"""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
UpperCAmelCase__  		=						pytest.mark.integration


UpperCAmelCase__  		=						{"""comet"""}
UpperCAmelCase__  		=						importlib.util.find_spec("""fairseq""") is not None

UpperCAmelCase__  		=						{"""code_eval"""}
UpperCAmelCase__  		=						os.name == """nt"""

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






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  @wraps(lowercase		)
  def wrapper(self   ,lowercase		):
    if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
      self.skipTest("""\"test requires Fairseq\""""		)
    else:
      test_case(self   ,lowercase		)

  return wrapper






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  @wraps(lowercase		)
  def wrapper(self   ,lowercase		):
    if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
      self.skipTest("""\"test requires transformers\""""		)
    else:
      test_case(self   ,lowercase		)

  return wrapper






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  @wraps(lowercase		)
  def wrapper(self   ,lowercase		):
    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     __UpperCAmelCase					(			):







  """simple docstring"""
  _UpperCAmelCase						 =      [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        a				(		parameterized.TestCase   ):
    _snake_case     :  Union[str, Any]     							=							{}
    _snake_case     :  Any     							=							None


    @pytest.mark.filterwarnings("""ignore:metric_module_factory is deprecated:FutureWarning"""					)
    @pytest.mark.filterwarnings("""ignore:load_metric is deprecated:FutureWarning"""					)
    def      lowerCAmelCase_  (					self	:  Union[str, Any]   ,				__lowerCAmelCase	:  Optional[Any]					):
      _UpperCAmelCase						 =      """[...]"""
      _UpperCAmelCase						 =      importlib.import_module(
          datasets.load.metric_module_factory(os.path.join("""metrics"""   ,				__lowerCAmelCase					)					).module_path					)
      _UpperCAmelCase						 =      datasets.load.import_main_class(metric_module.__name__   ,				dataset=__lowerCAmelCase					)
      # check parameters
      _UpperCAmelCase						 =      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(__lowerCAmelCase   ,				metric_module.__name__					):
        with self.use_local_metrics():
          try:
            _UpperCAmelCase						 =      doctest.testmod(__lowerCAmelCase   ,				verbose=__lowerCAmelCase   ,				raise_on_error=__lowerCAmelCase					)
          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	:  Union[str, Any]   ,				__lowerCAmelCase	:  List[Any]					):
      _UpperCAmelCase						 =      """[...]"""
      _UpperCAmelCase						 =      importlib.import_module(
          datasets.load.metric_module_factory(os.path.join("""metrics"""   ,				__lowerCAmelCase					)					).module_path					)
      # run doctest
      with self.use_local_metrics():
        _UpperCAmelCase						 =      doctest.testmod(__lowerCAmelCase   ,				verbose=__lowerCAmelCase   ,				raise_on_error=__lowerCAmelCase					)
      self.assertEqual(results.failed   ,				0					)
      self.assertGreater(results.attempted   ,				1					)


    @contextmanager
    def      lowerCAmelCase_  (					self	:  Dict   ,				__lowerCAmelCase	:  List[str]   ,				__lowerCAmelCase	:  Any					):
      if metric_name in self.INTENSIVE_CALLS_PATCHER:
        with self.INTENSIVE_CALLS_PATCHER[metric_name](__lowerCAmelCase					):
          yield
      else:
        yield


    @contextmanager
    def      lowerCAmelCase_  (					self	:  Dict					):
      def load_local_metric(__lowerCAmelCase	:  Any   ,				*__lowerCAmelCase	:  Dict   ,				**__lowerCAmelCase	:  List[Any]					):
        return load_metric(os.path.join("""metrics"""   ,				__lowerCAmelCase					)   ,				*__lowerCAmelCase   ,				**__lowerCAmelCase					)

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


    @classmethod
    def      lowerCAmelCase_  (					cls	:  List[Any]   ,				__lowerCAmelCase	:  Dict					):
      def wrapper(__lowerCAmelCase	:  Optional[Any]					):
        _UpperCAmelCase						 =      contextmanager(__lowerCAmelCase					)
        _UpperCAmelCase						 =      patcher
        return patcher

      return wrapper






@LocalMetricTest.register_intensive_calls_patcher("""bleurt"""		)
def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  import tensorflow.compat.va as tf
  from bleurt.score import Predictor

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


  class        a				(		lowerCAmelCase_   ):


      def      lowerCAmelCase_  (					self	:  int   ,				__lowerCAmelCase	:  Union[str, Any]					):
        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:
    _UpperCAmelCase						 =      MockedPredictor()
    yield






@LocalMetricTest.register_intensive_calls_patcher("""bertscore"""		)
def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  import torch

  def bert_cos_score_idf(lowercase   ,lowercase   ,*lowercase   ,**lowercase		):
    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:
    _UpperCAmelCase						 =      bert_cos_score_idf
    yield






@LocalMetricTest.register_intensive_calls_patcher("""comet"""		)
def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  def load_from_checkpoint(lowercase		):


    class        a				:


        def      lowerCAmelCase_  (					self	:  Union[str, Any]   ,				__lowerCAmelCase	:  Optional[Any]   ,				*__lowerCAmelCase	:  Union[str, Any]   ,				**__lowerCAmelCase	:  List[str]					):
          assert len(__lowerCAmelCase					) == 2
          _UpperCAmelCase						 =      [0.19, 0.92]
          return scores, sum(__lowerCAmelCase					) / len(__lowerCAmelCase					)



    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						 =      None
    with patch("""comet.load_from_checkpoint"""		) as mock_load_from_checkpoint:
      _UpperCAmelCase						 =      load_from_checkpoint
      yield






def     __UpperCAmelCase					(			):







  """simple docstring"""
  _UpperCAmelCase						 =      load_metric(os.path.join("""metrics"""   ,"""seqeval"""		)		)
  _UpperCAmelCase						 =      """ERROR"""
  _UpperCAmelCase						 =      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		)

"""simple docstring"""


import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
    UpperCAmelCase__  		=						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.""",
    )

    UpperCAmelCase__  		=						parser.parse_args()

    UpperCAmelCase__  		=						UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

    UpperCAmelCase__  		=						CLIPImageProcessor()
    UpperCAmelCase__  		=						CLIPVisionModelWithProjection.from_pretrained("""openai/clip-vit-large-patch14""")

    UpperCAmelCase__  		=						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)
