





"""simple docstring"""



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)
SCREAMING_SNAKE_CASE_					:	Optional[int]							  =						logging.getLogger()





def  _snake_case     (    UpperCAmelCase_  :					int					,   UpperCAmelCase_  :					Union[str, Any]			):
     A__										=  """\n""".join(a__			)
     Path(a__			).open("""w"""			).writelines(a__			)


SCREAMING_SNAKE_CASE_					:	Optional[int]							  =						'patrickvonplaten/t5-tiny-random'
SCREAMING_SNAKE_CASE_					:	List[str]							  =						'sshleifer/bart-tiny-random'
SCREAMING_SNAKE_CASE_					:	Union[str, Any]							  =						'sshleifer/tiny-mbart'

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



class 					a						(       __lowercase				):



    """simple docstring"""



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






         """simple docstring"""


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

         A__										=  str(Path(self.get_auto_remove_tmp_dir()       ) / """scores.json"""       )
         A__										=  """translation_en_to_de""" if model == T5_TINY else """summarization"""
         A__										=  f"""
            run_eval_search.py
            {model}
            {input_file_name}
            {output_file_name}
            --score_path {score_path}
            --task {task}
            --num_beams 2
            --length_penalty 2.0
            """.split()

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

    def 				UpperCamelCase	(     self: Any       ):






         """simple docstring"""


         self.run_eval_tester(__A       )

    @parameterized.expand([BART_TINY, MBART_TINY]       )
    @slow
    def 				UpperCamelCase	(     self: Optional[Any]    ,						UpperCamelCase: Optional[int]       ):






         """simple docstring"""


         self.run_eval_tester(__A       )





    @parameterized.expand([T5_TINY, MBART_TINY]       )
    @slow
    def 				UpperCamelCase	(     self: List[str]    ,						UpperCamelCase: Tuple       ):






         """simple docstring"""


         A__										=  Path(self.get_auto_remove_tmp_dir()       ) / """utest_input.source"""
         A__										=  input_file_name.parent / """utest_output.txt"""
         assert not output_file_name.exists()

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

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









"""simple docstring"""



import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()



class 					a						(       unittest.TestCase				):



    """simple docstring"""



    @property
    def 				UpperCamelCase	(     self: Any       ):






         """simple docstring"""


         torch.manual_seed(0       )
         A__										=  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





    def 				UpperCamelCase	(     self: str       ):






         """simple docstring"""


         A__										=  self.dummy_uncond_unet
         A__										=  PNDMScheduler()

         A__										=  PNDMPipeline(unet=UpperCamelCase    ,						scheduler=UpperCamelCase       )
         pndm.to(UpperCamelCase       )
         pndm.set_progress_bar_config(disable=UpperCamelCase       )

         A__										=  torch.manual_seed(0       )
         A__										=  pndm(generator=UpperCamelCase    ,						num_inference_steps=20    ,						output_type="""numpy"""       ).images

         A__										=  torch.manual_seed(0       )
         A__										=  pndm(generator=UpperCamelCase    ,						num_inference_steps=20    ,						output_type="""numpy"""    ,						return_dict=UpperCamelCase       )[0]

         A__										=  image[0, -3:, -3:, -1]
         A__										=  image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         A__										=  np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]       )

         assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1e-2



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



    """simple docstring"""



    def 				UpperCamelCase	(     self: Dict       ):






         """simple docstring"""


         A__										=  """google/ddpm-cifar10-32"""

         A__										=  UNetaDModel.from_pretrained(UpperCamelCase       )
         A__										=  PNDMScheduler()

         A__										=  PNDMPipeline(unet=UpperCamelCase    ,						scheduler=UpperCamelCase       )
         pndm.to(UpperCamelCase       )
         pndm.set_progress_bar_config(disable=UpperCamelCase       )
         A__										=  torch.manual_seed(0       )
         A__										=  pndm(generator=UpperCamelCase    ,						output_type="""numpy"""       ).images

         A__										=  image[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         A__										=  np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125]       )

         assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2






