


"""simple docstring"""







import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
     import jieba

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


lowerCAmelCase__				    =       logging.get_logger(__name__)

lowerCAmelCase__				    =       {'''vocab_file''': '''vocab.txt'''}

lowerCAmelCase__				    =       {
    '''vocab_file''': {
        '''openbmb/cpm-ant-10b''': '''https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt''',
    },
}

lowerCAmelCase__				    =       {
    '''openbmb/cpm-ant-10b''': 1_024,
}
def  a__      (    SCREAMING_SNAKE_CASE   :   Dict    ):


  '''simple docstring'''







  lowerCAmelCase					:					Tuple								=							collections.OrderedDict()
  with open(SCREAMING_SNAKE_CASE    ,				"r"    ,				encoding="utf-8"    ) as reader:
    lowerCAmelCase					:					List[str]								=							reader.readlines()
  for index, token in enumerate(SCREAMING_SNAKE_CASE    ):
    lowerCAmelCase					:					str								=							token.rstrip("\n"    )
    lowerCAmelCase					:					Optional[Any]								=							index
  return vocab


class 				SCREAMING_SNAKE_CASE__    (		lowercase    ):
       """simple docstring"""


       def __init__(						self		,					snake_case__		,					snake_case__="<unk>"		,					snake_case__=200						):






         """simple docstring"""





         lowerCAmelCase					:					List[str]								=							vocab
         lowerCAmelCase					:					List[Any]								=							unk_token
         lowerCAmelCase					:					Any								=							max_input_chars_per_word







       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					List[str]								=							list(snake_case__						)
         if len(snake_case__						) > self.max_input_chars_per_word:
           return [self.unk_token]

         lowerCAmelCase					:					List[Any]								=							0
         lowerCAmelCase					:					str								=							[]
         while start < len(snake_case__						):
           lowerCAmelCase					:					List[Any]								=							len(snake_case__						)
           lowerCAmelCase					:					Optional[int]								=							None
           while start < end:
             lowerCAmelCase					:					List[str]								=							"".join(chars[start:end]						)
             if substr in self.vocab:
               lowerCAmelCase					:					List[Any]								=							substr
               break
             end -= 1
           if cur_substr is None:
             sub_tokens.append(self.unk_token						)
             start += 1
           else:
             sub_tokens.append(snake_case__						)
             lowerCAmelCase					:					int								=							end

         return sub_tokens


class 				SCREAMING_SNAKE_CASE__    (		lowercase    ):
       """simple docstring"""


       a  :    List[Any]       =VOCAB_FILES_NAMES
       a  :    Optional[int]       =PRETRAINED_VOCAB_FILES_MAP
       a  :    List[Any]       =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       a  :    Optional[int]       =["input_ids", "attention_mask"]
       a  :    Optional[Any]       =False

       def __init__(						self		,					snake_case__		,					snake_case__="<d>"		,					snake_case__="</d>"		,					snake_case__="<s>"		,					snake_case__="</s>"		,					snake_case__="<pad>"		,					snake_case__="<unk>"		,					snake_case__="</n>"		,					snake_case__="</_>"		,					snake_case__="left"		,					**snake_case__		,					):






         """simple docstring"""





         requires_backends(self		,					["jieba"]						)
         super().__init__(
             bod_token=snake_case__		,					eod_token=snake_case__		,					bos_token=snake_case__		,					eos_token=snake_case__		,					pad_token=snake_case__		,					unk_token=snake_case__		,					line_token=snake_case__		,					space_token=snake_case__		,					padding_side=snake_case__		,					**snake_case__		,					)
         lowerCAmelCase					:					List[str]								=							bod_token
         lowerCAmelCase					:					Dict								=							eod_token
         lowerCAmelCase					:					Union[str, Any]								=							load_vocab(snake_case__						)
         lowerCAmelCase					:					List[Any]								=							self.encoder[space_token]
         lowerCAmelCase					:					Any								=							self.encoder[line_token]

         del self.encoder[space_token]
         del self.encoder[line_token]

         lowerCAmelCase					:					Tuple								=							collections.OrderedDict(sorted(self.encoder.items()		,					key=lambda snake_case__						: x[1]						)						)
         lowerCAmelCase					:					str								=							{v: k for k, v in self.encoder.items()}

         lowerCAmelCase					:					List[str]								=							WordpieceTokenizer(vocab=self.encoder		,					unk_token=self.unk_token						)

       @property
       def 	lowercase__							(						self						):






         """simple docstring"""





         return self.encoder[self.bod_token]

       @property
       def 	lowercase__							(						self						):






         """simple docstring"""





         return self.encoder[self.eod_token]

       @property
       def 	lowercase__							(						self						):






         """simple docstring"""





         return self.encoder["\n"]

       @property
       def 	lowercase__							(						self						):






