






import json
import os
from typing import Optional, Tuple

import regex as re

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


A__	:    Tuple			 = logging.get_logger(__name__)

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

A__	:    Optional[Any]			 = {
    '''vocab_file''': {'''ctrl''': '''https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json'''},
    '''merges_file''': {'''ctrl''': '''https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt'''},
}

A__	:    Optional[int]			 = {
    '''ctrl''': 256,
}

A__	:    Union[str, Any]			 = {
    '''Pregnancy''': 168_629,
    '''Christianity''': 7_675,
    '''Explain''': 106_423,
    '''Fitness''': 63_440,
    '''Saving''': 63_163,
    '''Ask''': 27_171,
    '''Ass''': 95_985,
    '''Joke''': 163_509,
    '''Questions''': 45_622,
    '''Thoughts''': 49_605,
    '''Retail''': 52_342,
    '''Feminism''': 164_338,
    '''Writing''': 11_992,
    '''Atheism''': 192_263,
    '''Netflix''': 48_616,
    '''Computing''': 39_639,
    '''Opinion''': 43_213,
    '''Alone''': 44_967,
    '''Funny''': 58_917,
    '''Gaming''': 40_358,
    '''Human''': 4_088,
    '''India''': 1_331,
    '''Joker''': 77_138,
    '''Diet''': 36_206,
    '''Legal''': 11_859,
    '''Norman''': 4_939,
    '''Tip''': 72_689,
    '''Weight''': 52_343,
    '''Movies''': 46_273,
    '''Running''': 23_425,
    '''Science''': 2_090,
    '''Horror''': 37_793,
    '''Confession''': 60_572,
    '''Finance''': 12_250,
    '''Politics''': 16_360,
    '''Scary''': 191_985,
    '''Support''': 12_654,
    '''Technologies''': 32_516,
    '''Teenage''': 66_160,
    '''Event''': 32_769,
    '''Learned''': 67_460,
    '''Notion''': 182_770,
    '''Wikipedia''': 37_583,
    '''Books''': 6_665,
    '''Extract''': 76_050,
    '''Confessions''': 102_701,
    '''Conspiracy''': 75_932,
    '''Links''': 63_674,
    '''Narcissus''': 150_425,
    '''Relationship''': 54_766,
    '''Relationships''': 134_796,
    '''Reviews''': 41_671,
    '''News''': 4_256,
    '''Translation''': 26_820,
    '''multilingual''': 128_406,
}

def 			_snake_case    (					lowerCamelCase__     :   Optional[Any]  )    ->     Tuple:
							lowerCamelCase_	:							Tuple									=set()
							lowerCamelCase_	:							Dict									=word[0]
							for char in word[1:]:
														pairs.add((prev_char, char)  )
														lowerCamelCase_	:							int									=char

							lowerCamelCase_	:							Optional[Any]									=set(__lowerCAmelCase  )
							return pairs







class        lowercase__      (							lowerCamelCase_       ):
			_UpperCAmelCase		:Optional[Any]             =			VOCAB_FILES_NAMES
			_UpperCAmelCase		:str             =			PRETRAINED_VOCAB_FILES_MAP
			_UpperCAmelCase		:str             =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_UpperCAmelCase		:int             =			CONTROL_CODES


			def __init__(				self					:  List[Any]       ,	snake_case__					:  str       ,	snake_case__					:  Dict       ,	snake_case__					:  Optional[Any]="<unk>"       ,	**snake_case__					:  str ):
										super().__init__(unk_token=__snake_case       ,	**__snake_case )

										with open(__snake_case       ,	encoding="utf-8" ) as vocab_handle:
																	lowerCamelCase_	:							List[str]									=json.load(__snake_case )
										lowerCamelCase_	:							Optional[Any]									={v: k for k, v in self.encoder.items()}
										with open(__snake_case       ,	encoding="utf-8" ) as merges_handle:
																	lowerCamelCase_	:							Optional[Any]									=merges_handle.read().split("\n" )[1:-1]
										lowerCamelCase_	:							List[Any]									=[tuple(merge.split() ) for merge in merges]
										lowerCamelCase_	:							Any									=dict(zip(__snake_case       ,	range(len(__snake_case ) ) ) )
										lowerCamelCase_	:							str									={}


