




"""simple docstring"""


import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING


_A													=      logging.get_logger(__name__)

_A													=      {
    """salesforce/blip2-opt-2.7b""": """https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json""",
}


class _lowerCamelCase    (					a_						):
   _lowerCamelCase    :Tuple     				=     "blip_2_vision_model"






   def __init__(		self       :		Union[str, Any]   ,     UpperCamelCase       :		Any=14_08   ,     UpperCamelCase       :		List[str]=61_44   ,     UpperCamelCase       :		Dict=39   ,     UpperCamelCase       :		str=16   ,     UpperCamelCase       :		int=2_24   ,     UpperCamelCase       :		List[Any]=14   ,     UpperCamelCase       :		Optional[int]="gelu"   ,     UpperCamelCase       :		Any=0.0_0001   ,     UpperCamelCase       :		Union[str, Any]=0.0   ,     UpperCamelCase       :		Any=1E-1_0   ,     UpperCamelCase       :		Dict=True   ,     **UpperCamelCase       :		Tuple   ,     )			->						str:

         """simple docstring"""
         super().__init__(**UpperCamelCase   )

         lowerCAmelCase__      :			List[str]   =	hidden_size
         lowerCAmelCase__      :			Dict   =	intermediate_size
         lowerCAmelCase__      :			List[Any]   =	num_hidden_layers
         lowerCAmelCase__      :			Any   =	num_attention_heads
         lowerCAmelCase__      :			int   =	patch_size
         lowerCAmelCase__      :			str   =	image_size
         lowerCAmelCase__      :			Dict   =	initializer_range
         lowerCAmelCase__      :			Tuple   =	attention_dropout
         lowerCAmelCase__      :			str   =	layer_norm_eps
         lowerCAmelCase__      :			str   =	hidden_act
         lowerCAmelCase__      :			int   =	qkv_bias






   @classmethod
   def 	_lowerCAmelCase   (		cls       :		Optional[int]   ,     UpperCamelCase       :		Union[str, os.PathLike]   ,     **UpperCamelCase       :		Optional[int]   )			->						"PretrainedConfig":

         """simple docstring"""
         cls._set_token_in_kwargs(UpperCamelCase   )

         lowerCAmelCase__      :			Optional[int]   =	cls.get_config_dict(UpperCamelCase   ,     **UpperCamelCase   )

         # get the vision config dict if we are loading from Blip2Config
         if config_dict.get("""model_type"""   ) == "blip-2":
               lowerCAmelCase__      :			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(UpperCamelCase   ,     **UpperCamelCase   )






class _lowerCamelCase    (					a_						):
   _lowerCamelCase    :Optional[int]     				=     "blip_2_qformer"






   def __init__(		self       :		List[Any]   ,     UpperCamelCase       :		Tuple=3_05_22   ,     UpperCamelCase       :		List[str]=7_68   ,     UpperCamelCase       :		Tuple=12   ,     UpperCamelCase       :		int=12   ,     UpperCamelCase       :		List[Any]=30_72   ,     UpperCamelCase       :		List[Any]="gelu"   ,     UpperCamelCase       :		Optional[int]=0.1   ,     UpperCamelCase       :		Optional[int]=0.1   ,     UpperCamelCase       :		Tuple=5_12   ,     UpperCamelCase       :		Tuple=0.02   ,     UpperCamelCase       :		int=1E-1_2   ,     UpperCamelCase       :		Union[str, Any]=0   ,     UpperCamelCase       :		Any="absolute"   ,     UpperCamelCase       :		List[Any]=2   ,     UpperCamelCase       :		List[Any]=14_08   ,     **UpperCamelCase       :		Optional[Any]   ,     )			->						str:

