


'''simple docstring'''




import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


__snake_case    						="""scheduler_config.json"""

class 		UpperCAmelCase_      (  __lowercase		):
     lowerCamelCase				:	Optional[int]		=     1
     lowerCamelCase				:	List[str]		=     2
     lowerCamelCase				:	str		=     3
     lowerCamelCase				:	Any		=     4
     lowerCamelCase				:	Dict		=     5




@dataclass
class 		UpperCAmelCase_      (  __lowercase		):
     lowerCamelCase				:	jnp.ndarray




class 		UpperCAmelCase_      :
     lowerCamelCase				:	Any		=     SCHEDULER_CONFIG_NAME
     lowerCamelCase				:	str		=     ['''dtype''']
     lowerCamelCase				:	int		=     []
     lowerCamelCase				:	Dict		=     True





     @classmethod
     def 							__UpperCAmelCase      ( cls       :      Union[str, Any]    ,      UpperCAmelCase__       :      Dict[str, Any] = None    ,      UpperCAmelCase__       :      Optional[str] = None    ,      UpperCAmelCase__       :      int=False    ,      **UpperCAmelCase__       :      str    ,      )				->  List[Any]:
        lowerCAmelCase     ,  lowerCAmelCase     =			cls.load_config(
            pretrained_model_name_or_path=UpperCAmelCase__    ,      subfolder=UpperCAmelCase__    ,      return_unused_kwargs=UpperCAmelCase__    ,      **UpperCAmelCase__    ,      )
        lowerCAmelCase     ,  lowerCAmelCase     =			cls.from_config(UpperCAmelCase__    ,      return_unused_kwargs=UpperCAmelCase__    ,      **UpperCAmelCase__    )

        if hasattr(UpperCAmelCase__    ,      'create_state'    ) and getattr(UpperCAmelCase__    ,      'has_state'    ,      UpperCAmelCase__    ):
           lowerCAmelCase     =			scheduler.create_state()

        if return_unused_kwargs:
           return scheduler, state, unused_kwargs

        return scheduler, state





     def 							__UpperCAmelCase      ( self       :      Dict    ,      UpperCAmelCase__       :      Union[str, os.PathLike]    ,      UpperCAmelCase__       :      bool = False    ,      **UpperCAmelCase__       :      Optional[int]    )				->  List[Any]:
        self.save_config(save_directory=UpperCAmelCase__    ,      push_to_hub=UpperCAmelCase__    ,      **UpperCAmelCase__    )





     @property
     def 							__UpperCAmelCase      ( self       :      Optional[int]    )				->  Dict:
        return self._get_compatibles()





     @classmethod
     def 							__UpperCAmelCase      ( cls       :      List[str]    )				->  List[str]:
        lowerCAmelCase     =			list(set([cls.__name__] + cls._compatibles    )    )
        lowerCAmelCase     =			importlib.import_module(__name__.split('.'    )[0]    )
        lowerCAmelCase     =			[
            getattr(UpperCAmelCase__    ,      UpperCAmelCase__    ) for c in compatible_classes_str if hasattr(UpperCAmelCase__    ,      UpperCAmelCase__    )
        ]
        return compatible_classes





def 	a_					( lowerCamelCase	:     jnp.ndarray	, lowerCamelCase	:     Tuple[int]				):
   assert len(lowerCamelCase				) >= x.ndim
   return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(lowerCamelCase				) - x.ndim)				)	, lowerCamelCase				)





def 	a_					( lowerCamelCase	:     int	, lowerCamelCase	:     Dict=0.999	, lowerCamelCase	:     Optional[Any]=jnp.floataa				):

   def alpha_bar(lowerCamelCase	:     Dict				):
      return math.cos((time_step + 0.008) / 1.008 * math.pi / 2				) ** 2

   lowerCAmelCase     =			[]
   for i in range(lowerCamelCase				):
      lowerCAmelCase     =			i / num_diffusion_timesteps
      lowerCAmelCase     =			(i + 1) / num_diffusion_timesteps
      betas.append(min(1 - alpha_bar(lowerCamelCase				) / alpha_bar(lowerCamelCase				)	, lowerCamelCase				)				)
   return jnp.array(lowerCamelCase	, dtype=lowerCamelCase				)

