





# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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

import torch

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






@dataclass
class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   a		:							torch.FloatTensor
   a		:							torch.FloatTensor






class 					lowercase__ (			__lowerCamelCase    ,       __lowerCamelCase       ):

   '''simple docstring'''


   a		:							List[Any]     	=	1




   @register_to_config
   def __init__(					self,	__magic_name__ = 2000,	__magic_name__ = 0.15,	__magic_name__ = 0.01,	__magic_name__ = 1348.0,	__magic_name__ = 1E-5,	__magic_name__ = 1,	)				->   int:






       """simple docstring"""
       # standard deviation of the initial noise distribution
       UpperCamelCase__							:       int      =   sigma_max

       # setable values
       UpperCamelCase__							:       Optional[int]      =   None

       self.set_sigmas(__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = None							)				->   torch.FloatTensor:






       """simple docstring"""
       return sample




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = None,	__magic_name__ = None							)				->   int:






       """simple docstring"""
       UpperCamelCase__							:       str      =   sampling_eps if sampling_eps is not None else self.config.sampling_eps

       UpperCamelCase__							:       List[Any]      =   torch.linspace(1,	__magic_name__,	__magic_name__,	device=__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = None,	__magic_name__ = None,	__magic_name__ = None							)				->   Dict:






       """simple docstring"""
       UpperCamelCase__							:       Tuple      =   sigma_min if sigma_min is not None else self.config.sigma_min
       UpperCamelCase__							:       str      =   sigma_max if sigma_max is not None else self.config.sigma_max
       UpperCamelCase__							:       Optional[int]      =   sampling_eps if sampling_eps is not None else self.config.sampling_eps
       if self.timesteps is None:
           self.set_timesteps(__magic_name__,	__magic_name__							)

       UpperCamelCase__							:       str      =   sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
       UpperCamelCase__							:       Optional[Any]      =   torch.exp(torch.linspace(math.log(__magic_name__							),	math.log(__magic_name__							),	__magic_name__							)							)
       UpperCamelCase__							:       Any      =   torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__							)				->   str:






       """simple docstring"""
       return torch.where(
           timesteps == 0,	torch.zeros_like(t.to(timesteps.device							)							),	self.discrete_sigmas[timesteps - 1].to(timesteps.device							),	)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__ = None,	__magic_name__ = True,	)				->   Union[SdeVeOutput, Tuple]:






       """simple docstring"""
       if self.timesteps is None:
           raise ValueError(
               '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''							)

       UpperCamelCase__							:       Any      =   timestep * torch.ones(
           sample.shape[0],	device=sample.device							)  # torch.repeat_interleave(timestep, sample.shape[0])
       UpperCamelCase__							:       Any      =   (timestep * (len(self.timesteps							) - 1)).long()

       # mps requires indices to be in the same device, so we use cpu as is the default with cuda
       UpperCamelCase__							:       List[Any]      =   timesteps.to(self.discrete_sigmas.device							)

       UpperCamelCase__							:       str      =   self.discrete_sigmas[timesteps].to(sample.device							)
       UpperCamelCase__							:       List[Any]      =   self.get_adjacent_sigma(__magic_name__,	__magic_name__							).to(sample.device							)
       UpperCamelCase__							:       Optional[Any]      =   torch.zeros_like(__magic_name__							)
       UpperCamelCase__							:       str      =   (sigma**2 - adjacent_sigma**2) ** 0.5

       # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
       # also equation 47 shows the analog from SDE models to ancestral sampling methods
       UpperCamelCase__							:       Union[str, Any]      =   diffusion.flatten()
       while len(diffusion.shape							) < len(sample.shape							):
           UpperCamelCase__							:       Any      =   diffusion.unsqueeze(-1							)
       UpperCamelCase__							:       str      =   drift - diffusion**2 * model_output

