import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class   lowercase      (       __lowercase       ):
       lowercase__  :    Optional[int]									=							(KDPMaDiscreteScheduler,)
       lowercase__  :    List[str]									=							10




       def 		__snake_case(  self				:   List[str]		,							**_UpperCamelCase				:   Tuple )   ->			Optional[int]:



            '''simple docstring'''

            SCREAMING_SNAKE_CASE           =    {
                '''num_train_timesteps''': 1_100,
                '''beta_start''': 0.0_0_0_1,
                '''beta_end''': 0.0_2,
                '''beta_schedule''': '''linear''',
            }

            config.update(**_a )
            return config




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



            '''simple docstring'''

            for timesteps in [10, 50, 100, 1_000]:
                 self.check_over_configs(num_train_timesteps=_a )




       def 		__snake_case(  self				:   Dict )   ->			Tuple:



            '''simple docstring'''

            for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]		,							[0.0_0_0_2, 0.0_0_2, 0.0_2] ):
                 self.check_over_configs(beta_start=_a		,							beta_end=_a )




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



            '''simple docstring'''

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




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



            '''simple docstring'''

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




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



            '''simple docstring'''

            SCREAMING_SNAKE_CASE           =    self.scheduler_classes[0]
            SCREAMING_SNAKE_CASE           =    self.get_scheduler_config(prediction_type="v_prediction" )
            SCREAMING_SNAKE_CASE           =    scheduler_class(**_a )

            scheduler.set_timesteps(self.num_inference_steps )

            SCREAMING_SNAKE_CASE           =    self.dummy_model()
            SCREAMING_SNAKE_CASE           =    self.dummy_sample_deter * scheduler.init_noise_sigma
            SCREAMING_SNAKE_CASE           =    sample.to(_a )

            for i, t in enumerate(scheduler.timesteps ):
                 SCREAMING_SNAKE_CASE           =    scheduler.scale_model_input(_a		,							_a )

                 SCREAMING_SNAKE_CASE           =    model(_a		,							_a )

                 SCREAMING_SNAKE_CASE           =    scheduler.step(_a		,							_a		,							_a )
                 SCREAMING_SNAKE_CASE           =    output.prev_sample

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

            if torch_device in ["cpu", "mps"]:
                 assert abs(result_sum.item() - 4.6_934e-07 ) < 1e-2
                 assert abs(result_mean.item() - 6.1_112e-10 ) < 1e-3
            else:
                 # CUDA
                 assert abs(result_sum.item() - 4.693_428_650_170_972e-07 ) < 1e-2
                 assert abs(result_mean.item() - 0.0_0_0_2 ) < 1e-3




       def 		__snake_case(  self				:   Optional[Any] )   ->			Dict:



            '''simple docstring'''

            if torch_device == "mps":
                 return
            SCREAMING_SNAKE_CASE           =    self.scheduler_classes[0]
            SCREAMING_SNAKE_CASE           =    self.get_scheduler_config()
            SCREAMING_SNAKE_CASE           =    scheduler_class(**_a )

            scheduler.set_timesteps(self.num_inference_steps )

            SCREAMING_SNAKE_CASE           =    self.dummy_model()
            SCREAMING_SNAKE_CASE           =    self.dummy_sample_deter * scheduler.init_noise_sigma
            SCREAMING_SNAKE_CASE           =    sample.to(_a )

            for i, t in enumerate(scheduler.timesteps ):
                 SCREAMING_SNAKE_CASE           =    scheduler.scale_model_input(_a		,							_a )

                 SCREAMING_SNAKE_CASE           =    model(_a		,							_a )

                 SCREAMING_SNAKE_CASE           =    scheduler.step(_a		,							_a		,							_a )
                 SCREAMING_SNAKE_CASE           =    output.prev_sample

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

            if torch_device in ["cpu", "mps"]:
                 assert abs(result_sum.item() - 2_0.4_1_2_5 ) < 1e-2
                 assert abs(result_mean.item() - 0.0_2_6_6 ) < 1e-3
            else:
                 # CUDA
                 assert abs(result_sum.item() - 2_0.4_1_2_5 ) < 1e-2
                 assert abs(result_mean.item() - 0.0_2_6_6 ) < 1e-3





       def 		__snake_case(  self				:   List[str] )   ->			Dict:



            '''simple docstring'''

            if torch_device == "mps":
                 return
            SCREAMING_SNAKE_CASE           =    self.scheduler_classes[0]
            SCREAMING_SNAKE_CASE           =    self.get_scheduler_config()
            SCREAMING_SNAKE_CASE           =    scheduler_class(**_a )

