





"""simple docstring"""




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},
}
"""

UpperCamelCase_           ="""\
SARI is a metric used for evaluating automatic text simplification systems.
The metric compares the predicted simplified sentences against the reference
and the source sentences. It explicitly measures the goodness of words that are
added, deleted and kept by the system.
Sari = (F1_add + F1_keep + P_del) / 3
where
F1_add: n-gram F1 score for add operation
F1_keep: n-gram F1 score for keep operation
P_del: n-gram precision score for delete operation
n = 4, as in the original paper.

This implementation is adapted from Tensorflow's tensor2tensor implementation [3].
It has two differences with the original GitHub [1] implementation:
  (1) Defines 0/0=1 instead of 0 to give higher scores for predictions that match
      a target exactly.
  (2) Fixes an alleged bug [2] in the keep score computation.
[1] https://github.com/cocoxu/simplification/blob/master/SARI.py
    (commit 0210f15)
[2] https://github.com/cocoxu/simplification/issues/6
[3] https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py
"""


UpperCamelCase_           ="""
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences.
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
Examples:
    >>> sources=[\"About 95 species are currently accepted .\"]
    >>> predictions=[\"About 95 you now get in .\"]
    >>> references=[[\"About 95 species are currently known .\",\"About 95 species are now accepted .\",\"95 species are now accepted .\"]]
    >>> sari = datasets.load_metric(\"sari\")
    >>> results = sari.compute(sources=sources, predictions=predictions, references=references)
    >>> print(results)
    {'sari': 26.953601953601954}
"""





def        a_				(							_lowercase       ,						_lowercase       ,						_lowercase       ,						_lowercase   ):
						_UpperCamelCase			:					Dict     =    [rgram for rgrams in rgramslist for rgram in rgrams]
						_UpperCamelCase			:					Optional[Any]     =    Counter(SCREAMING_SNAKE_CASE_   )

						_UpperCamelCase			:					int     =    Counter(SCREAMING_SNAKE_CASE_   )
						_UpperCamelCase			:					Dict     =    Counter()
						for sgram, scount in sgramcounter.items():
												_UpperCamelCase			:					Optional[Any]     =    scount * numref

						_UpperCamelCase			:					str     =    Counter(SCREAMING_SNAKE_CASE_   )
						_UpperCamelCase			:					Any     =    Counter()
						for cgram, ccount in cgramcounter.items():
												_UpperCamelCase			:					Tuple     =    ccount * numref

						# KEEP
						_UpperCamelCase			:					int     =    sgramcounter_rep & cgramcounter_rep
						_UpperCamelCase			:					int     =    keepgramcounter_rep & rgramcounter
						_UpperCamelCase			:					Union[str, Any]     =    sgramcounter_rep & rgramcounter

						_UpperCamelCase			:					Union[str, Any]     =    0
						_UpperCamelCase			:					Tuple     =    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.
						_UpperCamelCase			:					Union[str, Any]     =    1
						_UpperCamelCase			:					List[str]     =    1
						if len(SCREAMING_SNAKE_CASE_   ) > 0:
												_UpperCamelCase			:					Union[str, Any]     =    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)
												_UpperCamelCase			:					Dict     =    keeptmpscorea / sum(keepgramcounterall_rep.values()   )
						_UpperCamelCase			:					Tuple     =    0
						if keepscore_precision > 0 or keepscore_recall > 0:
												_UpperCamelCase			:					Tuple     =    2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

						# DELETION
						_UpperCamelCase			:					Tuple     =    sgramcounter_rep - cgramcounter_rep
						_UpperCamelCase			:					Tuple     =    delgramcounter_rep - rgramcounter
						_UpperCamelCase			:					List[Any]     =    sgramcounter_rep - rgramcounter
						_UpperCamelCase			:					Union[str, Any]     =    0
						_UpperCamelCase			:					List[str]     =    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.
						_UpperCamelCase			:					Optional[Any]     =    1
						if len(SCREAMING_SNAKE_CASE_   ) > 0:
												_UpperCamelCase			:					str     =    deltmpscorea / len(SCREAMING_SNAKE_CASE_   )