         """simple docstring"""
         super().__init__(pad_token_id=UpperCamelCase   ,     **UpperCamelCase   )

         lowerCAmelCase__      :			Union[str, Any]   =	vocab_size
         lowerCAmelCase__      :			Tuple   =	hidden_size
         lowerCAmelCase__      :			Union[str, Any]   =	num_hidden_layers
         lowerCAmelCase__      :			Union[str, Any]   =	num_attention_heads
         lowerCAmelCase__      :			Union[str, Any]   =	hidden_act
         lowerCAmelCase__      :			List[Any]   =	intermediate_size
         lowerCAmelCase__      :			Tuple   =	hidden_dropout_prob
         lowerCAmelCase__      :			List[Any]   =	attention_probs_dropout_prob
         lowerCAmelCase__      :			List[str]   =	max_position_embeddings
         lowerCAmelCase__      :			List[Any]   =	initializer_range
         lowerCAmelCase__      :			Any   =	layer_norm_eps
         lowerCAmelCase__      :			Optional[int]   =	position_embedding_type
         lowerCAmelCase__      :			Union[str, Any]   =	cross_attention_frequency
         lowerCAmelCase__      :			Optional[int]   =	encoder_hidden_size






   @classmethod
   def 	_lowerCAmelCase   (		cls       :		Dict   ,     UpperCamelCase       :		Union[str, os.PathLike]   ,     **UpperCamelCase       :		Any   )			->						"PretrainedConfig":

         """simple docstring"""
         cls._set_token_in_kwargs(UpperCamelCase   )

         lowerCAmelCase__      :			int   =	cls.get_config_dict(UpperCamelCase   ,     **UpperCamelCase   )

         # get the qformer config dict if we are loading from Blip2Config
         if config_dict.get("""model_type"""   ) == "blip-2":
               lowerCAmelCase__      :			Tuple   =	config_dict["""qformer_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(UpperCamelCase   ,     **UpperCamelCase   )






class _lowerCamelCase    (					a_						):
   _lowerCamelCase    :Optional[Any]     				=     "blip-2"
   _lowerCamelCase    :str     				=     True






   def __init__(		self       :		Dict   ,     UpperCamelCase       :		List[Any]=None   ,     UpperCamelCase       :		Optional[Any]=None   ,     UpperCamelCase       :		List[Any]=None   ,     UpperCamelCase       :		Tuple=32   ,     **UpperCamelCase       :		List[str]   )			->						Dict:

         """simple docstring"""
         super().__init__(**UpperCamelCase   )

         if vision_config is None:
               lowerCAmelCase__      :			Optional[Any]   =	{}
               logger.info("""vision_config is None. initializing the Blip2VisionConfig with default values."""   )

         if qformer_config is None:
               lowerCAmelCase__      :			Optional[int]   =	{}
               logger.info("""qformer_config is None. Initializing the Blip2QFormerConfig with default values."""   )

         if text_config is None:
               lowerCAmelCase__      :			Optional[int]   =	{}
               logger.info("""text_config is None. Initializing the text config with default values (`OPTConfig`)."""   )

         lowerCAmelCase__      :			Dict   =	BlipaVisionConfig(**UpperCamelCase   )
         lowerCAmelCase__      :			List[str]   =	BlipaQFormerConfig(**UpperCamelCase   )
         lowerCAmelCase__      :			Union[str, Any]   =	text_config["""model_type"""] if """model_type""" in text_config else """opt"""
         lowerCAmelCase__      :			Optional[int]   =	CONFIG_MAPPING[text_model_type](**UpperCamelCase   )

         lowerCAmelCase__      :			Union[str, Any]   =	self.text_config.tie_word_embeddings
         lowerCAmelCase__      :			int   =	self.text_config.is_encoder_decoder

         lowerCAmelCase__      :			Optional[Any]   =	num_query_tokens
         lowerCAmelCase__      :			Tuple   =	self.vision_config.hidden_size
         lowerCAmelCase__      :			List[str]   =	self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
         lowerCAmelCase__      :			Dict   =	1.0
         lowerCAmelCase__      :			int   =	0.02






   @classmethod
   def 	_lowerCAmelCase   (		cls       :		Tuple   ,     UpperCamelCase       :		BlipaVisionConfig   ,     UpperCamelCase       :		BlipaQFormerConfig   ,     UpperCamelCase       :		PretrainedConfig   ,     **UpperCamelCase       :		List[str]   ,     )			->						str:

