






import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch

import numpy as np
import pytest
from absl.testing import parameterized

import datasets
from datasets import load_metric

from .utils import for_all_test_methods, local, slow


# mark all tests as integration
lowerCamelCase     :							str    =pytest.mark.integration


lowerCamelCase     :							List[Any]    ={'''comet'''}
lowerCamelCase     :							int    =importlib.util.find_spec('''fairseq''') is not None

lowerCamelCase     :							Any    ={'''code_eval'''}
lowerCamelCase     :							Tuple    =os.name == '''nt'''

lowerCamelCase     :							str    ={'''bertscore''', '''frugalscore''', '''perplexity'''}
lowerCamelCase     :							Dict    =importlib.util.find_spec('''transformers''') is not None







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     List[str]:
     @wraps(__lowerCAmelCase      )
     def wrapper(self ,   __lowerCAmelCase      ):
          if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
               self.skipTest("\"test requires Fairseq\""      )
          else:
               test_case(self ,   __lowerCAmelCase      )

     return wrapper







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     Any:
     @wraps(__lowerCAmelCase      )
     def wrapper(self ,   __lowerCAmelCase      ):
          if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
               self.skipTest("\"test requires transformers\""      )
          else:
               test_case(self ,   __lowerCAmelCase      )

     return wrapper







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     str:
     @wraps(__lowerCAmelCase      )
     def wrapper(self ,   __lowerCAmelCase      ):
          if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
               self.skipTest("\"test not supported on Windows\""      )
          else:
               test_case(self ,   __lowerCAmelCase      )

     return wrapper







def 		SCREAMING_SNAKE_CASE							( )			->     Dict:
     UpperCamelCase__      :       List[Any]	    =  [metric_dir.split(os.sep      )[-2] for metric_dir in glob.glob("./metrics/*/"      )]
     return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"]  # gleu is unfinished





@parameterized.named_parameters(get_local_metric_names()	)
@for_all_test_methods(
    A__	,       A__	,       A__	)
@local
class       __a  (   parameterized.TestCase	):
 _lowerCAmelCase	:	str				=			{}
 _lowerCAmelCase	:	Dict				=			None







 @pytest.mark.filterwarnings("ignore:metric_module_factory is deprecated:FutureWarning"						)
 @pytest.mark.filterwarnings("ignore:load_metric is deprecated:FutureWarning"						)
 def  __lowercase (		self :       int							,       SCREAMING_SNAKE_CASE :       List[str]						):


      '''simple docstring'''






      UpperCamelCase__      :       Union[str, Any]	    =  "[...]"
      UpperCamelCase__      :       List[Any]	    =  importlib.import_module(
          datasets.load.metric_module_factory(os.path.join("metrics"							,       SCREAMING_SNAKE_CASE						)						).module_path						)
      UpperCamelCase__      :       str	    =  datasets.load.import_main_class(metric_module.__name__							,       dataset=SCREAMING_SNAKE_CASE						)
      # check parameters
      UpperCamelCase__      :       Any	    =  inspect.signature(metric._compute						).parameters
      self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values()						)						)  # no **kwargs
      # run doctest
      with self.patch_intensive_calls(SCREAMING_SNAKE_CASE							,       metric_module.__name__						):
           with self.use_local_metrics():
                try:
                     UpperCamelCase__      :       Optional[int]	    =  doctest.testmod(SCREAMING_SNAKE_CASE							,       verbose=SCREAMING_SNAKE_CASE							,       raise_on_error=SCREAMING_SNAKE_CASE						)
                except doctest.UnexpectedException as e:
                     raise e.exc_info[1]  # raise the exception that doctest caught
      self.assertEqual(results.failed							,       0						)
      self.assertGreater(results.attempted							,       1						)







 @slow
 def  __lowercase (		self :       str							,       SCREAMING_SNAKE_CASE :       Optional[int]						):


      '''simple docstring'''