@flax.struct.dataclass
class 		UpperCAmelCase_      :
     lowerCamelCase				:	jnp.ndarray
     lowerCamelCase				:	jnp.ndarray
     lowerCamelCase				:	jnp.ndarray





     @classmethod
     def 							__UpperCAmelCase      ( cls       :      Optional[Any]    ,      UpperCAmelCase__       :      Tuple    )				->  List[str]:
        lowerCAmelCase     =			scheduler.config

        if config.trained_betas is not None:
           lowerCAmelCase     =			jnp.asarray(config.trained_betas    ,      dtype=scheduler.dtype    )
        elif config.beta_schedule == "linear":
           lowerCAmelCase     =			jnp.linspace(config.beta_start    ,      config.beta_end    ,      config.num_train_timesteps    ,      dtype=scheduler.dtype    )
        elif config.beta_schedule == "scaled_linear":
           # this schedule is very specific to the latent diffusion model.
           lowerCAmelCase     =			(
               jnp.linspace(
                   config.beta_start**0.5    ,      config.beta_end**0.5    ,      config.num_train_timesteps    ,      dtype=scheduler.dtype    )
               ** 2
           )
        elif config.beta_schedule == "squaredcos_cap_v2":
           # Glide cosine schedule
           lowerCAmelCase     =			betas_for_alpha_bar(config.num_train_timesteps    ,      dtype=scheduler.dtype    )
        else:
           raise NotImplementedError(
               F'''beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}'''    )

        lowerCAmelCase     =			1.0 - betas

        lowerCAmelCase     =			jnp.cumprod(UpperCAmelCase__    ,      axis=0    )

        return cls(
            alphas=UpperCAmelCase__    ,      betas=UpperCAmelCase__    ,      alphas_cumprod=UpperCAmelCase__    ,      )





def 	a_					( lowerCamelCase	:     CommonSchedulerState	, lowerCamelCase	:     jnp.ndarray	, lowerCamelCase	:     jnp.ndarray	, lowerCamelCase	:     jnp.ndarray				):
   lowerCAmelCase     =			state.alphas_cumprod

   lowerCAmelCase     =			alphas_cumprod[timesteps] ** 0.5
   lowerCAmelCase     =			sqrt_alpha_prod.flatten()
   lowerCAmelCase     =			broadcast_to_shape_from_left(lowerCamelCase	, original_samples.shape				)

   lowerCAmelCase     =			(1 - alphas_cumprod[timesteps]) ** 0.5
   lowerCAmelCase     =			sqrt_one_minus_alpha_prod.flatten()
   lowerCAmelCase     =			broadcast_to_shape_from_left(lowerCamelCase	, original_samples.shape				)

   return sqrt_alpha_prod, sqrt_one_minus_alpha_prod





def 	a_					( lowerCamelCase	:     CommonSchedulerState	, lowerCamelCase	:     jnp.ndarray	, lowerCamelCase	:     jnp.ndarray	, lowerCamelCase	:     jnp.ndarray				):
   lowerCAmelCase     ,  lowerCAmelCase     =			get_sqrt_alpha_prod(lowerCamelCase	, lowerCamelCase	, lowerCamelCase	, lowerCamelCase				)
   lowerCAmelCase     =			sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
   return noisy_samples





def 	a_					( lowerCamelCase	:     CommonSchedulerState	, lowerCamelCase	:     jnp.ndarray	, lowerCamelCase	:     jnp.ndarray	, lowerCamelCase	:     jnp.ndarray				):
   lowerCAmelCase     ,  lowerCAmelCase     =			get_sqrt_alpha_prod(lowerCamelCase	, lowerCamelCase	, lowerCamelCase	, lowerCamelCase				)
   lowerCAmelCase     =			sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
   return velocity









'''simple docstring'''




import datasets

from .nmt_bleu import compute_bleu  # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py