         """simple docstring"""
         return cls(
             vision_config=vision_config.to_dict()   ,     qformer_config=qformer_config.to_dict()   ,     text_config=text_config.to_dict()   ,     **UpperCamelCase   ,     )






   def 	_lowerCAmelCase   (		self       :		int   )			->						List[Any]:

         """simple docstring"""
         lowerCAmelCase__      :			List[str]   =	copy.deepcopy(self.__dict__   )
         lowerCAmelCase__      :			Any   =	self.vision_config.to_dict()
         lowerCAmelCase__      :			Any   =	self.qformer_config.to_dict()
         lowerCAmelCase__      :			Optional[int]   =	self.text_config.to_dict()
         lowerCAmelCase__      :			Optional[Any]   =	self.__class__.model_type
         return output











"""simple docstring"""


import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


_A													=      logging.getLogger(__name__)


class _lowerCamelCase    (					a_						):






   def __init__(		self       :		List[Any]   ,     UpperCamelCase       :		Dict=-1   )			->						List[Any]:

         """simple docstring"""
         # in NER datasets, the last column is usually reserved for NER label
         lowerCAmelCase__      :			Optional[int]   =	label_idx






   def 	_lowerCAmelCase   (		self       :		Optional[int]   ,     UpperCamelCase       :		Optional[int]   ,     UpperCamelCase       :		Union[Split, str]   )			->						List[InputExample]:

         """simple docstring"""
         if isinstance(UpperCamelCase   ,     UpperCamelCase   ):
               lowerCAmelCase__      :			int   =	mode.value
         lowerCAmelCase__      :			Dict   =	os.path.join(UpperCamelCase   ,     f"""{mode}.txt"""   )
         lowerCAmelCase__      :			str   =	1
         lowerCAmelCase__      :			List[Any]   =	[]
         with open(UpperCamelCase   ,     encoding="""utf-8"""   ) as f:
               lowerCAmelCase__      :			str   =	[]
               lowerCAmelCase__      :			Any   =	[]
               for line in f:
                     if line.startswith("""-DOCSTART-"""   ) or line == "" or line == "\n":
                           if words:
                                 examples.append(InputExample(guid=f"""{mode}-{guid_index}"""   ,     words=UpperCamelCase   ,     labels=UpperCamelCase   )   )
                                 guid_index += 1
                                 lowerCAmelCase__      :			Optional[Any]   =	[]
                                 lowerCAmelCase__      :			Tuple   =	[]
                     else:
                           lowerCAmelCase__      :			Optional[int]   =	line.split(""" """   )
                           words.append(splits[0]   )
                           if len(UpperCamelCase   ) > 1:
                                 labels.append(splits[self.label_idx].replace("""\n"""   ,     """"""   )   )
                           else:
                                 # Examples could have no label for mode = "test"
                                 labels.append("""O"""   )
               if words:
                     examples.append(InputExample(guid=f"""{mode}-{guid_index}"""   ,     words=UpperCamelCase   ,     labels=UpperCamelCase   )   )
         return examples






   def 	_lowerCAmelCase   (		self       :		Any   ,     UpperCamelCase       :		TextIO   ,     UpperCamelCase       :		TextIO   ,     UpperCamelCase       :		List   )			->						List[Any]:

         """simple docstring"""
         lowerCAmelCase__      :			Dict   =	0
         for line in test_input_reader:
               if line.startswith("""-DOCSTART-"""   ) or line == "" or line == "\n":
                     writer.write(UpperCamelCase   )
                     if not preds_list[example_id]:
                           example_id += 1
               elif preds_list[example_id]:
                     lowerCAmelCase__      :			Union[str, Any]   =	line.split()[0] + """ """ + preds_list[example_id].pop(0   ) + """\n"""
                     writer.write(UpperCamelCase   )
               else:
                     logger.warning("""Maximum sequence length exceeded: No prediction for '%s'."""   ,     line.split()[0]   )






   def 	_lowerCAmelCase   (		self       :		str   ,     UpperCamelCase       :		str   )			->						List[str]:

