"""simple docstring"""
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
lowercase__					       =	pytest.mark.integration


lowercase__					       =	{'comet'}
lowercase__					       =	importlib.util.find_spec('fairseq') is not None

lowercase__					       =	{'code_eval'}
lowercase__					       =	os.name == 'nt'

lowercase__					       =	{'bertscore', 'frugalscore', 'perplexity'}
lowercase__					       =	importlib.util.find_spec('transformers') is not None







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->List[Any]:
  @wraps(_SCREAMING_SNAKE_CASE   )
  def wrapper(self		,     _SCREAMING_SNAKE_CASE   ):
    if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
      self.skipTest('"test requires Fairseq"'   )
    else:
      test_case(self		,     _SCREAMING_SNAKE_CASE   )

  return wrapper







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->Optional[int]:
  @wraps(_SCREAMING_SNAKE_CASE   )
  def wrapper(self		,     _SCREAMING_SNAKE_CASE   ):
    if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
      self.skipTest('"test requires transformers"'   )
    else:
      test_case(self		,     _SCREAMING_SNAKE_CASE   )

  return wrapper







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->Union[str, Any]:
  @wraps(_SCREAMING_SNAKE_CASE   )
  def wrapper(self		,     _SCREAMING_SNAKE_CASE   ):
    if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
      self.skipTest('"test not supported on Windows"'   )
    else:
      test_case(self		,     _SCREAMING_SNAKE_CASE   )

  return wrapper







def 						__a   (	)	->List[Any]:
  a__:     Optional[int]										=	[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(
    __lowerCAmelCase       ,      __lowerCAmelCase       ,      __lowerCAmelCase				)
@local
class    __snake_case       (     parameterized.TestCase				):
      a__        =		{}
      a__        =		None



      @pytest.mark.filterwarnings('ignore:metric_module_factory is deprecated:FutureWarning')
      @pytest.mark.filterwarnings('ignore:load_metric is deprecated:FutureWarning')
      def   lowerCamelCase_   (						self    ,			lowercase)	->			Dict:





        '''simple docstring'''





        a__:     Optional[Any]										=	'[...]'
        a__:     Optional[int]										=	importlib.import_module(
            datasets.load.metric_module_factory(os.path.join('metrics'    ,			lowercase)).module_path)
        a__:     int										=	datasets.load.import_main_class(metric_module.__name__    ,			dataset=lowercase)
        # check parameters
        a__:     Tuple										=	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(lowercase    ,			metric_module.__name__):
          with self.use_local_metrics():
            try:
              a__:     int										=	doctest.testmod(lowercase    ,			verbose=lowercase    ,			raise_on_error=lowercase)
            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   lowerCamelCase_   (						self    ,			lowercase)	->			Any:





        '''simple docstring'''





        a__:     Tuple										=	'[...]'
        a__:     str										=	importlib.import_module(
            datasets.load.metric_module_factory(os.path.join('metrics'    ,			lowercase)).module_path)
        # run doctest
        with self.use_local_metrics():
          a__:     str										=	doctest.testmod(lowercase    ,			verbose=lowercase    ,			raise_on_error=lowercase)
        self.assertEqual(results.failed    ,			0)
        self.assertGreater(results.attempted    ,			1)



      @contextmanager
      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase)	->			Tuple:





        '''simple docstring'''





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



      @contextmanager
      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        def load_local_metric(lowercase    ,			*lowercase    ,			**lowercase):
          return load_metric(os.path.join('metrics'    ,			lowercase)    ,			*lowercase    ,			**lowercase)

        with patch('datasets.load_metric') as mock_load_metric:
          a__:     Tuple										=	load_local_metric
          yield



      @classmethod
      def   lowerCamelCase_   (						cls    ,			lowercase)	->			int:





        '''simple docstring'''





        def wrapper(lowercase):
          a__:     Union[str, Any]										=	contextmanager(lowercase)
          a__:     Optional[Any]										=	patcher
          return patcher

        return wrapper







@LocalMetricTest.register_intensive_calls_patcher('bleurt'   )
def 						__a   (	_SCREAMING_SNAKE_CASE   )	->Tuple:
  import tensorflow.compat.va as tf
  from bleurt.score import Predictor

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




  class    __snake_case       (     __lowerCAmelCase				):



        def   lowerCamelCase_   (						self    ,			lowercase)	->			Optional[Any]:





          '''simple docstring'''





          assert len(input_dict['input_ids']) == 2
          return np.array([1.03, 1.04])

    # 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:
    a__:     int										=	MockedPredictor()
    yield







@LocalMetricTest.register_intensive_calls_patcher('bertscore'   )
def 						__a   (	_SCREAMING_SNAKE_CASE   )	->Union[str, Any]:
  import torch

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

  # 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:
    a__:     Union[str, Any]										=	bert_cos_score_idf
    yield







@LocalMetricTest.register_intensive_calls_patcher('comet'   )
def 						__a   (	_SCREAMING_SNAKE_CASE   )	->Any:
  def load_from_checkpoint(_SCREAMING_SNAKE_CASE   ):




    class    __snake_case       :



          def   lowerCamelCase_   (						self    ,			lowercase    ,			*lowercase    ,			**lowercase)	->			Tuple:





            '''simple docstring'''





            assert len(lowercase) == 2
            a__:     Dict										=	[0.19, 0.92]
            return scores, sum(lowercase) / len(lowercase)







    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:
    a__:     Dict										=	None
    with patch('comet.load_from_checkpoint'   ) as mock_load_from_checkpoint:
      a__:     Tuple										=	load_from_checkpoint
      yield







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



"""simple docstring"""
from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


lowercase__					       =	transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->Any:
  if isinstance(_SCREAMING_SNAKE_CASE		,     torch.Tensor   ):
    return image
  elif isinstance(_SCREAMING_SNAKE_CASE		,     PIL.Image.Image   ):
    a__:     Optional[int]										=	[image]

  a__:     str										=	[trans(img.convert('RGB'   )   ) for img in image]
  a__:     Any										=	torch.stack(_SCREAMING_SNAKE_CASE   )
  return image




class    __snake_case       (     __lowerCAmelCase				):



      def __init__(						self    ,			lowercase    ,			lowercase)	->			Optional[int]:





        '''simple docstring'''





        super().__init__()

        # make sure scheduler can always be converted to DDIM
        a__:     Dict										=	DDIMScheduler.from_config(scheduler.config)

        self.register_modules(unet=lowercase    ,			scheduler=lowercase)



      def   lowerCamelCase_   (						self    ,			lowercase)	->			int:





        '''simple docstring'''





        if strength < 0 or strength > 1:
          raise ValueError(f'The value of strength should in [0.0, 1.0] but is {strength}')



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase)	->			Dict:





        '''simple docstring'''





        a__:     int										=	min(int(num_inference_steps * strength)    ,			lowercase)

        a__:     Any										=	max(num_inference_steps - init_timestep    ,			0)
        a__:     Union[str, Any]										=	self.scheduler.timesteps[t_start:]

        return timesteps, num_inference_steps - t_start



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase=None)	->			List[Any]:





        '''simple docstring'''





        if not isinstance(lowercase    ,			(torch.Tensor, PIL.Image.Image, list)):
          raise ValueError(
              f'`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(lowercase)}')

        a__:     Tuple										=	image.to(device=lowercase    ,			dtype=lowercase)

        if isinstance(lowercase    ,			lowercase) and len(lowercase) != batch_size:
          raise ValueError(
              f'You have passed a list of generators of length {len(lowercase)}, but requested an effective batch'
              f' size of {batch_size}. Make sure the batch size matches the length of the generators.')

        a__:     List[str]										=	init_latents.shape
        a__:     List[Any]										=	randn_tensor(lowercase    ,			generator=lowercase    ,			device=lowercase    ,			dtype=lowercase)

        # get latents
        print('add noise to latents at timestep'    ,			lowercase)
        a__:     int										=	self.scheduler.add_noise(lowercase    ,			lowercase    ,			lowercase)
        a__:     Dict										=	init_latents

        return latents



      @torch.no_grad()
      def __call__(						self    ,			lowercase = None    ,			lowercase = 0.8    ,			lowercase = 1    ,			lowercase = None    ,			lowercase = 0.0    ,			lowercase = 50    ,			lowercase = None    ,			lowercase = "pil"    ,			lowercase = True    ,			)	->			Union[ImagePipelineOutput, Tuple]:





        '''simple docstring'''





        self.check_inputs(lowercase)

        # 2. Preprocess image
        a__:     Tuple										=	preprocess(lowercase)

        # 3. set timesteps
        self.scheduler.set_timesteps(lowercase    ,			device=self.device)
        a__							,    a__:     Union[str, Any]										=	self.get_timesteps(lowercase    ,			lowercase    ,			self.device)
        a__:     Optional[int]										=	timesteps[:1].repeat(lowercase)

        # 4. Prepare latent variables
        a__:     Union[str, Any]										=	self.prepare_latents(lowercase    ,			lowercase    ,			lowercase    ,			self.unet.dtype    ,			self.device    ,			lowercase)
        a__:     Optional[Any]										=	latents

        # 5. Denoising loop
        for t in self.progress_bar(lowercase):
          # 1. predict noise model_output
          a__:     Dict										=	self.unet(lowercase    ,			lowercase).sample

          # 2. predict previous mean of image x_t-1 and add variance depending on eta
          # eta corresponds to η in paper and should be between [0, 1]
          # do x_t -> x_t-1
          a__:     Optional[Any]										=	self.scheduler.step(
              lowercase    ,			lowercase    ,			lowercase    ,			eta=lowercase    ,			use_clipped_model_output=lowercase    ,			generator=lowercase    ,			).prev_sample

        a__:     Union[str, Any]										=	(image / 2 + 0.5).clamp(0    ,			1)
        a__:     Optional[int]										=	image.cpu().permute(0    ,			2    ,			3    ,			1).numpy()
        if output_type == "pil":
          a__:     Dict										=	self.numpy_to_pil(lowercase)

        if not return_dict:
          return (image, latent_timestep.item())

        return ImagePipelineOutput(images=lowercase)