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

            SCREAMING_SNAKE_CASE           =    self.dummy_model()
            SCREAMING_SNAKE_CASE           =    self.dummy_sample_deter.to(_a ) * scheduler.init_noise_sigma

            for t in scheduler.timesteps:
                 SCREAMING_SNAKE_CASE           =    scheduler.scale_model_input(_a		,							_a )

                 SCREAMING_SNAKE_CASE           =    model(_a		,							_a )

                 SCREAMING_SNAKE_CASE           =    scheduler.step(_a		,							_a		,							_a )
                 SCREAMING_SNAKE_CASE           =    output.prev_sample

            SCREAMING_SNAKE_CASE           =    torch.sum(torch.abs(_a ) )
            SCREAMING_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() - 2_0.4_1_2_5 ) < 1e-2
                 assert abs(result_mean.item() - 0.0_2_6_6 ) < 1e-3
            else:
                 # CUDA
                 assert abs(result_sum.item() - 2_0.4_1_2_5 ) < 1e-2
                 assert abs(result_mean.item() - 0.0_2_6_6 ) < 1e-3





from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


_lowerCamelCase   :				Optional[Any]     			=						logging.get_logger(__name__)

_lowerCamelCase   :				Any     			=						{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_lowerCamelCase   :				int     			=						{
    '''vocab_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'''
    },
}

_lowerCamelCase   :				Tuple     			=						{'''allegro/herbert-base-cased''': 5_14}
_lowerCamelCase   :				Optional[int]     			=						{}


class   lowercase      (       a       ):
							lowercase__  :    List[str]									=							VOCAB_FILES_NAMES
							lowercase__  :    str									=							PRETRAINED_VOCAB_FILES_MAP
							lowercase__  :    Tuple									=							PRETRAINED_INIT_CONFIGURATION
							lowercase__  :    Optional[int]									=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase__  :    str									=							HerbertTokenizer




							def __init__(  self				:   Dict		,							_UpperCamelCase				:   Any=None		,							_UpperCamelCase				:   Any=None		,							_UpperCamelCase				:   Optional[int]=None		,							_UpperCamelCase				:   Optional[int]="<s>"		,							_UpperCamelCase				:   Union[str, Any]="<unk>"		,							_UpperCamelCase				:   List[str]="<pad>"		,							_UpperCamelCase				:   List[str]="<mask>"		,							_UpperCamelCase				:   Tuple="</s>"		,							**_UpperCamelCase				:   Any		,							)   ->			str:



												'''simple docstring'''

												super().__init__(
												    _UpperCamelCase		,							_UpperCamelCase		,							tokenizer_file=_UpperCamelCase		,							cls_token=_UpperCamelCase		,							unk_token=_UpperCamelCase		,							pad_token=_UpperCamelCase		,							mask_token=_UpperCamelCase		,							sep_token=_UpperCamelCase		,							**_UpperCamelCase		,							)




							def 		__snake_case(  self				:   Optional[Any]		,							_UpperCamelCase				:   List[int]		,							_UpperCamelCase				:   Optional[List[int]] = None )   ->			List[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    [self.cls_token_id]
												SCREAMING_SNAKE_CASE           =    [self.sep_token_id]
												if token_ids_a is None:
																	return cls + token_ids_a + sep

												return cls + token_ids_a + sep + token_ids_a + sep




							def 		__snake_case(  self				:   Any		,							_UpperCamelCase				:   List[int]		,							_UpperCamelCase				:   Optional[List[int]] = None		,							_UpperCamelCase				:   bool = False )   ->			List[int]:



												'''simple docstring'''

												if already_has_special_tokens:
																	return super().get_special_tokens_mask(
																	    token_ids_a=_UpperCamelCase		,							token_ids_a=_UpperCamelCase		,							already_has_special_tokens=_UpperCamelCase )

												if token_ids_a is None:
																	return [1] + ([0] * len(_UpperCamelCase )) + [1]
												return [1] + ([0] * len(_UpperCamelCase )) + [1] + ([0] * len(_UpperCamelCase )) + [1]




							def 		__snake_case(  self				:   Union[str, Any]		,							_UpperCamelCase				:   List[int]		,							_UpperCamelCase				:   Optional[List[int]] = None )   ->			List[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    [self.sep_token_id]
												SCREAMING_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 		__snake_case(  self				:   str		,							_UpperCamelCase				:   str		,							_UpperCamelCase				:   Optional[str] = None )   ->			Tuple[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self._tokenizer.model.save(_UpperCamelCase		,							name=_UpperCamelCase )
												return tuple(_UpperCamelCase )




