


import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest


class     __UpperCAmelCase							(       __A							):


     """simple docstring"""
     _lowerCamelCase   =      (DEISMultistepScheduler,)
     _lowerCamelCase   =      (("""num_inference_steps""", 25),)


     def 	snake_case_  (	self  ,       **__A    ):
        __a					=  {
            """num_train_timesteps""": 1000,
            """beta_start""": 0.0001,
            """beta_end""": 0.02,
            """beta_schedule""": """linear""",
            """solver_order""": 2,
        }

        config.update(**__A    )
        return config


     def 	snake_case_  (	self  ,       __A=0  ,       **__A    ):
        __a					=  dict(self.forward_default_kwargs    )
        __a					=  kwargs.pop("""num_inference_steps"""  ,       __A    )
        __a					=  self.dummy_sample
        __a					=  0.1 * sample
        __a					=  [residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
           __a					=  self.get_scheduler_config(**__A    )
           __a					=  scheduler_class(**__A    )
           scheduler.set_timesteps(__A    )
           # copy over dummy past residuals
           __a					=  dummy_past_residuals[: scheduler.config.solver_order]

           with tempfile.TemporaryDirectory() as tmpdirname:
              scheduler.save_config(__A    )
              __a					=  scheduler_class.from_pretrained(__A    )
              new_scheduler.set_timesteps(__A    )
              # copy over dummy past residuals
              __a					=  dummy_past_residuals[: new_scheduler.config.solver_order]

           __a	,    __a					=  sample, sample
           for t in range(__A  ,       time_step + scheduler.config.solver_order + 1    ):
              __a					=  scheduler.step(__A  ,       __A  ,       __A  ,       **__A    ).prev_sample
              __a					=  new_scheduler.step(__A  ,       __A  ,       __A  ,       **__A    ).prev_sample

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


     def 	snake_case_  (	self    ):
        pass


     def 	snake_case_  (	self  ,       __A=0  ,       **__A    ):
        __a					=  dict(self.forward_default_kwargs    )
        __a					=  kwargs.pop("""num_inference_steps"""  ,       __A    )
        __a					=  self.dummy_sample
        __a					=  0.1 * sample
        __a					=  [residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
           __a					=  self.get_scheduler_config()
           __a					=  scheduler_class(**__A    )
           scheduler.set_timesteps(__A    )

           # copy over dummy past residuals (must be after setting timesteps)
           __a					=  dummy_past_residuals[: scheduler.config.solver_order]

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

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

           __a					=  scheduler.step(__A  ,       __A  ,       __A  ,       **__A    ).prev_sample
           __a					=  new_scheduler.step(__A  ,       __A  ,       __A  ,       **__A    ).prev_sample

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


     def 	snake_case_  (	self  ,       __A=None  ,       **__A    ):
        if scheduler is None:
           __a					=  self.scheduler_classes[0]
           __a					=  self.get_scheduler_config(**__A    )
           __a					=  scheduler_class(**__A    )

        __a					=  self.scheduler_classes[0]
        __a					=  self.get_scheduler_config(**__A    )
        __a					=  scheduler_class(**__A    )

        __a					=  10
        __a					=  self.dummy_model()
        __a					=  self.dummy_sample_deter
        scheduler.set_timesteps(__A    )

        for i, t in enumerate(scheduler.timesteps    ):
           __a					=  model(__A  ,       __A    )
           __a					=  scheduler.step(__A  ,       __A  ,       __A    ).prev_sample

        return sample


     def 	snake_case_  (	self    ):
        __a					=  dict(self.forward_default_kwargs    )

        __a					=  kwargs.pop("""num_inference_steps"""  ,       __A    )

        for scheduler_class in self.scheduler_classes:
           __a					=  self.get_scheduler_config()
           __a					=  scheduler_class(**__A    )

           __a					=  self.dummy_sample
           __a					=  0.1 * sample

           if num_inference_steps is not None and hasattr(__A  ,       """set_timesteps"""    ):
              scheduler.set_timesteps(__A    )
           elif num_inference_steps is not None and not hasattr(__A  ,       """set_timesteps"""    ):
              __a					=  num_inference_steps

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

           __a					=  scheduler.timesteps[5]
           __a					=  scheduler.timesteps[6]

           __a					=  scheduler.step(__A  ,       __A  ,       __A  ,       **__A    ).prev_sample
           __a					=  scheduler.step(__A  ,       __A  ,       __A  ,       **__A    ).prev_sample

