



'''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


a__      :				Dict   	=			"scheduler_config.json"



class       UpperCAmelCase__(							lowerCamelCase	):

   '''simple docstring'''




   A    :  Tuple       				=    1
   A    :  Union[str, Any]       				=    2
   A    :  str       				=    3
   A    :  Union[str, Any]       				=    4
   A    :  List[Any]       				=    5



@dataclass
class       UpperCAmelCase__(							lowerCamelCase	):

   '''simple docstring'''




   A    :  jnp.ndarray



class       UpperCAmelCase__:

   '''simple docstring'''




   A    :  Dict       				=    SCHEDULER_CONFIG_NAME
   A    :  int       				=    ["dtype"]
   A    :  int       				=    []
   A    :  Optional[Any]       				=    True


   @classmethod
   def 			UpperCAmelCase		(						cls  :			str			,				lowerCAmelCase  :			Dict[str, Any] = None			,				lowerCAmelCase  :			Optional[str] = None			,				lowerCAmelCase  :			List[str]=False			,				**lowerCAmelCase  :			List[str]			,				)	->				Optional[int]:




         """simple docstring"""

         lowercase__,	lowercase__     =  cls.load_config(
             pretrained_model_name_or_path=lowerCAmelCase			,				subfolder=lowerCAmelCase			,				return_unused_kwargs=lowerCAmelCase			,				**lowerCAmelCase			,				)
         lowercase__,	lowercase__     =  cls.from_config(lowerCAmelCase			,				return_unused_kwargs=lowerCAmelCase			,				**lowerCAmelCase)

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

         if return_unused_kwargs:
               return scheduler, state, unused_kwargs

         return scheduler, state


   def 			UpperCAmelCase		(						self  :			Optional[int]			,				lowerCAmelCase  :			Union[str, os.PathLike]			,				lowerCAmelCase  :			bool = False			,				**lowerCAmelCase  :			int)	->				str:




         """simple docstring"""

         self.save_config(save_directory=lowerCAmelCase			,				push_to_hub=lowerCAmelCase			,				**lowerCAmelCase)


   @property
   def 			UpperCAmelCase		(						self  :			Tuple)	->				Tuple:




         """simple docstring"""

         return self._get_compatibles()



   @classmethod
   def 			UpperCAmelCase		(						cls  :			Optional[Any])	->				Optional[Any]:




         """simple docstring"""

         lowercase__     =  list(set([cls.__name__] + cls._compatibles))
         lowercase__     =  importlib.import_module(__name__.split('.')[0])
         lowercase__     =  [
             getattr(lowerCAmelCase			,				lowerCAmelCase) for c in compatible_classes_str if hasattr(lowerCAmelCase			,				lowerCAmelCase)
         ]
         return compatible_classes

def   _lowerCAmelCase   (						A__						,  A__	):
      assert len(A__	) >= x.ndim
      return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(A__	) - x.ndim)	)						,  A__	)

def   _lowerCAmelCase   (						A__						,  A__=0.9_99						,  A__=jnp.floataa	):

      def alpha_bar(A__	):
            return math.cos((time_step + 0.0_08) / 1.0_08 * math.pi / 2	) ** 2

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



@flax.struct.dataclass
class       UpperCAmelCase__:

   '''simple docstring'''




   A    :  jnp.ndarray
   A    :  jnp.ndarray
   A    :  jnp.ndarray


   @classmethod
   def 			UpperCAmelCase		(						cls  :			Tuple			,				lowerCAmelCase  :			str)	->				str:




         """simple docstring"""

         lowercase__     =  scheduler.config

         if config.trained_betas is not None:
               lowercase__     =  jnp.asarray(config.trained_betas			,				dtype=scheduler.dtype)
         elif config.beta_schedule == "linear":
               lowercase__     =  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.
               lowercase__     =  (
                   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
               lowercase__     =  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__}''')

         lowercase__     =  1.0 - betas

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

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

def   _lowerCAmelCase   (						A__						,  A__						,  A__						,  A__	):
      lowercase__     =  state.alphas_cumprod

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

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

      return sqrt_alpha_prod, sqrt_one_minus_alpha_prod

def   _lowerCAmelCase   (						A__						,  A__						,  A__						,  A__	):
      lowercase__,	lowercase__     =  get_sqrt_alpha_prod(A__						,  A__						,  A__						,  A__	)
      lowercase__     =  sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
      return noisy_samples

def   _lowerCAmelCase   (						A__						,  A__						,  A__						,  A__	):
      lowercase__,	lowercase__     =  get_sqrt_alpha_prod(A__						,  A__						,  A__						,  A__	)
      lowercase__     =  sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
      return velocity





from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


a__      :				int   	=			"\\n@misc{wu2016googles,\n      title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n      author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey\n              and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin\n              Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto\n              Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and\n              Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes\n              and Jeffrey Dean},\n      year={2016},\n      eprint={1609.08144},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}\n"

a__      :				Optional[Any]   	=			"\\nThe BLEU score has some undesirable properties when used for single\nsentences, as it was designed to be a corpus measure. We therefore\nuse a slightly different score for our RL experiments which we call\nthe 'GLEU score'. For the GLEU score, we record all sub-sequences of\n1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then\ncompute a recall, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the target (ground truth) sequence,\nand a precision, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the generated output sequence. Then\nGLEU score is simply the minimum of recall and precision. This GLEU\nscore's range is always between 0 (no matches) and 1 (all match) and\nit is symmetrical when switching output and target. According to\nour experiments, GLEU score correlates quite well with the BLEU\nmetric on a corpus level but does not have its drawbacks for our per\nsentence reward objective.\n"

a__      :				Tuple   	=			"\\nComputes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.\nInstead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching\ntokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.\n\nArgs:\n    predictions (list of str): list of translations to score.\n        Each translation should be tokenized into a list of tokens.\n    references (list of list of str): list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.\n    max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.\n\nReturns:\n    'google_bleu': google_bleu score\n\nExamples:\n    Example 1:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.44\n\n    Example 2:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n        ...          'heed', 'the', 'cat', 'commands']\n        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n        ...          'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.61\n\n    Example 3:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n        ...          'heed', 'the', 'cat', 'commands']\n        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n        ...          'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.53\n\n    Example 4:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n        ...          'heed', 'the', 'cat', 'commands']\n        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n        ...          'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.4\n"



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

   '''simple docstring'''




   def 			UpperCAmelCase		(						self  :			List[Any])	->				MetricInfo:




         """simple docstring"""

         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'),
                 })			,				)



   def 			UpperCAmelCase		(						self  :			int			,				lowerCAmelCase  :			List[List[List[str]]]			,				lowerCAmelCase  :			List[List[str]]			,				lowerCAmelCase  :			int = 1			,				lowerCAmelCase  :			int = 4			,				)	->				Dict[str, float]:




         """simple docstring"""

         return {
             "google_bleu": gleu_score.corpus_gleu(
                 list_of_references=lowerCAmelCase			,				hypotheses=lowerCAmelCase			,				min_len=lowerCAmelCase			,				max_len=lowerCAmelCase)
         }
