






"""simple docstring"""






import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_   : Optional[Any]   			= logging.get_logger(__name__)

UpperCAmelCase_   : Optional[Any]   			= {
    """Salesforce/blip-vqa-base""": """https://huggingface.co/Salesforce/blip-vqa-base/resolve/main/config.json""",
    """Salesforce/blip-vqa-capfit-large""": (
        """https://huggingface.co/Salesforce/blip-vqa-base-capfit/resolve/main/config.json"""
    ),
    """Salesforce/blip-image-captioning-base""": (
        """https://huggingface.co/Salesforce/blip-image-captioning-base/resolve/main/config.json"""
    ),
    """Salesforce/blip-image-captioning-large""": (
        """https://huggingface.co/Salesforce/blip-image-captioning-large/resolve/main/config.json"""
    ),
    """Salesforce/blip-itm-base-coco""": """https://huggingface.co/Salesforce/blip-itm-base-coco/resolve/main/config.json""",
    """Salesforce/blip-itm-large-coco""": """https://huggingface.co/Salesforce/blip-itm-large-coco/resolve/main/config.json""",
    """Salesforce/blip-itm-base-flikr""": """https://huggingface.co/Salesforce/blip-itm-base-flikr/resolve/main/config.json""",
    """Salesforce/blip-itm-large-flikr""": (
        """https://huggingface.co/Salesforce/blip-itm-large-flikr/resolve/main/config.json"""
    ),
}




class 							lowerCAmelCase__				(			UpperCAmelCase__ ):






      '''simple docstring'''

      __UpperCamelCase	 =  "blip_text_model"
      def __init__(						self     :  Tuple						,	lowercase_     :  str=30524						,	lowercase_     :  Union[str, Any]=768						,	lowercase_     :  Optional[Any]=768						,	lowercase_     :  Dict=3072						,	lowercase_     :  Optional[Any]=768						,	lowercase_     :  Any=12						,	lowercase_     :  Dict=8						,	lowercase_     :  str=512						,	lowercase_     :  Any="gelu"						,	lowercase_     :  str=1e-12						,	lowercase_     :  List[str]=0.0						,	lowercase_     :  Any=0.0						,	lowercase_     :  List[str]=0.02						,	lowercase_     :  int=30522						,	lowercase_     :  Any=2						,	lowercase_     :  Optional[int]=0						,	lowercase_     :  Union[str, Any]=102						,	lowercase_     :  Optional[Any]=True						,	lowercase_     :  str=True						,	**lowercase_     :  Optional[Any]						,	):




          '''simple docstring'''


          super().__init__(
              pad_token_id=lowercase_						,	bos_token_id=lowercase_						,	eos_token_id=lowercase_						,	sep_token_id=lowercase_						,	**lowercase_						,	)

          SCREAMING_SNAKE_CASE_      :  List[str]    						=		vocab_size
          SCREAMING_SNAKE_CASE_      :  Any    						=		hidden_size
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		encoder_hidden_size
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		intermediate_size
          SCREAMING_SNAKE_CASE_      :  str    						=		projection_dim
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		hidden_dropout_prob
          SCREAMING_SNAKE_CASE_      :  str    						=		num_hidden_layers
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		num_attention_heads
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		max_position_embeddings
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		layer_norm_eps
          SCREAMING_SNAKE_CASE_      :  int    						=		hidden_act
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		initializer_range
          SCREAMING_SNAKE_CASE_      :  str    						=		attention_probs_dropout_prob
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		is_decoder
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		use_cache



      @classmethod
      def _SCREAMING_SNAKE_CASE (						cls     :  Optional[Any]						,	lowercase_     :  Union[str, os.PathLike]						,	**lowercase_     :  Any):




          '''simple docstring'''


          cls._set_token_in_kwargs(lowercase_)

          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  str    						=		cls.get_config_dict(lowercase_						,	**lowercase_)

          # get the text config dict if we are loading from BlipConfig
          if config_dict.get('''model_type''') == "blip":
              SCREAMING_SNAKE_CASE_      :  str    						=		config_dict['''text_config''']

          if "model_type" in config_dict and hasattr(cls						,	'''model_type''') and config_dict["model_type"] != cls.model_type:
              logger.warning(
                  F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
                  F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.')

          return cls.from_dict(lowercase_						,	**lowercase_)




class 							lowerCAmelCase__				(			UpperCAmelCase__ ):






      '''simple docstring'''

      __UpperCamelCase	 =  "blip_vision_model"
      def __init__(						self     :  Optional[Any]						,	lowercase_     :  Tuple=768						,	lowercase_     :  Tuple=3072						,	lowercase_     :  Tuple=512						,	lowercase_     :  int=12						,	lowercase_     :  Optional[int]=12						,	lowercase_     :  List[Any]=384						,	lowercase_     :  List[str]=16						,	lowercase_     :  Any="gelu"						,	lowercase_     :  Optional[int]=1e-5						,	lowercase_     :  List[str]=0.0						,	lowercase_     :  List[str]=1e-10						,	**lowercase_     :  Dict						,	):




          '''simple docstring'''


          super().__init__(**lowercase_)