						# ADDITION
						_UpperCamelCase			:					Union[str, Any]     =    set(SCREAMING_SNAKE_CASE_   ) - set(SCREAMING_SNAKE_CASE_   )
						_UpperCamelCase			:					Union[str, Any]     =    set(SCREAMING_SNAKE_CASE_   ) & set(SCREAMING_SNAKE_CASE_   )
						_UpperCamelCase			:					int     =    set(SCREAMING_SNAKE_CASE_   ) - set(SCREAMING_SNAKE_CASE_   )

						_UpperCamelCase			:					Tuple     =    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.
						_UpperCamelCase			:					Dict     =    1
						_UpperCamelCase			:					Any     =    1
						if len(SCREAMING_SNAKE_CASE_   ) > 0:
												_UpperCamelCase			:					List[Any]     =    addtmpscore / len(SCREAMING_SNAKE_CASE_   )
						if len(SCREAMING_SNAKE_CASE_   ) > 0:
												_UpperCamelCase			:					Dict     =    addtmpscore / len(SCREAMING_SNAKE_CASE_   )
						_UpperCamelCase			:					str     =    0
						if addscore_precision > 0 or addscore_recall > 0:
												_UpperCamelCase			:					List[Any]     =    2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

						return (keepscore, delscore_precision, addscore)





def        a_				(							_lowercase       ,						_lowercase       ,						_lowercase   ):
						_UpperCamelCase			:					List[Any]     =    len(SCREAMING_SNAKE_CASE_   )

						_UpperCamelCase			:					Optional[int]     =    ssent.split(''' '''   )
						_UpperCamelCase			:					List[Any]     =    csent.split(''' '''   )
						_UpperCamelCase			:					Dict     =    []
						_UpperCamelCase			:					Dict     =    []
						_UpperCamelCase			:					Union[str, Any]     =    []
						_UpperCamelCase			:					Dict     =    []
						_UpperCamelCase			:					Tuple     =    []
						_UpperCamelCase			:					Union[str, Any]     =    []

						_UpperCamelCase			:					Tuple     =    []
						_UpperCamelCase			:					Dict     =    []
						_UpperCamelCase			:					List[str]     =    []
						_UpperCamelCase			:					Optional[Any]     =    []
						for rsent in rsents:
												_UpperCamelCase			:					Any     =    rsent.split(''' '''   )
												_UpperCamelCase			:					str     =    []
												_UpperCamelCase			:					List[str]     =    []
												_UpperCamelCase			:					Union[str, Any]     =    []
												ragramslist.append(SCREAMING_SNAKE_CASE_   )
												for i in range(0       ,						len(SCREAMING_SNAKE_CASE_   ) - 1   ):
																		if i < len(SCREAMING_SNAKE_CASE_   ) - 1:
																								_UpperCamelCase			:					int     =    ragrams[i] + ''' ''' + ragrams[i + 1]
																								ragrams.append(SCREAMING_SNAKE_CASE_   )
																		if i < len(SCREAMING_SNAKE_CASE_   ) - 2:
																								_UpperCamelCase			:					Optional[int]     =    ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2]
																								ragrams.append(SCREAMING_SNAKE_CASE_   )
																		if i < len(SCREAMING_SNAKE_CASE_   ) - 3:
																								_UpperCamelCase			:					Optional[int]     =    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:
																		_UpperCamelCase			:					Any     =    sagrams[i] + ''' ''' + sagrams[i + 1]
																		sagrams.append(SCREAMING_SNAKE_CASE_   )
												if i < len(SCREAMING_SNAKE_CASE_   ) - 2:
																		_UpperCamelCase			:					Any     =    sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2]
																		sagrams.append(SCREAMING_SNAKE_CASE_   )
												if i < len(SCREAMING_SNAKE_CASE_   ) - 3:
																		_UpperCamelCase			:					Optional[Any]     =    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:
																		_UpperCamelCase			:					int     =    cagrams[i] + ''' ''' + cagrams[i + 1]
																		cagrams.append(SCREAMING_SNAKE_CASE_   )
												if i < len(SCREAMING_SNAKE_CASE_   ) - 2:
																		_UpperCamelCase			:					Optional[int]     =    cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2]
																		cagrams.append(SCREAMING_SNAKE_CASE_   )
												if i < len(SCREAMING_SNAKE_CASE_   ) - 3:
																		_UpperCamelCase			:					Dict     =    cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2] + ''' ''' + cagrams[i + 3]
																		cagrams.append(SCREAMING_SNAKE_CASE_   )

