

import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_=0.999			,						SCREAMING_SNAKE_CASE_="cosine"			,						)->						List[str]:



    """simple docstring"""






    if alpha_transform_type == "cosine":

        def alpha_bar_fn(SCREAMING_SNAKE_CASE_			):
            return math.cos((t + 0.008) / 1.008 * math.pi / 2			) ** 2

    elif alpha_transform_type == "exp":

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

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

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






class 						__magic_name__					(							snake_case			,      snake_case				):
     UpperCamelCase_	:str										=      [e.name for e in KarrasDiffusionSchedulers]
     UpperCamelCase_	:str										=      2






     @register_to_config
     def __init__(				self		,						_lowercase = 1_000		,						_lowercase = 0.00_085		,						_lowercase = 0.012		,						_lowercase = "linear"		,						_lowercase = None		,						_lowercase = "epsilon"		,						_lowercase = False		,						_lowercase = False		,						_lowercase = 1.0		,						_lowercase = "linspace"		,						_lowercase = 0		,						)->     List[Any]:
         if trained_betas is not None:
             UpperCamelCase_			  =						torch.tensor(_lowercase		,						dtype=torch.floataa	)
         elif beta_schedule == "linear":
             UpperCamelCase_			  =						torch.linspace(_lowercase		,						_lowercase		,						_lowercase		,						dtype=torch.floataa	)
         elif beta_schedule == "scaled_linear":
             # this schedule is very specific to the latent diffusion model.
             UpperCamelCase_			  =						(
                 torch.linspace(beta_start**0.5		,						beta_end**0.5		,						_lowercase		,						dtype=torch.floataa	) ** 2
             )
         elif beta_schedule == "squaredcos_cap_v2":
             # Glide cosine schedule
             UpperCamelCase_			  =						betas_for_alpha_bar(_lowercase		,						alpha_transform_type="cosine"	)
         elif beta_schedule == "exp":
             UpperCamelCase_			  =						betas_for_alpha_bar(_lowercase		,						alpha_transform_type="exp"	)
         else:
             raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"	)

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

         #  set all values
         self.set_timesteps(_lowercase		,						_lowercase		,						_lowercase	)
         UpperCamelCase_			  =						use_karras_sigmas






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase=None	)->     Optional[int]:
         if schedule_timesteps is None:
             UpperCamelCase_			  =						self.timesteps

         UpperCamelCase_			  =						(schedule_timesteps == timestep).nonzero()

         # The sigma index that is taken for the **very** first `step`
         # is always the second index (or the last index if there is only 1)
         # This way we can ensure we don't accidentally skip a sigma in
         # case we start in the middle of the denoising schedule (e.g. for image-to-image)
         if len(self._index_counter	) == 0:
             UpperCamelCase_			  =						1 if len(_lowercase	) > 1 else 0
         else:
             UpperCamelCase_			  =						timestep.cpu().item() if torch.is_tensor(_lowercase	) else timestep
             UpperCamelCase_			  =						self._index_counter[timestep_int]

         return indices[pos].item()






     @property
     def 	UpperCAmelCase_				(				self	)->     Optional[Any]:
         # standard deviation of the initial noise distribution
         if self.config.timestep_spacing in ["linspace", "trailing"]:
             return self.sigmas.max()

         return (self.sigmas.max() ** 2 + 1) ** 0.5






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase		,						)->     torch.FloatTensor:
         UpperCamelCase_			  =						self.index_for_timestep(_lowercase	)

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






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase = None		,						_lowercase = None		,						)->     Optional[Any]:
         UpperCamelCase_			  =						num_inference_steps

         UpperCamelCase_			  =						num_train_timesteps or self.config.num_train_timesteps

         # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
         if self.config.timestep_spacing == "linspace":
             UpperCamelCase_			  =						np.linspace(0		,						num_train_timesteps - 1		,						_lowercase		,						dtype=_lowercase	)[::-1].copy()
         elif self.config.timestep_spacing == "leading":
             UpperCamelCase_			  =						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
             UpperCamelCase_			  =						(np.arange(0		,						_lowercase	) * step_ratio).round()[::-1].copy().astype(_lowercase	)
             timesteps += self.config.steps_offset
         elif self.config.timestep_spacing == "trailing":
             UpperCamelCase_			  =						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
             UpperCamelCase_			  =						(np.arange(_lowercase		,						0		,						-step_ratio	)).round().copy().astype(_lowercase	)
             timesteps -= 1
         else:
             raise ValueError(
                 F"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."	)

