + sep					) * [0] + len(token_ids_a + sep					) * [1]






	def 							_UpperCamelCase			(       self       ,			a       ,			a = None					)       ->    Tuple[str]:
								snake_case_				=							self._tokenizer.model.save(a       ,			name=a					)
								return tuple(a					)





