


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
_snake_case			 =	pytest.mark.integration


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

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

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






def 						lowercase_(    SCREAMING_SNAKE_CASE_      ):


  '''simple docstring'''

  @wraps(_lowercase      )
  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 ,		_lowercase      )

  return wrapper






def 						lowercase_(    SCREAMING_SNAKE_CASE_      ):


  '''simple docstring'''

  @wraps(_lowercase      )
  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 ,		_lowercase      )

  return wrapper






def 						lowercase_(    SCREAMING_SNAKE_CASE_      ):


  '''simple docstring'''

  @wraps(_lowercase      )
  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 ,		_lowercase      )

  return wrapper






def 						lowercase_(    ):


  '''simple docstring'''

  lowerCamelCase					:   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       UpperCAmelCase_   (   parameterized.TestCase       ):

   '''simple docstring'''




   __A					: Optional[Any]							=						{}
   __A					: str							=						None






   @pytest.mark.filterwarnings("ignore:metric_module_factory is deprecated:FutureWarning"					)
   @pytest.mark.filterwarnings("ignore:load_metric is deprecated:FutureWarning"					)
   def _snake_case       (						self		,							__A					):






     """simple docstring"""





     lowerCamelCase					:   List[str]       =							"[...]"
     lowerCamelCase					:   str       =							importlib.import_module(
         datasets.load.metric_module_factory(os.path.join("metrics"		,							A_					)					).module_path					)
     lowerCamelCase					:   List[Any]       =							datasets.load.import_main_class(metric_module.__name__		,							dataset=A_					)
     # check parameters
     lowerCamelCase					:   Union[str, 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(A_		,							metric_module.__name__					):
       with self.use_local_metrics():
         try:
           lowerCamelCase					:   Dict       =							doctest.testmod(A_		,							verbose=A_		,							raise_on_error=A_					)
         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 _snake_case       (						self		,							__A					):






     """simple docstring"""





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






   @contextmanager
   def _snake_case       (						self		,							__A		,							__A					):






     """simple docstring"""





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






   @contextmanager
   def _snake_case       (						self					):






     """simple docstring"""





     def load_local_metric(__A		,							*__A		,							**__A					):
       return load_metric(os.path.join("metrics"		,							A_					)		,							*A_		,							**A_					)

     with patch("datasets.load_metric"					) as mock_load_metric:
       lowerCamelCase					:   Tuple       =							load_local_metric
       yield






   @classmethod
   def _snake_case       (						cls		,							__A					):






     """simple docstring"""





     def wrapper(__A					):
       lowerCamelCase					:   str       =							contextmanager(A_					)
       lowerCamelCase					:   Union[str, Any]       =							patcher
       return patcher

     return wrapper






@LocalMetricTest.register_intensive_calls_patcher("bleurt"      )
def 						lowercase_(    SCREAMING_SNAKE_CASE_      ):


  '''simple docstring'''

  import tensorflow.compat.va as tf
  from bleurt.score import Predictor

  tf.flags.DEFINE_string("sv" ,		"" ,		""      )  # handle pytest cli flags
  class       UpperCAmelCase_   (   _a       ):

     '''simple docstring'''




     def _snake_case       (						self		,							__A					):






       """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:
    lowerCamelCase					:   Union[str, Any]       =							MockedPredictor()
    yield






@LocalMetricTest.register_intensive_calls_patcher("bertscore"      )
def 						lowercase_(    SCREAMING_SNAKE_CASE_      ):


  '''simple docstring'''

  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(_lowercase      )      )

  # 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:
    lowerCamelCase					:   Tuple       =							bert_cos_score_idf
    yield






@LocalMetricTest.register_intensive_calls_patcher("comet"      )
def 						lowercase_(    SCREAMING_SNAKE_CASE_      ):


  '''simple docstring'''

  def load_from_checkpoint(SCREAMING_SNAKE_CASE_      ):
    class       UpperCAmelCase_   :

       '''simple docstring'''




       def _snake_case       (						self		,							__A		,							*__A		,							**__A					):






         """simple docstring"""





         assert len(A_					) == 2
         lowerCamelCase					:   int       =							[0.19, 0.92]
         return scores, sum(A_					) / len(A_					)



    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:
    lowerCamelCase					:   List[Any]       =							None
    with patch("comet.load_from_checkpoint"      ) as mock_load_from_checkpoint:
      lowerCamelCase					:   Dict       =							load_from_checkpoint
      yield







def 						lowercase_(    ):


  '''simple docstring'''

  lowerCamelCase					:   List[str]       =							load_metric(os.path.join("metrics" ,		"seqeval"      )      )
  lowerCamelCase					:   Optional[int]       =							"ERROR"
  lowerCamelCase					:   Tuple       =							f"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}"""
  with pytest.raises(_lowercase ,		match=re.escape(_lowercase      )      ):
    metric.compute(predictions=[] ,		references=[] ,		scheme=_lowercase      )







import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
    __snake_case   							=						argparse.ArgumentParser()

    parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

    parser.add_argument(
        '''--txt2img_unclip''',
        default='''kakaobrain/karlo-v1-alpha''',
        type=str,
        required=False,
        help='''The pretrained txt2img unclip.''',
    )

    __snake_case   							=						parser.parse_args()

    __snake_case   							=						UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

    __snake_case   							=						CLIPImageProcessor()
    __snake_case   							=						CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')

    __snake_case   							=						UnCLIPImageVariationPipeline(
        decoder=txtaimg.decoder,
        text_encoder=txtaimg.text_encoder,
        tokenizer=txtaimg.tokenizer,
        text_proj=txtaimg.text_proj,
        feature_extractor=feature_extractor,
        image_encoder=image_encoder,
        super_res_first=txtaimg.super_res_first,
        super_res_last=txtaimg.super_res_last,
        decoder_scheduler=txtaimg.decoder_scheduler,
        super_res_scheduler=txtaimg.super_res_scheduler,
    )

    imgaimg.save_pretrained(args.dump_path)