			@property
			def      UpperCAmelCase__						(				self					:  int ):
										return len(self.encoder )


			def      UpperCAmelCase__						(				self					:  str ):
										return dict(self.encoder       ,	**self.added_tokens_encoder )


			def      UpperCAmelCase__						(				self					:  int       ,	snake_case__					:  Any ):
										if token in self.cache:
																	return self.cache[token]
										lowerCamelCase_	:							Optional[Any]									=tuple(__snake_case )
										lowerCamelCase_	:							Union[str, Any]									=tuple(list(word[:-1] ) + [word[-1] + "</w>"] )
										lowerCamelCase_	:							Optional[Any]									=get_pairs(__snake_case )

										if not pairs:
																	return token

										while True:
																	lowerCamelCase_	:							List[str]									=min(__snake_case       ,	key=lambda snake_case__ : self.bpe_ranks.get(__snake_case       ,	float("inf" ) ) )
																	if bigram not in self.bpe_ranks:
																								break
																	lowerCamelCase_							,						lowerCamelCase_	:							Dict									=bigram
																	lowerCamelCase_	:							int									=[]
																	lowerCamelCase_	:							Optional[Any]									=0
																	while i < len(__snake_case ):
																								try:
																															lowerCamelCase_	:							Any									=word.index(__snake_case       ,	__snake_case )
																								except ValueError:
																															new_word.extend(word[i:] )
																															break
																								else:
																															new_word.extend(word[i:j] )
																															lowerCamelCase_	:							Optional[Any]									=j

																								if word[i] == first and i < len(__snake_case ) - 1 and word[i + 1] == second:
																															new_word.append(first + second )
																															i += 2
																								else:
																															new_word.append(word[i] )
																															i += 1
																	lowerCamelCase_	:							str									=tuple(__snake_case )
																	lowerCamelCase_	:							List[Any]									=new_word
																	if len(__snake_case ) == 1:
																								break
																	else:
																								lowerCamelCase_	:							str									=get_pairs(__snake_case )
										lowerCamelCase_	:							List[str]									="@@ ".join(__snake_case )
										lowerCamelCase_	:							Tuple									=word[:-4]
										lowerCamelCase_	:							List[Any]									=word
										return word


			def      UpperCAmelCase__						(				self					:  Optional[Any]       ,	snake_case__					:  Any ):
										lowerCamelCase_	:							int									=[]

										lowerCamelCase_	:							List[Any]									=re.findall(r"\S+\n?"       ,	__snake_case )

										for token in words:
																	split_tokens.extend(list(self.bpe(__snake_case ).split(" " ) ) )
										return split_tokens


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


			def      UpperCAmelCase__						(				self					:  int       ,	snake_case__					:  Dict ):
										return self.decoder.get(__snake_case       ,	self.unk_token )


			def      UpperCAmelCase__						(				self					:  Optional[int]       ,	snake_case__					:  Dict ):
										lowerCamelCase_	:							str									=" ".join(__snake_case ).replace("@@ "       ,	"" ).strip()
										return out_string


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

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

										lowerCamelCase_	:							Optional[int]									=0
										with open(__snake_case       ,	"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 snake_case__ : 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!" )
																															lowerCamelCase_	:							Any									=token_index
																								writer.write(" ".join(__snake_case ) + "\n" )
																								index += 1

										return vocab_file, merge_file

			# def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
			#     filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
			#     tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
			#     tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
			#     return ''.join(tokens_generated_so_far)











"""simple docstring"""



# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)







@flax.struct.dataclass
class        lowercase__      :
			_UpperCAmelCase		:CommonSchedulerState

			# setable values
			_UpperCAmelCase		:jnp.ndarray
			_UpperCAmelCase		:jnp.ndarray
			_UpperCAmelCase		:Optional[int]             =			None


			@classmethod
			def      UpperCAmelCase__						(				cls					:  int       ,	snake_case__					:  CommonSchedulerState       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  jnp.ndarray ):
										return cls(common=snake_case__       ,	init_noise_sigma=snake_case__       ,	timesteps=snake_case__ )