          SCREAMING_SNAKE_CASE_      :  Tuple    						=		hidden_size
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		intermediate_size
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		projection_dim
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		num_hidden_layers
          SCREAMING_SNAKE_CASE_      :  Any    						=		num_attention_heads
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		patch_size
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		image_size
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		initializer_range
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		attention_dropout
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		layer_norm_eps
          SCREAMING_SNAKE_CASE_      :  Dict    						=		hidden_act



      @classmethod
      def _SCREAMING_SNAKE_CASE (						cls     :  str						,	lowercase_     :  Union[str, os.PathLike]						,	**lowercase_     :  int):




          '''simple docstring'''


          cls._set_token_in_kwargs(lowercase_)

          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		cls.get_config_dict(lowercase_						,	**lowercase_)

          # get the vision config dict if we are loading from BlipConfig
          if config_dict.get('''model_type''') == "blip":
              SCREAMING_SNAKE_CASE_      :  int    						=		config_dict['''vision_config''']

          if "model_type" in config_dict and hasattr(cls						,	'''model_type''') and config_dict["model_type"] != cls.model_type:
              logger.warning(
                  F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
                  F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.')

          return cls.from_dict(lowercase_						,	**lowercase_)




class 							lowerCAmelCase__				(			UpperCAmelCase__ ):






      '''simple docstring'''

      __UpperCamelCase	 =  "blip"
      __UpperCamelCase	 =  True
      def __init__(						self     :  List[Any]						,	lowercase_     :  str=None						,	lowercase_     :  List[str]=None						,	lowercase_     :  Tuple=512						,	lowercase_     :  Optional[Any]=2.65_92						,	lowercase_     :  Dict=256						,	**lowercase_     :  Tuple						,	):




          '''simple docstring'''


          super().__init__(**lowercase_)

          if text_config is None:
              SCREAMING_SNAKE_CASE_      :  Any    						=		{}
              logger.info('''`text_config` is `None`. Initializing the `BlipTextConfig` with default values.''')

          if vision_config is None:
              SCREAMING_SNAKE_CASE_      :  Dict    						=		{}
              logger.info('''`vision_config` is `None`. Initializing the `BlipVisionConfig` with default values.''')

          SCREAMING_SNAKE_CASE_      :  List[str]    						=		BlipTextConfig(**lowercase_)
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		BlipVisionConfig(**lowercase_)

          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		self.vision_config.hidden_size

          SCREAMING_SNAKE_CASE_      :  List[str]    						=		projection_dim
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		logit_scale_init_value
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		1.0
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		0.02
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		image_text_hidden_size
      @classmethod
      def _SCREAMING_SNAKE_CASE (						cls     :  int						,	lowercase_     :  BlipTextConfig						,	lowercase_     :  BlipVisionConfig						,	**lowercase_     :  Any):




          '''simple docstring'''


          return cls(text_config=text_config.to_dict()						,	vision_config=vision_config.to_dict()						,	**lowercase_)



      def _SCREAMING_SNAKE_CASE (						self     :  Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  int    						=		copy.deepcopy(self.__dict__)
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		self.text_config.to_dict()
          SCREAMING_SNAKE_CASE_      :  str    						=		self.vision_config.to_dict()
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		self.__class__.model_type
          return output




"""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__      :    List[Any]    							= logging.getLogger()

def lowercase_  (   _snake_case   ,_snake_case						):
							SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   """\n""".join(_snake_case						)
							Path(_snake_case						).open("""w"""						).writelines(_snake_case						)


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

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



class 	lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		str:



											"""simple docstring"""




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

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



				def 	__magic_name__    (self    )					->		Dict:



											"""simple docstring"""




											self.run_eval_tester(SCREAMING_SNAKE_CASE__    )



				@parameterized.expand([BART_TINY, MBART_TINY]    )
				@slow
				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Dict:



											"""simple docstring"""




											self.run_eval_tester(SCREAMING_SNAKE_CASE__    )



				@parameterized.expand([T5_TINY, MBART_TINY]    )
				@slow
				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Dict:



											"""simple docstring"""




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

											SCREAMING_SNAKE_CASE__						:       Any           =   {
											    """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!""",
											    ],
											}

											SCREAMING_SNAKE_CASE__						:       List[str]           =   Path(self.get_auto_remove_tmp_dir()    )
											SCREAMING_SNAKE_CASE__						:       Tuple           =   str(tmp_dir / """scores.json"""    )
											SCREAMING_SNAKE_CASE__						:       Tuple           =   str(tmp_dir / """val.target"""    )
											_dump_articles(SCREAMING_SNAKE_CASE__   ,       text["""en"""]    )
											_dump_articles(SCREAMING_SNAKE_CASE__   ,       text["""de"""]    )
											SCREAMING_SNAKE_CASE__						:       str           =   """translation_en_to_de""" if model == T5_TINY else """summarization"""
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   F'''
            run_eval_search.py
            {model}
            {str(SCREAMING_SNAKE_CASE__    )}
            {str(SCREAMING_SNAKE_CASE__    )}
            --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(SCREAMING_SNAKE_CASE__   ,       """argv"""   ,       SCREAMING_SNAKE_CASE__    ):
																		with CaptureStdout() as cs:
																									run_search()
																		SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   [""" num_beams | length_penalty""", model, """Best score args"""]
																		SCREAMING_SNAKE_CASE__						:       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__    )    )




