
import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


UpperCAmelCase	      = '''
@inproceedings{xu-etal-2016-optimizing,
    title = {Optimizing Statistical Machine Translation for Text Simplification},
    authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},
    journal = {Transactions of the Association for Computational Linguistics},
    volume = {4},
    year={2016},
    url = {https://www.aclweb.org/anthology/Q16-1029},
    pages = {401--415
},
@inproceedings{post-2018-call,
    title = "A Call for Clarity in Reporting {BLEU} Scores",
    author = "Post, Matt",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6319",
    pages = "186--191",
}
'''

UpperCAmelCase	      = '''\
WIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU
It can be used to evaluate the quality of machine-generated texts.
'''


UpperCAmelCase	      = '''
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.
Args:
    sources: list of source sentences where each sentence should be a string.
    predictions: list of predicted sentences where each sentence should be a string.
    references: list of lists of reference sentences where each sentence should be a string.
Returns:
    sari: sari score
    sacrebleu: sacrebleu score
    exact: exact score

Examples:
    >>> sources=["About 95 species are currently accepted ."]
    >>> predictions=["About 95 you now get in ."]
    >>> references=[["About 95 species are currently known ."]]
    >>> wiki_split = datasets.load_metric("wiki_split")
    >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)
    >>> print(results)
    {\'sari\': 21.805555555555557, \'sacrebleu\': 14.535768424205482, \'exact\': 0.0}
'''






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):

	def remove_articles(__SCREAMING_SNAKE_CASE							):
		lowercase       =    re.compile(r'\b(a|an|the)\b' ,							re.UNICODE							)
		return re.sub(__SCREAMING_SNAKE_CASE ,							' ' ,							__SCREAMING_SNAKE_CASE							)

	def white_space_fix(__SCREAMING_SNAKE_CASE							):
		return " ".join(text.split()							)

	def remove_punc(__SCREAMING_SNAKE_CASE							):
		lowercase       =    set(string.punctuation							)
		return "".join(ch for ch in text if ch not in exclude							)

	def lower(__SCREAMING_SNAKE_CASE							):
		return text.lower()

	return white_space_fix(remove_articles(remove_punc(lower(__SCREAMING_SNAKE_CASE							)							)							)							)






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	return int(normalize_answer(__SCREAMING_SNAKE_CASE							) == normalize_answer(__SCREAMING_SNAKE_CASE							)							)






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	lowercase       =    [any(compute_exact(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							) for ref in refs							) for pred, refs in zip(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)]
	return (sum(__SCREAMING_SNAKE_CASE							) / len(__SCREAMING_SNAKE_CASE							)) * 100






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	lowercase       =    [rgram for rgrams in rgramslist for rgram in rgrams]
	lowercase       =    Counter(__SCREAMING_SNAKE_CASE							)

	lowercase       =    Counter(__SCREAMING_SNAKE_CASE							)
	lowercase       =    Counter()
	for sgram, scount in sgramcounter.items():
		lowercase       =    scount * numref

	lowercase       =    Counter(__SCREAMING_SNAKE_CASE							)
	lowercase       =    Counter()
	for cgram, ccount in cgramcounter.items():
		lowercase       =    ccount * numref

	# KEEP
	lowercase       =    sgramcounter_rep & cgramcounter_rep
	lowercase       =    keepgramcounter_rep & rgramcounter
	lowercase       =    sgramcounter_rep & rgramcounter

	lowercase       =    0
	lowercase       =    0
	for keepgram in keepgramcountergood_rep:
		keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
		# Fix an alleged bug [2] in the keep score computation.
		# keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
		keeptmpscorea += keepgramcountergood_rep[keepgram]
	# Define 0/0=1 instead of 0 to give higher scores for predictions that match
	#      a target exactly.
	lowercase       =    1
	lowercase       =    1
	if len(__SCREAMING_SNAKE_CASE							) > 0:
		lowercase       =    keeptmpscorea / len(__SCREAMING_SNAKE_CASE							)
	if len(__SCREAMING_SNAKE_CASE							) > 0:
		# Fix an alleged bug [2] in the keep score computation.
		# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
		lowercase       =    keeptmpscorea / sum(keepgramcounterall_rep.values()							)
	lowercase       =    0
	if keepscore_precision > 0 or keepscore_recall > 0:
		lowercase       =    2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

