





import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
_lowerCAmelCase				:   List[str]						       =						logging.getLogger()

def   __snake_case    (       _lowerCAmelCase			:			Path					,					_lowerCAmelCase			:			list						)			->    List[Any]:
   A_       :					Any        =      "\n".join(_lowerCAmelCase						)
   Path(_lowerCAmelCase						).open("w"						).writelines(_lowerCAmelCase						)


_lowerCAmelCase				:   Any						       =						'''patrickvonplaten/t5-tiny-random'''
_lowerCAmelCase				:   int						       =						'''sshleifer/bart-tiny-random'''
_lowerCAmelCase				:   List[Any]						       =						'''sshleifer/tiny-mbart'''

_lowerCAmelCase				:   int						       =						logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks





class        __magic_name__	(				lowerCamelCase__				):





     """simple docstring"""



     def     SCREAMING_SNAKE_CASE		(				self							:List[str]      ,   snake_case							:str	):





        '''simple docstring'''




        A_       :					Dict        =      Path(self.get_auto_remove_tmp_dir()	) / "utest_input.source"
        A_       :					Any        =      input_file_name.parent / "utest_output.txt"
        assert not output_file_name.exists()
        A_       :					str        =      [" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."]
        _dump_articles(snake_case      ,   snake_case	)

        A_       :					int        =      str(Path(self.get_auto_remove_tmp_dir()	) / "scores.json"	)
        A_       :					List[Any]        =      "translation_en_to_de" if model == T5_TINY else "summarization"
        A_       :					Union[str, Any]        =      f"\n            run_eval_search.py\n            {model}\n            {input_file_name}\n            {output_file_name}\n            --score_path {score_path}\n            --task {task}\n            --num_beams 2\n            --length_penalty 2.0\n            ".split()

        with patch.object(snake_case      ,   "argv"      ,   snake_case	):
           run_generate()
           assert Path(snake_case	).exists()
           # os.remove(Path(output_file_name))



     def     SCREAMING_SNAKE_CASE		(				self							:Union[str, Any]	):





        '''simple docstring'''




        self.run_eval_tester(snake_case	)



     @parameterized.expand([BART_TINY, MBART_TINY]	)
     @slow
     def     SCREAMING_SNAKE_CASE		(				self							:List[str]      ,   snake_case							:Union[str, Any]	):





        '''simple docstring'''




        self.run_eval_tester(snake_case	)





     @parameterized.expand([T5_TINY, MBART_TINY]	)
     @slow
     def     SCREAMING_SNAKE_CASE		(				self							:Optional[Any]      ,   snake_case							:Dict	):





        '''simple docstring'''




        A_       :					Union[str, Any]        =      Path(self.get_auto_remove_tmp_dir()	) / "utest_input.source"
        A_       :					List[str]        =      input_file_name.parent / "utest_output.txt"
        assert not output_file_name.exists()

        A_       :					int        =      {
            "en": ["Machine learning is great, isn't it?", "I like to eat bananas", "Tomorrow is another great day!"],
            "de": [
                "Maschinelles Lernen ist großartig, oder?",
                "Ich esse gerne Bananen",
                "Morgen ist wieder ein toller Tag!",
            ],
        }

        A_       :					Any        =      Path(self.get_auto_remove_tmp_dir()	)
        A_       :					Union[str, Any]        =      str(tmp_dir / "scores.json"	)
        A_       :					int        =      str(tmp_dir / "val.target"	)
        _dump_articles(snake_case      ,   text["en"]	)
        _dump_articles(snake_case      ,   text["de"]	)
        A_       :					Optional[int]        =      "translation_en_to_de" if model == T5_TINY else "summarization"
        A_       :					str        =      f"\n            run_eval_search.py\n            {model}\n            {str(snake_case	)}\n            {str(snake_case	)}\n            --score_path {score_path}\n            --reference_path {reference_path}\n            --task {task}\n            ".split()
        testargs.extend(["--search", "num_beams=1:2 length_penalty=0.9:1.0"]	)

        with patch.object(snake_case      ,   "argv"      ,   snake_case	):
           with CaptureStdout() as cs:
              run_search()
           A_       :					str        =      [" num_beams | length_penalty", model, "Best score args"]
           A_       :					Dict        =      ["Info"]
           if "translation" in task:
              expected_strings.append("bleu"	)
           else:
              expected_strings.extend(snake_case	)
           for w in expected_strings:
              assert w in cs.out
           for w in un_expected_strings:
              assert w not in cs.out
           assert Path(snake_case	).exists()
           os.remove(Path(snake_case	)	)







import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class        __magic_name__	(				unittest.TestCase				):