         """simple docstring"""
         if path:
               with open(UpperCamelCase   ,     """r"""   ) as f:
                     lowerCAmelCase__      :			Any   =	f.read().splitlines()
               if "O" not in labels:
                     lowerCAmelCase__      :			List[str]   =	["""O"""] + labels
               return labels
         else:
               return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]






class _lowerCamelCase    (					a_						):






   def __init__(		self       :		Union[str, Any]   )			->						Any:

         """simple docstring"""
         # in CONLL2003 dataset chunk column is second-to-last
         super().__init__(label_idx=-2   )






   def 	_lowerCAmelCase   (		self       :		Optional[Any]   ,     UpperCamelCase       :		str   )			->						List[str]:

         """simple docstring"""
         if path:
               with open(UpperCamelCase   ,     """r"""   ) as f:
                     lowerCAmelCase__      :			Any   =	f.read().splitlines()
               if "O" not in labels:
                     lowerCAmelCase__      :			str   =	["""O"""] + labels
               return labels
         else:
               return [
                   "O",
                   "B-ADVP",
                   "B-INTJ",
                   "B-LST",
                   "B-PRT",
                   "B-NP",
                   "B-SBAR",
                   "B-VP",
                   "B-ADJP",
                   "B-CONJP",
                   "B-PP",
                   "I-ADVP",
                   "I-INTJ",
                   "I-LST",
                   "I-PRT",
                   "I-NP",
                   "I-SBAR",
                   "I-VP",
                   "I-ADJP",
                   "I-CONJP",
                   "I-PP",
               ]






class _lowerCamelCase    (					a_						):






   def 	_lowerCAmelCase   (		self       :		Union[str, Any]   ,     UpperCamelCase       :		Any   ,     UpperCamelCase       :		Union[Split, str]   )			->						List[InputExample]:

         """simple docstring"""
         if isinstance(UpperCamelCase   ,     UpperCamelCase   ):
               lowerCAmelCase__      :			Optional[Any]   =	mode.value
         lowerCAmelCase__      :			int   =	os.path.join(UpperCamelCase   ,     f"""{mode}.txt"""   )
         lowerCAmelCase__      :			Optional[int]   =	1
         lowerCAmelCase__      :			List[Any]   =	[]

         with open(UpperCamelCase   ,     encoding="""utf-8"""   ) as f:
               for sentence in parse_incr(UpperCamelCase   ):
                     lowerCAmelCase__      :			Union[str, Any]   =	[]
                     lowerCAmelCase__      :			List[Any]   =	[]
                     for token in sentence:
                           words.append(token["""form"""]   )
                           labels.append(token["""upos"""]   )
                     assert len(UpperCamelCase   ) == len(UpperCamelCase   )
                     if words:
                           examples.append(InputExample(guid=f"""{mode}-{guid_index}"""   ,     words=UpperCamelCase   ,     labels=UpperCamelCase   )   )
                           guid_index += 1
         return examples






   def 	_lowerCAmelCase   (		self       :		List[str]   ,     UpperCamelCase       :		TextIO   ,     UpperCamelCase       :		TextIO   ,     UpperCamelCase       :		List   )			->						List[Any]:

         """simple docstring"""
         lowerCAmelCase__      :			Any   =	0
         for sentence in parse_incr(UpperCamelCase   ):
               lowerCAmelCase__      :			Union[str, Any]   =	preds_list[example_id]
               lowerCAmelCase__      :			List[Any]   =	""""""
               for token in sentence:
                     out += f"""{token['form']} ({token['upos']}|{s_p.pop(0   )}) """
               out += "\n"
               writer.write(UpperCamelCase   )
               example_id += 1






   def 	_lowerCAmelCase   (		self       :		Dict   ,     UpperCamelCase       :		str   )			->						List[str]:

         """simple docstring"""
         if path:
               with open(UpperCamelCase   ,     """r"""   ) as f:
                     return f.read().splitlines()
         else:
               return [
                   "ADJ",
                   "ADP",
                   "ADV",
                   "AUX",
                   "CCONJ",
                   "DET",
                   "INTJ",
                   "NOUN",
                   "NUM",
                   "PART",
                   "PRON",
                   "PROPN",
                   "PUNCT",
                   "SCONJ",
                   "SYM",
                   "VERB",
                   "X",
               ]





