

"""simple docstring"""





import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


_a       =							logging.get_logger(__name__)


_a       =							{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

# See all LED models at https://huggingface.co/models?filter=LED
_a       =							{
    'vocab_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json',
    },
    'merges_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json',
    },
}

_a       =							{
    'allenai/led-base-16384': 1_63_84,
}




@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def   _A  (  )	->						Union[str, Any]:
   '''simple docstring'''


   __lowercase								=						(
       list(range(ord("!"),			ord("~") + 1)) + list(range(ord("¡"),			ord("¬") + 1)) + list(range(ord("®"),			ord("ÿ") + 1))
   )
   __lowercase								=						bs[:]
   __lowercase								=						0
   for b in range(2**8):
      if b not in bs:
         bs.append(UpperCamelCase_)
         cs.append(2**8 + n)
         n += 1
   __lowercase								=						[chr(UpperCamelCase_) for n in cs]
   return dict(zip(UpperCamelCase_,			UpperCamelCase_))




def   _A  (  UpperCamelCase_     :       List[Any])	->						Any:
   '''simple docstring'''


   __lowercase								=						set()
   __lowercase								=						word[0]
   for char in word[1:]:
      pairs.add((prev_char, char))
      __lowercase								=						char
   return pairs




class 					_lowerCAmelCase  (	lowercase   ):







 """simple docstring"""







 __UpperCAmelCase	:					Any   		=	VOCAB_FILES_NAMES
 __UpperCAmelCase	:					Tuple   		=	PRETRAINED_VOCAB_FILES_MAP
 __UpperCAmelCase	:					Optional[int]   		=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __UpperCAmelCase	:					List[Any]   		=	["input_ids", "attention_mask"]


 def __init__(			self							:			Optional[Any],					UpperCAmelCase__							:			List[str],					UpperCAmelCase__							:			List[Any],					UpperCAmelCase__							:			Union[str, Any]="replace",					UpperCAmelCase__							:			Tuple="<s>",					UpperCAmelCase__							:			Optional[Any]="</s>",					UpperCAmelCase__							:			Any="</s>",					UpperCAmelCase__							:			Optional[int]="<s>",					UpperCAmelCase__							:			Tuple="<unk>",					UpperCAmelCase__							:			Dict="<pad>",					UpperCAmelCase__							:			int="<mask>",					UpperCAmelCase__							:			Any=False,					**UpperCAmelCase__							:			List[Any],					):
    __lowercase								=						AddedToken(UpperCAmelCase__,					lstrip=UpperCAmelCase__,					rstrip=UpperCAmelCase__	) if isinstance(UpperCAmelCase__,					UpperCAmelCase__	) else bos_token
    __lowercase								=						AddedToken(UpperCAmelCase__,					lstrip=UpperCAmelCase__,					rstrip=UpperCAmelCase__	) if isinstance(UpperCAmelCase__,					UpperCAmelCase__	) else eos_token
    __lowercase								=						AddedToken(UpperCAmelCase__,					lstrip=UpperCAmelCase__,					rstrip=UpperCAmelCase__	) if isinstance(UpperCAmelCase__,					UpperCAmelCase__	) else sep_token
    __lowercase								=						AddedToken(UpperCAmelCase__,					lstrip=UpperCAmelCase__,					rstrip=UpperCAmelCase__	) if isinstance(UpperCAmelCase__,					UpperCAmelCase__	) else cls_token
    __lowercase								=						AddedToken(UpperCAmelCase__,					lstrip=UpperCAmelCase__,					rstrip=UpperCAmelCase__	) if isinstance(UpperCAmelCase__,					UpperCAmelCase__	) else unk_token
    __lowercase								=						AddedToken(UpperCAmelCase__,					lstrip=UpperCAmelCase__,					rstrip=UpperCAmelCase__	) if isinstance(UpperCAmelCase__,					UpperCAmelCase__	) else pad_token

    # Mask token behave like a normal word, i.e. include the space before it
    __lowercase								=						AddedToken(UpperCAmelCase__,					lstrip=UpperCAmelCase__,					rstrip=UpperCAmelCase__	) if isinstance(UpperCAmelCase__,					UpperCAmelCase__	) else mask_token

