import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
    from .tokenization_pegasus import PegasusTokenizer
else:
    lowerCAmelCase			:							Any     =None


lowerCAmelCase			:							Tuple     =logging.get_logger(__name__)


lowerCAmelCase			:							Tuple     ="▁"

lowerCAmelCase			:							str     ={"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

lowerCAmelCase			:							int     ={
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"},
    "tokenizer_file": {
        "google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"
    },
}

lowerCAmelCase			:							Tuple     ={
    "google/pegasus-xsum": 512,
}




class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      _snake_case    		=       VOCAB_FILES_NAMES
      _snake_case    		=       PRETRAINED_VOCAB_FILES_MAP
      _snake_case    		=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _snake_case    		=       PegasusTokenizer
      _snake_case    		=       ['input_ids', 'attention_mask']



      def __init__(					self     :				Tuple    ,  _UpperCamelCase     :				int=None    ,  _UpperCamelCase     :				Union[str, Any]=None    ,  _UpperCamelCase     :				List[str]="<pad>"    ,  _UpperCamelCase     :				Tuple="</s>"    ,  _UpperCamelCase     :				int="<unk>"    ,  _UpperCamelCase     :				Any="<mask_2>"    ,  _UpperCamelCase     :				Optional[int]="<mask_1>"    ,  _UpperCamelCase     :				int=None    ,  _UpperCamelCase     :				Union[str, Any]=103    ,  **_UpperCamelCase     :				Tuple    ,  )				->Optional[int]:


          """simple docstring"""


          _lowerCamelCase     :		List[str] 						=						offset

          if additional_special_tokens is not None:
              if not isinstance(_UpperCamelCase    ,  _UpperCamelCase):
                  raise TypeError(
                      F"""additional_special_tokens should be of type {type(_UpperCamelCase)}, but is"""
                      F""" {type(_UpperCamelCase)}""")

              _lowerCamelCase     :		Any 						=						(
                  ([mask_token_sent] + additional_special_tokens)
                  if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                  else additional_special_tokens
              )
              # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
              additional_special_tokens_extended += [
                  F"""<unk_{i}>""" for i in range(len(_UpperCamelCase)    ,  self.offset - 1)
              ]

              if len(set(_UpperCamelCase)) != len(_UpperCamelCase):
                  raise ValueError(
                      """Please make sure that the provided additional_special_tokens do not contain an incorrectly"""
                      F""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.""")
              _lowerCamelCase     :		str 						=						additional_special_tokens_extended
          else:
              _lowerCamelCase     :		List[str] 						=						[mask_token_sent] if mask_token_sent is not None else []
              additional_special_tokens += [F"""<unk_{i}>""" for i in range(2    ,  self.offset)]

          super().__init__(
              _UpperCamelCase    ,  tokenizer_file=_UpperCamelCase    ,  pad_token=_UpperCamelCase    ,  eos_token=_UpperCamelCase    ,  unk_token=_UpperCamelCase    ,  mask_token=_UpperCamelCase    ,  mask_token_sent=_UpperCamelCase    ,  offset=_UpperCamelCase    ,  additional_special_tokens=_UpperCamelCase    ,  **_UpperCamelCase    ,  )
          _lowerCamelCase     :		str 						=						vocab_file
          _lowerCamelCase     :		Any 						=						False if not self.vocab_file else True



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str]    ,  _UpperCamelCase     :				Any)				->List[str]:


          """simple docstring"""


          _lowerCamelCase     :		Dict 						=						set(self.all_special_ids)  # call it once instead of inside list comp
          all_special_ids.remove(self.unk_token_id)  # <unk> is only sometimes special

          if all_special_ids != set(range(len(self.additional_special_tokens) + 3)):
              raise ValueError(
                  """There should be 3 special tokens: mask_token, pad_token, and eos_token +"""
                  F""" {len(self.additional_special_tokens)} additional_special_tokens, but got {all_special_ids}""")

          return [1 if x in all_special_ids else 0 for x in seq]



