

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


_SCREAMING_SNAKE_CASE					:  Any             =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE					:  List[Any]             =      {
    '''Salesforce/instruct-blip-flan-t5''': '''https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json''',
}






class 	UpperCAmelCase__		(	A__	):






  """simple docstring"""


  a									= "instructblip_vision_model"






  def __init__(							self						:		Tuple     ,       __lowerCamelCase						:		str=1408     ,       __lowerCamelCase						:		int=6144     ,       __lowerCamelCase						:		Any=39     ,       __lowerCamelCase						:		Dict=16     ,       __lowerCamelCase						:		Dict=224     ,       __lowerCamelCase						:		Tuple=14     ,       __lowerCamelCase						:		Optional[Any]="gelu"     ,       __lowerCamelCase						:		Tuple=1e-6     ,       __lowerCamelCase						:		str=0.0     ,       __lowerCamelCase						:		Any=1e-10     ,       __lowerCamelCase						:		Dict=True     ,       **__lowerCamelCase						:		List[Any]     ,       )		->							Optional[int]:
       super().__init__(**__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   hidden_size
       SCREAMING_SNAKE_CASE__   =   intermediate_size
       SCREAMING_SNAKE_CASE__   =   num_hidden_layers
       SCREAMING_SNAKE_CASE__   =   num_attention_heads
       SCREAMING_SNAKE_CASE__   =   patch_size
       SCREAMING_SNAKE_CASE__   =   image_size
       SCREAMING_SNAKE_CASE__   =   initializer_range
       SCREAMING_SNAKE_CASE__   =   attention_dropout
       SCREAMING_SNAKE_CASE__   =   layer_norm_eps
       SCREAMING_SNAKE_CASE__   =   hidden_act
       SCREAMING_SNAKE_CASE__   =   qkv_bias







  @classmethod
  def 				lowercase_		(							cls						:		List[str]     ,       __lowerCamelCase						:		Union[str, os.PathLike]     ,       **__lowerCamelCase						:		List[Any] )		->							"PretrainedConfig":
       cls._set_token_in_kwargs(__lowerCamelCase )

       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   cls.get_config_dict(__lowerCamelCase     ,       **__lowerCamelCase )

       # get the vision config dict if we are loading from InstructBlipConfig
       if config_dict.get('''model_type''' ) == "instructblip":
            SCREAMING_SNAKE_CASE__   =   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(__lowerCamelCase     ,       **__lowerCamelCase )






class 	UpperCAmelCase__		(	A__	):






  """simple docstring"""


  a									= "instructblip_qformer"






  def __init__(							self						:		Dict     ,       __lowerCamelCase						:		Dict=3_0522     ,       __lowerCamelCase						:		str=768     ,       __lowerCamelCase						:		Dict=12     ,       __lowerCamelCase						:		Dict=12     ,       __lowerCamelCase						:		Union[str, Any]=3072     ,       __lowerCamelCase						:		List[str]="gelu"     ,       __lowerCamelCase						:		Union[str, Any]=0.1     ,       __lowerCamelCase						:		Any=0.1     ,       __lowerCamelCase						:		Optional[Any]=512     ,       __lowerCamelCase						:		str=0.02     ,       __lowerCamelCase						:		Union[str, Any]=1e-12     ,       __lowerCamelCase						:		int=0     ,       __lowerCamelCase						:		List[Any]="absolute"     ,       __lowerCamelCase						:		Any=2     ,       __lowerCamelCase						:		str=1408     ,       **__lowerCamelCase						:		Union[str, Any]     ,       )		->							Any:
       super().__init__(pad_token_id=__lowerCamelCase     ,       **__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   vocab_size
       SCREAMING_SNAKE_CASE__   =   hidden_size
       SCREAMING_SNAKE_CASE__   =   num_hidden_layers
       SCREAMING_SNAKE_CASE__   =   num_attention_heads
       SCREAMING_SNAKE_CASE__   =   hidden_act
       SCREAMING_SNAKE_CASE__   =   intermediate_size
       SCREAMING_SNAKE_CASE__   =   hidden_dropout_prob
       SCREAMING_SNAKE_CASE__   =   attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE__   =   max_position_embeddings
       SCREAMING_SNAKE_CASE__   =   initializer_range
       SCREAMING_SNAKE_CASE__   =   layer_norm_eps
       SCREAMING_SNAKE_CASE__   =   position_embedding_type
       SCREAMING_SNAKE_CASE__   =   cross_attention_frequency
       SCREAMING_SNAKE_CASE__   =   encoder_hidden_size







  @classmethod
  def 				lowercase_		(							cls						:		str     ,       __lowerCamelCase						:		Union[str, os.PathLike]     ,       **__lowerCamelCase						:		List[str] )		->							"PretrainedConfig":
       cls._set_token_in_kwargs(__lowerCamelCase )

       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   cls.get_config_dict(__lowerCamelCase     ,       **__lowerCamelCase )

       # get the qformer config dict if we are loading from InstructBlipConfig
       if config_dict.get('''model_type''' ) == "instructblip":
            SCREAMING_SNAKE_CASE__   =   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(__lowerCamelCase     ,       **__lowerCamelCase )






class 	UpperCAmelCase__		(	A__	):






  """simple docstring"""


  a									= "instructblip"
  a									= True






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

       if vision_config is None:
            SCREAMING_SNAKE_CASE__   =   {}
            logger.info('''vision_config is None. initializing the InstructBlipVisionConfig with default values.''' )

       if qformer_config is None:
            SCREAMING_SNAKE_CASE__   =   {}
            logger.info('''qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.''' )

       if text_config is None:
            SCREAMING_SNAKE_CASE__   =   {}
            logger.info('''text_config is None. Initializing the text config with default values (`OPTConfig`).''' )