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

    with open(UpperCAmelCase__,					encoding="utf-8"	) as vocab_handle:
       __lowercase								=						json.load(UpperCAmelCase__	)
    __lowercase								=						{v: k for k, v in self.encoder.items()}
    __lowercase								=						errors  # how to handle errors in decoding
    __lowercase								=						bytes_to_unicode()
    __lowercase								=						{v: k for k, v in self.byte_encoder.items()}
    with open(UpperCAmelCase__,					encoding="utf-8"	) as merges_handle:
       __lowercase								=						merges_handle.read().split("\n"	)[1:-1]
    __lowercase								=						[tuple(merge.split()	) for merge in bpe_merges]
    __lowercase								=						dict(zip(UpperCAmelCase__,					range(len(UpperCAmelCase__	)	)	)	)
    __lowercase								=						{}
    __lowercase								=						add_prefix_space

    # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
    __lowercase								=						re.compile(r"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"	)


 @property
 # Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
 def 					_lowercase (			self							:			List[str]	):
    return len(self.encoder	)


 def 					_lowercase (			self							:			int	):
    return dict(self.encoder,					**self.added_tokens_encoder	)


 def 					_lowercase (			self							:			str,					UpperCAmelCase__							:			str	):
    if token in self.cache:
       return self.cache[token]
    __lowercase								=						tuple(UpperCAmelCase__	)
    __lowercase								=						get_pairs(UpperCAmelCase__	)

    if not pairs:
       return token

    while True:
       __lowercase								=						min(UpperCAmelCase__,					key=lambda UpperCAmelCase__	: self.bpe_ranks.get(UpperCAmelCase__,					float("inf"	)	)	)
       if bigram not in self.bpe_ranks:
          break
       __lowercase		,__lowercase								=						bigram
       __lowercase								=						[]
       __lowercase								=						0
       while i < len(UpperCAmelCase__	):
          try:
             __lowercase								=						word.index(UpperCAmelCase__,					UpperCAmelCase__	)
          except ValueError:
             new_word.extend(word[i:]	)
             break
          else:
             new_word.extend(word[i:j]	)
             __lowercase								=						j

          if word[i] == first and i < len(UpperCAmelCase__	) - 1 and word[i + 1] == second:
             new_word.append(first + second	)
             i += 2
          else:
             new_word.append(word[i]	)
             i += 1
       __lowercase								=						tuple(UpperCAmelCase__	)
       __lowercase								=						new_word
       if len(UpperCAmelCase__	) == 1:
          break
       else:
          __lowercase								=						get_pairs(UpperCAmelCase__	)
    __lowercase								=						" ".join(UpperCAmelCase__	)
    __lowercase								=						word
    return word


 def 					_lowercase (			self							:			Tuple,					UpperCAmelCase__							:			Tuple	):
    __lowercase								=						[]
    for token in re.findall(self.pat,					UpperCAmelCase__	):
       __lowercase								=						"".join(
           self.byte_encoder[b] for b in token.encode("utf-8"	)	)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
       bpe_tokens.extend(bpe_token for bpe_token in self.bpe(UpperCAmelCase__	).split(" "	)	)
    return bpe_tokens


 def 					_lowercase (			self							:			Optional[int],					UpperCAmelCase__							:			int	):
    return self.encoder.get(UpperCAmelCase__,					self.encoder.get(self.unk_token	)	)


 def 					_lowercase (			self							:			Optional[Any],					UpperCAmelCase__							:			str	):
    return self.decoder.get(UpperCAmelCase__	)


 def 					_lowercase (			self							:			List[Any],					UpperCAmelCase__							:			Any	):
    __lowercase								=						"".join(UpperCAmelCase__	)
    __lowercase								=						bytearray([self.byte_decoder[c] for c in text]	).decode("utf-8",					errors=self.errors	)
    return text


 def 					_lowercase (			self							:			str,					UpperCAmelCase__							:			str,					UpperCAmelCase__							:			Optional[str] = None	):
    if not os.path.isdir(UpperCAmelCase__	):
       logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""	)
       return
    __lowercase								=						os.path.join(
        UpperCAmelCase__,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]	)
    __lowercase								=						os.path.join(
        UpperCAmelCase__,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]	)

    with open(UpperCAmelCase__,					"w",					encoding="utf-8"	) as f:
       f.write(json.dumps(self.encoder,					indent=2,					sort_keys=UpperCAmelCase__,					ensure_ascii=UpperCAmelCase__	) + "\n"	)