@dataclass
class        lowercase__      (							snake_case__       ):
			_UpperCAmelCase		:DDPMSchedulerState







class        lowercase__      (							snake_case__,       snake_case__       ):
			_UpperCAmelCase		:Any             =			[e.name for e in FlaxKarrasDiffusionSchedulers]

			_UpperCAmelCase		:jnp.dtype


			@property
			def      UpperCAmelCase__						(				self					:  Optional[Any] ):
										return True


			@register_to_config
			def __init__(				self					:  Optional[int]       ,	snake_case__					:  int = 1000       ,	snake_case__					:  float = 0.0_001       ,	snake_case__					:  float = 0.02       ,	snake_case__					:  str = "linear"       ,	snake_case__					:  Optional[jnp.ndarray] = None       ,	snake_case__					:  str = "fixed_small"       ,	snake_case__					:  bool = True       ,	snake_case__					:  str = "epsilon"       ,	snake_case__					:  jnp.dtype = jnp.floataa       ,	):
										lowerCamelCase_	:							str									=dtype


			def      UpperCAmelCase__						(				self					:  List[str]       ,	snake_case__					:  Optional[CommonSchedulerState] = None ):
										if common is None:
																	lowerCamelCase_	:							int									=CommonSchedulerState.create(self )

										# standard deviation of the initial noise distribution
										lowerCamelCase_	:							Optional[Any]									=jnp.array(1.0       ,	dtype=self.dtype )

										lowerCamelCase_	:							str									=jnp.arange(0       ,	self.config.num_train_timesteps ).round()[::-1]

										return DDPMSchedulerState.create(
										    common=snake_case__       ,	init_noise_sigma=snake_case__       ,	timesteps=snake_case__       ,	)


			def      UpperCAmelCase__						(				self					:  Optional[int]       ,	snake_case__					:  DDPMSchedulerState       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  Optional[int] = None ):
										return sample


			def      UpperCAmelCase__						(				self					:  Optional[Any]       ,	snake_case__					:  DDPMSchedulerState       ,	snake_case__					:  int       ,	snake_case__					:  Tuple = () ):
										lowerCamelCase_	:							Any									=self.config.num_train_timesteps // num_inference_steps
										# creates integer timesteps by multiplying by ratio
										# rounding to avoid issues when num_inference_step is power of 3
										lowerCamelCase_	:							List[str]									=(jnp.arange(0       ,	snake_case__ ) * step_ratio).round()[::-1]

										return state.replace(
										    num_inference_steps=snake_case__       ,	timesteps=snake_case__       ,	)


			def      UpperCAmelCase__						(				self					:  Union[str, Any]       ,	snake_case__					:  DDPMSchedulerState       ,	snake_case__					:  Union[str, Any]       ,	snake_case__					:  List[Any]=None       ,	snake_case__					:  Any=None ):
										lowerCamelCase_	:							List[str]									=state.common.alphas_cumprod[t]
										lowerCamelCase_	:							Union[str, Any]									=jnp.where(t > 0       ,	state.common.alphas_cumprod[t - 1]       ,	jnp.array(1.0       ,	dtype=self.dtype ) )

										# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
										# and sample from it to get previous sample
										# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
										lowerCamelCase_	:							Tuple									=(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

										if variance_type is None:
																	lowerCamelCase_	:							List[Any]									=self.config.variance_type

										# hacks - were probably added for training stability
										if variance_type == "fixed_small":
																	lowerCamelCase_	:							List[str]									=jnp.clip(snake_case__       ,	a_min=1E-20 )
										# for rl-diffuser https://arxiv.org/abs/2205.09991
										elif variance_type == "fixed_small_log":
																	lowerCamelCase_	:							Dict									=jnp.log(jnp.clip(snake_case__       ,	a_min=1E-20 ) )
										elif variance_type == "fixed_large":
																	lowerCamelCase_	:							Optional[Any]									=state.common.betas[t]
										elif variance_type == "fixed_large_log":
																	# Glide max_log
																	lowerCamelCase_	:							Any									=jnp.log(state.common.betas[t] )
										elif variance_type == "learned":
																	return predicted_variance
										elif variance_type == "learned_range":
																	lowerCamelCase_	:							List[str]									=variance
																	lowerCamelCase_	:							Optional[int]									=state.common.betas[t]
																	lowerCamelCase_	:							Dict									=(predicted_variance + 1) / 2
																	lowerCamelCase_	:							Dict									=frac * max_log + (1 - frac) * min_log