       SCREAMING_SNAKE_CASE__   =   InstructBlipVisionConfig(**__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   InstructBlipQFormerConfig(**__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   text_config['''model_type'''] if '''model_type''' in text_config else '''opt'''
       SCREAMING_SNAKE_CASE__   =   CONFIG_MAPPING[text_model_type](**__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   self.text_config.tie_word_embeddings
       SCREAMING_SNAKE_CASE__   =   self.text_config.is_encoder_decoder

       SCREAMING_SNAKE_CASE__   =   num_query_tokens
       SCREAMING_SNAKE_CASE__   =   self.vision_config.hidden_size
       SCREAMING_SNAKE_CASE__   =   self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
       SCREAMING_SNAKE_CASE__   =   1.0
       SCREAMING_SNAKE_CASE__   =   0.02






  @classmethod
  def 				lowercase_		(							cls						:		Dict     ,       __lowerCamelCase						:		InstructBlipVisionConfig     ,       __lowerCamelCase						:		InstructBlipQFormerConfig     ,       __lowerCamelCase						:		PretrainedConfig     ,       **__lowerCamelCase						:		Tuple     ,       )		->							int:
       return cls(
           vision_config=vision_config.to_dict()     ,       qformer_config=qformer_config.to_dict()     ,       text_config=text_config.to_dict()     ,       **__lowerCamelCase     ,       )







  def 				lowercase_		(							self						:		Dict )		->							Optional[int]:
       SCREAMING_SNAKE_CASE__   =   copy.deepcopy(self.__dict__ )
       SCREAMING_SNAKE_CASE__   =   self.vision_config.to_dict()
       SCREAMING_SNAKE_CASE__   =   self.qformer_config.to_dict()
       SCREAMING_SNAKE_CASE__   =   self.text_config.to_dict()
       SCREAMING_SNAKE_CASE__   =   self.__class__.model_type
       return output





import datasets

from .evaluate import evaluate


_SCREAMING_SNAKE_CASE					:  Union[str, Any]             =      '''\
@inproceedings{Rajpurkar2016SQuAD10,
  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
  booktitle={EMNLP},
  year={2016}
}
'''

_SCREAMING_SNAKE_CASE					:  Dict             =      '''
This metric wrap the official scoring script for version 1 of the Stanford Question Answering Dataset (SQuAD).

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by
crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,
from the corresponding reading passage, or the question might be unanswerable.
'''

_SCREAMING_SNAKE_CASE					:  str             =      '''
Computes SQuAD scores (F1 and EM).
Args:
    predictions: List of question-answers dictionaries with the following key-values:
        - \'id\': id of the question-answer pair as given in the references (see below)
        - \'prediction_text\': the text of the answer
    references: List of question-answers dictionaries with the following key-values:
        - \'id\': id of the question-answer pair (see above),
        - \'answers\': a Dict in the SQuAD dataset format
            {
                \'text\': list of possible texts for the answer, as a list of strings
                \'answer_start\': list of start positions for the answer, as a list of ints
            }
            Note that answer_start values are not taken into account to compute the metric.
Returns:
    \'exact_match\': Exact match (the normalized answer exactly match the gold answer)
    \'f1\': The F-score of predicted tokens versus the gold answer
Examples:

    >>> predictions = [{\'prediction_text\': \'1976\', \'id\': \'56e10a3be3433e1400422b22\'}]
    >>> references = [{\'answers\': {\'answer_start\': [97], \'text\': [\'1976\']}, \'id\': \'56e10a3be3433e1400422b22\'}]
    >>> squad_metric = datasets.load_metric("squad")
    >>> results = squad_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'exact_match\': 100.0, \'f1\': 100.0}
'''






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,					_KWARGS_DESCRIPTION	)
class 	UpperCAmelCase__		(	datasets.Metric	):






  """simple docstring"""


  def 				lowercase_		(							self						:		List[Any] )		->							Optional[int]:
       return datasets.MetricInfo(
           description=_DESCRIPTION     ,       citation=_CITATION     ,       inputs_description=_KWARGS_DESCRIPTION     ,       features=datasets.Features(
               {
                   '''predictions''': {'''id''': datasets.Value('''string''' ), '''prediction_text''': datasets.Value('''string''' )},
                   '''references''': {
                       '''id''': datasets.Value('''string''' ),
                       '''answers''': datasets.features.Sequence(
                           {
                               '''text''': datasets.Value('''string''' ),
                               '''answer_start''': datasets.Value('''int32''' ),
                           } ),
                   },
               } )     ,       codebase_urls=['''https://rajpurkar.github.io/SQuAD-explorer/''']     ,       reference_urls=['''https://rajpurkar.github.io/SQuAD-explorer/''']     ,       )







  def 				lowercase_		(							self						:		Optional[Any]     ,       __lowerCamelCase						:		Dict     ,       __lowerCamelCase						:		List[Any] )		->							Optional[int]:
       SCREAMING_SNAKE_CASE__   =   {prediction['''id''']: prediction['''prediction_text'''] for prediction in predictions}
       SCREAMING_SNAKE_CASE__   =   [
           {
               '''paragraphs''': [
                   {
                       '''qas''': [
                           {
                               '''answers''': [{'''text''': answer_text} for answer_text in ref['''answers''']['''text''']],
                               '''id''': ref['''id'''],
                           }
                           for ref in references
                       ]
                   }
               ]
           }
       ]
       SCREAMING_SNAKE_CASE__   =   evaluate(dataset=__lowerCamelCase     ,       predictions=__lowerCamelCase )
       return score


