
import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class 					lowerCamelCase		(  A_  ):
     UpperCAmelCase__    :							Any									=		(KDPMaDiscreteScheduler,)
     UpperCAmelCase__    :							Any									=		10





     def 		UpperCAmelCase(self				:   int    ,   **_A				:   Tuple       )						->						str:
         snake_case								= {
             "num_train_timesteps": 1_1_0_0,
             "beta_start": 0.00_01,
             "beta_end": 0.02,
             "beta_schedule": "linear",
         }

         config.update(**_A       )
         return config





     def 		UpperCAmelCase(self				:   Union[str, Any]       )						->						Optional[Any]:
         for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
             self.check_over_configs(num_train_timesteps=_A       )





     def 		UpperCAmelCase(self				:   Optional[int]       )						->						Any:
         for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]    ,   [0.00_02, 0.0_02, 0.02]       ):
             self.check_over_configs(beta_start=_A    ,   beta_end=_A       )





     def 		UpperCAmelCase(self				:   int       )						->						Union[str, Any]:
         for schedule in ["linear", "scaled_linear"]:
             self.check_over_configs(beta_schedule=_A       )





     def 		UpperCAmelCase(self				:   int       )						->						Union[str, Any]:
         for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=_A       )





     def 		UpperCAmelCase(self				:   List[Any]       )						->						str:
         snake_case								= self.scheduler_classes[0]
         snake_case								= self.get_scheduler_config(prediction_type="v_prediction"       )
         snake_case								= scheduler_class(**_A       )

         scheduler.set_timesteps(self.num_inference_steps       )

         snake_case								= self.dummy_model()
         snake_case								= self.dummy_sample_deter * scheduler.init_noise_sigma
         snake_case								= sample.to(_A       )

         for i, t in enumerate(scheduler.timesteps       ):
             snake_case								= scheduler.scale_model_input(_A    ,   _A       )

             snake_case								= model(_A    ,   _A       )

             snake_case								= scheduler.step(_A    ,   _A    ,   _A       )
             snake_case								= output.prev_sample

         snake_case								= torch.sum(torch.abs(_A       )       )
         snake_case								= torch.mean(torch.abs(_A       )       )

         if torch_device in ["cpu", "mps"]:
             assert abs(result_sum.item() - 4.69_34E-07       ) < 1E-2
             assert abs(result_mean.item() - 6.11_12E-10       ) < 1E-3
         else:
             # CUDA
             assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72E-07       ) < 1E-2
             assert abs(result_mean.item() - 0.00_02       ) < 1E-3





     def 		UpperCAmelCase(self				:   Union[str, Any]       )						->						Optional[Any]:
         if torch_device == "mps":
             return
         snake_case								= self.scheduler_classes[0]
         snake_case								= self.get_scheduler_config()
         snake_case								= scheduler_class(**_A       )

         scheduler.set_timesteps(self.num_inference_steps       )

         snake_case								= self.dummy_model()
         snake_case								= self.dummy_sample_deter * scheduler.init_noise_sigma
         snake_case								= sample.to(_A       )

         for i, t in enumerate(scheduler.timesteps       ):
             snake_case								= scheduler.scale_model_input(_A    ,   _A       )

             snake_case								= model(_A    ,   _A       )

             snake_case								= scheduler.step(_A    ,   _A    ,   _A       )
             snake_case								= output.prev_sample

         snake_case								= torch.sum(torch.abs(_A       )       )
         snake_case								= torch.mean(torch.abs(_A       )       )

         if torch_device in ["cpu", "mps"]:
             assert abs(result_sum.item() - 20.41_25       ) < 1E-2
             assert abs(result_mean.item() - 0.02_66       ) < 1E-3
         else:
             # CUDA
             assert abs(result_sum.item() - 20.41_25       ) < 1E-2
             assert abs(result_mean.item() - 0.02_66       ) < 1E-3





     def 		UpperCAmelCase(self				:   Optional[int]       )						->						Tuple:
         if torch_device == "mps":
             return
         snake_case								= self.scheduler_classes[0]
         snake_case								= self.get_scheduler_config()
         snake_case								= scheduler_class(**_A       )

         scheduler.set_timesteps(self.num_inference_steps    ,   device=_A       )

         snake_case								= self.dummy_model()
         snake_case								= self.dummy_sample_deter.to(_A       ) * scheduler.init_noise_sigma

         for t in scheduler.timesteps:
             snake_case								= scheduler.scale_model_input(_A    ,   _A       )

             snake_case								= model(_A    ,   _A       )

             snake_case								= scheduler.step(_A    ,   _A    ,   _A       )
             snake_case								= output.prev_sample

         snake_case								= torch.sum(torch.abs(_A       )       )
         snake_case								= torch.mean(torch.abs(_A       )       )

         if str(_A       ).startswith("cpu"       ):
             # The following sum varies between 148 and 156 on mps. Why?
             assert abs(result_sum.item() - 20.41_25       ) < 1E-2
             assert abs(result_mean.item() - 0.02_66       ) < 1E-3
         else:
             # CUDA
             assert abs(result_sum.item() - 20.41_25       ) < 1E-2
             assert abs(result_mean.item() - 0.02_66       ) < 1E-3



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer


_A   =			logging.get_logger(__name__)

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

_A   =			{
    "vocab_file": {"mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/vocab.txt"},
    "tokenizer_file": {
        "mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/tokenizer.json"
    },
}

_A   =			{"mobilebert-uncased": 5_12}


_A   =			{}







class 					lowerCamelCase		(  A_  ):
     UpperCAmelCase__    :							Tuple									=		VOCAB_FILES_NAMES
     UpperCAmelCase__    :							Any									=		PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase__    :							Tuple									=		PRETRAINED_INIT_CONFIGURATION
     UpperCAmelCase__    :							Tuple									=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase__    :							Optional[Any]									=		MobileBertTokenizer





     def __init__(self				:   Any    ,   _A				:   str=None    ,   _A				:   str=None    ,   _A				:   Union[str, Any]=True    ,   _A				:   Optional[Any]="[UNK]"    ,   _A				:   int="[SEP]"    ,   _A				:   Dict="[PAD]"    ,   _A				:   int="[CLS]"    ,   _A				:   Union[str, Any]="[MASK]"    ,   _A				:   Any=True    ,   _A				:   Dict=None    ,   **_A				:   List[str]    ,   )						->						List[str]:
         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				:   List[str]    ,   _A				:   Union[str, Any]    ,   _A				:   Dict=None       )						->						Optional[Any]:
         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				:   Union[str, Any]    ,   _A				:   List[int]    ,   _A				:   Optional[List[int]] = 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				:   int    ,   _A				:   str    ,   _A				:   Optional[str] = None       )						->						Tuple[str]:
         snake_case								= self._tokenizer.model.save(_A    ,   name=_A       )
         return tuple(_A       )