										return variance


			def      UpperCAmelCase__						(				self					:  int       ,	snake_case__					:  DDPMSchedulerState       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  int       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  Optional[jax.random.KeyArray] = None       ,	snake_case__					:  bool = True       ,	):
										lowerCamelCase_	:							Union[str, Any]									=timestep

										if key is None:
																	lowerCamelCase_	:							Dict									=jax.random.PRNGKey(0 )

										if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
																	lowerCamelCase_							,						lowerCamelCase_	:							Optional[Any]									=jnp.split(snake_case__       ,	sample.shape[1]       ,	axis=1 )
										else:
																	lowerCamelCase_	:							List[str]									=None

										# 1. compute alphas, betas
										lowerCamelCase_	:							Union[str, Any]									=state.common.alphas_cumprod[t]
										lowerCamelCase_	:							Dict									=jnp.where(t > 0       ,	state.common.alphas_cumprod[t - 1]       ,	jnp.array(1.0       ,	dtype=self.dtype ) )
										lowerCamelCase_	:							Any									=1 - alpha_prod_t
										lowerCamelCase_	:							List[str]									=1 - alpha_prod_t_prev

										# 2. compute predicted original sample from predicted noise also called
										# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
										if self.config.prediction_type == "epsilon":
																	lowerCamelCase_	:							int									=(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
										elif self.config.prediction_type == "sample":
																	lowerCamelCase_	:							List[Any]									=model_output
										elif self.config.prediction_type == "v_prediction":
																	lowerCamelCase_	:							Tuple									=(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
										else:
																	raise ValueError(
																	    F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` """
																	    " for the FlaxDDPMScheduler." )

										# 3. Clip "predicted x_0"
										if self.config.clip_sample:
																	lowerCamelCase_	:							List[Any]									=jnp.clip(snake_case__       ,	-1       ,	1 )

										# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
										# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
										lowerCamelCase_	:							int									=(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
										lowerCamelCase_	:							Optional[Any]									=state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

										# 5. Compute predicted previous sample µ_t
										# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
										lowerCamelCase_	:							Any									=pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

										# 6. Add noise
										def random_variance():
																	lowerCamelCase_	:							Union[str, Any]									=jax.random.split(snake_case__       ,	num=1 )
																	lowerCamelCase_	:							List[Any]									=jax.random.normal(snake_case__       ,	shape=model_output.shape       ,	dtype=self.dtype )
																	return (self._get_variance(snake_case__       ,	snake_case__       ,	predicted_variance=snake_case__ ) ** 0.5) * noise

										lowerCamelCase_	:							Tuple									=jnp.where(t > 0       ,	random_variance()       ,	jnp.zeros(model_output.shape       ,	dtype=self.dtype ) )

										lowerCamelCase_	:							str									=pred_prev_sample + variance

										if not return_dict:
																	return (pred_prev_sample, state)

										return FlaxDDPMSchedulerOutput(prev_sample=snake_case__       ,	state=snake_case__ )


			def      UpperCAmelCase__						(				self					:  Dict       ,	snake_case__					:  DDPMSchedulerState       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  jnp.ndarray       ,	):
										return add_noise_common(state.common       ,	snake_case__       ,	snake_case__       ,	snake_case__ )


			def      UpperCAmelCase__						(				self					:  int       ,	snake_case__					:  DDPMSchedulerState       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  jnp.ndarray       ,	):
										return get_velocity_common(state.common       ,	snake_case__       ,	snake_case__       ,	snake_case__ )


			def __len__(				self					:  Tuple ):
										return self.config.num_train_timesteps