         """simple docstring"""





         return len(self.encoder						)

       def 	lowercase__							(						self						):






         """simple docstring"""





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

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					str								=							[]
         for x in jieba.cut(snake_case__		,					cut_all=snake_case__						):
           output_tokens.extend(self.wordpiece_tokenizer.tokenize(snake_case__						)						)
         return output_tokens

       def 	lowercase__							(						self		,					snake_case__		,					**snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					Optional[Any]								=							[i for i in token_ids if i >= 0]
         lowerCAmelCase					:					int								=							[
             x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
         ]
         return super()._decode(snake_case__		,					**snake_case__						)

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         return token in self.encoder

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         return "".join(snake_case__						)

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         return self.encoder.get(snake_case__		,					self.encoder.get(self.unk_token						)						)

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         return self.decoder.get(snake_case__		,					self.unk_token						)

       def 	lowercase__							(						self		,					snake_case__		,					snake_case__ = None						):






         """simple docstring"""





         if os.path.isdir(snake_case__						):
           lowerCAmelCase					:					Union[str, Any]								=							os.path.join(
               snake_case__		,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)
         else:
           lowerCAmelCase					:					Dict								=							(filename_prefix + "-" if filename_prefix else "") + save_directory
         lowerCAmelCase					:					List[Any]								=							0
         if " " in self.encoder:
           lowerCAmelCase					:					Union[str, Any]								=							self.encoder[" "]
           del self.encoder[" "]
         if "\n" in self.encoder:
           lowerCAmelCase					:					Optional[int]								=							self.encoder["\n"]
           del self.encoder["\n"]
         lowerCAmelCase					:					Dict								=							collections.OrderedDict(sorted(self.encoder.items()		,					key=lambda snake_case__						: x[1]						)						)
         with open(snake_case__		,					"w"		,					encoding="utf-8"						) as writer:
           for token, token_index in self.encoder.items():
             if index != token_index:
               logger.warning(
                   f"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
                   " Please check that the vocabulary is not corrupted!"						)
               lowerCAmelCase					:					Tuple								=							token_index
             writer.write(token + "\n"						)
             index += 1
         return (vocab_file,)

       def 	lowercase__							(						self		,					snake_case__		,					snake_case__ = None						):






         """simple docstring"""





         if token_ids_a is None:
           return [self.bos_token_id] + token_ids_a
         return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a







       def 	lowercase__							(						self		,					snake_case__		,					snake_case__ = None		,					snake_case__ = False						):






         """simple docstring"""





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

         if token_ids_a is not None:
           return [1] + ([0] * len(snake_case__						)) + [1] + ([0] * len(snake_case__						))
         return [1] + ([0] * len(snake_case__						))









"""simple docstring"""







import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest


class 				SCREAMING_SNAKE_CASE__    (		lowercase    ):
       """simple docstring"""


       a  :    Optional[int]       =(DEISMultistepScheduler,)
       a  :    str       =(("num_inference_steps", 25),)

       def 	lowercase__							(						self		,					**snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					Tuple								=							{
             "num_train_timesteps": 1_000,
             "beta_start": 0.0001,
             "beta_end": 0.02,
             "beta_schedule": "linear",
             "solver_order": 2,
         }

         config.update(**snake_case__						)
         return config

       def 	lowercase__							(						self		,					snake_case__=0		,					**snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					List[str]								=							dict(self.forward_default_kwargs						)
         lowerCAmelCase					:					List[str]								=							kwargs.pop("num_inference_steps"		,					snake_case__						)
         lowerCAmelCase					:					List[str]								=							self.dummy_sample
         lowerCAmelCase					:					int								=							0.1 * sample
         lowerCAmelCase					:					Tuple								=							[residual + 0.2, residual + 0.15, residual + 0.10]

         for scheduler_class in self.scheduler_classes:
           lowerCAmelCase					:					Any								=							self.get_scheduler_config(**snake_case__						)
           lowerCAmelCase					:					List[str]								=							scheduler_class(**snake_case__						)
           scheduler.set_timesteps(snake_case__						)
           # copy over dummy past residuals
           lowerCAmelCase					:					str								=							dummy_past_residuals[: scheduler.config.solver_order]

           with tempfile.TemporaryDirectory() as tmpdirname:
             scheduler.save_config(snake_case__						)
             lowerCAmelCase					:					List[str]								=							scheduler_class.from_pretrained(snake_case__						)
             new_scheduler.set_timesteps(snake_case__						)
             # copy over dummy past residuals
             lowerCAmelCase					:					Optional[int]								=							dummy_past_residuals[: new_scheduler.config.solver_order]

           lowerCAmelCase						,  lowerCAmelCase					:					Union[str, Any]								=							sample, sample
           for t in range(snake_case__		,					time_step + scheduler.config.solver_order + 1						):
             lowerCAmelCase					:					Any								=							scheduler.step(snake_case__		,					snake_case__		,					snake_case__		,					**snake_case__						).prev_sample
             lowerCAmelCase					:					str								=							new_scheduler.step(snake_case__		,					snake_case__		,					snake_case__		,					**snake_case__						).prev_sample

             assert torch.sum(torch.abs(output - new_output						)						) < 1e-5, "Scheduler outputs are not identical"

       def 	lowercase__							(						self						):