         UpperCamelCase_			  =						np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
         UpperCamelCase_			  =						np.log(_lowercase	)
         UpperCamelCase_			  =						np.interp(_lowercase		,						np.arange(0		,						len(_lowercase	)	)		,						_lowercase	)

         if self.config.use_karras_sigmas:
             UpperCamelCase_			  =						self._convert_to_karras(in_sigmas=_lowercase		,						num_inference_steps=self.num_inference_steps	)
             UpperCamelCase_			  =						np.array([self._sigma_to_t(_lowercase		,						_lowercase	) for sigma in sigmas]	)

         UpperCamelCase_			  =						np.concatenate([sigmas, [0.0]]	).astype(np.floataa	)
         UpperCamelCase_			  =						torch.from_numpy(_lowercase	).to(device=_lowercase	)
         UpperCamelCase_			  =						torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2	), sigmas[-1:]]	)

         UpperCamelCase_			  =						torch.from_numpy(_lowercase	)
         UpperCamelCase_			  =						torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2	)]	)

         if str(_lowercase	).startswith("mps"	):
             # mps does not support float64
             UpperCamelCase_			  =						timesteps.to(_lowercase		,						dtype=torch.floataa	)
         else:
             UpperCamelCase_			  =						timesteps.to(device=_lowercase	)

         # empty dt and derivative
         UpperCamelCase_			  =						None
         UpperCamelCase_			  =						None

         # for exp beta schedules, such as the one for `pipeline_shap_e.py`
         # we need an index counter
         UpperCamelCase_			  =						defaultdict(_lowercase	)






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase	)->     List[str]:
         # get log sigma
         UpperCamelCase_			  =						np.log(_lowercase	)

         # get distribution
         UpperCamelCase_			  =						log_sigma - log_sigmas[:, np.newaxis]

         # get sigmas range
         UpperCamelCase_			  =						np.cumsum((dists >= 0)		,						axis=0	).argmax(axis=0	).clip(max=log_sigmas.shape[0] - 2	)
         UpperCamelCase_			  =						low_idx + 1

         UpperCamelCase_			  =						log_sigmas[low_idx]
         UpperCamelCase_			  =						log_sigmas[high_idx]

         # interpolate sigmas
         UpperCamelCase_			  =						(low - log_sigma) / (low - high)
         UpperCamelCase_			  =						np.clip(_lowercase		,						0		,						1	)

         # transform interpolation to time range
         UpperCamelCase_			  =						(1 - w) * low_idx + w * high_idx
         UpperCamelCase_			  =						t.reshape(sigma.shape	)
         return t






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase	)->     torch.FloatTensor:
         UpperCamelCase_			  =						in_sigmas[-1].item()
         UpperCamelCase_			  =						in_sigmas[0].item()

         UpperCamelCase_			  =						7.0  # 7.0 is the value used in the paper
         UpperCamelCase_			  =						np.linspace(0		,						1		,						_lowercase	)
         UpperCamelCase_			  =						sigma_min ** (1 / rho)
         UpperCamelCase_			  =						sigma_max ** (1 / rho)
         UpperCamelCase_			  =						(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
         return sigmas






     @property
     def 	UpperCAmelCase_				(				self	)->     Optional[Any]:
         return self.dt is None






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase		,						_lowercase		,						_lowercase = True		,						)->     Union[SchedulerOutput, Tuple]:
         UpperCamelCase_			  =						self.index_for_timestep(_lowercase	)

         # advance index counter by 1
         UpperCamelCase_			  =						timestep.cpu().item() if torch.is_tensor(_lowercase	) else timestep
         self._index_counter[timestep_int] += 1

         if self.state_in_first_order:
             UpperCamelCase_			  =						self.sigmas[step_index]
             UpperCamelCase_			  =						self.sigmas[step_index + 1]
         else:
             # 2nd order / Heun's method
             UpperCamelCase_			  =						self.sigmas[step_index - 1]
             UpperCamelCase_			  =						self.sigmas[step_index]

         # currently only gamma=0 is supported. This usually works best anyways.
         # We can support gamma in the future but then need to scale the timestep before
         # passing it to the model which requires a change in API
         UpperCamelCase_			  =						0
         UpperCamelCase_			  =						sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

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

         if self.config.clip_sample:
             UpperCamelCase_			  =						pred_original_sample.clamp(
                 -self.config.clip_sample_range		,						self.config.clip_sample_range	)

         if self.state_in_first_order:
             # 2. Convert to an ODE derivative for 1st order
             UpperCamelCase_			  =						(sample - pred_original_sample) / sigma_hat
             # 3. delta timestep
             UpperCamelCase_			  =						sigma_next - sigma_hat

             # store for 2nd order step
             UpperCamelCase_			  =						derivative
             UpperCamelCase_			  =						dt
             UpperCamelCase_			  =						sample
         else:
             # 2. 2nd order / Heun's method
             UpperCamelCase_			  =						(sample - pred_original_sample) / sigma_next
             UpperCamelCase_			  =						(self.prev_derivative + derivative) / 2