	# DELETION
	lowercase       =    sgramcounter_rep - cgramcounter_rep
	lowercase       =    delgramcounter_rep - rgramcounter
	lowercase       =    sgramcounter_rep - rgramcounter
	lowercase       =    0
	lowercase       =    0
	for delgram in delgramcountergood_rep:
		deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
		deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
	# Define 0/0=1 instead of 0 to give higher scores for predictions that match
	# a target exactly.
	lowercase       =    1
	if len(__SCREAMING_SNAKE_CASE							) > 0:
		lowercase       =    deltmpscorea / len(__SCREAMING_SNAKE_CASE							)

	# ADDITION
	lowercase       =    set(__SCREAMING_SNAKE_CASE							) - set(__SCREAMING_SNAKE_CASE							)
	lowercase       =    set(__SCREAMING_SNAKE_CASE							) & set(__SCREAMING_SNAKE_CASE							)
	lowercase       =    set(__SCREAMING_SNAKE_CASE							) - set(__SCREAMING_SNAKE_CASE							)

	lowercase       =    0
	for addgram in addgramcountergood:
		addtmpscore += 1

	# Define 0/0=1 instead of 0 to give higher scores for predictions that match
	# a target exactly.
	lowercase       =    1
	lowercase       =    1
	if len(__SCREAMING_SNAKE_CASE							) > 0:
		lowercase       =    addtmpscore / len(__SCREAMING_SNAKE_CASE							)
	if len(__SCREAMING_SNAKE_CASE							) > 0:
		lowercase       =    addtmpscore / len(__SCREAMING_SNAKE_CASE							)
	lowercase       =    0
	if addscore_precision > 0 or addscore_recall > 0:
		lowercase       =    2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

	return (keepscore, delscore_precision, addscore)






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	lowercase       =    len(__SCREAMING_SNAKE_CASE							)

	lowercase       =    ssent.split(' '							)
	lowercase       =    csent.split(' '							)
	lowercase       =    []
	lowercase       =    []
	lowercase       =    []
	lowercase       =    []
	lowercase       =    []
	lowercase       =    []

	lowercase       =    []
	lowercase       =    []
	lowercase       =    []
	lowercase       =    []
	for rsent in rsents:
		lowercase       =    rsent.split(' '							)
		lowercase       =    []
		lowercase       =    []
		lowercase       =    []
		ragramslist.append(__SCREAMING_SNAKE_CASE							)
		for i in range(0 ,							len(__SCREAMING_SNAKE_CASE							) - 1							):
			if i < len(__SCREAMING_SNAKE_CASE							) - 1:
				lowercase       =    ragrams[i] + ' ' + ragrams[i + 1]
				ragrams.append(__SCREAMING_SNAKE_CASE							)
			if i < len(__SCREAMING_SNAKE_CASE							) - 2:
				lowercase       =    ragrams[i] + ' ' + ragrams[i + 1] + ' ' + ragrams[i + 2]
				ragrams.append(__SCREAMING_SNAKE_CASE							)
			if i < len(__SCREAMING_SNAKE_CASE							) - 3:
				lowercase       =    ragrams[i] + ' ' + ragrams[i + 1] + ' ' + ragrams[i + 2] + ' ' + ragrams[i + 3]
				ragrams.append(__SCREAMING_SNAKE_CASE							)
		ragramslist.append(__SCREAMING_SNAKE_CASE							)
		ragramslist.append(__SCREAMING_SNAKE_CASE							)
		ragramslist.append(__SCREAMING_SNAKE_CASE							)

	for i in range(0 ,							len(__SCREAMING_SNAKE_CASE							) - 1							):
		if i < len(__SCREAMING_SNAKE_CASE							) - 1:
			lowercase       =    sagrams[i] + ' ' + sagrams[i + 1]
			sagrams.append(__SCREAMING_SNAKE_CASE							)
		if i < len(__SCREAMING_SNAKE_CASE							) - 2:
			lowercase       =    sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2]
			sagrams.append(__SCREAMING_SNAKE_CASE							)
		if i < len(__SCREAMING_SNAKE_CASE							) - 3:
			lowercase       =    sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2] + ' ' + sagrams[i + 3]
			sagrams.append(__SCREAMING_SNAKE_CASE							)

	for i in range(0 ,							len(__SCREAMING_SNAKE_CASE							) - 1							):
		if i < len(__SCREAMING_SNAKE_CASE							) - 1:
			lowercase       =    cagrams[i] + ' ' + cagrams[i + 1]
			cagrams.append(__SCREAMING_SNAKE_CASE							)
		if i < len(__SCREAMING_SNAKE_CASE							) - 2:
			lowercase       =    cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2]
			cagrams.append(__SCREAMING_SNAKE_CASE							)
		if i < len(__SCREAMING_SNAKE_CASE							) - 3:
			lowercase       =    cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2] + ' ' + cagrams[i + 3]
			cagrams.append(__SCREAMING_SNAKE_CASE							)