         """simple docstring"""





         pass

       def 	lowercase__							(						self		,					snake_case__=0		,					**snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					Dict								=							dict(self.forward_default_kwargs						)
         lowerCAmelCase					:					List[str]								=							kwargs.pop("num_inference_steps"		,					snake_case__						)
         lowerCAmelCase					:					List[str]								=							self.dummy_sample
         lowerCAmelCase					:					Optional[int]								=							0.1 * sample
         lowerCAmelCase					:					List[Any]								=							[residual + 0.2, residual + 0.15, residual + 0.10]

         for scheduler_class in self.scheduler_classes:
           lowerCAmelCase					:					int								=							self.get_scheduler_config()
           lowerCAmelCase					:					Any								=							scheduler_class(**snake_case__						)
           scheduler.set_timesteps(snake_case__						)

           # copy over dummy past residuals (must be after setting timesteps)
           lowerCAmelCase					:					Union[str, Any]								=							dummy_past_residuals[: scheduler.config.solver_order]

           with tempfile.TemporaryDirectory() as tmpdirname:
             scheduler.save_config(snake_case__						)
             lowerCAmelCase					:					Any								=							scheduler_class.from_pretrained(snake_case__						)
             # copy over dummy past residuals
             new_scheduler.set_timesteps(snake_case__						)

             # copy over dummy past residual (must be after setting timesteps)
             lowerCAmelCase					:					Optional[int]								=							dummy_past_residuals[: new_scheduler.config.solver_order]

           lowerCAmelCase					:					Union[str, Any]								=							scheduler.step(snake_case__		,					snake_case__		,					snake_case__		,					**snake_case__						).prev_sample
           lowerCAmelCase					:					int								=							new_scheduler.step(snake_case__		,					snake_case__		,					snake_case__		,					**snake_case__						).prev_sample

           assert torch.sum(torch.abs(output - new_output						)						) < 1e-5, "Scheduler outputs are not identical"

       def 	lowercase__							(						self		,					snake_case__=None		,					**snake_case__						):






         """simple docstring"""





         if scheduler is None:
           lowerCAmelCase					:					List[str]								=							self.scheduler_classes[0]
           lowerCAmelCase					:					Optional[int]								=							self.get_scheduler_config(**snake_case__						)
           lowerCAmelCase					:					Any								=							scheduler_class(**snake_case__						)

         lowerCAmelCase					:					List[str]								=							self.scheduler_classes[0]
         lowerCAmelCase					:					Tuple								=							self.get_scheduler_config(**snake_case__						)
         lowerCAmelCase					:					List[str]								=							scheduler_class(**snake_case__						)

         lowerCAmelCase					:					int								=							10
         lowerCAmelCase					:					List[str]								=							self.dummy_model()
         lowerCAmelCase					:					List[Any]								=							self.dummy_sample_deter
         scheduler.set_timesteps(snake_case__						)

         for i, t in enumerate(scheduler.timesteps						):
           lowerCAmelCase					:					Any								=							model(snake_case__		,					snake_case__						)
           lowerCAmelCase					:					List[Any]								=							scheduler.step(snake_case__		,					snake_case__		,					snake_case__						).prev_sample

         return sample

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Dict								=							dict(self.forward_default_kwargs						)

         lowerCAmelCase					:					Optional[Any]								=							kwargs.pop("num_inference_steps"		,					snake_case__						)

         for scheduler_class in self.scheduler_classes:
           lowerCAmelCase					:					Dict								=							self.get_scheduler_config()
           lowerCAmelCase					:					List[Any]								=							scheduler_class(**snake_case__						)

           lowerCAmelCase					:					int								=							self.dummy_sample
           lowerCAmelCase					:					int								=							0.1 * sample

           if num_inference_steps is not None and hasattr(snake_case__		,					"set_timesteps"						):
             scheduler.set_timesteps(snake_case__						)
           elif num_inference_steps is not None and not hasattr(snake_case__		,					"set_timesteps"						):
             lowerCAmelCase					:					List[str]								=							num_inference_steps

