






"""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		:  Optional[int]						=		logging.getLogger()
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Any		,					__snake_case			:  Optional[int]    ):





  '''simple docstring'''





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


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

_UpperCamelCase		:  int						=		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       )       )










from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
							import timm


if is_torch_available():
							from torch import Tensor
class       _UpperCAmelCase					(       A__		,A__	):

				"""simple docstring"""




				lowercase__											=       """pixel_values"""
				lowercase__											=       False
				lowercase__											=       TimmBackboneConfig




				def __init__(     self						:    Tuple,  lowerCamelCase						:    List[str],  **lowerCamelCase						:    List[str]  ):

								'''simple docstring'''
								requires_backends(self,  '''timm'''  )
								super().__init__(lowerCamelCase  )
								lowercase__    =			config

								if config.backbone is None:
												raise ValueError('''backbone is not set in the config. Please set it to a timm model name.'''  )

								if config.backbone not in timm.list_models():
												raise ValueError(F"""backbone {config.backbone} is not supported by timm."""  )

								if hasattr(lowerCamelCase,  '''out_features'''  ) and config.out_features is not None:
												raise ValueError('''out_features is not supported by TimmBackbone. Please use out_indices instead.'''  )

								lowercase__    =			getattr(lowerCamelCase,  '''use_pretrained_backbone''',  lowerCamelCase  )
								if pretrained is None:
												raise ValueError('''use_pretrained_backbone is not set in the config. Please set it to True or False.'''  )

								# We just take the final layer by default. This matches the default for the transformers models.
								lowercase__    =			config.out_indices if getattr(lowerCamelCase,  '''out_indices''',  lowerCamelCase  ) is not None else (-1,)

								lowercase__    =			timm.create_model(
								    config.backbone,  pretrained=lowerCamelCase,  features_only=config.features_only,  in_chans=config.num_channels,  out_indices=lowerCamelCase,  **lowerCamelCase,  )
								# These are used to control the output of the model when called. If output_hidden_states is True, then
								# return_layers is modified to include all layers.
								lowercase__    =			self._backbone.return_layers
								lowercase__    =			{layer['''module''']: str(lowerCamelCase  ) for i, layer in enumerate(self._backbone.feature_info.info  )}
								super()._init_backbone(lowerCamelCase  )




				@classmethod
				def 				lowercase__   (     cls						:    List[str],  lowerCamelCase						:    List[str],  *lowerCamelCase						:    Optional[int],  **lowerCamelCase						:    List[Any]  ):

								'''simple docstring'''
								requires_backends(cls,  ['''vision''', '''timm''']  )
								from ...models.timm_backbone import TimmBackboneConfig

								lowercase__    =			kwargs.pop('''config''',  TimmBackboneConfig()  )

								lowercase__    =			kwargs.pop('''use_timm_backbone''',  lowerCamelCase  )
								if not use_timm:
												raise ValueError('''use_timm_backbone must be True for timm backbones'''  )

								lowercase__    =			kwargs.pop('''num_channels''',  config.num_channels  )
								lowercase__    =			kwargs.pop('''features_only''',  config.features_only  )
								lowercase__    =			kwargs.pop('''use_pretrained_backbone''',  config.use_pretrained_backbone  )
								lowercase__    =			kwargs.pop('''out_indices''',  config.out_indices  )
								lowercase__    =			TimmBackboneConfig(
								    backbone=lowerCamelCase,  num_channels=lowerCamelCase,  features_only=lowerCamelCase,  use_pretrained_backbone=lowerCamelCase,  out_indices=lowerCamelCase,  )
								return super()._from_config(lowerCamelCase,  **lowerCamelCase  )




				def 				lowercase__   (     self						:    List[Any],  lowerCamelCase						:    Optional[Any]  ):

								'''simple docstring'''
								pass




				def 				lowercase__   (     self						:    int,  lowerCamelCase						:    int,  lowerCamelCase						:    Optional[int]=None,  lowerCamelCase						:    List[Any]=None,  lowerCamelCase						:    int=None,  **lowerCamelCase						:    Optional[int]  ):

								'''simple docstring'''
								lowercase__    =			return_dict if return_dict is not None else self.config.use_return_dict
								lowercase__    =			(
								    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
								)
								lowercase__    =			output_attentions if output_attentions is not None else self.config.output_attentions

								if output_attentions:
												raise ValueError('''Cannot output attentions for timm backbones at the moment'''  )

								if output_hidden_states:
												# We modify the return layers to include all the stages of the backbone
												lowercase__    =			self._all_layers
												lowercase__    =			self._backbone(lowerCamelCase,  **lowerCamelCase  )
												lowercase__    =			self._return_layers
												lowercase__    =			tuple(hidden_states[i] for i in self.out_indices  )
								else:
												lowercase__    =			self._backbone(lowerCamelCase,  **lowerCamelCase  )
												lowercase__    =			None

								lowercase__    =			tuple(lowerCamelCase  )
								lowercase__    =			tuple(lowerCamelCase  ) if hidden_states is not None else None

								if not return_dict:
												lowercase__    =			(feature_maps,)
												if output_hidden_states:
																lowercase__    =			output + (hidden_states,)
												return output

								return BackboneOutput(feature_maps=lowerCamelCase,  hidden_states=lowerCamelCase,  attentions=lowerCamelCase  )