	((lowercase)  ,					(lowercase)  ,					(lowercase))       =    SARIngram(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)
	((lowercase)  ,					(lowercase)  ,					(lowercase))       =    SARIngram(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)
	((lowercase)  ,					(lowercase)  ,					(lowercase))       =    SARIngram(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)
	((lowercase)  ,					(lowercase)  ,					(lowercase))       =    SARIngram(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)
	lowercase       =    sum([keepascore, keepascore, keepascore, keepascore]							) / 4
	lowercase       =    sum([delascore, delascore, delascore, delascore]							) / 4
	lowercase       =    sum([addascore, addascore, addascore, addascore]							) / 4
	lowercase       =    (avgkeepscore + avgdelscore + avgaddscore) / 3
	return finalscore






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE = True ,							__SCREAMING_SNAKE_CASE = "13a" ,							__SCREAMING_SNAKE_CASE = True							):
	# Normalization is requried for the ASSET dataset (one of the primary
	# datasets in sentence simplification) to allow using space
	# to split the sentence. Even though Wiki-Auto and TURK datasets,
	# do not require normalization, we do it for consistency.
	# Code adapted from the EASSE library [1] written by the authors of the ASSET dataset.
	# [1] https://github.com/feralvam/easse/blob/580bba7e1378fc8289c663f864e0487188fe8067/easse/utils/preprocessing.py#L7

	if lowercase:
		lowercase       =    sentence.lower()

	if tokenizer in ["13a", "intl"]:
		if version.parse(sacrebleu.__version__							).major >= 2:
			lowercase       =    sacrebleu.metrics.bleu._get_tokenizer(__SCREAMING_SNAKE_CASE							)()(__SCREAMING_SNAKE_CASE							)
		else:
			lowercase       =    sacrebleu.TOKENIZERS[tokenizer]()(__SCREAMING_SNAKE_CASE							)
	elif tokenizer == "moses":
		lowercase       =    sacremoses.MosesTokenizer().tokenize(__SCREAMING_SNAKE_CASE ,							return_str=__SCREAMING_SNAKE_CASE ,							escape=__SCREAMING_SNAKE_CASE							)
	elif tokenizer == "penn":
		lowercase       =    sacremoses.MosesTokenizer().penn_tokenize(__SCREAMING_SNAKE_CASE ,							return_str=__SCREAMING_SNAKE_CASE							)
	else:
		lowercase       =    sentence

	if not return_str:
		lowercase       =    normalized_sent.split()

	return normalized_sent






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	if not (len(__SCREAMING_SNAKE_CASE							) == len(__SCREAMING_SNAKE_CASE							) == len(__SCREAMING_SNAKE_CASE							)):
		raise ValueError('Sources length must match predictions and references lengths.'							)
	lowercase       =    0
	for src, pred, refs in zip(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
		sari_score += SARIsent(normalize(__SCREAMING_SNAKE_CASE							) ,							normalize(__SCREAMING_SNAKE_CASE							) ,							[normalize(__SCREAMING_SNAKE_CASE							) for sent in refs]							)
	lowercase       =    sari_score / len(__SCREAMING_SNAKE_CASE							)
	return 100 * sari_score






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE="exp" ,							__SCREAMING_SNAKE_CASE=None ,							__SCREAMING_SNAKE_CASE=False ,							__SCREAMING_SNAKE_CASE=False ,							__SCREAMING_SNAKE_CASE=False ,							):
	lowercase       =    len(references[0]							)
	if any(len(__SCREAMING_SNAKE_CASE							) != references_per_prediction for refs in references							):
		raise ValueError('Sacrebleu requires the same number of references for each prediction'							)
	lowercase       =    [[refs[i] for refs in references] for i in range(__SCREAMING_SNAKE_CASE							)]
	lowercase       =    sacrebleu.corpus_bleu(
	    __SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							smooth_method=__SCREAMING_SNAKE_CASE ,							smooth_value=__SCREAMING_SNAKE_CASE ,							force=__SCREAMING_SNAKE_CASE ,							lowercase=__SCREAMING_SNAKE_CASE ,							use_effective_order=__SCREAMING_SNAKE_CASE ,							)
	return output.score







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




	'''simple docstring'''


