import absl  # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import six  # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring

import datasets


UpperCAmelCase_								=							'''\\n@inproceedings{lin-2004-rouge,\n    title = \"{ROUGE}: A Package for Automatic Evaluation of Summaries\",\n    author = \"Lin, Chin-Yew\",\n    booktitle = \"Text Summarization Branches Out\",\n    month = jul,\n    year = \"2004\",\n    address = \"Barcelona, Spain\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W04-1013\",\n    pages = \"74--81\",\n}\n'''

UpperCAmelCase_								=							'''\\nROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for\nevaluating automatic summarization and machine translation software in natural language processing.\nThe metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.\n\nNote that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.\n\nThis metrics is a wrapper around Google Research reimplementation of ROUGE:\nhttps://github.com/google-research/google-research/tree/master/rouge\n'''

UpperCAmelCase_								=							'''\nCalculates average rouge scores for a list of hypotheses and references\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    rouge_types: A list of rouge types to calculate.\n        Valid names:\n        `\"rouge{n}\"` (e.g. `\"rouge1\"`, `\"rouge2\"`) where: {n} is the n-gram based scoring,\n        `\"rougeL\"`: Longest common subsequence based scoring.\n        `\"rougeLSum\"`: rougeLsum splits text using `\"\n\"`.\n        See details in https://github.com/huggingface/datasets/issues/617\n    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.\n    use_aggregator: Return aggregates if this is set to True\nReturns:\n    rouge1: rouge_1 (precision, recall, f1),\n    rouge2: rouge_2 (precision, recall, f1),\n    rougeL: rouge_l (precision, recall, f1),\n    rougeLsum: rouge_lsum (precision, recall, f1)\nExamples:\n\n    >>> rouge = datasets.load_metric(\'rouge\')\n    >>> predictions = [\"hello there\", \"general kenobi\"]\n    >>> references = [\"hello there\", \"general kenobi\"]\n    >>> results = rouge.compute(predictions=predictions, references=references)\n    >>> print(list(results.keys()))\n    [\'rouge1\', \'rouge2\', \'rougeL\', \'rougeLsum\']\n    >>> print(results[\"rouge1\"])\n    AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))\n    >>> print(results[\"rouge1\"].mid.fmeasure)\n    1.0\n'''





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



     def 			a__  (					self       ):
            return datasets.MetricInfo(
                description=_DESCRIPTION				,      citation=_CITATION				,      inputs_description=_KWARGS_DESCRIPTION				,      features=datasets.Features(
                    {
                        'predictions': datasets.Value('string'				,      id='sequence'       ),
                        'references': datasets.Value('string'				,      id='sequence'       ),
                    }       )				,      codebase_urls=['https://github.com/google-research/google-research/tree/master/rouge']				,      reference_urls=[
                    'https://en.wikipedia.org/wiki/ROUGE_(metric)',
                    'https://github.com/google-research/google-research/tree/master/rouge',
                ]				,      )





     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__=None				,      lowerCAmelCase__=True				,      lowerCAmelCase__=False       ):
            if rouge_types is None:
                   _A=	['rouge1', 'rouge2', 'rougeL', 'rougeLsum']

            _A=	rouge_scorer.RougeScorer(rouge_types=lowerCAmelCase__				,      use_stemmer=lowerCAmelCase__       )
            if use_aggregator:
                   _A=	scoring.BootstrapAggregator()
            else:
                   _A=	[]

            for ref, pred in zip(lowerCAmelCase__				,      lowerCAmelCase__       ):
                   _A=	scorer.score(lowerCAmelCase__				,      lowerCAmelCase__       )
                   if use_aggregator:
                          aggregator.add_scores(lowerCAmelCase__       )
                   else:
                          scores.append(lowerCAmelCase__       )

            if use_aggregator:
                   _A=	aggregator.aggregate()
            else:
                   _A=	{}
                   for key in scores[0]:
                          _A=	[score[key] for score in scores]

            return result
import copy
import os
from typing import Union

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


UpperCAmelCase_								=							logging.get_logger(__name__)

