
import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


lowerCamelCase__    :		Union[str, Any]          =   logging.get_logger(__name__)


lowerCamelCase__    :		Union[str, Any]          =   {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

# See all BART models at https://huggingface.co/models?filter=bart
lowerCamelCase__    :		Any          =   {
    """vocab_file""": {
        """facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/vocab.json""",
        """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/vocab.json""",
        """facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json""",
        """facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json""",
        """facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json""",
        """yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/merges.txt""",
        """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/merges.txt""",
        """facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt""",
        """facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt""",
        """facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt""",
        """yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json""",
        """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json""",
        """facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json""",
        """facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json""",
        """facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json""",
        """yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json""",
    },
}

lowerCamelCase__    :		Optional[Any]          =   {
    """facebook/bart-base""": 1_0_2_4,
    """facebook/bart-large""": 1_0_2_4,
    """facebook/bart-large-mnli""": 1_0_2_4,
    """facebook/bart-large-cnn""": 1_0_2_4,
    """facebook/bart-large-xsum""": 1_0_2_4,
    """yjernite/bart_eli5""": 1_0_2_4,
}



class       _snake_case   (	UpperCAmelCase_							):
	__lowerCAmelCase  :     List[str]        =			VOCAB_FILES_NAMES
	__lowerCAmelCase  :     Any        =			PRETRAINED_VOCAB_FILES_MAP
	__lowerCAmelCase  :     List[str]        =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__lowerCAmelCase  :     str        =			['input_ids', 'attention_mask']
	__lowerCAmelCase  :     int        =			BartTokenizer


	def __init__(					self					,  SCREAMING_SNAKE_CASE_=None					,  SCREAMING_SNAKE_CASE_=None					,  SCREAMING_SNAKE_CASE_=None					,  SCREAMING_SNAKE_CASE_="replace"					,  SCREAMING_SNAKE_CASE_="<s>"					,  SCREAMING_SNAKE_CASE_="</s>"					,  SCREAMING_SNAKE_CASE_="</s>"					,  SCREAMING_SNAKE_CASE_="<s>"					,  SCREAMING_SNAKE_CASE_="<unk>"					,  SCREAMING_SNAKE_CASE_="<pad>"					,  SCREAMING_SNAKE_CASE_="<mask>"					,  SCREAMING_SNAKE_CASE_=False					,  SCREAMING_SNAKE_CASE_=True					,  **SCREAMING_SNAKE_CASE_					,  ):


			'''simple docstring'''




			super().__init__(
			    SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  tokenizer_file=SCREAMING_SNAKE_CASE_					,  errors=SCREAMING_SNAKE_CASE_					,  bos_token=SCREAMING_SNAKE_CASE_					,  eos_token=SCREAMING_SNAKE_CASE_					,  sep_token=SCREAMING_SNAKE_CASE_					,  cls_token=SCREAMING_SNAKE_CASE_					,  unk_token=SCREAMING_SNAKE_CASE_					,  pad_token=SCREAMING_SNAKE_CASE_					,  mask_token=SCREAMING_SNAKE_CASE_					,  add_prefix_space=SCREAMING_SNAKE_CASE_					,  trim_offsets=SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_					,  )

			lowercase__  :   Optional[int]    		=      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
			if pre_tok_state.get("""add_prefix_space"""					,  SCREAMING_SNAKE_CASE_) != add_prefix_space:
					lowercase__  :   Dict    		=      getattr(SCREAMING_SNAKE_CASE_					,  pre_tok_state.pop("""type"""))
					lowercase__  :   Union[str, Any]    		=      add_prefix_space
					lowercase__  :   Tuple    		=      pre_tok_class(**SCREAMING_SNAKE_CASE_)

			lowercase__  :   Optional[Any]    		=      add_prefix_space

			# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
			lowercase__  :   List[str]    		=      'post_processor'
			lowercase__  :   List[str]    		=      getattr(self.backend_tokenizer					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)
			if tokenizer_component_instance:
					lowercase__  :   int    		=      json.loads(tokenizer_component_instance.__getstate__())

					# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
					if "sep" in state:
							lowercase__  :   Union[str, Any]    		=      tuple(state["""sep"""])
					if "cls" in state:
							lowercase__  :   Union[str, Any]    		=      tuple(state["""cls"""])

					lowercase__  :   Any    		=      False

					if state.get("""add_prefix_space"""					,  SCREAMING_SNAKE_CASE_) != add_prefix_space:
							lowercase__  :   Optional[int]    		=      add_prefix_space
							lowercase__  :   Optional[Any]    		=      True

					if state.get("""trim_offsets"""					,  SCREAMING_SNAKE_CASE_) != trim_offsets:
							lowercase__  :   str    		=      trim_offsets
							lowercase__  :   Union[str, Any]    		=      True

					if changes_to_apply:
							lowercase__  :   Dict    		=      getattr(SCREAMING_SNAKE_CASE_					,  state.pop("""type"""))
							lowercase__  :   int    		=      component_class(**SCREAMING_SNAKE_CASE_)
							setattr(self.backend_tokenizer					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)


	@property
	def      lowercase__     (					self):


			'''simple docstring'''




			if self._mask_token is None:
					if self.verbose:
							logger.error("""Using mask_token, but it is not set yet.""")
					return None
			return str(self._mask_token)


	@mask_token.setter
	def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_):


