






"""simple docstring"""



import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch

@require_torch
class        a     :







    @property
    def 			UpperCamelCase_      (			self       ):
      return self.get_dummy_input()







    @property
    def 			UpperCamelCase_      (			self       ):
      if self.block_type == "down":
        return (4, 3_2, 1_6, 1_6)
      elif self.block_type == "mid":
        return (4, 3_2, 3_2, 3_2)
      elif self.block_type == "up":
        return (4, 3_2, 6_4, 6_4)

      raise ValueError(F'\'{self.block_type}\' is not a supported block_type. Set it to \'up\', \'mid\', or \'down\'.'       )







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase=True							,		_lowerCamelCase=False							,		_lowerCamelCase=False							,		_lowerCamelCase=False							,		):
      lowercase							    =       4
      lowercase							    =       3_2
      lowercase							    =       (3_2, 3_2)

      lowercase							    =       torch.manual_seed(0       )
      lowercase							    =       torch.device(_lowerCamelCase       )
      lowercase							    =       (batch_size, num_channels) + sizes
      lowercase							    =       randn_tensor(_lowerCamelCase							,		generator=_lowerCamelCase							,		device=_lowerCamelCase       )
      lowercase							    =       {'hidden_states': hidden_states}

      if include_temb:
        lowercase							    =       1_2_8
        lowercase							    =       randn_tensor((batch_size, temb_channels)							,		generator=_lowerCamelCase							,		device=_lowerCamelCase       )

      if include_res_hidden_states_tuple:
        lowercase							    =       torch.manual_seed(1       )
        lowercase							    =       (randn_tensor(_lowerCamelCase							,		generator=_lowerCamelCase							,		device=_lowerCamelCase       ),)

      if include_encoder_hidden_states:
        lowercase							    =       floats_tensor((batch_size, 3_2, 3_2)       ).to(_lowerCamelCase       )

      if include_skip_sample:
        lowercase							    =       randn_tensor(((batch_size, 3) + sizes)							,		generator=_lowerCamelCase							,		device=_lowerCamelCase       )

      return dummy_input







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       {
          'in_channels': 3_2,
          'out_channels': 3_2,
          'temb_channels': 1_2_8,
      }
      if self.block_type == "up":
        lowercase							    =       3_2

      if self.block_type == "mid":
        init_dict.pop('out_channels'       )

      lowercase							    =       self.dummy_input
      return init_dict, inputs_dict







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase       ):
      lowercase    ,     lowercase							    =       self.prepare_init_args_and_inputs_for_common()
      lowercase							    =       self.block_class(**_lowerCamelCase       )
      unet_block.to(_lowerCamelCase       )
      unet_block.eval()

      with torch.no_grad():
        lowercase							    =       unet_block(**_lowerCamelCase       )

      if isinstance(_lowerCamelCase							,		_lowerCamelCase       ):
        lowercase							    =       output[0]

      self.assertEqual(output.shape							,		self.output_shape       )

      lowercase							    =       output[0, -1, -3:, -3:]
      lowercase							    =       torch.tensor(_lowerCamelCase       ).to(_lowerCamelCase       )
      assert torch_all_close(output_slice.flatten()							,		_lowerCamelCase							,		atol=5e-3       )







    @unittest.skipIf(torch_device == 'mps'							,		'Training is not supported in mps'       )
    def 			UpperCamelCase_      (			self       ):
      lowercase    ,     lowercase							    =       self.prepare_init_args_and_inputs_for_common()
      lowercase							    =       self.block_class(**_lowerCamelCase       )
      model.to(_lowerCamelCase       )
      model.train()
      lowercase							    =       model(**_lowerCamelCase       )

      if isinstance(_lowerCamelCase							,		_lowerCamelCase       ):
        lowercase							    =       output[0]

      lowercase							    =       torch.device(_lowerCamelCase       )
      lowercase							    =       randn_tensor(output.shape							,		device=_lowerCamelCase       )
      lowercase							    =       torch.nn.functional.mse_loss(_lowerCamelCase							,		_lowerCamelCase       )
      loss.backward()










"""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)
_UpperCamelCase		:  Tuple						=		logging.getLogger()
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Path		,					__snake_case			:  list    ):





  '''simple docstring'''





  lowercase							    =       '\n'.join(__snake_case    )
  Path(__snake_case    ).open('w'    ).writelines(__snake_case    )


_UpperCamelCase		:  Union[str, Any]						=		'patrickvonplaten/t5-tiny-random'
_UpperCamelCase		:  Union[str, Any]						=		'sshleifer/bart-tiny-random'
_UpperCamelCase		:  Tuple						=		'sshleifer/tiny-mbart'

_UpperCamelCase		:  Union[str, Any]						=		logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks



class        a     (   a_	):







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

      lowercase							    =       str(Path(self.get_auto_remove_tmp_dir()       ) / 'scores.json'       )
      lowercase							    =       'translation_en_to_de' if model == T5_TINY else 'summarization'
      lowercase							    =       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(_lowerCamelCase							,		'argv'							,		_lowerCamelCase       ):
        run_generate()
        assert Path(_lowerCamelCase       ).exists()
        # os.remove(Path(output_file_name))







    def 			UpperCamelCase_      (			self       ):
      self.run_eval_tester(_lowerCamelCase       )







    @parameterized.expand([BART_TINY, MBART_TINY]       )
    @slow
    def 			UpperCamelCase_      (			self							,		_lowerCamelCase       ):
      self.run_eval_tester(_lowerCamelCase       )







    @parameterized.expand([T5_TINY, MBART_TINY]       )
    @slow
    def 			UpperCamelCase_      (			self							,		_lowerCamelCase       ):
      lowercase							    =       Path(self.get_auto_remove_tmp_dir()       ) / 'utest_input.source'
      lowercase							    =       input_file_name.parent / 'utest_output.txt'
      assert not output_file_name.exists()

      lowercase							    =       {
          '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!',
          ],
      }

      lowercase							    =       Path(self.get_auto_remove_tmp_dir()       )
      lowercase							    =       str(tmp_dir / 'scores.json'       )
      lowercase							    =       str(tmp_dir / 'val.target'       )
      _dump_articles(_lowerCamelCase							,		text['en']       )
      _dump_articles(_lowerCamelCase							,		text['de']       )
      lowercase							    =       'translation_en_to_de' if model == T5_TINY else 'summarization'
      lowercase							    =       F'\n            run_eval_search.py\n            {model}\n            {str(_lowerCamelCase       )}\n            {str(_lowerCamelCase       )}\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(_lowerCamelCase							,		'argv'							,		_lowerCamelCase       ):
        with CaptureStdout() as cs:
          run_search()
        lowercase							    =       [' num_beams | length_penalty', model, 'Best score args']
        lowercase							    =       ['Info']
        if "translation" in task:
          expected_strings.append('bleu'       )
        else:
          expected_strings.extend(_lowerCamelCase       )
        for w in expected_strings:
          assert w in cs.out
        for w in un_expected_strings:
          assert w not in cs.out
        assert Path(_lowerCamelCase       ).exists()
        os.remove(Path(_lowerCamelCase       )       )