      UpperCamelCase__      :       str	    =  "[...]"
      UpperCamelCase__      :       Union[str, Any]	    =  importlib.import_module(
          datasets.load.metric_module_factory(os.path.join("metrics"							,       SCREAMING_SNAKE_CASE						)						).module_path						)
      # run doctest
      with self.use_local_metrics():
           UpperCamelCase__      :       Optional[Any]	    =  doctest.testmod(SCREAMING_SNAKE_CASE							,       verbose=SCREAMING_SNAKE_CASE							,       raise_on_error=SCREAMING_SNAKE_CASE						)
      self.assertEqual(results.failed							,       0						)
      self.assertGreater(results.attempted							,       1						)







 @contextmanager
 def  __lowercase (		self :       Optional[int]							,       SCREAMING_SNAKE_CASE :       str							,       SCREAMING_SNAKE_CASE :       int						):


      '''simple docstring'''






      if metric_name in self.INTENSIVE_CALLS_PATCHER:
           with self.INTENSIVE_CALLS_PATCHER[metric_name](SCREAMING_SNAKE_CASE						):
                yield
      else:
           yield







 @contextmanager
 def  __lowercase (		self :       int						):


      '''simple docstring'''






      def load_local_metric(SCREAMING_SNAKE_CASE :       Dict							,       *SCREAMING_SNAKE_CASE :       int							,       **SCREAMING_SNAKE_CASE :       Dict						):
           return load_metric(os.path.join("metrics"							,       SCREAMING_SNAKE_CASE						)							,       *SCREAMING_SNAKE_CASE							,       **SCREAMING_SNAKE_CASE						)

      with patch("datasets.load_metric"						) as mock_load_metric:
           UpperCamelCase__      :       str	    =  load_local_metric
           yield







 @classmethod
 def  __lowercase (		cls :       Optional[int]							,       SCREAMING_SNAKE_CASE :       Optional[Any]						):


      '''simple docstring'''






      def wrapper(SCREAMING_SNAKE_CASE :       Any						):
           UpperCamelCase__      :       Dict	    =  contextmanager(SCREAMING_SNAKE_CASE						)
           UpperCamelCase__      :       Dict	    =  patcher
           return patcher

      return wrapper







@LocalMetricTest.register_intensive_calls_patcher("bleurt"      )
def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     Tuple:
     import tensorflow.compat.va as tf
     from bleurt.score import Predictor

     tf.flags.DEFINE_string("sv" ,   "" ,   ""      )  # handle pytest cli flags





     class       __a  (   A__	):







      def  __lowercase (		self :       str							,       SCREAMING_SNAKE_CASE :       List[Any]						):


           '''simple docstring'''






           assert len(input_dict["input_ids"]						) == 2
           return np.array([1.0_3, 1.0_4]						)

    # mock predict_fn which is supposed to do a forward pass with a bleurt model
     with patch("bleurt.score._create_predictor"      ) as mock_create_predictor:
          UpperCamelCase__      :       Union[str, Any]	    =  MockedPredictor()
          yield







@LocalMetricTest.register_intensive_calls_patcher("bertscore"      )
def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     Optional[Any]:
     import torch

     def bert_cos_score_idf(__lowerCAmelCase ,   __lowerCAmelCase ,   *__lowerCAmelCase ,   **__lowerCAmelCase      ):
          return torch.tensor([[1.0, 1.0, 1.0]] * len(__lowerCAmelCase      )      )

     # mock get_model which is supposed to do download a bert model
     # mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
     with patch("bert_score.scorer.get_model"      ), patch(
         "bert_score.scorer.bert_cos_score_idf"      ) as mock_bert_cos_score_idf:
          UpperCamelCase__      :       int	    =  bert_cos_score_idf
          yield







@LocalMetricTest.register_intensive_calls_patcher("comet"      )
def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     int:
     def load_from_checkpoint(__lowerCAmelCase      ):





          class       __a  :







           def  __lowercase (		self :       str							,       SCREAMING_SNAKE_CASE :       str							,       *SCREAMING_SNAKE_CASE :       Union[str, Any]							,       **SCREAMING_SNAKE_CASE :       Optional[int]						):