						((_UpperCamelCase)   ,							(_UpperCamelCase)   ,							(_UpperCamelCase))			:					Dict     =    SARIngram(SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_   )
						((_UpperCamelCase)   ,							(_UpperCamelCase)   ,							(_UpperCamelCase))			:					List[str]     =    SARIngram(SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_   )
						((_UpperCamelCase)   ,							(_UpperCamelCase)   ,							(_UpperCamelCase))			:					str     =    SARIngram(SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_   )
						((_UpperCamelCase)   ,							(_UpperCamelCase)   ,							(_UpperCamelCase))			:					Any     =    SARIngram(SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_   )
						_UpperCamelCase			:					Dict     =    sum([keepascore, keepascore, keepascore, keepascore]   ) / 4
						_UpperCamelCase			:					Optional[Any]     =    sum([delascore, delascore, delascore, delascore]   ) / 4
						_UpperCamelCase			:					Union[str, Any]     =    sum([addascore, addascore, addascore, addascore]   ) / 4
						_UpperCamelCase			:					List[Any]     =    (avgkeepscore + avgdelscore + avgaddscore) / 3
						return finalscore





def        a_				(							_lowercase       ,						_lowercase = True       ,						_lowercase = "13a"       ,						_lowercase = True   ):






						if lowercase:
												_UpperCamelCase			:					Optional[Any]     =    sentence.lower()

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

						if not return_str:
												_UpperCamelCase			:					str     =    normalized_sent.split()

						return normalized_sent






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






				def 				snake_case	( self		:    List[Any]							)	->      List[str]:




										'''simple docstring'''



										return datasets.MetricInfo(
										    description=_DESCRIPTION, citation=_CITATION, inputs_description=_KWARGS_DESCRIPTION, features=datasets.Features(
										        {
										            '''sources''': datasets.Value('''string''', id='''sequence'''							),
										            '''predictions''': datasets.Value('''string''', id='''sequence'''							),
										            '''references''': datasets.Sequence(datasets.Value('''string''', id='''sequence'''							), id='''references'''							),
										        }							), codebase_urls=[
										        '''https://github.com/cocoxu/simplification/blob/master/SARI.py''',
										        '''https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py''',
										    ], reference_urls=['''https://www.aclweb.org/anthology/Q16-1029.pdf'''], )






				def 				snake_case	( self		:    Dict, lowerCAmelCase__		:    str, lowerCAmelCase__		:    Optional[Any], lowerCAmelCase__		:    str							)	->      List[Any]:




										'''simple docstring'''



										if not (len(_lowercase							) == len(_lowercase							) == len(_lowercase							)):
																raise ValueError('''Sources length must match predictions and references lengths.'''							)
										_UpperCamelCase			:					int     =    0
										for src, pred, refs in zip(_lowercase, _lowercase, _lowercase							):
																sari_score += SARIsent(normalize(_lowercase							), normalize(_lowercase							), [normalize(_lowercase							) for sent in refs]							)
										_UpperCamelCase			:					Dict     =    sari_score / len(_lowercase							)
										return {"sari": 1_0_0 * sari_score}






import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		List[Any]:



    """simple docstring"""






    UpperCamelCase_   ,    UpperCamelCase_   ,    UpperCamelCase_          =		pt_attn_layer.in_proj_weight.chunk(3       ,		dim=0       )
    UpperCamelCase_   ,    UpperCamelCase_   ,    UpperCamelCase_          =		pt_attn_layer.in_proj_bias.chunk(3       ,		dim=0       )

    UpperCamelCase_          =		pt_attn_layer.out_proj.weight
    UpperCamelCase_          =		pt_attn_layer.out_proj.bias

    UpperCamelCase_          =		q_proj
    UpperCamelCase_          =		q_proj_bias

    UpperCamelCase_          =		k_proj
    UpperCamelCase_          =		k_proj_bias

    UpperCamelCase_          =		v_proj
    UpperCamelCase_          =		v_proj_bias

    UpperCamelCase_          =		out_proj_weights
    UpperCamelCase_          =		out_proj_bias




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		Union[str, Any]:



    """simple docstring"""






    copy_linear(hf_mlp.fca       ,		pt_mlp.c_fc       )
    copy_linear(hf_mlp.fca       ,		pt_mlp.c_proj       )




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		List[Any]:



    """simple docstring"""






    UpperCamelCase_          =		pt_linear.weight
    UpperCamelCase_          =		pt_linear.bias




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		List[Any]:



    """simple docstring"""






    copy_linear(hf_layer.layer_norma       ,		pt_layer.ln_a       )
    copy_linear(hf_layer.layer_norma       ,		pt_layer.ln_a       )

    # copy MLP
    copy_mlp(hf_layer.mlp       ,		pt_layer.mlp       )

    # copy attn
    copy_attn_layer(hf_layer.self_attn       ,		pt_layer.attn       )




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		List[Any]:



    """simple docstring"""






    for hf_layer, pt_layer in zip(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ):
        copy_layer(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		Optional[Any]:



    """simple docstring"""






    UpperCamelCase_          =		pt_model.token_embedding.weight
    UpperCamelCase_          =		pt_model.positional_embedding

    # copy layer norm
    copy_linear(hf_encoder.final_layer_norm       ,		pt_model.ln_final       )

    # copy hidden layers
    copy_layers(hf_encoder.encoder.layers       ,		pt_model.transformer.resblocks       )




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		Any:



    """simple docstring"""






    UpperCamelCase_          =		pt_model.text_projection.data.T

    # copy text encoder
    copy_encoder(hf_model.text_model       ,		SCREAMING_SNAKE_CASE_       )




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		Any:



    """simple docstring"""






    UpperCamelCase_          =		pt_model.visual.proj.data.T

    # copy layer norms
    copy_linear(hf_model.vision_model.pre_layrnorm       ,		pt_model.visual.ln_pre       )
    copy_linear(hf_model.vision_model.post_layernorm       ,		pt_model.visual.ln_post       )

    # copy embeds
    UpperCamelCase_          =		pt_model.visual.conva.weight.data
    UpperCamelCase_          =		pt_model.visual.class_embedding
    UpperCamelCase_          =		pt_model.visual.positional_embedding.data

    # copy encoder
    copy_layers(hf_model.vision_model.encoder.layers       ,		pt_model.visual.transformer.resblocks       )




@torch.no_grad()
def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_=None       )->		List[Any]:



    """simple docstring"""






    if config_path is not None:
        UpperCamelCase_          =		CLIPConfig.from_pretrained(SCREAMING_SNAKE_CASE_       )
    else:
        UpperCamelCase_          =		CLIPConfig(projection_dim=5_1_2       ,		text_config={}       ,		vision_config={}       )

    UpperCamelCase_          =		CLIPModel(SCREAMING_SNAKE_CASE_       ).eval()

    UpperCamelCase_   ,    UpperCamelCase_          =		load(SCREAMING_SNAKE_CASE_       ,		device="cpu"       ,		jit=SCREAMING_SNAKE_CASE_       )
    UpperCamelCase_          =		pt_model.eval()

    copy_text_model_and_projection(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )
    copy_vison_model_and_projection(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )
    UpperCamelCase_          =		pt_model.logit_scale

    UpperCamelCase_          =		torch.arange(0       ,		7_7       ).unsqueeze(0       )
    UpperCamelCase_          =		torch.randn(1       ,		3       ,		2_2_4       ,		2_2_4       )

    UpperCamelCase_          =		hf_model(input_ids=SCREAMING_SNAKE_CASE_       ,		pixel_values=SCREAMING_SNAKE_CASE_       ,		return_dict=SCREAMING_SNAKE_CASE_       )
    UpperCamelCase_          =		hf_outputs.logits_per_image
    UpperCamelCase_          =		hf_outputs.logits_per_text
    UpperCamelCase_   ,    UpperCamelCase_          =		pt_model(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )

    assert torch.allclose(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		atol=1E-3       )
    assert torch.allclose(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		atol=1E-3       )

    hf_model.save_pretrained(SCREAMING_SNAKE_CASE_       )


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE						:Any      =			argparse.ArgumentParser()
 parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
 parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
 parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
 SCREAMING_SNAKE_CASE						:Dict      =			parser.parse_args()

 convert_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