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


          """simple docstring"""


          if already_has_special_tokens:
              return self._special_token_mask(_UpperCamelCase)
          elif token_ids_a is None:
              return self._special_token_mask(_UpperCamelCase) + [1]
          else:
              return self._special_token_mask(token_ids_a + token_ids_a) + [1]



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


          """simple docstring"""


          if token_ids_a is None:
              return token_ids_a + [self.eos_token_id]
          # We don't expect to process pairs, but leave the pair logic for API consistency
          return token_ids_a + token_ids_a + [self.eos_token_id]




      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				Optional[str] = None)				->Tuple[str]:


          """simple docstring"""


          if not self.can_save_slow_tokenizer:
              raise ValueError(
                  """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
                  """tokenizer.""")

          if not os.path.isdir(_UpperCamelCase):
              logger.error(F"""Vocabulary path ({save_directory}) should be a directory""")
              return
          _lowerCamelCase     :		List[str] 						=						os.path.join(
              _UpperCamelCase    ,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])

          if os.path.abspath(self.vocab_file) != os.path.abspath(_UpperCamelCase):
              copyfile(self.vocab_file    ,  _UpperCamelCase)

          return (out_vocab_file,)





import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      _snake_case    		=       (EulerDiscreteScheduler,)
      _snake_case    		=       10



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple    ,  **_UpperCamelCase     :				Optional[Any])				->Optional[Any]:


          """simple docstring"""


          _lowerCamelCase     :		Optional[int] 						=						{
              """num_train_timesteps""": 1100,
              """beta_start""": 0.0_0_0_1,
              """beta_end""": 0.0_2,
              """beta_schedule""": """linear""",
          }

          config.update(**_UpperCamelCase)
          return config



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str])				->List[str]:


          """simple docstring"""


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



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


          """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=_UpperCamelCase    ,  beta_end=_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->Dict:


          """simple docstring"""


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



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


          """simple docstring"""


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



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


          """simple docstring"""


          _lowerCamelCase     :		List[Any] 						=						self.scheduler_classes[0]
          _lowerCamelCase     :		str 						=						self.get_scheduler_config()
          _lowerCamelCase     :		Any 						=						scheduler_class(**_UpperCamelCase)

          scheduler.set_timesteps(self.num_inference_steps)

          _lowerCamelCase     :		str 						=						torch.manual_seed(0)

          _lowerCamelCase     :		str 						=						self.dummy_model()
          _lowerCamelCase     :		List[Any] 						=						self.dummy_sample_deter * scheduler.init_noise_sigma
          _lowerCamelCase     :		int 						=						sample.to(_UpperCamelCase)

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

              _lowerCamelCase     :		List[str] 						=						model(_UpperCamelCase    ,  _UpperCamelCase)

              _lowerCamelCase     :		str 						=						scheduler.step(_UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase    ,  generator=_UpperCamelCase)
              _lowerCamelCase     :		Dict 						=						output.prev_sample

          _lowerCamelCase     :		Any 						=						torch.sum(torch.abs(_UpperCamelCase))
          _lowerCamelCase     :		Any 						=						torch.mean(torch.abs(_UpperCamelCase))

          assert abs(result_sum.item() - 1_0.0_8_0_7) < 1E-2
          assert abs(result_mean.item() - 0.0_1_3_1) < 1E-3



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


          """simple docstring"""


          _lowerCamelCase     :		int 						=						self.scheduler_classes[0]
          _lowerCamelCase     :		Optional[Any] 						=						self.get_scheduler_config(prediction_type="""v_prediction""")
          _lowerCamelCase     :		int 						=						scheduler_class(**_UpperCamelCase)

          scheduler.set_timesteps(self.num_inference_steps)

          _lowerCamelCase     :		Any 						=						torch.manual_seed(0)

          _lowerCamelCase     :		int 						=						self.dummy_model()
          _lowerCamelCase     :		int 						=						self.dummy_sample_deter * scheduler.init_noise_sigma
          _lowerCamelCase     :		Dict 						=						sample.to(_UpperCamelCase)