           # copy over dummy past residuals (must be done after set_timesteps)
           lowerCAmelCase					:					int								=							[residual + 0.2, residual + 0.15, residual + 0.10]
           lowerCAmelCase					:					Tuple								=							dummy_past_residuals[: scheduler.config.solver_order]

           lowerCAmelCase					:					int								=							scheduler.timesteps[5]
           lowerCAmelCase					:					str								=							scheduler.timesteps[6]

           lowerCAmelCase					:					str								=							scheduler.step(snake_case__		,					snake_case__		,					snake_case__		,					**snake_case__						).prev_sample
           lowerCAmelCase					:					Union[str, Any]								=							scheduler.step(snake_case__		,					snake_case__		,					snake_case__		,					**snake_case__						).prev_sample

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

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					int								=							DEISMultistepScheduler(**self.get_scheduler_config()						)
         lowerCAmelCase					:					Dict								=							self.full_loop(scheduler=snake_case__						)
         lowerCAmelCase					:					Optional[Any]								=							torch.mean(torch.abs(snake_case__						)						)

         assert abs(result_mean.item() - 0.23916						) < 1e-3

         lowerCAmelCase					:					List[str]								=							DPMSolverSinglestepScheduler.from_config(scheduler.config						)
         lowerCAmelCase					:					Any								=							DPMSolverMultistepScheduler.from_config(scheduler.config						)
         lowerCAmelCase					:					str								=							UniPCMultistepScheduler.from_config(scheduler.config						)
         lowerCAmelCase					:					Union[str, Any]								=							DEISMultistepScheduler.from_config(scheduler.config						)

         lowerCAmelCase					:					Union[str, Any]								=							self.full_loop(scheduler=snake_case__						)
         lowerCAmelCase					:					str								=							torch.mean(torch.abs(snake_case__						)						)

         assert abs(result_mean.item() - 0.23916						) < 1e-3

       def 	lowercase__							(						self						):






         """simple docstring"""





         for timesteps in [25, 50, 100, 999, 1_000]:
           self.check_over_configs(num_train_timesteps=snake_case__						)

       def 	lowercase__							(						self						):






         """simple docstring"""





         self.check_over_configs(thresholding=snake_case__						)
         for order in [1, 2, 3]:
           for solver_type in ["logrho"]:
             for threshold in [0.5, 1.0, 2.0]:
               for prediction_type in ["epsilon", "sample"]:
                 self.check_over_configs(
                     thresholding=snake_case__		,					prediction_type=snake_case__		,					sample_max_value=snake_case__		,					algorithm_type="deis"		,					solver_order=snake_case__		,					solver_type=snake_case__		,					)

       def 	lowercase__							(						self						):






         """simple docstring"""





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

       def 	lowercase__							(						self						):






         """simple docstring"""





         for algorithm_type in ["deis"]:
           for solver_type in ["logrho"]:
             for order in [1, 2, 3]:
               for prediction_type in ["epsilon", "sample"]:
                 self.check_over_configs(
                     solver_order=snake_case__		,					solver_type=snake_case__		,					prediction_type=snake_case__		,					algorithm_type=snake_case__		,					)
                 lowerCAmelCase					:					Any								=							self.full_loop(
                     solver_order=snake_case__		,					solver_type=snake_case__		,					prediction_type=snake_case__		,					algorithm_type=snake_case__		,					)
                 assert not torch.isnan(snake_case__						).any(), "Samples have nan numbers"

       def 	lowercase__							(						self						):






         """simple docstring"""





         self.check_over_configs(lower_order_final=snake_case__						)
         self.check_over_configs(lower_order_final=snake_case__						)

       def 	lowercase__							(						self						):






         """simple docstring"""





         for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
           self.check_over_forward(num_inference_steps=snake_case__		,					time_step=0						)

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					List[str]								=							self.full_loop()
         lowerCAmelCase					:					Dict								=							torch.mean(torch.abs(snake_case__						)						)

         assert abs(result_mean.item() - 0.23916						) < 1e-3

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Optional[Any]								=							self.full_loop(prediction_type="v_prediction"						)
         lowerCAmelCase					:					List[Any]								=							torch.mean(torch.abs(snake_case__						)						)

         assert abs(result_mean.item() - 0.091						) < 1e-3







       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Dict								=							self.scheduler_classes[0]
         lowerCAmelCase					:					str								=							self.get_scheduler_config(thresholding=snake_case__		,					dynamic_thresholding_ratio=0						)
         lowerCAmelCase					:					Optional[Any]								=							scheduler_class(**snake_case__						)

         lowerCAmelCase					:					Optional[Any]								=							10
         lowerCAmelCase					:					Tuple								=							self.dummy_model()
         lowerCAmelCase					:					List[str]								=							self.dummy_sample_deter.half()
         scheduler.set_timesteps(snake_case__						)

         for i, t in enumerate(scheduler.timesteps						):
           lowerCAmelCase					:					Union[str, Any]								=							model(snake_case__		,					snake_case__						)
           lowerCAmelCase					:					Tuple								=							scheduler.step(snake_case__		,					snake_case__		,					snake_case__						).prev_sample

         assert sample.dtype == torch.floataa