	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		return datasets.MetricInfo(
		    description=_DESCRIPTION		,    citation=_CITATION		,    inputs_description=_KWARGS_DESCRIPTION		,    features=datasets.Features(
		        {
		            'predictions': datasets.Value('string'		,    id='sequence'       ),
		            'references': datasets.Sequence(datasets.Value('string'		,    id='sequence'       )		,    id='references'       ),
		        }       )		,    codebase_urls=[
		        'https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py',
		        'https://github.com/cocoxu/simplification/blob/master/SARI.py',
		        'https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py',
		        'https://github.com/mjpost/sacreBLEU',
		    ]		,    reference_urls=[
		        'https://www.aclweb.org/anthology/Q16-1029.pdf',
		        'https://github.com/mjpost/sacreBLEU',
		        'https://en.wikipedia.org/wiki/BLEU',
		        'https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213',
		    ]		,    )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case       ):
		lowercase       =    {}
		result.update({'sari': compute_sari(sources=snake_case		,    predictions=snake_case		,    references=snake_case       )}       )
		result.update({'sacrebleu': compute_sacrebleu(predictions=snake_case		,    references=snake_case       )}       )
		result.update({'exact': compute_em(predictions=snake_case		,    references=snake_case       )}       )
		return result



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/blip2-opt-2.7b''': '''https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json''',
}







class        A_   (			__lowerCamelCase   ):




	'''simple docstring'''


	_UpperCamelCase     :     Optional[Any]							 =       """blip_2_vision_model"""





	def __init__(       self		,    snake_case=1408		,    snake_case=6144		,    snake_case=39		,    snake_case=16		,    snake_case=224		,    snake_case=14		,    snake_case="gelu"		,    snake_case=0.00_001		,    snake_case=0.0		,    snake_case=1E-10		,    snake_case=True		,    **snake_case		,    ):
		super().__init__(**snake_case       )

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





	@classmethod
	def 	SCREAMING_SNAKE_CASE__						(       cls		,    snake_case		,    **snake_case       ):
		cls._set_token_in_kwargs(snake_case       )

		lowercase  ,					lowercase       =    cls.get_config_dict(snake_case		,    **snake_case       )

		# get the vision config dict if we are loading from Blip2Config
		if config_dict.get('model_type'       ) == "blip-2":
			lowercase       =    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_   (			__lowerCamelCase   ):




	'''simple docstring'''


	_UpperCamelCase     :     int							 =       """blip_2_qformer"""





	def __init__(       self		,    snake_case=3_0522		,    snake_case=768		,    snake_case=12		,    snake_case=12		,    snake_case=3072		,    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=1408		,    **snake_case		,    ):
		super().__init__(pad_token_id=snake_case		,    **snake_case       )

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





	@classmethod
	def 	SCREAMING_SNAKE_CASE__						(       cls		,    snake_case		,    **snake_case       ):
		cls._set_token_in_kwargs(snake_case       )

		lowercase  ,					lowercase       =    cls.get_config_dict(snake_case		,    **snake_case       )

		# get the qformer config dict if we are loading from Blip2Config
		if config_dict.get('model_type'       ) == "blip-2":
			lowercase       =    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_   (			__lowerCamelCase   ):




	'''simple docstring'''


	_UpperCamelCase     :     List[Any]							 =       """blip-2"""
	_UpperCamelCase     :     str							 =       True





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

		if vision_config is None:
			lowercase       =    {}
			logger.info('vision_config is None. initializing the Blip2VisionConfig with default values.'       )

		if qformer_config is None:
			lowercase       =    {}
			logger.info('qformer_config is None. Initializing the Blip2QFormerConfig with default values.'       )

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

		lowercase       =    BlipaVisionConfig(**snake_case       )
		lowercase       =    BlipaQFormerConfig(**snake_case       )
		lowercase       =    text_config['model_type'] if 'model_type' in text_config else 'opt'
		lowercase       =    CONFIG_MAPPING[text_model_type](**snake_case       )

		lowercase       =    self.text_config.tie_word_embeddings
		lowercase       =    self.text_config.is_encoder_decoder

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





	@classmethod
	def 	SCREAMING_SNAKE_CASE__						(       cls		,    snake_case		,    snake_case		,    snake_case		,    **snake_case		,    ):
		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       ):
		lowercase       =    copy.deepcopy(self.__dict__       )
		lowercase       =    self.vision_config.to_dict()
		lowercase       =    self.qformer_config.to_dict()
		lowercase       =    self.text_config.to_dict()
		lowercase       =    self.__class__.model_type
		return output