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

              _lowerCamelCase     :		str 						=						model(_UpperCamelCase    ,  _UpperCamelCase)

              _lowerCamelCase     :		List[Any] 						=						scheduler.step(_UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase    ,  generator=_UpperCamelCase)
              _lowerCamelCase     :		Tuple 						=						output.prev_sample

          _lowerCamelCase     :		Union[str, Any] 						=						torch.sum(torch.abs(_UpperCamelCase))
          _lowerCamelCase     :		Optional[int] 						=						torch.mean(torch.abs(_UpperCamelCase))

          assert abs(result_sum.item() - 0.0_0_0_2) < 1E-2
          assert abs(result_mean.item() - 2.2_6_7_6E-0_6) < 1E-3



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


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						self.scheduler_classes[0]
          _lowerCamelCase     :		int 						=						self.get_scheduler_config()
          _lowerCamelCase     :		List[Any] 						=						scheduler_class(**_UpperCamelCase)

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

          _lowerCamelCase     :		Optional[Any] 						=						torch.manual_seed(0)

          _lowerCamelCase     :		Tuple 						=						self.dummy_model()
          _lowerCamelCase     :		Optional[Any] 						=						self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
          _lowerCamelCase     :		Tuple 						=						sample.to(_UpperCamelCase)

          for t in scheduler.timesteps:
              _lowerCamelCase     :		List[Any] 						=						scheduler.scale_model_input(_UpperCamelCase    ,  _UpperCamelCase)

              _lowerCamelCase     :		List[str] 						=						model(_UpperCamelCase    ,  _UpperCamelCase)

              _lowerCamelCase     :		Any 						=						scheduler.step(_UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase    ,  generator=_UpperCamelCase)
              _lowerCamelCase     :		List[Any] 						=						output.prev_sample

          _lowerCamelCase     :		Any 						=						torch.sum(torch.abs(_UpperCamelCase))
          _lowerCamelCase     :		List[Any] 						=						torch.mean(torch.abs(_UpperCamelCase))

          assert abs(result_sum.item() - 1_0.0_8_0_7) < 1E-2
          assert abs(result_mean.item() - 0.0_1_3_1) < 1E-3




      def 	_SCREAMING_SNAKE_CASE    (					self     :				int)				->Tuple:


          """simple docstring"""


          _lowerCamelCase     :		List[str] 						=						self.scheduler_classes[0]
          _lowerCamelCase     :		Optional[int] 						=						self.get_scheduler_config()
          _lowerCamelCase     :		int 						=						scheduler_class(**_UpperCamelCase    ,  use_karras_sigmas=_UpperCamelCase)

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

          _lowerCamelCase     :		int 						=						torch.manual_seed(0)

          _lowerCamelCase     :		Tuple 						=						self.dummy_model()
          _lowerCamelCase     :		str 						=						self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
          _lowerCamelCase     :		Optional[int] 						=						sample.to(_UpperCamelCase)

          for t in scheduler.timesteps:
              _lowerCamelCase     :		Tuple 						=						scheduler.scale_model_input(_UpperCamelCase    ,  _UpperCamelCase)

              _lowerCamelCase     :		Any 						=						model(_UpperCamelCase    ,  _UpperCamelCase)

              _lowerCamelCase     :		List[str] 						=						scheduler.step(_UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase    ,  generator=_UpperCamelCase)
              _lowerCamelCase     :		int 						=						output.prev_sample

          _lowerCamelCase     :		Tuple 						=						torch.sum(torch.abs(_UpperCamelCase))
          _lowerCamelCase     :		List[str] 						=						torch.mean(torch.abs(_UpperCamelCase))

          assert abs(result_sum.item() - 1_2_4.5_2_2_9_9_4_9_9_5_1_1_7_1_9) < 1E-2
          assert abs(result_mean.item() - 0.1_6_2_1_3_9_3_2_6_3_3_3_9_9_9_6_3) < 1E-3




