
'''simple docstring'''





import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
						from transformers.pipelines.conversational import Conversation

UpperCAmelCase            = logging.get_logger(__name__)

UpperCAmelCase            = {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
}

UpperCAmelCase            = {
    "vocab_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/vocab.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/vocab.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/vocab.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/vocab.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/vocab.json",
    },
    "merges_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/merges.txt",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/merges.txt",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/merges.txt",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/merges.txt",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/merges.txt",
    },
}

UpperCAmelCase            = {
    "gpt2": 1_024,
    "gpt2-medium": 1_024,
    "gpt2-large": 1_024,
    "gpt2-xl": 1_024,
    "distilgpt2": 1_024,
}

@lru_cache()
def   A			(							):
						snake_case							:  Dict			=     (
						    list(range(ord('''!'''							)     ,				ord('''~'''							) + 1							)							) + list(range(ord('''¡'''							)     ,				ord('''¬'''							) + 1							)							) + list(range(ord('''®'''							)     ,				ord('''ÿ'''							) + 1							)							)
						)
						snake_case							:  Tuple			=     bs[:]
						snake_case							:  int			=     0
						for b in range(2**8							):
												if b not in bs:
																		bs.append(A_							)
																		cs.append(2**8 + n							)
																		n += 1
						snake_case							:  Union[str, Any]			=     [chr(A_							) for n in cs]
						return dict(zip(A_     ,				A_							)							)

def   A			(							A_				:       Any							):
						snake_case							:  List[str]			=     set()
						snake_case							:  int			=     word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)							)
												snake_case							:  List[str]			=     char
						return pairs





class     a (					__magic_name__     ):
				_snake_case		     =			VOCAB_FILES_NAMES
				_snake_case		     =			PRETRAINED_VOCAB_FILES_MAP
				_snake_case		     =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_snake_case		     =			['''input_ids''', '''attention_mask''']




				def __init__(				self						:					Any,     SCREAMING_SNAKE_CASE_						:					Optional[Any],     SCREAMING_SNAKE_CASE_						:					str,     SCREAMING_SNAKE_CASE_						:					Tuple="replace",     SCREAMING_SNAKE_CASE_						:					Dict="<|endoftext|>",     SCREAMING_SNAKE_CASE_						:					str="<|endoftext|>",     SCREAMING_SNAKE_CASE_						:					Any="<|endoftext|>",     SCREAMING_SNAKE_CASE_						:					List[str]=None,     SCREAMING_SNAKE_CASE_						:					Union[str, Any]=False,     SCREAMING_SNAKE_CASE_						:					Tuple=False,     **SCREAMING_SNAKE_CASE_						:					str,     ):
										snake_case							:  Dict			=     AddedToken(SCREAMING_SNAKE_CASE_,     lstrip=SCREAMING_SNAKE_CASE_,     rstrip=SCREAMING_SNAKE_CASE_   ) if isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   ) else bos_token
										snake_case							:  Union[str, Any]			=     AddedToken(SCREAMING_SNAKE_CASE_,     lstrip=SCREAMING_SNAKE_CASE_,     rstrip=SCREAMING_SNAKE_CASE_   ) if isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   ) else eos_token
										snake_case							:  List[Any]			=     AddedToken(SCREAMING_SNAKE_CASE_,     lstrip=SCREAMING_SNAKE_CASE_,     rstrip=SCREAMING_SNAKE_CASE_   ) if isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   ) else unk_token
										snake_case							:  Tuple			=     AddedToken(SCREAMING_SNAKE_CASE_,     lstrip=SCREAMING_SNAKE_CASE_,     rstrip=SCREAMING_SNAKE_CASE_   ) if isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   ) else pad_token
										super().__init__(
										    errors=SCREAMING_SNAKE_CASE_,     unk_token=SCREAMING_SNAKE_CASE_,     bos_token=SCREAMING_SNAKE_CASE_,     eos_token=SCREAMING_SNAKE_CASE_,     pad_token=SCREAMING_SNAKE_CASE_,     add_prefix_space=SCREAMING_SNAKE_CASE_,     add_bos_token=SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_,     )
										snake_case							:  int			=     add_bos_token