     """simple docstring"""



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





        '''simple docstring'''




        torch.manual_seed(0	)
        A_       :					Optional[Any]        =      UNetaDModel(
            block_out_channels=(32, 64)      ,   layers_per_block=2      ,   sample_size=32      ,   in_channels=3      ,   out_channels=3      ,   down_block_types=("DownBlock2D", "AttnDownBlock2D")      ,   up_block_types=("AttnUpBlock2D", "UpBlock2D")      ,   )
        return model



     @property
     def     SCREAMING_SNAKE_CASE		(				self							:int	):





        '''simple docstring'''




        torch.manual_seed(0	)
        A_       :					Any        =      VQModel(
            block_out_channels=[32, 64]      ,   in_channels=3      ,   out_channels=3      ,   down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]      ,   up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]      ,   latent_channels=3      ,   )
        return model



     @property
     def     SCREAMING_SNAKE_CASE		(				self							:Any	):





        '''simple docstring'''




        torch.manual_seed(0	)
        A_       :					int        =      CLIPTextConfig(
            bos_token_id=0      ,   eos_token_id=2      ,   hidden_size=32      ,   intermediate_size=37      ,   layer_norm_eps=1e-05      ,   num_attention_heads=4      ,   num_hidden_layers=5      ,   pad_token_id=1      ,   vocab_size=1_000      ,   )
        return CLIPTextModel(snake_case	)





     def     SCREAMING_SNAKE_CASE		(				self							:List[Any]	):





        '''simple docstring'''




        A_       :					Dict        =      self.dummy_uncond_unet
        A_       :					Any        =      DDIMScheduler()
        A_       :					List[str]        =      self.dummy_vq_model

        A_       :					Union[str, Any]        =      LDMPipeline(unet=snake_case      ,   vqvae=snake_case      ,   scheduler=snake_case	)
        ldm.to(snake_case	)
        ldm.set_progress_bar_config(disable=snake_case	)

        A_       :					Any        =      torch.manual_seed(0	)
        A_       :					Optional[Any]        =      ldm(generator=snake_case      ,   num_inference_steps=2      ,   output_type="numpy"	).images

        A_       :					Dict        =      torch.manual_seed(0	)
        A_       :					Any        =      ldm(generator=snake_case      ,   num_inference_steps=2      ,   output_type="numpy"      ,   return_dict=snake_case	)[0]

        A_       :					int        =      image[0, -3:, -3:, -1]
        A_       :					Any        =      image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        A_       :					str        =      np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172]	)
        A_       :					List[str]        =      1e-2 if torch_device != "mps" else 3e-2

        assert np.abs(image_slice.flatten() - expected_slice	).max() < tolerance
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < tolerance




@slow
@require_torch
class        __magic_name__	(				unittest.TestCase				):





     """simple docstring"""



     def     SCREAMING_SNAKE_CASE		(				self							:Tuple	):





        '''simple docstring'''




        A_       :					int        =      LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256"	)
        ldm.to(snake_case	)
        ldm.set_progress_bar_config(disable=snake_case	)

        A_       :					int        =      torch.manual_seed(0	)
        A_       :					List[str]        =      ldm(generator=snake_case      ,   num_inference_steps=5      ,   output_type="numpy"	).images

        A_       :					Tuple        =      image[0, -3:, -3:, -1]

        assert image.shape == (1, 256, 256, 3)
        A_       :					Any        =      np.array([0.4399, 0.44975, 0.46825, 0.474, 0.4359, 0.4581, 0.45095, 0.4341, 0.4447]	)
        A_       :					List[Any]        =      1e-2 if torch_device != "mps" else 3e-2

        assert np.abs(image_slice.flatten() - expected_slice	).max() < tolerance