__snake_case    						="""\
@INPROCEEDINGS{Papineni02bleu:a,
    author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},
    title = {BLEU: a Method for Automatic Evaluation of Machine Translation},
    booktitle = {},
    year = {2002},
    pages = {311--318}
}
@inproceedings{lin-och-2004-orange,
    title = \"{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation\",
    author = \"Lin, Chin-Yew  and
      Och, Franz Josef\",
    booktitle = \"{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics\",
    month = \"aug 23{--}aug 27\",
    year = \"2004\",
    address = \"Geneva, Switzerland\",
    publisher = \"COLING\",
    url = \"https://www.aclweb.org/anthology/C04-1072\",
    pages = \"501--507\",
}
"""

__snake_case    						="""\
BLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.
Quality is considered to be the correspondence between a machine's output and that of a human: \"the closer a machine translation is to a professional human translation,
the better it is\" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and
remains one of the most popular automated and inexpensive metrics.

Scores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.
Those scores are then averaged over the whole corpus to reach an estimate of the translation's overall quality. Intelligibility or grammatical correctness
are not taken into account[citation needed].

BLEU's output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1
representing more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the
reference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional
reference translations will increase the BLEU score.
"""

__snake_case    						="""
Computes BLEU score of translated segments against one or more references.
Args:
    predictions: list of translations to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
    max_order: Maximum n-gram order to use when computing BLEU score.
    smooth: Whether or not to apply Lin et al. 2004 smoothing.
Returns:
    'bleu': bleu score,
    'precisions': geometric mean of n-gram precisions,
    'brevity_penalty': brevity penalty,
    'length_ratio': ratio of lengths,
    'translation_length': translation_length,
    'reference_length': reference_length
Examples:

    >>> predictions = [
    ...     [\"hello\", \"there\", \"general\", \"kenobi\"],                             # tokenized prediction of the first sample
    ...     [\"foo\", \"bar\", \"foobar\"]                                             # tokenized prediction of the second sample
    ... ]
    >>> references = [
    ...     [[\"hello\", \"there\", \"general\", \"kenobi\"], [\"hello\", \"there\", \"!\"]],  # tokenized references for the first sample (2 references)
    ...     [[\"foo\", \"bar\", \"foobar\"]]                                           # tokenized references for the second sample (1 reference)
    ... ]
    >>> bleu = datasets.load_metric(\"bleu\")
    >>> results = bleu.compute(predictions=predictions, references=references)
    >>> print(results[\"bleu\"])
    1.0
"""

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       ,	_KWARGS_DESCRIPTION		)
class 		UpperCAmelCase_      (  datasets.Metric		):





     def 							__UpperCAmelCase      ( self       :      Tuple    )				->  int:
        return datasets.MetricInfo(
            description=_DESCRIPTION    ,      citation=_CITATION    ,      inputs_description=_KWARGS_DESCRIPTION    ,      features=datasets.Features(
                {
                    'predictions': datasets.Sequence(datasets.Value('string'    ,      id='token'    )    ,      id='sequence'    ),
                    'references': datasets.Sequence(
                        datasets.Sequence(datasets.Value('string'    ,      id='token'    )    ,      id='sequence'    )    ,      id='references'    ),
                }    )    ,      codebase_urls=['https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py']    ,      reference_urls=[
                'https://en.wikipedia.org/wiki/BLEU',
                'https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213',
            ]    ,      )





     def 							__UpperCAmelCase      ( self       :      Optional[int]    ,      UpperCAmelCase__       :      Tuple    ,      UpperCAmelCase__       :      Any    ,      UpperCAmelCase__       :      Union[str, Any]=4    ,      UpperCAmelCase__       :      Optional[int]=False    )				->  int:
        lowerCAmelCase     =			compute_bleu(
            reference_corpus=UpperCAmelCase__    ,      translation_corpus=UpperCAmelCase__    ,      max_order=UpperCAmelCase__    ,      smooth=UpperCAmelCase__    )
        ((lowerCAmelCase)     ,  (lowerCAmelCase)     ,  (lowerCAmelCase)     ,  (lowerCAmelCase)     ,  (lowerCAmelCase)     ,  (lowerCAmelCase))     =			score
        return {
            "bleu": bleu,
            "precisions": precisions,
            "brevity_penalty": bp,
            "length_ratio": ratio,
            "translation_length": translation_length,
            "reference_length": reference_length,
        }