UpperCAmelCase_								=							{
    '''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						(					_a			):
     _SCREAMING_SNAKE_CASE     :     List[Any]		="""bridgetower_vision_model"""



     def __init__(					self				,      lowerCAmelCase__=768				,      lowerCAmelCase__=12				,      lowerCAmelCase__=3				,      lowerCAmelCase__=16				,      lowerCAmelCase__=288				,      lowerCAmelCase__=1				,      lowerCAmelCase__=1E-05				,      lowerCAmelCase__=False				,      lowerCAmelCase__=True				,      lowerCAmelCase__=False				,      **lowerCAmelCase__				,      ):
            super().__init__(**lowerCAmelCase__       )
            _A=	hidden_size
            _A=	num_hidden_layers
            _A=	num_channels
            _A=	patch_size
            _A=	image_size
            _A=	initializer_factor
            _A=	layer_norm_eps
            _A=	stop_gradient
            _A=	share_layernorm
            _A=	remove_last_layer





     @classmethod
     def 			a__  (					cls				,      lowerCAmelCase__				,      **lowerCAmelCase__       ):
            _A,			_A=	cls.get_config_dict(lowerCAmelCase__				,      **lowerCAmelCase__       )

            if config_dict.get('model_type'       ) == "bridgetower":
                   _A=	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(lowerCAmelCase__				,      **lowerCAmelCase__       )





class      lowerCAmelCase						(					_a			):
     _SCREAMING_SNAKE_CASE     :     List[Any]		="""bridgetower_text_model"""



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

            _A=	vocab_size
            _A=	hidden_size
            _A=	num_hidden_layers
            _A=	num_attention_heads
            _A=	hidden_act
            _A=	initializer_factor
            _A=	intermediate_size
            _A=	hidden_dropout_prob
            _A=	attention_probs_dropout_prob
            _A=	max_position_embeddings
            _A=	type_vocab_size
            _A=	layer_norm_eps
            _A=	position_embedding_type
            _A=	use_cache
            _A=	pad_token_id
            _A=	bos_token_id
            _A=	eos_token_id





     @classmethod
     def 			a__  (					cls				,      lowerCAmelCase__				,      **lowerCAmelCase__       ):
            _A,			_A=	cls.get_config_dict(lowerCAmelCase__				,      **lowerCAmelCase__       )

            if config_dict.get('model_type'       ) == "bridgetower":
                   _A=	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(lowerCAmelCase__				,      **lowerCAmelCase__       )





class      lowerCAmelCase						(					_a			):
     _SCREAMING_SNAKE_CASE     :     Dict		="""bridgetower"""



     def __init__(					self				,      lowerCAmelCase__=True				,      lowerCAmelCase__="gelu"				,      lowerCAmelCase__=768				,      lowerCAmelCase__=1				,      lowerCAmelCase__=1E-05				,      lowerCAmelCase__=False				,      lowerCAmelCase__="add"				,      lowerCAmelCase__=12				,      lowerCAmelCase__=6				,      lowerCAmelCase__=False				,      lowerCAmelCase__=False				,      lowerCAmelCase__=None				,      lowerCAmelCase__=None				,      **lowerCAmelCase__				,      ):
            # TODO: remove this once the Hub files are updated.
            _A=	kwargs.pop('text_config_dict'				,      lowerCAmelCase__       )
            _A=	kwargs.pop('vision_config_dict'				,      lowerCAmelCase__       )

            super().__init__(**lowerCAmelCase__       )
            _A=	share_cross_modal_transformer_layers
            _A=	hidden_act
            _A=	hidden_size
            _A=	initializer_factor
            _A=	layer_norm_eps
            _A=	share_link_tower_layers
            _A=	link_tower_type
            _A=	num_attention_heads
            _A=	num_hidden_layers
            _A=	tie_word_embeddings
            _A=	init_layernorm_from_vision_encoder

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

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

            _A=	BridgeTowerTextConfig(**lowerCAmelCase__       )
            _A=	BridgeTowerVisionConfig(**lowerCAmelCase__       )



     @classmethod
     def 			a__  (					cls				,      lowerCAmelCase__				,      lowerCAmelCase__				,      **lowerCAmelCase__       ):
            return cls(text_config=text_config.to_dict()				,      vision_config=vision_config.to_dict()				,      **lowerCAmelCase__       )





     def 			a__  (					self       ):
            _A=	copy.deepcopy(self.__dict__       )
            _A=	self.text_config.to_dict()
            _A=	self.vision_config.to_dict()
            _A=	self.__class__.model_type
            return output