                '''simple docstring'''






                assert len(SCREAMING_SNAKE_CASE						) == 2
                UpperCamelCase__      :       Any	    =  [0.1_9, 0.9_2]
                return scores, sum(SCREAMING_SNAKE_CASE						) / len(SCREAMING_SNAKE_CASE						)







          return Model()

     # mock load_from_checkpoint which is supposed to do download a bert model
     # mock load_from_checkpoint which is supposed to do download a bert model
     with patch("comet.download_model"      ) as mock_download_model:
          UpperCamelCase__      :       Union[str, Any]	    =  None
          with patch("comet.load_from_checkpoint"      ) as mock_load_from_checkpoint:
               UpperCamelCase__      :       Any	    =  load_from_checkpoint
               yield







def 		SCREAMING_SNAKE_CASE							( )			->     str:
     UpperCamelCase__      :       List[str]	    =  load_metric(os.path.join("metrics" ,   "seqeval"      )      )
     UpperCamelCase__      :       Dict	    =  "ERROR"
     UpperCamelCase__      :       Dict	    =  f'Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}'
     with pytest.raises(__lowerCAmelCase ,   match=re.escape(__lowerCAmelCase      )      ):
          metric.compute(predictions=[] ,   references=[] ,   scheme=__lowerCAmelCase      )







# 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       __a  :
 _lowerCAmelCase	:	CommonSchedulerState

 # setable values
 _lowerCAmelCase	:	jnp.ndarray
 _lowerCAmelCase	:	jnp.ndarray
 _lowerCAmelCase	:	Optional[int]				=			None







 @classmethod
 def  __lowercase (		cls :       Optional[Any]							,       SCREAMING_SNAKE_CASE :       CommonSchedulerState							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       SCREAMING_SNAKE_CASE :       jnp.ndarray						):


      '''simple docstring'''






      return cls(common=SCREAMING_SNAKE_CASE							,       init_noise_sigma=SCREAMING_SNAKE_CASE							,       timesteps=SCREAMING_SNAKE_CASE						)







@dataclass
class       __a  (   A__	):
 _lowerCAmelCase	:	DDPMSchedulerState







class       __a  (   A__	,       A__	):
 _lowerCAmelCase	:	Tuple				=			[e.name for e in FlaxKarrasDiffusionSchedulers]

 _lowerCAmelCase	:	jnp.dtype







 @property
 def  __lowercase (		self :       List[str]						):


      '''simple docstring'''






      return True







 @register_to_config
 def __init__(		self :       Dict							,       SCREAMING_SNAKE_CASE :       int = 10_00							,       SCREAMING_SNAKE_CASE :       float = 0.0_0_0_1							,       SCREAMING_SNAKE_CASE :       float = 0.0_2							,       SCREAMING_SNAKE_CASE :       str = "linear"							,       SCREAMING_SNAKE_CASE :       Optional[jnp.ndarray] = None							,       SCREAMING_SNAKE_CASE :       str = "fixed_small"							,       SCREAMING_SNAKE_CASE :       bool = True							,       SCREAMING_SNAKE_CASE :       str = "epsilon"							,       SCREAMING_SNAKE_CASE :       jnp.dtype = jnp.floataa							,       ):


      '''simple docstring'''






      UpperCamelCase__      :       int	    =  dtype







 def  __lowercase (		self :       List[Any]							,       SCREAMING_SNAKE_CASE :       Optional[CommonSchedulerState] = None						):


      '''simple docstring'''






      if common is None:
           UpperCamelCase__      :       Tuple	    =  CommonSchedulerState.create(self						)

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

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

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







 def  __lowercase (		self :       Tuple							,       SCREAMING_SNAKE_CASE :       DDPMSchedulerState							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       SCREAMING_SNAKE_CASE :       Optional[int] = None						):


      '''simple docstring'''






      return sample







 def  __lowercase (		self :       Tuple							,       SCREAMING_SNAKE_CASE :       DDPMSchedulerState							,       SCREAMING_SNAKE_CASE :       int							,       SCREAMING_SNAKE_CASE :       Tuple = ()						):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[Any]	    =  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__      :       Optional[int]	    =  (jnp.arange(0							,       SCREAMING_SNAKE_CASE						) * step_ratio).round()[::-1]