             # 3. take prev timestep & sample
             UpperCamelCase_			  =						self.dt
             UpperCamelCase_			  =						self.sample

             # free dt and derivative
             # Note, this puts the scheduler in "first order mode"
             UpperCamelCase_			  =						None
             UpperCamelCase_			  =						None
             UpperCamelCase_			  =						None

         UpperCamelCase_			  =						sample + derivative * dt

         if not return_dict:
             return (prev_sample,)

         return SchedulerOutput(prev_sample=_lowercase	)






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase		,						_lowercase		,						)->     torch.FloatTensor:
         # Make sure sigmas and timesteps have the same device and dtype as original_samples
         UpperCamelCase_			  =						self.sigmas.to(device=original_samples.device		,						dtype=original_samples.dtype	)
         if original_samples.device.type == "mps" and torch.is_floating_point(_lowercase	):
             # mps does not support float64
             UpperCamelCase_			  =						self.timesteps.to(original_samples.device		,						dtype=torch.floataa	)
             UpperCamelCase_			  =						timesteps.to(original_samples.device		,						dtype=torch.floataa	)
         else:
             UpperCamelCase_			  =						self.timesteps.to(original_samples.device	)
             UpperCamelCase_			  =						timesteps.to(original_samples.device	)

         UpperCamelCase_			  =						[self.index_for_timestep(_lowercase		,						_lowercase	) for t in timesteps]

         UpperCamelCase_			  =						sigmas[step_indices].flatten()
         while len(sigma.shape	) < len(original_samples.shape	):
             UpperCamelCase_			  =						sigma.unsqueeze(-1	)

         UpperCamelCase_			  =						original_samples + noise * sigma
         return noisy_samples






     def __len__(				self	)->     Any:
         return self.config.num_train_timesteps



import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_barthez import BarthezTokenizer
else:
 SCREAMING_SNAKE_CASE		:Optional[Any]						=     None

SCREAMING_SNAKE_CASE		:List[str]						=     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE		:Optional[Any]						=     {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE		:int						=     {
    """vocab_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json"""
        ),
    },
}

SCREAMING_SNAKE_CASE		:List[str]						=     {
    """moussaKam/mbarthez""": 1024,
    """moussaKam/barthez""": 1024,
    """moussaKam/barthez-orangesum-title""": 1024,
}

SCREAMING_SNAKE_CASE		:Optional[Any]						=     """▁"""






class 						__magic_name__					(							snake_case				):
     UpperCamelCase_	:Dict										=      VOCAB_FILES_NAMES
     UpperCamelCase_	:Optional[int]										=      PRETRAINED_VOCAB_FILES_MAP
     UpperCamelCase_	:Any										=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCamelCase_	:Any										=      ["""input_ids""", """attention_mask"""]
     UpperCamelCase_	:List[Any]										=      BarthezTokenizer






     def __init__(				self		,						_lowercase=None		,						_lowercase=None		,						_lowercase="<s>"		,						_lowercase="</s>"		,						_lowercase="</s>"		,						_lowercase="<s>"		,						_lowercase="<unk>"		,						_lowercase="<pad>"		,						_lowercase="<mask>"		,						**_lowercase		,						)->     Optional[int]:
         # Mask token behave like a normal word, i.e. include the space before it
         UpperCamelCase_			  =						AddedToken(_lowercase		,						lstrip=_lowercase		,						rstrip=_lowercase	) if isinstance(_lowercase		,						_lowercase	) else mask_token

         super().__init__(
             _lowercase		,						tokenizer_file=_lowercase		,						bos_token=_lowercase		,						eos_token=_lowercase		,						unk_token=_lowercase		,						sep_token=_lowercase		,						cls_token=_lowercase		,						pad_token=_lowercase		,						mask_token=_lowercase		,						**_lowercase		,						)

         UpperCamelCase_			  =						vocab_file
         UpperCamelCase_			  =						False if not self.vocab_file else True






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase = None	)->     List[int]:

         if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         UpperCamelCase_			  =						[self.cls_token_id]
         UpperCamelCase_			  =						[self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase = None	)->     List[int]:
         UpperCamelCase_			  =						[self.sep_token_id]
         UpperCamelCase_			  =						[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 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase = None	)->     Tuple[str]:
         if not self.can_save_slow_tokenizer:
             raise ValueError(
                 "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                 "tokenizer."	)

         if not os.path.isdir(_lowercase	):
             logger.error(F"Vocabulary path ({save_directory}) should be a directory"	)
             return
         UpperCamelCase_			  =						os.path.join(
             _lowercase		,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]	)

         if os.path.abspath(self.vocab_file	) != os.path.abspath(_lowercase	):
             copyfile(self.vocab_file		,						_lowercase	)

         return (out_vocab_file,)