       #  equation 6: sample noise for the diffusion term of
       UpperCamelCase__							:       Union[str, Any]      =   randn_tensor(
           sample.shape,	layout=sample.layout,	generator=__magic_name__,	device=sample.device,	dtype=sample.dtype							)
       UpperCamelCase__							:       Optional[Any]      =   sample - drift  # subtract because `dt` is a small negative timestep
       # TODO is the variable diffusion the correct scaling term for the noise?
       UpperCamelCase__							:       str      =   prev_sample_mean + diffusion * noise  # add impact of diffusion field g

       if not return_dict:
           return (prev_sample, prev_sample_mean)

       return SdeVeOutput(prev_sample=__magic_name__,	prev_sample_mean=__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__ = None,	__magic_name__ = True,	)				->   Union[SchedulerOutput, Tuple]:






       """simple docstring"""
       if self.timesteps is None:
           raise ValueError(
               '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''							)

       # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
       # sample noise for correction
       UpperCamelCase__							:       List[str]      =   randn_tensor(sample.shape,	layout=sample.layout,	generator=__magic_name__							).to(sample.device							)

       # compute step size from the model_output, the noise, and the snr
       UpperCamelCase__							:       str      =   torch.norm(model_output.reshape(model_output.shape[0],	-1							),	dim=-1							).mean()
       UpperCamelCase__							:       Tuple      =   torch.norm(noise.reshape(noise.shape[0],	-1							),	dim=-1							).mean()
       UpperCamelCase__							:       Union[str, Any]      =   (self.config.snr * noise_norm / grad_norm) ** 2 * 2
       UpperCamelCase__							:       Optional[Any]      =   step_size * torch.ones(sample.shape[0]							).to(sample.device							)
       # self.repeat_scalar(step_size, sample.shape[0])

       # compute corrected sample: model_output term and noise term
       UpperCamelCase__							:       Tuple      =   step_size.flatten()
       while len(step_size.shape							) < len(sample.shape							):
           UpperCamelCase__							:       int      =   step_size.unsqueeze(-1							)
       UpperCamelCase__							:       int      =   sample + step_size * model_output
       UpperCamelCase__							:       List[Any]      =   prev_sample_mean + ((step_size * 2) ** 0.5) * noise

       if not return_dict:
           return (prev_sample,)

       return SchedulerOutput(prev_sample=__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__,	)				->   torch.FloatTensor:






       """simple docstring"""
       # Make sure sigmas and timesteps have the same device and dtype as original_samples
       UpperCamelCase__							:       Any      =   timesteps.to(original_samples.device							)
       UpperCamelCase__							:       List[str]      =   self.discrete_sigmas.to(original_samples.device							)[timesteps]
       UpperCamelCase__							:       Any      =   (
           noise * sigmas[:, None, None, None]
           if noise is not None
           else torch.randn_like(__magic_name__							) * sigmas[:, None, None, None]
       )
       UpperCamelCase__							:       int      =   noise + original_samples
       return noisy_samples




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






       """simple docstring"""
       return self.config.num_train_timesteps








import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
  if is_torch_available():
    import torch
  if is_tf_available():
    import tensorflow as tf

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_codegen import CodeGenTokenizer


UpperCAmelCase_  							=				logging.get_logger(__name__)

UpperCAmelCase_  							=				{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

UpperCAmelCase_  							=				{
    'vocab_file': {
        'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json',
    },
    'merges_file': {
        'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'Salesforce/codegen-350M-mono': (
            'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json'
        ),
    },
}

UpperCAmelCase_  							=				{
    'Salesforce/codegen-350M-mono': 2048,
}






class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   a		:							Union[str, Any]     	=	VOCAB_FILES_NAMES
   a		:							List[str]     	=	PRETRAINED_VOCAB_FILES_MAP
   a		:							Optional[int]     	=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   a		:							List[Any]     	=	["input_ids", "attention_mask"]
   a		:							Any     	=	CodeGenTokenizer




   def __init__(					self,	__magic_name__=None,	__magic_name__=None,	__magic_name__=None,	__magic_name__="<|endoftext|>",	__magic_name__="<|endoftext|>",	__magic_name__="<|endoftext|>",	__magic_name__=False,	**__magic_name__,	)				->   Optional[int]:






       """simple docstring"""
       super().__init__(
           __magic_name__,	__magic_name__,	tokenizer_file=__magic_name__,	unk_token=__magic_name__,	bos_token=__magic_name__,	eos_token=__magic_name__,	add_prefix_space=__magic_name__,	**__magic_name__,	)

       if kwargs.pop('''add_bos_token''',	__magic_name__							):
           UpperCamelCase__							:       List[Any]      =   kwargs.pop('''name_or_path''',	''''''							)
           raise ValueError(
               '''Currenty GPT2\'s fast tokenizer does NOT support adding a BOS token.'''
               '''Instead you should use GPT2\'s slow tokenizer class `CodeGenTokenizer` as follows: \n'''
               f"`CodeGenTokenizer.from_pretrained('{model_id}')`\nor\n"
               f"`AutoTokenizer.from_pretrained('{model_id}', use_fast=False)`\n"
               '''This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005.'''
               ''' so that the fast tokenizer works correctly.'''							)

       UpperCamelCase__							:       Dict      =   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()							)
       if pre_tok_state.get('''add_prefix_space''',	__magic_name__							) != add_prefix_space:
           UpperCamelCase__							:       List[str]      =   getattr(__magic_name__,	pre_tok_state.pop('''type'''							)							)
           UpperCamelCase__							:       int      =   add_prefix_space
           UpperCamelCase__							:       List[str]      =   pre_tok_class(**__magic_name__							)

       UpperCamelCase__							:       List[Any]      =   add_prefix_space




   def    UpperCamelCase__						(					self,	*__magic_name__,	**__magic_name__							)				->   BatchEncoding:






       """simple docstring"""
       UpperCamelCase__							:       List[Any]      =   kwargs.get('''is_split_into_words''',	__magic_name__							)
       assert self.add_prefix_space or not is_split_into_words, (
           f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
           "to use it with pretokenized inputs."
       )

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




   def    UpperCamelCase__						(					self,	*__magic_name__,	**__magic_name__							)				->   BatchEncoding:






       """simple docstring"""
       UpperCamelCase__							:       Dict      =   kwargs.get('''is_split_into_words''',	__magic_name__							)

       assert self.add_prefix_space or not is_split_into_words, (
           f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
           "to use it with pretokenized inputs."
       )

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




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = None							)				->   Tuple[str]:






       """simple docstring"""
       UpperCamelCase__							:       List[Any]      =   self._tokenizer.model.save(__magic_name__,	name=__magic_name__							)
       return tuple(__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = False,	__magic_name__ = None,	__magic_name__ = None,	**__magic_name__,	)				->   str:






       """simple docstring"""
       UpperCamelCase__							:       Dict      =   super().decode(
           token_ids=__magic_name__,	skip_special_tokens=__magic_name__,	clean_up_tokenization_spaces=__magic_name__,	**__magic_name__,	)

       if truncate_before_pattern is not None and len(__magic_name__							) > 0:
           UpperCamelCase__							:       str      =   self.truncate(__magic_name__,	__magic_name__							)

       return decoded_text




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__							)				->   List[str]:






       """simple docstring"""
       def find_re(__magic_name__,	__magic_name__,	__magic_name__							):
           UpperCamelCase__							:       Dict      =   pattern.search(__magic_name__,	__magic_name__							)
           return m.start() if m else -1

       UpperCamelCase__							:       Union[str, Any]      =   [re.compile(__magic_name__,	re.MULTILINE							) for pattern in truncate_before_pattern]

       UpperCamelCase__							:       List[Any]      =   list(re.finditer('''^print''',	__magic_name__,	re.MULTILINE							)							)

       if len(__magic_name__							) > 1:
           UpperCamelCase__							:       Tuple      =   completion[: prints[1].start()]

       UpperCamelCase__							:       Optional[int]      =   list(re.finditer('''^def''',	__magic_name__,	re.MULTILINE							)							)

       if len(__magic_name__							) > 1:
           UpperCamelCase__							:       Optional[Any]      =   completion[: defs[1].start()]

       UpperCamelCase__							:       str      =   0

       UpperCamelCase__							:       Tuple      =   [
           pos for pos in [find_re(__magic_name__,	__magic_name__,	__magic_name__							) for terminal in terminals] if pos != -1
       ]

       if len(__magic_name__							) > 0:
           return completion[: min(__magic_name__							)]
       else:
           return completion