										with open(SCREAMING_SNAKE_CASE_,     encoding='''utf-8'''   ) as vocab_handle:
																snake_case							:  Optional[int]			=     json.load(SCREAMING_SNAKE_CASE_   )
										snake_case							:  int			=     {v: k for k, v in self.encoder.items()}
										snake_case							:  Tuple			=     errors  # how to handle errors in decoding
										snake_case							:  List[str]			=     bytes_to_unicode()
										snake_case							:  Optional[int]			=     {v: k for k, v in self.byte_encoder.items()}
										with open(SCREAMING_SNAKE_CASE_,     encoding='''utf-8'''   ) as merges_handle:
																snake_case							:  str			=     merges_handle.read().split('''\n'''   )[1:-1]
										snake_case							:  Optional[Any]			=     [tuple(merge.split()   ) for merge in bpe_merges]
										snake_case							:  Tuple			=     dict(zip(SCREAMING_SNAKE_CASE_,     range(len(SCREAMING_SNAKE_CASE_   )   )   )   )
										snake_case							:  str			=     {}
										snake_case							:  Dict			=     add_prefix_space

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




				@property
				def 						__snake_case				(				self						:					Tuple   ):
										return len(self.encoder   )




				def 						__snake_case				(				self						:					List[Any]   ):
										return dict(self.encoder,     **self.added_tokens_encoder   )




				def 						__snake_case				(				self						:					int,     SCREAMING_SNAKE_CASE_						:					Tuple   ):
										if token in self.cache:
																return self.cache[token]
										snake_case							:  List[Any]			=     tuple(SCREAMING_SNAKE_CASE_   )
										snake_case							:  Optional[Any]			=     get_pairs(SCREAMING_SNAKE_CASE_   )

										if not pairs:
																return token

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

																						if word[i] == first and i < len(SCREAMING_SNAKE_CASE_   ) - 1 and word[i + 1] == second:
																												new_word.append(first + second   )
																												i += 2
																						else:
																												new_word.append(word[i]   )
																												i += 1
																snake_case							:  Tuple			=     tuple(SCREAMING_SNAKE_CASE_   )
																snake_case							:  Tuple			=     new_word
																if len(SCREAMING_SNAKE_CASE_   ) == 1:
																						break
																else:
																						snake_case							:  List[str]			=     get_pairs(SCREAMING_SNAKE_CASE_   )
										snake_case							:  Union[str, Any]			=     ''' '''.join(SCREAMING_SNAKE_CASE_   )
										snake_case							:  List[Any]			=     word
										return word




				def 						__snake_case				(				self						:					Union[str, Any],     SCREAMING_SNAKE_CASE_						:					Optional[int],     SCREAMING_SNAKE_CASE_						:					Optional[Any]=None   ):
										if self.add_bos_token:
																snake_case							:  int			=     [self.bos_token_id]
										else:
																snake_case							:  Dict			=     []

										snake_case							:  List[str]			=     bos_token_ids + token_ids_a

										if token_ids_a is None:
																return output

										return output + bos_token_ids + token_ids_a




				def 						__snake_case				(				self						:					Dict,     SCREAMING_SNAKE_CASE_						:					List[int],     SCREAMING_SNAKE_CASE_						:					Optional[List[int]] = None,     SCREAMING_SNAKE_CASE_						:					bool = False   ):
										if already_has_special_tokens:
																return super().get_special_tokens_mask(
																    token_ids_a=SCREAMING_SNAKE_CASE_,     token_ids_a=SCREAMING_SNAKE_CASE_,     already_has_special_tokens=SCREAMING_SNAKE_CASE_   )

										if not self.add_bos_token:
																return super().get_special_tokens_mask(
																    token_ids_a=SCREAMING_SNAKE_CASE_,     token_ids_a=SCREAMING_SNAKE_CASE_,     already_has_special_tokens=SCREAMING_SNAKE_CASE_   )