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


     def 	snake_case_  (	self    ):
        # make sure that iterating over schedulers with same config names gives same results
        # for defaults
        __a					=  DEISMultistepScheduler(**self.get_scheduler_config()    )
        __a					=  self.full_loop(scheduler=__A    )
        __a					=  torch.mean(torch.abs(__A    )    )

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

        __a					=  DPMSolverSinglestepScheduler.from_config(scheduler.config    )
        __a					=  DPMSolverMultistepScheduler.from_config(scheduler.config    )
        __a					=  UniPCMultistepScheduler.from_config(scheduler.config    )
        __a					=  DEISMultistepScheduler.from_config(scheduler.config    )

        __a					=  self.full_loop(scheduler=__A    )
        __a					=  torch.mean(torch.abs(__A    )    )

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


     def 	snake_case_  (	self    ):
        for timesteps in [25, 50, 100, 999, 1000]:
           self.check_over_configs(num_train_timesteps=__A    )


     def 	snake_case_  (	self    ):
        self.check_over_configs(thresholding=__A    )
        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=__A  ,       prediction_type=__A  ,       sample_max_value=__A  ,       algorithm_type="""deis"""  ,       solver_order=__A  ,       solver_type=__A  ,       )


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


     def 	snake_case_  (	self    ):
        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=__A  ,       solver_type=__A  ,       prediction_type=__A  ,       algorithm_type=__A  ,       )
                    __a					=  self.full_loop(
                        solver_order=__A  ,       solver_type=__A  ,       prediction_type=__A  ,       algorithm_type=__A  ,       )
                    assert not torch.isnan(__A    ).any(), "Samples have nan numbers"


     def 	snake_case_  (	self    ):
        self.check_over_configs(lower_order_final=__A    )
        self.check_over_configs(lower_order_final=__A    )


     def 	snake_case_  (	self    ):
        for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
           self.check_over_forward(num_inference_steps=__A  ,       time_step=0    )


     def 	snake_case_  (	self    ):
        __a					=  self.full_loop()
        __a					=  torch.mean(torch.abs(__A    )    )

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


     def 	snake_case_  (	self    ):
        __a					=  self.full_loop(prediction_type="""v_prediction"""    )
        __a					=  torch.mean(torch.abs(__A    )    )

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


     def 	snake_case_  (	self    ):
        __a					=  self.scheduler_classes[0]
        __a					=  self.get_scheduler_config(thresholding=__A  ,       dynamic_thresholding_ratio=0    )
        __a					=  scheduler_class(**__A    )

        __a					=  10
        __a					=  self.dummy_model()
        __a					=  self.dummy_sample_deter.half()
        scheduler.set_timesteps(__A    )

        for i, t in enumerate(scheduler.timesteps    ):
           __a					=  model(__A  ,       __A    )
           __a					=  scheduler.step(__A  ,       __A  ,       __A    ).prev_sample

        assert sample.dtype == torch.floataa







import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

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


SCREAMING_SNAKE_CASE							    =							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							    =							'▁'

SCREAMING_SNAKE_CASE							    =							{'vocab_file': 'prophetnet.tokenizer'}

SCREAMING_SNAKE_CASE							    =							{
    'vocab_file': {
        'microsoft/xprophetnet-large-wiki100-cased': (
            'https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer'
        ),
    }
}

SCREAMING_SNAKE_CASE							    =							{
    'microsoft/xprophetnet-large-wiki100-cased': {'do_lower_case': False},
}

SCREAMING_SNAKE_CASE							    =							{
    'microsoft/xprophetnet-large-wiki100-cased': 5_1_2,
}




def  a   (lowerCAmelCase__   ):
   __a					=  collections.OrderedDict()
   with open(lowerCAmelCase__		,				"""r"""		,				encoding="""utf-8"""   ) as reader:
      __a					=  reader.readlines()
   for index, token in enumerate(lowerCAmelCase__   ):
      __a					=  token.rstrip("""\n"""   )
      __a					=  index
   return vocab


class     __UpperCAmelCase							(       __A							):