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







 def  __lowercase (		self :       str							,       SCREAMING_SNAKE_CASE :       DDPMSchedulerState							,       SCREAMING_SNAKE_CASE :       List[str]							,       SCREAMING_SNAKE_CASE :       Dict=None							,       SCREAMING_SNAKE_CASE :       Optional[Any]=None						):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[int]	    =  state.common.alphas_cumprod[t]
      UpperCamelCase__      :       Tuple	    =  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__      :       int	    =  (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

      if variance_type is None:
           UpperCamelCase__      :       List[Any]	    =  self.config.variance_type

      # hacks - were probably added for training stability
      if variance_type == "fixed_small":
           UpperCamelCase__      :       Union[str, Any]	    =  jnp.clip(SCREAMING_SNAKE_CASE							,       a_min=1e-2_0						)
      # for rl-diffuser https://arxiv.org/abs/2205.09991
      elif variance_type == "fixed_small_log":
           UpperCamelCase__      :       Optional[Any]	    =  jnp.log(jnp.clip(SCREAMING_SNAKE_CASE							,       a_min=1e-2_0						)						)
      elif variance_type == "fixed_large":
           UpperCamelCase__      :       Dict	    =  state.common.betas[t]
      elif variance_type == "fixed_large_log":
           # Glide max_log
           UpperCamelCase__      :       List[str]	    =  jnp.log(state.common.betas[t]						)
      elif variance_type == "learned":
           return predicted_variance
      elif variance_type == "learned_range":
           UpperCamelCase__      :       Tuple	    =  variance
           UpperCamelCase__      :       int	    =  state.common.betas[t]
           UpperCamelCase__      :       Union[str, Any]	    =  (predicted_variance + 1) / 2
           UpperCamelCase__      :       List[str]	    =  frac * max_log + (1 - frac) * min_log

      return variance







 def  __lowercase (		self :       Optional[Any]							,       SCREAMING_SNAKE_CASE :       DDPMSchedulerState							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       SCREAMING_SNAKE_CASE :       int							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       SCREAMING_SNAKE_CASE :       Optional[jax.random.KeyArray] = None							,       SCREAMING_SNAKE_CASE :       bool = True							,       ):


      '''simple docstring'''






      UpperCamelCase__      :       str	    =  timestep

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

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

      # 1. compute alphas, betas
      UpperCamelCase__      :       Optional[int]	    =  state.common.alphas_cumprod[t]
      UpperCamelCase__      :       Dict	    =  jnp.where(t > 0							,       state.common.alphas_cumprod[t - 1]							,       jnp.array(1.0							,       dtype=self.dtype						)						)
      UpperCamelCase__      :       Any	    =  1 - alpha_prod_t
      UpperCamelCase__      :       List[str]	    =  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__      :       Optional[Any]	    =  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
      elif self.config.prediction_type == "sample":
           UpperCamelCase__      :       Dict	    =  model_output
      elif self.config.prediction_type == "v_prediction":
           UpperCamelCase__      :       str	    =  (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__      :       List[str]	    =  jnp.clip(SCREAMING_SNAKE_CASE							,       -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__      :       List[Any]	    =  (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
      UpperCamelCase__      :       str	    =  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__      :       Any	    =  pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

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

      UpperCamelCase__      :       List[str]	    =  pred_prev_sample + variance

      if not return_dict:
           return (pred_prev_sample, state)

      return FlaxDDPMSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE							,       state=SCREAMING_SNAKE_CASE						)







 def  __lowercase (		self :       Any							,       SCREAMING_SNAKE_CASE :       DDPMSchedulerState							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       ):


      '''simple docstring'''






      return add_noise_common(state.common							,       SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE						)







 def  __lowercase (		self :       Dict							,       SCREAMING_SNAKE_CASE :       DDPMSchedulerState							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       ):


      '''simple docstring'''






      return get_velocity_common(state.common							,       SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE						)







 def __len__(		self :       int						):


      '''simple docstring'''






      return self.config.num_train_timesteps