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




				def 						__snake_case				(				self						:					str,     SCREAMING_SNAKE_CASE_						:					List[Any]   ):
										snake_case							:  Optional[int]			=     []
										for token in re.findall(self.pat,     SCREAMING_SNAKE_CASE_   ):
																snake_case							:  Dict			=     ''''''.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(SCREAMING_SNAKE_CASE_   ).split(''' '''   )   )
										return bpe_tokens




				def 						__snake_case				(				self						:					Optional[Any],     SCREAMING_SNAKE_CASE_						:					List[str]   ):
										return self.encoder.get(SCREAMING_SNAKE_CASE_,     self.encoder.get(self.unk_token   )   )




				def 						__snake_case				(				self						:					List[str],     SCREAMING_SNAKE_CASE_						:					Tuple   ):
										return self.decoder.get(SCREAMING_SNAKE_CASE_   )




				def 						__snake_case				(				self						:					List[Any],     SCREAMING_SNAKE_CASE_						:					Dict   ):
										snake_case							:  Union[str, Any]			=     ''''''.join(SCREAMING_SNAKE_CASE_   )
										snake_case							:  Any			=     bytearray([self.byte_decoder[c] for c in text]   ).decode('''utf-8''',     errors=self.errors   )
										return text




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

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

										snake_case							:  Optional[int]			=     0
										with open(SCREAMING_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 SCREAMING_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!'''   )
																												snake_case							:  Union[str, Any]			=     token_index
																						writer.write(''' '''.join(SCREAMING_SNAKE_CASE_   ) + '''\n'''   )
																						index += 1

										return vocab_file, merge_file




				def 						__snake_case				(				self						:					Optional[int],     SCREAMING_SNAKE_CASE_						:					Tuple,     SCREAMING_SNAKE_CASE_						:					Dict=False,     **SCREAMING_SNAKE_CASE_						:					Optional[int]   ):
										snake_case							:  Any			=     kwargs.pop('''add_prefix_space''',     self.add_prefix_space   )
										if is_split_into_words or add_prefix_space:
																snake_case							:  Dict			=     ''' ''' + text
										return (text, kwargs)




				def 						__snake_case				(				self						:					Union[str, Any],     SCREAMING_SNAKE_CASE_						:					"Conversation"   ):
										snake_case							:  List[str]			=     []
										for is_user, text in conversation.iter_texts():
																input_ids.extend(self.encode(SCREAMING_SNAKE_CASE_,     add_special_tokens=SCREAMING_SNAKE_CASE_   ) + [self.eos_token_id]   )
										if len(SCREAMING_SNAKE_CASE_   ) > self.model_max_length:
																snake_case							:  str			=     input_ids[-self.model_max_length :]
										return input_ids








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

import flax
import jax.numpy as jnp
from scipy import integrate

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

@flax.struct.dataclass
class SCREAMING_SNAKE_CASE_			:





					"""simple docstring"""


					A__								=     42

					# setable values
					A__								=     42
					A__								=     42
					A__								=     42
					A__								=     None

					# running values
					A__								=     None





					@classmethod
					def        __magic_name__			(			cls						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						):
										return cls(common=_lowerCAmelCase						,	init_noise_sigma=_lowerCAmelCase						,	timesteps=_lowerCAmelCase						,	sigmas=_lowerCAmelCase						)

@dataclass
class SCREAMING_SNAKE_CASE_			(       lowercase_ ):





					"""simple docstring"""


					A__								=     42

class SCREAMING_SNAKE_CASE_			(       lowercase_				,							lowercase_ ):





					"""simple docstring"""


					A__								=     [e.name for e in FlaxKarrasDiffusionSchedulers]

					A__								=     42





					@property
					def        __magic_name__			(			self						):
										return True





					@register_to_config
					def __init__(			self						,	_lowerCAmelCase = 1000						,	_lowerCAmelCase = 0.0_001						,	_lowerCAmelCase = 0.02						,	_lowerCAmelCase = "linear"						,	_lowerCAmelCase = None						,	_lowerCAmelCase = "epsilon"						,	_lowerCAmelCase = jnp.floataa						,	):
										lowerCamelCase__      =							dtype





					def        __magic_name__			(			self						,	_lowerCAmelCase = None						):
										if common is None:
															lowerCamelCase__      =							CommonSchedulerState.create(self						)

										lowerCamelCase__      =							jnp.arange(0						,	self.config.num_train_timesteps						).round()[::-1]
										lowerCamelCase__      =							((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