     """simple docstring"""
     _lowerCamelCase   =      VOCAB_FILES_NAMES
     _lowerCamelCase   =      PRETRAINED_VOCAB_FILES_MAP
     _lowerCamelCase   =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _lowerCamelCase   =      ["""input_ids""", """attention_mask"""]


     def __init__(	self  ,       __A  ,       __A="[SEP]"  ,       __A="[SEP]"  ,       __A="[SEP]"  ,       __A="[UNK]"  ,       __A="[PAD]"  ,       __A="[CLS]"  ,       __A="[MASK]"  ,       __A = None  ,       **__A  ,       ):
        __a					=  {} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            bos_token=__A  ,       eos_token=__A  ,       sep_token=__A  ,       unk_token=__A  ,       pad_token=__A  ,       cls_token=__A  ,       mask_token=__A  ,       sp_model_kwargs=self.sp_model_kwargs  ,       **__A  ,       )

        try:
           import sentencepiece as spm
        except ImportError:
           logger.warning(
               """You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"""
               """ pip install sentencepiece"""    )
           raise

        __a					=  spm.SentencePieceProcessor(**self.sp_model_kwargs    )
        self.sp_model.Load(str(__A    )    )
        __a					=  vocab_file

        # Original fairseq vocab and spm vocab must be "aligned":
        # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
        # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
        # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
        # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

        # put special tokens and [unused] tokens into the vocab
        __a					=  {"""[PAD]""": 0, """[CLS]""": 1, """[SEP]""": 2, """[UNK]""": 3, """[MASK]""": 4}

        for i in range(10    ):
           __a					=  f'''[unused{i}]'''
           __a					=  5 + i

        # The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
        __a					=  12
        __a					=  {v: k for k, v in self.fairseq_tokens_to_ids.items()}
        for k in self.fairseq_tokens_to_ids.keys():
           self.unique_no_split_tokens.append(__A    )


     def __getstate__(	self    ):
        __a					=  self.__dict__.copy()
        __a					=  None
        return state


     def __setstate__(	self  ,       __A    ):
        __a					=  d
        try:
           import sentencepiece as spm
        except ImportError:
           logger.warning(
               """You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"""
               """ pip install sentencepiece"""    )
           raise

        # for backward compatibility
        if not hasattr(self  ,       """sp_model_kwargs"""    ):
           __a					=  {}

        __a					=  spm.SentencePieceProcessor(**self.sp_model_kwargs    )
        self.sp_model.Load(self.vocab_file    )


     def 	snake_case_  (	self  ,       __A  ,       __A = None  ,       __A = False    ):

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

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


     def 	snake_case_  (	self  ,       __A  ,       __A = None    ):
        __a					=  [self.sep_token_id]

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


     @property
     def 	snake_case_  (	self    ):
        return len(self.sp_model    ) + self.fairseq_offset


     def 	snake_case_  (	self    ):
        __a					=  {self.convert_ids_to_tokens(__A    ): i for i in range(self.vocab_size    )}
        vocab.update(self.added_tokens_encoder    )
        return vocab


     def 	snake_case_  (	self  ,       __A    ):
        return self.sp_model.encode(__A  ,       out_type=__A    )


     def 	snake_case_  (	self  ,       __A    ):
        if token in self.fairseq_tokens_to_ids:
           return self.fairseq_tokens_to_ids[token]
        __a					=  self.sp_model.PieceToId(__A    )

        # Need to return unknown token if the SP model returned 0
        return spm_id + self.fairseq_offset if spm_id else self.unk_token_id


     def 	snake_case_  (	self  ,       __A    ):
        if index in self.fairseq_ids_to_tokens:
           return self.fairseq_ids_to_tokens[index]
        return self.sp_model.IdToPiece(index - self.fairseq_offset    )


     def 	snake_case_  (	self  ,       __A    ):
        __a					=  """""".join(__A    ).replace(__A  ,       """ """    ).strip()
        return out_string


     def 	snake_case_  (	self  ,       __A  ,       __A = None    ):
        if not os.path.isdir(__A    ):
           logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''    )
           return
        __a					=  os.path.join(
            __A  ,       (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]    )

        if os.path.abspath(self.vocab_file    ) != os.path.abspath(__A    ) and os.path.isfile(self.vocab_file    ):
           copyfile(self.vocab_file  ,       __A    )
        elif not os.path.isfile(self.vocab_file    ):
           with open(__A  ,       """wb"""    ) as fi:
              __a					=  self.sp_model.serialized_model_proto()
              fi.write(__A    )

        return (out_vocab_file,)


     def 	snake_case_  (	self  ,       __A  ,       __A = None    ):

        if token_ids_a is None:
           return token_ids_a + [self.sep_token_id]
        __a					=  [self.sep_token_id]
        return token_ids_a + sep + token_ids_a + sep