    __lowercase								=						0
    with open(UpperCAmelCase__,					"w",					encoding="utf-8"	) as writer:
       writer.write("#version: 0.2\n"	)
       for bpe_tokens, token_index in sorted(self.bpe_ranks.items(),					key=lambda UpperCAmelCase__	: kv[1]	):
          if index != token_index:
             logger.warning(
                 F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
                 " Please check that the tokenizer is not corrupted!"	)
             __lowercase								=						token_index
          writer.write(" ".join(UpperCAmelCase__	) + "\n"	)
          index += 1

    return vocab_file, merge_file


 def 					_lowercase (			self							:			Union[str, Any],					UpperCAmelCase__							:			List[int],					UpperCAmelCase__							:			Optional[List[int]] = None	):
    if token_ids_a is None:
       return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
    __lowercase								=						[self.cls_token_id]
    __lowercase								=						[self.sep_token_id]
    return cls + token_ids_a + sep + sep + token_ids_a + sep


 def 					_lowercase (			self							:			Any,					UpperCAmelCase__							:			List[int],					UpperCAmelCase__							:			Optional[List[int]] = None,					UpperCAmelCase__							:			bool = False	):
    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, 1] + ([0] * len(UpperCAmelCase__	)) + [1]


 def 					_lowercase (			self							:			List[Any],					UpperCAmelCase__							:			List[int],					UpperCAmelCase__							:			Optional[List[int]] = None	):
    __lowercase								=						[self.sep_token_id]
    __lowercase								=						[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]


 def 					_lowercase (			self							:			Dict,					UpperCAmelCase__							:			List[Any],					UpperCAmelCase__							:			str=False,					**UpperCAmelCase__							:			List[Any]	):
    __lowercase								=						kwargs.pop("add_prefix_space",					self.add_prefix_space	)
    if (is_split_into_words or add_prefix_space) and (len(UpperCAmelCase__	) > 0 and not text[0].isspace()):
       __lowercase								=						" " + text
    return (text, kwargs)



 def 					_lowercase (			self							:			Dict,					UpperCAmelCase__							:			Union[Dict[str, EncodedInput], BatchEncoding],					UpperCAmelCase__							:			Optional[int] = None,					UpperCAmelCase__							:			PaddingStrategy = PaddingStrategy.DO_NOT_PAD,					UpperCAmelCase__							:			Optional[int] = None,					UpperCAmelCase__							:			Optional[bool] = None,					):
    __lowercase								=						super()._pad(
        encoded_inputs=UpperCAmelCase__,					max_length=UpperCAmelCase__,					padding_strategy=UpperCAmelCase__,					pad_to_multiple_of=UpperCAmelCase__,					return_attention_mask=UpperCAmelCase__,					)

    # Load from model defaults
    if return_attention_mask is None:
       __lowercase								=						"attention_mask" in self.model_input_names

    if return_attention_mask and "global_attention_mask" in encoded_inputs:
       __lowercase								=						encoded_inputs[self.model_input_names[0]]
       # `global_attention_mask` need to have the same length as other (sequential) inputs.
       __lowercase								=						len(encoded_inputs["global_attention_mask"]	) != len(UpperCAmelCase__	)

       if needs_to_be_padded:
          __lowercase								=						len(UpperCAmelCase__	) - len(encoded_inputs["global_attention_mask"]	)

          if self.padding_side == "right":
             # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
             __lowercase								=						(
                 encoded_inputs["global_attention_mask"] + [-1] * difference
             )
          elif self.padding_side == "left":
             __lowercase								=						[-1] * difference + encoded_inputs[
                 "global_attention_mask"
             ]
          else:
             raise ValueError("Invalid padding strategy:" + str(self.padding_side	)	)

    return encoded_inputs









# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate



@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""
	__A							=							42
	__A							=							None






def     a(      A       :    Optional[Any] ,     A       :    Any=0.999 ,     A       :    Dict="cosine" ,     )		->       Optional[int]:
					"""simple docstring"""





					if alpha_transform_type == "cosine":

										def alpha_bar_fn(A       :    Optional[int]       ):
															return math.cos((t + 0.008) / 1.008 * math.pi / 2       ) ** 2

					elif alpha_transform_type == "exp":

										def alpha_bar_fn(A       :    Any       ):
															return math.exp(t * -12.0       )

					else:
										raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}'''       )

					a					     =   []
					for i in range(A       ):
										a					     =   i / num_diffusion_timesteps
										a					     =   (i + 1) / num_diffusion_timesteps
										betas.append(min(1 - alpha_bar_fn(A       ) / alpha_bar_fn(A       ) ,     A       )       )
					return torch.tensor(A ,     dtype=torch.floataa       )







class 	_lowercase							(   lowerCAmelCase,      lowerCAmelCase	):





