



"""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)
A:   Dict								=		logging.getLogger()

def       _snake_case						(						UpperCamelCase						:		Path			,		UpperCamelCase						:		list						):
 UpperCAmelCase	:					str	=	"""\n""".join(UpperCamelCase						)
 Path(UpperCamelCase						).open("""w"""						).writelines(UpperCamelCase						)


A:   int								=		"patrickvonplaten/t5-tiny-random"
A:   Dict								=		"sshleifer/bart-tiny-random"
A:   Tuple								=		"sshleifer/tiny-mbart"

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



class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__ ):

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      int:





    '''simple docstring'''




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

    UpperCAmelCase	:					Union[str, Any]	=	str(Path(self.get_auto_remove_tmp_dir()						) / """scores.json"""						)
    UpperCAmelCase	:					str	=	"""translation_en_to_de""" if model == T5_TINY else """summarization"""
    UpperCAmelCase	:					Dict	=	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(_SCREAMING_SNAKE_CASE      ,     """argv"""      ,     _SCREAMING_SNAKE_CASE						):
     run_generate()
     assert Path(_SCREAMING_SNAKE_CASE						).exists()
     # os.remove(Path(output_file_name))

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      List[str]:





    '''simple docstring'''




    self.run_eval_tester(_SCREAMING_SNAKE_CASE						)

   @parameterized.expand([BART_TINY, MBART_TINY]						)
   @slow
   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      Optional[Any]:





    '''simple docstring'''




    self.run_eval_tester(_SCREAMING_SNAKE_CASE						)




   @parameterized.expand([T5_TINY, MBART_TINY]						)
   @slow
   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      Any:





    '''simple docstring'''




    UpperCAmelCase	:					Optional[Any]	=	Path(self.get_auto_remove_tmp_dir()						) / """utest_input.source"""
    UpperCAmelCase	:					int	=	input_file_name.parent / """utest_output.txt"""
    assert not output_file_name.exists()

    UpperCAmelCase	:					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!""",
        ],
    }

    UpperCAmelCase	:					Tuple	=	Path(self.get_auto_remove_tmp_dir()						)
    UpperCAmelCase	:					Optional[int]	=	str(tmp_dir / """scores.json"""						)
    UpperCAmelCase	:					Dict	=	str(tmp_dir / """val.target"""						)
    _dump_articles(_SCREAMING_SNAKE_CASE      ,     text["""en"""]						)
    _dump_articles(_SCREAMING_SNAKE_CASE      ,     text["""de"""]						)
    UpperCAmelCase	:					Tuple	=	"""translation_en_to_de""" if model == T5_TINY else """summarization"""
    UpperCAmelCase	:					Union[str, Any]	=	F"\n            run_eval_search.py\n            {model}\n            {str(_SCREAMING_SNAKE_CASE						)}\n            {str(_SCREAMING_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(_SCREAMING_SNAKE_CASE      ,     """argv"""      ,     _SCREAMING_SNAKE_CASE						):
     with CaptureStdout() as cs:
      run_search()
     UpperCAmelCase	:					str	=	[""" num_beams | length_penalty""", model, """Best score args"""]
     UpperCAmelCase	:					Union[str, Any]	=	["""Info"""]
     if "translation" in task:
      expected_strings.append("""bleu"""						)
     else:
      expected_strings.extend(_SCREAMING_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(_SCREAMING_SNAKE_CASE						).exists()
     os.remove(Path(_SCREAMING_SNAKE_CASE						)						)





"""simple docstring"""
import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


A:   List[str]								=		logging.get_logger(__name__)

A:   Dict								=		{
    "microsoft/conditional-detr-resnet-50": (
        "https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"
    ),
}



class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__ ):
   __lowerCAmelCase  :   Any						=						'conditional_detr'
   __lowerCAmelCase  :   Union[str, Any]						=						['past_key_values']
   __lowerCAmelCase  :   int						=						{
       'hidden_size': 'd_model',
       'num_attention_heads': 'encoder_attention_heads',
   }

