

"""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


_UpperCamelCase		     =    logging.get_logger(__name__)

_UpperCamelCase		     =    {
    """Salesforce/instruct-blip-flan-t5""": """https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json""",
}






class 			__a					(						__magic_name__       ):





       """simple docstring"""


       __UpperCamelCase					:      str    =						'instructblip_vision_model'






       def __init__(   self			,					snake_case=1_408			,					snake_case=6_144			,					snake_case=39			,					snake_case=16			,					snake_case=224			,					snake_case=14			,					snake_case="gelu"			,					snake_case=1e-6			,					snake_case=0.0			,					snake_case=1e-10			,					snake_case=True			,					**snake_case			,					):





        """simple docstring"""

        super().__init__(**snake_case   )

        lowerCAmelCase__   :				Any 			=				hidden_size
        lowerCAmelCase__   :				List[str] 			=				intermediate_size
        lowerCAmelCase__   :				List[Any] 			=				num_hidden_layers
        lowerCAmelCase__   :				Optional[Any] 			=				num_attention_heads
        lowerCAmelCase__   :				List[Any] 			=				patch_size
        lowerCAmelCase__   :				Union[str, Any] 			=				image_size
        lowerCAmelCase__   :				Optional[Any] 			=				initializer_range
        lowerCAmelCase__   :				List[Any] 			=				attention_dropout
        lowerCAmelCase__   :				Union[str, Any] 			=				layer_norm_eps
        lowerCAmelCase__   :				Optional[Any] 			=				hidden_act
        lowerCAmelCase__   :				Optional[Any] 			=				qkv_bias






       @classmethod
       def 			SCREAMING_SNAKE_CASE_	(   cls			,					snake_case			,					**snake_case   ):





        """simple docstring"""

        cls._set_token_in_kwargs(snake_case   )

        lowerCAmelCase__			,						lowerCAmelCase__   :				str 			=				cls.get_config_dict(snake_case			,					**snake_case   )

        # get the vision config dict if we are loading from InstructBlipConfig
        if config_dict.get("model_type"   ) == "instructblip":
         lowerCAmelCase__   :				List[Any] 			=				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(snake_case			,					**snake_case   )






class 			__a					(						__magic_name__       ):





       """simple docstring"""


       __UpperCamelCase					:      Optional[int]    =						'instructblip_qformer'






       def __init__(   self			,					snake_case=30_522			,					snake_case=768			,					snake_case=12			,					snake_case=12			,					snake_case=3_072			,					snake_case="gelu"			,					snake_case=0.1			,					snake_case=0.1			,					snake_case=512			,					snake_case=0.02			,					snake_case=1e-12			,					snake_case=0			,					snake_case="absolute"			,					snake_case=2			,					snake_case=1_408			,					**snake_case			,					):





        """simple docstring"""

        super().__init__(pad_token_id=snake_case			,					**snake_case   )

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






       @classmethod
       def 			SCREAMING_SNAKE_CASE_	(   cls			,					snake_case			,					**snake_case   ):





        """simple docstring"""

        cls._set_token_in_kwargs(snake_case   )

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

        # get the qformer config dict if we are loading from InstructBlipConfig
        if config_dict.get("model_type"   ) == "instructblip":
         lowerCAmelCase__   :				Dict 			=				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(snake_case			,					**snake_case   )






class 			__a					(						__magic_name__       ):





       """simple docstring"""


       __UpperCamelCase					:      List[str]    =						'instructblip'
       __UpperCamelCase					:      Tuple    =						True






       def __init__(   self			,					snake_case=None			,					snake_case=None			,					snake_case=None			,					snake_case=32			,					**snake_case   ):





        """simple docstring"""

        super().__init__(**snake_case   )

        if vision_config is None:
         lowerCAmelCase__   :				str 			=				{}
         logger.info("vision_config is None. initializing the InstructBlipVisionConfig with default values."   )

        if qformer_config is None:
         lowerCAmelCase__   :				str 			=				{}
         logger.info("qformer_config is None. Initializing the InstructBlipQFormerConfig with default values."   )

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

        lowerCAmelCase__   :				Optional[Any] 			=				InstructBlipVisionConfig(**snake_case   )
        lowerCAmelCase__   :				str 			=				InstructBlipQFormerConfig(**snake_case   )
        lowerCAmelCase__   :				List[Any] 			=				text_config["model_type"] if "model_type" in text_config else "opt"
        lowerCAmelCase__   :				str 			=				CONFIG_MAPPING[text_model_type](**snake_case   )

