




"""simple docstring"""






import copy
import os
from typing import Union

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


lowercase__  		=		logging.get_logger(__name__)

lowercase__  		=		{
    """BridgeTower/bridgetower-base""": """https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json""",
    """BridgeTower/bridgetower-base-itm-mlm""": (
        """https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm/blob/main/config.json"""
    ),
}






class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			lowerCamelCase__   =    """bridgetower_vision_model"""
			def __init__(						self						,   lowercase=768						,   lowercase=12						,   lowercase=3						,   lowercase=16						,   lowercase=288						,   lowercase=1						,   lowercase=1E-05						,   lowercase=False						,   lowercase=True						,   lowercase=False						,   **lowercase						,   ):
							super().__init__(**lowercase						)
							_lowerCamelCase  :    Any					=					hidden_size
							_lowerCamelCase  :    Optional[int]					=					num_hidden_layers
							_lowerCamelCase  :    Optional[Any]					=					num_channels
							_lowerCamelCase  :    Optional[int]					=					patch_size
							_lowerCamelCase  :    str					=					image_size
							_lowerCamelCase  :    int					=					initializer_factor
							_lowerCamelCase  :    List[Any]					=					layer_norm_eps
							_lowerCamelCase  :    List[Any]					=					stop_gradient
							_lowerCamelCase  :    Any					=					share_layernorm
							_lowerCamelCase  :    Union[str, Any]					=					remove_last_layer






			@classmethod
			def       A_					(						cls						,   lowercase						,   **lowercase						):
							_lowerCamelCase  :    Dict					=					cls.get_config_dict(lowercase						,   **lowercase						)

							if config_dict.get('model_type'						) == "bridgetower":
											_lowerCamelCase  :    List[Any]					=					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__			(  lowercase		):






			'''simple docstring'''



			lowerCamelCase__   =    """bridgetower_text_model"""
			def __init__(						self						,   lowercase=50265						,   lowercase=768						,   lowercase=12						,   lowercase=12						,   lowercase=1						,   lowercase=3072						,   lowercase="gelu"						,   lowercase=0.1						,   lowercase=0.1						,   lowercase=514						,   lowercase=1						,   lowercase=1E-05						,   lowercase=1						,   lowercase=0						,   lowercase=2						,   lowercase="absolute"						,   lowercase=True						,   **lowercase						,   ):
							super().__init__(**lowercase						)

							_lowerCamelCase  :    List[Any]					=					vocab_size
							_lowerCamelCase  :    Union[str, Any]					=					hidden_size
							_lowerCamelCase  :    Any					=					num_hidden_layers
							_lowerCamelCase  :    str					=					num_attention_heads
							_lowerCamelCase  :    Optional[Any]					=					hidden_act
							_lowerCamelCase  :    Tuple					=					initializer_factor
							_lowerCamelCase  :    Any					=					intermediate_size
							_lowerCamelCase  :    Dict					=					hidden_dropout_prob
							_lowerCamelCase  :    Tuple					=					attention_probs_dropout_prob
							_lowerCamelCase  :    Union[str, Any]					=					max_position_embeddings
							_lowerCamelCase  :    str					=					type_vocab_size
							_lowerCamelCase  :    Any					=					layer_norm_eps
							_lowerCamelCase  :    List[str]					=					position_embedding_type
							_lowerCamelCase  :    Dict					=					use_cache
							_lowerCamelCase  :    Union[str, Any]					=					pad_token_id
							_lowerCamelCase  :    str					=					bos_token_id
							_lowerCamelCase  :    Tuple					=					eos_token_id






			@classmethod
			def       A_					(						cls						,   lowercase						,   **lowercase						):
							_lowerCamelCase  :    Dict					=					cls.get_config_dict(lowercase						,   **lowercase						)

							if config_dict.get('model_type'						) == "bridgetower":
											_lowerCamelCase  :    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__			(  lowercase		):






			'''simple docstring'''



			lowerCamelCase__   =    """bridgetower"""
			def __init__(						self						,   lowercase=True						,   lowercase="gelu"						,   lowercase=768						,   lowercase=1						,   lowercase=1E-05						,   lowercase=False						,   lowercase="add"						,   lowercase=12						,   lowercase=6						,   lowercase=False						,   lowercase=False						,   lowercase=None						,   lowercase=None						,   **lowercase						,   ):
							# TODO: remove this once the Hub files are updated.
							_lowerCamelCase  :    Optional[Any]					=					kwargs.pop('text_config_dict'						,   lowercase						)
							_lowerCamelCase  :    Optional[Any]					=					kwargs.pop('vision_config_dict'						,   lowercase						)

							super().__init__(**lowercase						)
							_lowerCamelCase  :    List[str]					=					share_cross_modal_transformer_layers
							_lowerCamelCase  :    Optional[int]					=					hidden_act
							_lowerCamelCase  :    Union[str, Any]					=					hidden_size
							_lowerCamelCase  :    Any					=					initializer_factor
							_lowerCamelCase  :    Tuple					=					layer_norm_eps
							_lowerCamelCase  :    str					=					share_link_tower_layers
							_lowerCamelCase  :    Tuple					=					link_tower_type
							_lowerCamelCase  :    Optional[int]					=					num_attention_heads
							_lowerCamelCase  :    str					=					num_hidden_layers
							_lowerCamelCase  :    Optional[int]					=					tie_word_embeddings
							_lowerCamelCase  :    Tuple					=					init_layernorm_from_vision_encoder

							if text_config is None:
											_lowerCamelCase  :    Tuple					=					{}
											logger.info('`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values.'						)

							if vision_config is None:
											_lowerCamelCase  :    Tuple					=					{}
											logger.info('`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values.'						)

							_lowerCamelCase  :    Dict					=					BridgeTowerTextConfig(**lowercase						)
							_lowerCamelCase  :    Union[str, Any]					=					BridgeTowerVisionConfig(**lowercase						)
			@classmethod
			def       A_					(						cls						,   lowercase						,   lowercase						,   **lowercase						):
							return cls(text_config=text_config.to_dict()						,   vision_config=vision_config.to_dict()						,   **lowercase						)






			def       A_					(						self						):
							_lowerCamelCase  :    Any					=					copy.deepcopy(self.__dict__						)
							_lowerCamelCase  :    Dict					=					self.text_config.to_dict()
							_lowerCamelCase  :    List[Any]					=					self.vision_config.to_dict()
							_lowerCamelCase  :    Optional[int]					=					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


lowercase__  		=		"""\
@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}
}
"""

lowercase__  		=		"""\
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.
"""

lowercase__  		=		"""\
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 lowerCAmelCase__			(  datasets.Metric		):






			'''simple docstring'''



			def       A_					(						self						):
							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       A_					(						self						,   lowercase						,   lowercase						,   lowercase = 1						,   lowercase = 4						,   ):
							return {
							    "google_bleu": gleu_score.corpus_gleu(
							        list_of_references=lowercase						,   hypotheses=lowercase						,   min_len=lowercase						,   max_len=lowercase						)
							}