import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      UpperCAmelCase_      :      Tuple      							=   (KDPMaDiscreteScheduler,)
      UpperCAmelCase_      :      List[str]      							=   10




      def 		lowerCAmelCase__					(				self					,**lowerCamelCase_							)			->			Dict:



         '''simple docstring'''



         UpperCAmelCase__							:				str   				=  {
             '''num_train_timesteps''': 1100,
             '''beta_start''': 0.0001,
             '''beta_end''': 0.02,
             '''beta_schedule''': '''linear''',
         }

         config.update(**lowerCamelCase_							)
         return config




      def 		lowerCAmelCase__					(				self							)			->			str:



         '''simple docstring'''



         for timesteps in [10, 50, 100, 1000]:
            self.check_over_configs(num_train_timesteps=lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self							)			->			Any:



         '''simple docstring'''



         for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]					,[0.0002, 0.002, 0.02]							):
            self.check_over_configs(beta_start=lowerCamelCase_					,beta_end=lowerCamelCase_							)




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



         '''simple docstring'''



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




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



         '''simple docstring'''



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




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



         '''simple docstring'''



         UpperCAmelCase__							:				Union[str, Any]   				=  self.scheduler_classes[0]
         UpperCAmelCase__							:				List[str]   				=  self.get_scheduler_config(prediction_type='''v_prediction'''							)
         UpperCAmelCase__							:				List[Any]   				=  scheduler_class(**lowerCamelCase_							)

         scheduler.set_timesteps(self.num_inference_steps							)

         UpperCAmelCase__							:				int   				=  self.dummy_model()
         UpperCAmelCase__							:				str   				=  self.dummy_sample_deter * scheduler.init_noise_sigma
         UpperCAmelCase__							:				Union[str, Any]   				=  sample.to(lowerCamelCase_							)

         for i, t in enumerate(scheduler.timesteps							):
            UpperCAmelCase__							:				Any   				=  scheduler.scale_model_input(lowerCamelCase_					,lowerCamelCase_							)

            UpperCAmelCase__							:				Any   				=  model(lowerCamelCase_					,lowerCamelCase_							)

            UpperCAmelCase__							:				List[str]   				=  scheduler.step(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)
            UpperCAmelCase__							:				Tuple   				=  output.prev_sample

         UpperCAmelCase__							:				str   				=  torch.sum(torch.abs(lowerCamelCase_							)							)
         UpperCAmelCase__							:				Dict   				=  torch.mean(torch.abs(lowerCamelCase_							)							)

         if torch_device in ["cpu", "mps"]:
            assert abs(result_sum.item() - 4.6934e-07							) < 1e-2
            assert abs(result_mean.item() - 6.1112e-10							) < 1e-3
         else:
            # CUDA
            assert abs(result_sum.item() - 4.693428650170972e-07							) < 1e-2
            assert abs(result_mean.item() - 0.0002							) < 1e-3




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



         '''simple docstring'''



         if torch_device == "mps":
            return
         UpperCAmelCase__							:				Dict   				=  self.scheduler_classes[0]
         UpperCAmelCase__							:				Union[str, Any]   				=  self.get_scheduler_config()
         UpperCAmelCase__							:				Union[str, Any]   				=  scheduler_class(**lowerCamelCase_							)

         scheduler.set_timesteps(self.num_inference_steps							)

         UpperCAmelCase__							:				List[str]   				=  self.dummy_model()
         UpperCAmelCase__							:				List[Any]   				=  self.dummy_sample_deter * scheduler.init_noise_sigma
         UpperCAmelCase__							:				Union[str, Any]   				=  sample.to(lowerCamelCase_							)

         for i, t in enumerate(scheduler.timesteps							):
            UpperCAmelCase__							:				Optional[int]   				=  scheduler.scale_model_input(lowerCamelCase_					,lowerCamelCase_							)

            UpperCAmelCase__							:				List[Any]   				=  model(lowerCamelCase_					,lowerCamelCase_							)

            UpperCAmelCase__							:				Dict   				=  scheduler.step(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)
            UpperCAmelCase__							:				int   				=  output.prev_sample

         UpperCAmelCase__							:				List[Any]   				=  torch.sum(torch.abs(lowerCamelCase_							)							)
         UpperCAmelCase__							:				List[str]   				=  torch.mean(torch.abs(lowerCamelCase_							)							)

         if torch_device in ["cpu", "mps"]:
            assert abs(result_sum.item() - 20.4125							) < 1e-2
            assert abs(result_mean.item() - 0.0266							) < 1e-3
         else:
            # CUDA
            assert abs(result_sum.item() - 20.4125							) < 1e-2
            assert abs(result_mean.item() - 0.0266							) < 1e-3




      def 		lowerCAmelCase__					(				self							)			->			Any:



         '''simple docstring'''



         if torch_device == "mps":
            return
         UpperCAmelCase__							:				Dict   				=  self.scheduler_classes[0]
         UpperCAmelCase__							:				Optional[Any]   				=  self.get_scheduler_config()
         UpperCAmelCase__							:				Optional[int]   				=  scheduler_class(**lowerCamelCase_							)

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

         UpperCAmelCase__							:				Optional[int]   				=  self.dummy_model()
         UpperCAmelCase__							:				Union[str, Any]   				=  self.dummy_sample_deter.to(lowerCamelCase_							) * scheduler.init_noise_sigma

         for t in scheduler.timesteps:
            UpperCAmelCase__							:				Union[str, Any]   				=  scheduler.scale_model_input(lowerCamelCase_					,lowerCamelCase_							)

            UpperCAmelCase__							:				Any   				=  model(lowerCamelCase_					,lowerCamelCase_							)

            UpperCAmelCase__							:				str   				=  scheduler.step(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)
            UpperCAmelCase__							:				List[Any]   				=  output.prev_sample

         UpperCAmelCase__							:				Dict   				=  torch.sum(torch.abs(lowerCamelCase_							)							)
         UpperCAmelCase__							:				Dict   				=  torch.mean(torch.abs(lowerCamelCase_							)							)

         if str(lowerCamelCase_							).startswith('''cpu'''							):
            # The following sum varies between 148 and 156 on mps. Why?
            assert abs(result_sum.item() - 20.4125							) < 1e-2
            assert abs(result_mean.item() - 0.0266							) < 1e-3
         else:
            # CUDA
            assert abs(result_sum.item() - 20.4125							) < 1e-2
            assert abs(result_mean.item() - 0.0266							) < 1e-3





'''simple docstring'''

import json
import os
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCamelCase__     :	int				=			logging.get_logger(__name__)

UpperCamelCase__     :	Optional[Any]				=			{'vocab_file': 'vocab.json'}

UpperCamelCase__     :	Tuple				=			{
    'vocab_file': {
        'mgp-str': 'https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json',
    }
}

UpperCamelCase__     :	List[Any]				=			{'mgp-str': 27}


class 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      UpperCAmelCase_      :      Any      							=   VOCAB_FILES_NAMES
      UpperCAmelCase_      :      Dict      							=   PRETRAINED_VOCAB_FILES_MAP
      UpperCAmelCase_      :      int      							=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES




      def __init__(				self					,lowerCamelCase_					,lowerCamelCase_="[GO]"					,lowerCamelCase_="[GO]"					,lowerCamelCase_="[s]"					,lowerCamelCase_="[GO]"					,**lowerCamelCase_							)			->			str:



         '''simple docstring'''



         super().__init__(
             unk_token=lowerCamelCase_					,bos_token=lowerCamelCase_					,eos_token=lowerCamelCase_					,pad_token=lowerCamelCase_					,**lowerCamelCase_					,)

         with open(lowerCamelCase_					,encoding='''utf-8'''							) as vocab_handle:
            UpperCAmelCase__							:				Optional[Any]   				=  json.load(lowerCamelCase_							)
         UpperCAmelCase__							:				Any   				=  {v: k for k, v in self.vocab.items()}




      @property
      def 		lowerCAmelCase__					(				self							)			->			Tuple:



         '''simple docstring'''



         return len(self.vocab							)




      def 		lowerCAmelCase__					(				self							)			->			Dict:



         '''simple docstring'''



         return dict(self.vocab					,**self.added_tokens_encoder							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			Any:



         '''simple docstring'''



         UpperCAmelCase__							:				List[Any]   				=  []
         for s in text:
            char_tokens.extend(lowerCamelCase_							)
         return char_tokens




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			str:



         '''simple docstring'''



         return self.vocab.get(lowerCamelCase_					,self.vocab.get(self.unk_token							)							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			Any:



         '''simple docstring'''



         return self.decoder.get(lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_ = None							)			->			Tuple[str]:



         '''simple docstring'''



         if not os.path.isdir(lowerCamelCase_							):
            logger.error('''Vocabulary path ({}) should be a directory'''.format(lowerCamelCase_							)							)
            return
         UpperCAmelCase__							:				Union[str, Any]   				=  os.path.join(
             lowerCamelCase_					,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)

         with open(lowerCamelCase_					,'''w'''					,encoding='''utf-8'''							) as f:
            f.write(json.dumps(self.vocab					,indent=2					,sort_keys=lowerCamelCase_					,ensure_ascii=lowerCamelCase_							) + '''\n'''							)

         return (vocab_file,)