			'''simple docstring'''




			lowercase__  :   Dict    		=      AddedToken(SCREAMING_SNAKE_CASE_					,  lstrip=SCREAMING_SNAKE_CASE_					,  rstrip=SCREAMING_SNAKE_CASE_) if isinstance(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_) else value
			lowercase__  :   Optional[Any]    		=      value


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


			'''simple docstring'''




			lowercase__  :   str    		=      kwargs.get("""is_split_into_words"""					,  SCREAMING_SNAKE_CASE_)

			if is_split_into_words and not self.add_prefix_space:
					raise ValueError(
					    f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
					    """to use it with pretokenized inputs.""")

			return super()._batch_encode_plus(*SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_)


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


			'''simple docstring'''




			lowercase__  :   List[Any]    		=      kwargs.get("""is_split_into_words"""					,  SCREAMING_SNAKE_CASE_)

			if is_split_into_words and not self.add_prefix_space:
					raise ValueError(
					    f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
					    """to use it with pretokenized inputs.""")

			return super()._encode_plus(*SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_)


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


			'''simple docstring'''




			lowercase__  :   Dict    		=      self._tokenizer.model.save(SCREAMING_SNAKE_CASE_					,  name=SCREAMING_SNAKE_CASE_)
			return tuple(SCREAMING_SNAKE_CASE_)


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


			'''simple docstring'''




			lowercase__  :   Optional[int]    		=      [self.bos_token_id] + token_ids_a + [self.eos_token_id]
			if token_ids_a is None:
					return output

			return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]



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


			'''simple docstring'''




			lowercase__  :   str    		=      [self.sep_token_id]
			lowercase__  :   Dict    		=      [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 + sep + token_ids_a + sep) * [0]








from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin



@dataclass
class       _snake_case   (	UpperCAmelCase_							):
 __lowerCAmelCase  :     torch.FloatTensor
 __lowerCAmelCase  :     torch.FloatTensor
 __lowerCAmelCase  :     Optional[torch.FloatTensor]        =			None



class       _snake_case   (	UpperCAmelCase_  ,						UpperCAmelCase_							):
 __lowerCAmelCase  :     Union[str, Any]        =			2


 @register_to_config
 def __init__(					self					,  SCREAMING_SNAKE_CASE_ = 0.0_2					,  SCREAMING_SNAKE_CASE_ = 1_00					,  SCREAMING_SNAKE_CASE_ = 1.0_0_7					,  SCREAMING_SNAKE_CASE_ = 80					,  SCREAMING_SNAKE_CASE_ = 0.0_5					,  SCREAMING_SNAKE_CASE_ = 50					,  ):


   '''simple docstring'''




   lowercase__  :   List[Any]    		=      sigma_max

   # setable values
   lowercase__  :   int    		=      None
   lowercase__  :   np.IntTensor    		=      None
   lowercase__  :   torch.FloatTensor    		=      None  # sigma(t_i)


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


   '''simple docstring'''




   return sample


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


   '''simple docstring'''




   lowercase__  :   Dict    		=      num_inference_steps
   lowercase__  :   List[Any]    		=      np.arange(0					,  self.num_inference_steps)[::-1].copy()
   lowercase__  :   Optional[Any]    		=      torch.from_numpy(SCREAMING_SNAKE_CASE_).to(SCREAMING_SNAKE_CASE_)
   lowercase__  :   str    		=      [
       (
           self.config.sigma_max**2
           * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
       )
       for i in self.timesteps
   ]
   lowercase__  :   int    		=      torch.tensor(SCREAMING_SNAKE_CASE_					,  dtype=torch.floataa					,  device=SCREAMING_SNAKE_CASE_)


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


   '''simple docstring'''




   if self.config.s_min <= sigma <= self.config.s_max:
     lowercase__  :   Optional[Any]    		=      min(self.config.s_churn / self.num_inference_steps					,  2**0.5 - 1)
   else:
     lowercase__  :   Tuple    		=      0

   # sample eps ~ N(0, S_noise^2 * I)
   lowercase__  :   Dict    		=      self.config.s_noise * randn_tensor(sample.shape					,  generator=SCREAMING_SNAKE_CASE_).to(sample.device)
   lowercase__  :   int    		=      sigma + gamma * sigma
   lowercase__  :   Optional[int]    		=      sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

   return sample_hat, sigma_hat


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_ = True					,  ):


   '''simple docstring'''




   lowercase__  :   str    		=      sample_hat + sigma_hat * model_output
   lowercase__  :   str    		=      (sample_hat - pred_original_sample) / sigma_hat
   lowercase__  :   Optional[Any]    		=      sample_hat + (sigma_prev - sigma_hat) * derivative

   if not return_dict:
     return (sample_prev, derivative)

   return KarrasVeOutput(
       prev_sample=SCREAMING_SNAKE_CASE_					,  derivative=SCREAMING_SNAKE_CASE_					,  pred_original_sample=SCREAMING_SNAKE_CASE_)


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_ = True					,  ):


   '''simple docstring'''




   lowercase__  :   str    		=      sample_prev + sigma_prev * model_output
   lowercase__  :   Dict    		=      (sample_prev - pred_original_sample) / sigma_prev
   lowercase__  :   Optional[int]    		=      sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

   if not return_dict:
     return (sample_prev, derivative)

   return KarrasVeOutput(
       prev_sample=SCREAMING_SNAKE_CASE_					,  derivative=SCREAMING_SNAKE_CASE_					,  pred_original_sample=SCREAMING_SNAKE_CASE_)



 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   raise NotImplementedError()