	"""simple docstring"""
	__A							=							1




	@register_to_config
	def __init__(self		,       lowerCamelCase_ = 1000		,       lowerCamelCase_ = 0.0001		,       lowerCamelCase_ = 0.02		,       lowerCamelCase_ = "linear"		,       lowerCamelCase_ = None		,       lowerCamelCase_ = True		,       lowerCamelCase_ = True		,       lowerCamelCase_ = 0		,       lowerCamelCase_ = "epsilon"		,       lowerCamelCase_ = 1.0		,       **lowerCamelCase_		,       ):
						"""simple docstring"""

						if kwargs.get("set_alpha_to_one"		,       lowerCamelCase_		) is not None:
											a					     =   (
											    "The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead."
											)
											deprecate("set_alpha_to_one"		,       "1.0.0"		,       lowerCamelCase_		,       standard_warn=lowerCamelCase_		)
											a					     =   kwargs["set_alpha_to_one"]
						if trained_betas is not None:
											a					     =   torch.tensor(lowerCamelCase_		,       dtype=torch.floataa		)
						elif beta_schedule == "linear":
											a					     =   torch.linspace(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       dtype=torch.floataa		)
						elif beta_schedule == "scaled_linear":
											# this schedule is very specific to the latent diffusion model.
											a					     =   (
											    torch.linspace(beta_start**0.5		,       beta_end**0.5		,       lowerCamelCase_		,       dtype=torch.floataa		) ** 2
											)
						elif beta_schedule == "squaredcos_cap_v2":
											# Glide cosine schedule
											a					     =   betas_for_alpha_bar(lowerCamelCase_		)
						else:
											raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''		)

						a					     =   1.0 - self.betas
						a					     =   torch.cumprod(self.alphas		,       dim=0		)

						# At every step in inverted ddim, we are looking into the next alphas_cumprod
						# For the final step, there is no next alphas_cumprod, and the index is out of bounds
						# `set_alpha_to_zero` decides whether we set this parameter simply to zero
						# in this case, self.step() just output the predicted noise
						# or whether we use the final alpha of the "non-previous" one.
						a					     =   torch.tensor(0.0		) if set_alpha_to_zero else self.alphas_cumprod[-1]

						# standard deviation of the initial noise distribution
						a					     =   1.0

						# setable values
						a					     =   None
						a					     =   torch.from_numpy(np.arange(0		,       lowerCamelCase_		).copy().astype(np.intaa		)		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_ = None		):
						"""simple docstring"""

						return sample




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_ = None		):
						"""simple docstring"""

						if num_inference_steps > self.config.num_train_timesteps:
											raise ValueError(
											    F'''`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'''
											    F''' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'''
											    F''' maximal {self.config.num_train_timesteps} timesteps.'''		)

						a					     =   num_inference_steps
						a					     =   self.config.num_train_timesteps // self.num_inference_steps
						# creates integer timesteps by multiplying by ratio
						# casting to int to avoid issues when num_inference_step is power of 3
						a					     =   (np.arange(0		,       lowerCamelCase_		) * step_ratio).round().copy().astype(np.intaa		)
						a					     =   torch.from_numpy(lowerCamelCase_		).to(lowerCamelCase_		)
						self.timesteps += self.config.steps_offset




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_ = 0.0		,       lowerCamelCase_ = False		,       lowerCamelCase_ = None		,       lowerCamelCase_ = True		,       ):
						"""simple docstring"""

						a					     =   timestep + self.config.num_train_timesteps // self.num_inference_steps

						# 2. compute alphas, betas
						# change original implementation to exactly match noise levels for analogous forward process
						a					     =   self.alphas_cumprod[timestep]
						a					     =   (
						    self.alphas_cumprod[prev_timestep]
						    if prev_timestep < self.config.num_train_timesteps
						    else self.final_alpha_cumprod
						)

						a					     =   1 - alpha_prod_t

						# 3. compute predicted original sample from predicted noise also called
						# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
						if self.config.prediction_type == "epsilon":
											a					     =   (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
											a					     =   model_output
						elif self.config.prediction_type == "sample":
											a					     =   model_output
											a					     =   (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
						elif self.config.prediction_type == "v_prediction":
											a					     =   (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
											a					     =   (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
						else:
											raise ValueError(
											    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'''
											    " `v_prediction`"		)

						# 4. Clip or threshold "predicted x_0"
						if self.config.clip_sample:
											a					     =   pred_original_sample.clamp(
											    -self.config.clip_sample_range		,       self.config.clip_sample_range		)

						# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
						a					     =   (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

						# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
						a					     =   alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

						if not return_dict:
											return (prev_sample, pred_original_sample)
						return DDIMSchedulerOutput(prev_sample=lowerCamelCase_		,       pred_original_sample=lowerCamelCase_		)




	def __len__(self		):
						"""simple docstring"""

						return self.config.num_train_timesteps