   def __init__(   self      ,     _SCREAMING_SNAKE_CASE=True      ,     _SCREAMING_SNAKE_CASE=None      ,     _SCREAMING_SNAKE_CASE=3      ,     _SCREAMING_SNAKE_CASE=300      ,     _SCREAMING_SNAKE_CASE=6      ,     _SCREAMING_SNAKE_CASE=2048      ,     _SCREAMING_SNAKE_CASE=8      ,     _SCREAMING_SNAKE_CASE=6      ,     _SCREAMING_SNAKE_CASE=2048      ,     _SCREAMING_SNAKE_CASE=8      ,     _SCREAMING_SNAKE_CASE=0.0      ,     _SCREAMING_SNAKE_CASE=0.0      ,     _SCREAMING_SNAKE_CASE=True      ,     _SCREAMING_SNAKE_CASE="relu"      ,     _SCREAMING_SNAKE_CASE=256      ,     _SCREAMING_SNAKE_CASE=0.1      ,     _SCREAMING_SNAKE_CASE=0.0      ,     _SCREAMING_SNAKE_CASE=0.0      ,     _SCREAMING_SNAKE_CASE=0.02      ,     _SCREAMING_SNAKE_CASE=1.0      ,     _SCREAMING_SNAKE_CASE=False      ,     _SCREAMING_SNAKE_CASE="sine"      ,     _SCREAMING_SNAKE_CASE="resnet50"      ,     _SCREAMING_SNAKE_CASE=True      ,     _SCREAMING_SNAKE_CASE=False      ,     _SCREAMING_SNAKE_CASE=2      ,     _SCREAMING_SNAKE_CASE=5      ,     _SCREAMING_SNAKE_CASE=2      ,     _SCREAMING_SNAKE_CASE=1      ,     _SCREAMING_SNAKE_CASE=1      ,     _SCREAMING_SNAKE_CASE=2      ,     _SCREAMING_SNAKE_CASE=5      ,     _SCREAMING_SNAKE_CASE=2      ,     _SCREAMING_SNAKE_CASE=0.25      ,     **_SCREAMING_SNAKE_CASE      ,     )  ->      Tuple:





    '''simple docstring'''




    if backbone_config is not None and use_timm_backbone:
     raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`."""						)

    if not use_timm_backbone:
     if backbone_config is None:
      logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""						)
      UpperCAmelCase	:					Tuple	=	CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]						)
     elif isinstance(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						):
      UpperCAmelCase	:					str	=	backbone_config.get("""model_type"""						)
      UpperCAmelCase	:					int	=	CONFIG_MAPPING[backbone_model_type]
      UpperCAmelCase	:					Union[str, Any]	=	config_class.from_dict(_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					Union[str, Any]	=	use_timm_backbone
    UpperCAmelCase	:					Optional[int]	=	backbone_config
    UpperCAmelCase	:					List[str]	=	num_channels
    UpperCAmelCase	:					Any	=	num_queries
    UpperCAmelCase	:					Union[str, Any]	=	d_model
    UpperCAmelCase	:					List[str]	=	encoder_ffn_dim
    UpperCAmelCase	:					Optional[int]	=	encoder_layers
    UpperCAmelCase	:					Union[str, Any]	=	encoder_attention_heads
    UpperCAmelCase	:					Optional[Any]	=	decoder_ffn_dim
    UpperCAmelCase	:					Any	=	decoder_layers
    UpperCAmelCase	:					Optional[int]	=	decoder_attention_heads
    UpperCAmelCase	:					Optional[int]	=	dropout
    UpperCAmelCase	:					Dict	=	attention_dropout
    UpperCAmelCase	:					Dict	=	activation_dropout
    UpperCAmelCase	:					Any	=	activation_function
    UpperCAmelCase	:					Any	=	init_std
    UpperCAmelCase	:					Tuple	=	init_xavier_std
    UpperCAmelCase	:					Optional[int]	=	encoder_layerdrop
    UpperCAmelCase	:					Any	=	decoder_layerdrop
    UpperCAmelCase	:					Any	=	encoder_layers
    UpperCAmelCase	:					Optional[Any]	=	auxiliary_loss
    UpperCAmelCase	:					List[Any]	=	position_embedding_type
    UpperCAmelCase	:					Union[str, Any]	=	backbone
    UpperCAmelCase	:					List[Any]	=	use_pretrained_backbone
    UpperCAmelCase	:					Dict	=	dilation
    # Hungarian matcher
    UpperCAmelCase	:					Optional[int]	=	class_cost
    UpperCAmelCase	:					List[str]	=	bbox_cost
    UpperCAmelCase	:					List[str]	=	giou_cost
    # Loss coefficients
    UpperCAmelCase	:					List[Any]	=	mask_loss_coefficient
    UpperCAmelCase	:					List[str]	=	dice_loss_coefficient
    UpperCAmelCase	:					Optional[int]	=	cls_loss_coefficient
    UpperCAmelCase	:					Union[str, Any]	=	bbox_loss_coefficient
    UpperCAmelCase	:					Union[str, Any]	=	giou_loss_coefficient
    UpperCAmelCase	:					int	=	focal_alpha
    super().__init__(is_encoder_decoder=_SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE						)

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      int:





    '''simple docstring'''




    return self.encoder_attention_heads

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      int:





    '''simple docstring'''




    return self.d_model




   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Any:





    '''simple docstring'''




    UpperCAmelCase	:					List[Any]	=	copy.deepcopy(self.__dict__						)
    if self.backbone_config is not None:
     UpperCAmelCase	:					Union[str, Any]	=	self.backbone_config.to_dict()
    UpperCAmelCase	:					Dict	=	self.__class__.model_type
    return output



class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__ ):
   __lowerCAmelCase  :   Any						=						version.parse('1.11' )

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Mapping[str, Mapping[int, str]]:





    '''simple docstring'''




    return OrderedDict(
        [
            ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
            ("""pixel_mask""", {0: """batch"""}),
        ]						)

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      float:





    '''simple docstring'''




    return 1E-5




   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      int:





    '''simple docstring'''




    return 12