										# standard deviation of the initial noise distribution
										lowerCamelCase__      =							sigmas.max()

										return LMSDiscreteSchedulerState.create(
										    common=_lowerCAmelCase						,	init_noise_sigma=_lowerCAmelCase						,	timesteps=_lowerCAmelCase						,	sigmas=_lowerCAmelCase						,	)





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						):
										((lowerCamelCase__)     ,		)      =							jnp.where(state.timesteps == timestep						,	size=1						)
										lowerCamelCase__      =							step_index[0]

										lowerCamelCase__      =							state.sigmas[step_index]
										lowerCamelCase__      =							sample / ((sigma**2 + 1) ** 0.5)
										return sample





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						):

										def lms_derivative(_lowerCAmelCase						):
															lowerCamelCase__      =							1.0
															for k in range(_lowerCAmelCase						):
																				if current_order == k:
																									continue
																				prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
															return prod

										lowerCamelCase__      =							integrate.quad(_lowerCAmelCase						,	state.sigmas[t]						,	state.sigmas[t + 1]						,	epsrel=1E-4						)[0]

										return integrated_coeff





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase = ()						):
										lowerCamelCase__      =							jnp.linspace(self.config.num_train_timesteps - 1						,	0						,	_lowerCAmelCase						,	dtype=self.dtype						)

										lowerCamelCase__      =							jnp.floor(_lowerCAmelCase						).astype(jnp.intaa						)
										lowerCamelCase__      =							jnp.ceil(_lowerCAmelCase						).astype(jnp.intaa						)

										lowerCamelCase__      =							jnp.mod(_lowerCAmelCase						,	1.0						)

										lowerCamelCase__      =							((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
										lowerCamelCase__      =							(1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
										lowerCamelCase__      =							jnp.concatenate([sigmas, jnp.array([0.0]						,	dtype=self.dtype						)]						)

										lowerCamelCase__      =							timesteps.astype(jnp.intaa						)

										# initial running values
										lowerCamelCase__      =							jnp.zeros((0,) + shape						,	dtype=self.dtype						)

										return state.replace(
										    timesteps=_lowerCAmelCase						,	sigmas=_lowerCAmelCase						,	num_inference_steps=_lowerCAmelCase						,	derivatives=_lowerCAmelCase						,	)





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase = 4						,	_lowerCAmelCase = True						,	):
										if state.num_inference_steps is None:
															raise ValueError(
															    "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"						)

										lowerCamelCase__      =							state.sigmas[timestep]

										# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
										if self.config.prediction_type == "epsilon":
															lowerCamelCase__      =							sample - sigma * model_output
										elif self.config.prediction_type == "v_prediction":
															# * c_out + input * c_skip
															lowerCamelCase__      =							model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
										else:
															raise ValueError(
															    F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"						)

										# 2. Convert to an ODE derivative
										lowerCamelCase__      =							(sample - pred_original_sample) / sigma
										lowerCamelCase__      =							state.replace(derivatives=jnp.append(state.derivatives						,	_lowerCAmelCase						)						)
										if len(state.derivatives						) > order:
															lowerCamelCase__      =							state.replace(derivatives=jnp.delete(state.derivatives						,	0						)						)

										# 3. Compute linear multistep coefficients
										lowerCamelCase__      =							min(timestep + 1						,	_lowerCAmelCase						)
										lowerCamelCase__      =							[self.get_lms_coefficient(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						) for curr_order in range(_lowerCAmelCase						)]

										# 4. Compute previous sample based on the derivatives path
										lowerCamelCase__      =							sample + sum(
										    coeff * derivative for coeff, derivative in zip(_lowerCAmelCase						,	reversed(state.derivatives						)						)						)

										if not return_dict:
															return (prev_sample, state)

										return FlaxLMSSchedulerOutput(prev_sample=_lowerCAmelCase						,	state=_lowerCAmelCase						)





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	):
										lowerCamelCase__      =							state.sigmas[timesteps].flatten()
										lowerCamelCase__      =							broadcast_to_shape_from_left(_lowerCAmelCase						,	noise.shape						)

										lowerCamelCase__      =							original_samples + noise * sigma

										return noisy_samples






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

