





'''simple docstring'''




# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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

import flax
import jax
import jax.numpy as jnp

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






@flax.struct.dataclass
class UpperCAmelCase				:
    _A			:      int     =							42

    # setable values
    _A			:      int     =							42
    _A			:      Optional[int]     =							42
    _A			:      Union[str, Any]     =							None



    @classmethod
    def        __lowerCamelCase    (	cls  ,      __A  ,      __A  ,      __A       ):
     return cls(common=__A  ,      init_noise_sigma=__A  ,      timesteps=__A       )






@dataclass
class UpperCAmelCase				(		__A				):
    _A			:      List[Any]     =							42






class UpperCAmelCase				(		__A       ,      __A				):
    _A			:      Dict     =							[e.name for e in FlaxKarrasDiffusionSchedulers]

    _A			:      Dict     =							42



    @property
    def        __lowerCamelCase    (	self       ):
     return True



    @register_to_config
    def __init__(	self  ,      __A = 1_000  ,      __A = 0.0_0_0_1  ,      __A = 0.0_2  ,      __A = "linear"  ,      __A = None  ,      __A = "fixed_small"  ,      __A = True  ,      __A = "epsilon"  ,      __A = jnp.floataa  ,      ):
     __UpperCAmelCase       = dtype



    def        __lowerCamelCase    (	self  ,      __A = None       ):
     if common is None:
      __UpperCAmelCase       = CommonSchedulerState.create(self       )

     # standard deviation of the initial noise distribution
     __UpperCAmelCase       = jnp.array(1.0  ,      dtype=self.dtype       )

     __UpperCAmelCase       = jnp.arange(0  ,      self.config.num_train_timesteps       ).round()[::-1]

     return DDPMSchedulerState.create(
         common=__A  ,      init_noise_sigma=__A  ,      timesteps=__A  ,      )



    def        __lowerCamelCase    (	self  ,      __A  ,      __A  ,      __A = None       ):
     return sample



    def        __lowerCamelCase    (	self  ,      __A  ,      __A  ,      __A = ()       ):
     __UpperCAmelCase       = self.config.num_train_timesteps // num_inference_steps
     # creates integer timesteps by multiplying by ratio
     # rounding to avoid issues when num_inference_step is power of 3
     __UpperCAmelCase       = (jnp.arange(0  ,      __A       ) * step_ratio).round()[::-1]

     return state.replace(
         num_inference_steps=__A  ,      timesteps=__A  ,      )



    def        __lowerCamelCase    (	self  ,      __A  ,      __A  ,      __A=None  ,      __A=None       ):
     __UpperCAmelCase       = state.common.alphas_cumprod[t]
     __UpperCAmelCase       = jnp.where(t > 0  ,      state.common.alphas_cumprod[t - 1]  ,      jnp.array(1.0  ,      dtype=self.dtype       )       )

     # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
     # and sample from it to get previous sample
     # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
     __UpperCAmelCase       = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

     if variance_type is None:
      __UpperCAmelCase       = self.config.variance_type

     # hacks - were probably added for training stability
     if variance_type == "fixed_small":
      __UpperCAmelCase       = jnp.clip(__A  ,      a_min=1E-20       )
     # for rl-diffuser https://arxiv.org/abs/2205.09991
     elif variance_type == "fixed_small_log":
      __UpperCAmelCase       = jnp.log(jnp.clip(__A  ,      a_min=1E-20       )       )
     elif variance_type == "fixed_large":
      __UpperCAmelCase       = state.common.betas[t]
     elif variance_type == "fixed_large_log":
      # Glide max_log
      __UpperCAmelCase       = jnp.log(state.common.betas[t]       )
     elif variance_type == "learned":
      return predicted_variance
     elif variance_type == "learned_range":
      __UpperCAmelCase       = variance
      __UpperCAmelCase       = state.common.betas[t]
      __UpperCAmelCase       = (predicted_variance + 1) / 2
      __UpperCAmelCase       = frac * max_log + (1 - frac) * min_log

     return variance



    def        __lowerCamelCase    (	self  ,      __A  ,      __A  ,      __A  ,      __A  ,      __A = None  ,      __A = True  ,      ):
     __UpperCAmelCase       = timestep

     if key is None:
      __UpperCAmelCase       = jax.random.PRNGKey(0       )

     if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
      __UpperCAmelCase       ,     __UpperCAmelCase       = jnp.split(__A  ,      sample.shape[1]  ,      axis=1       )
     else:
      __UpperCAmelCase       = None

     # 1. compute alphas, betas
     __UpperCAmelCase       = state.common.alphas_cumprod[t]
     __UpperCAmelCase       = jnp.where(t > 0  ,      state.common.alphas_cumprod[t - 1]  ,      jnp.array(1.0  ,      dtype=self.dtype       )       )
     __UpperCAmelCase       = 1 - alpha_prod_t
     __UpperCAmelCase       = 1 - alpha_prod_t_prev

     # 2. compute predicted original sample from predicted noise also called
     # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
     if self.config.prediction_type == "epsilon":
      __UpperCAmelCase       = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
     elif self.config.prediction_type == "sample":
      __UpperCAmelCase       = model_output
     elif self.config.prediction_type == "v_prediction":
      __UpperCAmelCase       = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
     else:
      raise ValueError(
          f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '
          ' for the FlaxDDPMScheduler.'       )

     # 3. Clip "predicted x_0"
     if self.config.clip_sample:
      __UpperCAmelCase       = jnp.clip(__A  ,      -1  ,      1       )

     # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
     # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
     __UpperCAmelCase       = (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
     __UpperCAmelCase       = state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

     # 5. Compute predicted previous sample µ_t
     # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
     __UpperCAmelCase       = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

     # 6. Add noise
     def random_variance():
      __UpperCAmelCase       = jax.random.split(__A  ,      num=1       )
      __UpperCAmelCase       = jax.random.normal(__A  ,      shape=model_output.shape  ,      dtype=self.dtype       )
      return (self._get_variance(__A  ,      __A  ,      predicted_variance=__A       ) ** 0.5) * noise

     __UpperCAmelCase       = jnp.where(t > 0  ,      random_variance()  ,      jnp.zeros(model_output.shape  ,      dtype=self.dtype       )       )

     __UpperCAmelCase       = pred_prev_sample + variance

     if not return_dict:
      return (pred_prev_sample, state)

     return FlaxDDPMSchedulerOutput(prev_sample=__A  ,      state=__A       )



    def        __lowerCamelCase    (	self  ,      __A  ,      __A  ,      __A  ,      __A  ,      ):
     return add_noise_common(state.common  ,      __A  ,      __A  ,      __A       )



    def        __lowerCamelCase    (	self  ,      __A  ,      __A  ,      __A  ,      __A  ,      ):
     return get_velocity_common(state.common  ,      __A  ,      __A  ,      __A       )




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









import comet  # From: unbabel-comet
import torch

import datasets


SCREAMING_SNAKE_CASE							    =							datasets.logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							    =							'\\n@inproceedings{rei-EtAl:2020:WMT,\n   author    = {Rei, Ricardo  and  Stewart, Craig  and  Farinha, Ana C  and  Lavie, Alon},\n   title     = {Unbabel\'s Participation in the WMT20 Metrics Shared Task},\n   booktitle      = {Proceedings of the Fifth Conference on Machine Translation},\n   month          = {November},\n   year           = {2020},\n   address        = {Online},\n   publisher      = {Association for Computational Linguistics},\n   pages     = {909--918},\n}\n@inproceedings{rei-etal-2020-comet,\n   title = "{COMET}: A Neural Framework for {MT} Evaluation",\n   author = "Rei, Ricardo  and\n      Stewart, Craig  and\n      Farinha, Ana C  and\n      Lavie, Alon",\n   booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",\n   month = nov,\n   year = "2020",\n   address = "Online",\n   publisher = "Association for Computational Linguistics",\n   url = "https://www.aclweb.org/anthology/2020.emnlp-main.213",\n   pages = "2685--2702",\n}\n'

SCREAMING_SNAKE_CASE							    =							'\\nCrosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA\'s or MQM).\nWith the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.\n\nSee the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.\n'

SCREAMING_SNAKE_CASE							    =							'\nCOMET score.\n\nArgs:\n\n`sources` (list of str): Source sentences\n`predictions` (list of str): candidate translations\n`references` (list of str): reference translations\n`cuda` (bool): If set to True, runs COMET using GPU\n`show_progress` (bool): Shows progress\n`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.\n\nReturns:\n    `samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.\n    `scores`: List of scores.\n\nExamples:\n\n    >>> comet_metric = datasets.load_metric(\'comet\')\n    >>> # comet_metric = load_metric(\'comet\', \'wmt20-comet-da\')  # you can also choose which model to use\n    >>> source = ["Dem Feuer konnte Einhalt geboten werden", "Schulen und Kindergärten wurden eröffnet."]\n    >>> hypothesis = ["The fire could be stopped", "Schools and kindergartens were open"]\n    >>> reference = ["They were able to control the fire.", "Schools and kindergartens opened"]\n    >>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)\n    >>> print([round(v, 2) for v in results["scores"]])\n    [0.19, 0.92]\n'


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


     """simple docstring"""


     def 	snake_case_  (	self    ):
        return datasets.MetricInfo(
            description=_DESCRIPTION  ,       citation=_CITATION  ,       homepage="""https://unbabel.github.io/COMET/html/index.html"""  ,       inputs_description=_KWARGS_DESCRIPTION  ,       features=datasets.Features(
                {
                    """sources""": datasets.Value("""string"""  ,       id="""sequence"""    ),
                    """predictions""": datasets.Value("""string"""  ,       id="""sequence"""    ),
                    """references""": datasets.Value("""string"""  ,       id="""sequence"""    ),
                }    )  ,       codebase_urls=["""https://github.com/Unbabel/COMET"""]  ,       reference_urls=[
                """https://github.com/Unbabel/COMET""",
                """https://www.aclweb.org/anthology/2020.emnlp-main.213/""",
                """http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6""",
            ]  ,       )


     def 	snake_case_  (	self  ,       __A    ):
        if self.config_name == "default":
           __a					=  comet.load_from_checkpoint(comet.download_model("""wmt20-comet-da"""    )    )
        else:
           __a					=  comet.load_from_checkpoint(comet.download_model(self.config_name    )    )


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A  ,       __A=None  ,       __A=False    ):
        if gpus is None:
           __a					=  1 if torch.cuda.is_available() else 0
        __a					=  {"""src""": sources, """mt""": predictions, """ref""": references}
        __a					=  [dict(zip(__A  ,       __A    )    ) for t in zip(*data.values()    )]
        __a	,    __a					=  self.scorer.predict(__A  ,       gpus=__A  ,       progress_bar=__A    )
        return {"mean_score": mean_score, "scores": scores}