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

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






       @classmethod
       def 			SCREAMING_SNAKE_CASE_	(   cls			,					snake_case			,					snake_case			,					snake_case			,					**snake_case			,					):





        """simple docstring"""

        return cls(
            vision_config=vision_config.to_dict()			,					qformer_config=qformer_config.to_dict()			,					text_config=text_config.to_dict()			,					**snake_case			,					)






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Tuple 			=				copy.deepcopy(self.__dict__   )
        lowerCAmelCase__   :				Any 			=				self.vision_config.to_dict()
        lowerCAmelCase__   :				List[Any] 			=				self.qformer_config.to_dict()
        lowerCAmelCase__   :				Dict 			=				self.text_config.to_dict()
        lowerCAmelCase__   :				List[Any] 			=				self.__class__.model_type
        return output









"""simple docstring"""

from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


_UpperCamelCase		     =    """\
@misc{wu2016googles,
      title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
      author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey
              and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin
              Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto
              Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and
              Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes
              and Jeffrey Dean},
      year={2016},
      eprint={1609.08144},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
"""

_UpperCamelCase		     =    """\
The BLEU score has some undesirable properties when used for single
sentences, as it was designed to be a corpus measure. We therefore
use a slightly different score for our RL experiments which we call
the 'GLEU score'. For the GLEU score, we record all sub-sequences of
1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then
compute a recall, which is the ratio of the number of matching n-grams
to the number of total n-grams in the target (ground truth) sequence,
and a precision, which is the ratio of the number of matching n-grams
to the number of total n-grams in the generated output sequence. Then
GLEU score is simply the minimum of recall and precision. This GLEU
score's range is always between 0 (no matches) and 1 (all match) and
it is symmetrical when switching output and target. According to
our experiments, GLEU score correlates quite well with the BLEU
metric on a corpus level but does not have its drawbacks for our per
sentence reward objective.
"""

_UpperCamelCase		     =    """\
Computes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.
Instead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching
tokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.

Args:
    predictions (list of str): list of translations to score.
        Each translation should be tokenized into a list of tokens.
    references (list of list of str): list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
    min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.
    max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.

Returns:
    'google_bleu': google_bleu score

Examples:
    Example 1:
        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',
        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']
        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']

        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
        ...         'interested', 'in', 'world', 'history']
        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
        ...          'because', 'he', 'read', 'the', 'book']

        >>> list_of_references = [[ref1a], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric(\"google_bleu\")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
        >>> print(round(results[\"google_bleu\"], 2))
        0.44

    Example 2:
        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',
        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']
        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']
        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',
        ...          'heed', 'the', 'cat', 'commands']
        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',
        ...          'of', 'the', 'cat']

        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
        ...         'interested', 'in', 'world', 'history']
        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
        ...          'because', 'he', 'read', 'the', 'book']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric(\"google_bleu\")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
        >>> print(round(results[\"google_bleu\"], 2))
        0.61

    Example 3:
        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',
        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']
        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']
        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',
        ...          'heed', 'the', 'cat', 'commands']
        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',
        ...          'of', 'the', 'cat']

        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
        ...         'interested', 'in', 'world', 'history']
        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
        ...          'because', 'he', 'read', 'the', 'book']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric(\"google_bleu\")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)
        >>> print(round(results[\"google_bleu\"], 2))
        0.53

    Example 4:
        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',
        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']
        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']
        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',
        ...          'heed', 'the', 'cat', 'commands']
        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',
        ...          'of', 'the', 'cat']

        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
        ...         'interested', 'in', 'world', 'history']
        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
        ...          'because', 'he', 'read', 'the', 'book']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric(\"google_bleu\")
        >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)
        >>> print(round(results[\"google_bleu\"], 2))
        0.4
"""






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





       """simple docstring"""


       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        return datasets.MetricInfo(
            description=_DESCRIPTION			,					citation=_CITATION			,					inputs_description=_KWARGS_DESCRIPTION			,					features=datasets.Features(
                {
                    "predictions": datasets.Sequence(datasets.Value("string"			,					id="token"   )			,					id="sequence"   ),
                    "references": datasets.Sequence(
                        datasets.Sequence(datasets.Value("string"			,					id="token"   )			,					id="sequence"   )			,					id="references"   ),
                }   )			,					)






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case			,					snake_case = 1			,					snake_case = 4			,					):





        """simple docstring"""

        return {
            "google_bleu": gleu_score.corpus_gleu(
                list_of_references=snake_case			,					hypotheses=snake_case			,					min_len=snake_case			,					max_len=snake_case   )
        }






