





import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


snake_case_				=			"""
@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\",
}
"""

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


snake_case_				=			"""
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 lowerCamelCase__    (							snake_case_	:  int       )      ->				List[Any]:

   def remove_articles(snake_case_	:  int       ):
      __snake_case							       =   re.compile(R'''\b(a|an|the)\b'''       ,      re.UNICODE       )
      return re.sub(_lowercase       ,      ''' '''       ,      _lowercase       )

   def white_space_fix(snake_case_	:  Dict       ):
      return " ".join(text.split()       )

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

   def lower(snake_case_	:  Optional[int]       ):
      return text.lower()

   return white_space_fix(remove_articles(remove_punc(lower(_lowercase       )       )       )       )






def lowerCamelCase__    (							snake_case_	:  Tuple       ,      snake_case_	:  Union[str, Any]       )      ->				List[Any]:
   return int(normalize_answer(_lowercase       ) == normalize_answer(_lowercase       )       )






def lowerCamelCase__    (							snake_case_	:  Optional[Any]       ,      snake_case_	:  Optional[int]       )      ->				Tuple:
   __snake_case							       =   [any(compute_exact(_lowercase       ,      _lowercase       ) for ref in refs       ) for pred, refs in zip(_lowercase       ,      _lowercase       )]
   return (sum(_lowercase       ) / len(_lowercase       )) * 100






def lowerCamelCase__    (							snake_case_	:  Optional[Any]       ,      snake_case_	:  Optional[Any]       ,      snake_case_	:  str       ,      snake_case_	:  Optional[Any]       )      ->				Dict:
   __snake_case							       =   [rgram for rgrams in rgramslist for rgram in rgrams]
   __snake_case							       =   Counter(_lowercase       )

   __snake_case							       =   Counter(_lowercase       )
   __snake_case							       =   Counter()
   for sgram, scount in sgramcounter.items():
      __snake_case							       =   scount * numref

   __snake_case							       =   Counter(_lowercase       )
   __snake_case							       =   Counter()
   for cgram, ccount in cgramcounter.items():
      __snake_case							       =   ccount * numref

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

   __snake_case							       =   0
   __snake_case							       =   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.
   __snake_case							       =   1
   __snake_case							       =   1
   if len(_lowercase       ) > 0:
      __snake_case							       =   keeptmpscorea / len(_lowercase       )
   if len(_lowercase       ) > 0:
      # Fix an alleged bug [2] in the keep score computation.
      # keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
      __snake_case							       =   keeptmpscorea / sum(keepgramcounterall_rep.values()       )
   __snake_case							       =   0
   if keepscore_precision > 0 or keepscore_recall > 0:
      __snake_case							       =   2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

   # DELETION
   __snake_case							       =   sgramcounter_rep - cgramcounter_rep
   __snake_case							       =   delgramcounter_rep - rgramcounter
   __snake_case							       =   sgramcounter_rep - rgramcounter
   __snake_case							       =   0
   __snake_case							       =   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.
   __snake_case							       =   1
   if len(_lowercase       ) > 0:
      __snake_case							       =   deltmpscorea / len(_lowercase       )

   # ADDITION
   __snake_case							       =   set(_lowercase       ) - set(_lowercase       )
   __snake_case							       =   set(_lowercase       ) & set(_lowercase       )
   __snake_case							       =   set(_lowercase       ) - set(_lowercase       )

   __snake_case							       =   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.
   __snake_case							       =   1
   __snake_case							       =   1
   if len(_lowercase       ) > 0:
      __snake_case							       =   addtmpscore / len(_lowercase       )
   if len(_lowercase       ) > 0:
      __snake_case							       =   addtmpscore / len(_lowercase       )
   __snake_case							       =   0
   if addscore_precision > 0 or addscore_recall > 0:
      __snake_case							       =   2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

   return (keepscore, delscore_precision, addscore)






def lowerCamelCase__    (							snake_case_	:  Tuple       ,      snake_case_	:  Tuple       ,      snake_case_	:  Dict       )      ->				str:
   __snake_case							       =   len(_lowercase       )

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

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

   for i in range(0       ,      len(_lowercase       ) - 1       ):
      if i < len(_lowercase       ) - 1:
         __snake_case							       =   sagrams[i] + """ """ + sagrams[i + 1]
         sagrams.append(_lowercase       )
      if i < len(_lowercase       ) - 2:
         __snake_case							       =   sagrams[i] + """ """ + sagrams[i + 1] + """ """ + sagrams[i + 2]
         sagrams.append(_lowercase       )
      if i < len(_lowercase       ) - 3:
         __snake_case							       =   sagrams[i] + """ """ + sagrams[i + 1] + """ """ + sagrams[i + 2] + """ """ + sagrams[i + 3]
         sagrams.append(_lowercase       )

   for i in range(0       ,      len(_lowercase       ) - 1       ):
      if i < len(_lowercase       ) - 1:
         __snake_case							       =   cagrams[i] + """ """ + cagrams[i + 1]
         cagrams.append(_lowercase       )
      if i < len(_lowercase       ) - 2:
         __snake_case							       =   cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2]
         cagrams.append(_lowercase       )
      if i < len(_lowercase       ) - 3:
         __snake_case							       =   cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2] + """ """ + cagrams[i + 3]
         cagrams.append(_lowercase       )

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






def lowerCamelCase__    (							snake_case_	:  Optional[int]       ,      snake_case_	:  str = True       ,      snake_case_	:  Dict = "13a"       ,      snake_case_	:  int = True       )      ->				Union[str, Any]:
   # 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:
      __snake_case							       =   sentence.lower()

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

   if not return_str:
      __snake_case							       =   normalized_sent.split()

   return normalized_sent






def lowerCamelCase__    (							snake_case_	:  int       ,      snake_case_	:  str       ,      snake_case_	:  Optional[Any]       )      ->				Any:
   if not (len(_lowercase       ) == len(_lowercase       ) == len(_lowercase       )):
      raise ValueError('''Sources length must match predictions and references lengths.'''       )
   __snake_case							       =   0
   for src, pred, refs in zip(_lowercase       ,      _lowercase       ,      _lowercase       ):
      sari_score += SARIsent(normalize(_lowercase       )       ,      normalize(_lowercase       )       ,      [normalize(_lowercase       ) for sent in refs]       )
   __snake_case							       =   sari_score / len(_lowercase       )
   return 100 * sari_score






def lowerCamelCase__    (							snake_case_	:  Any       ,      snake_case_	:  Union[str, Any]       ,      snake_case_	:  List[Any]="exp"       ,      snake_case_	:  str=None       ,      snake_case_	:  str=False       ,      snake_case_	:  Tuple=False       ,      snake_case_	:  Optional[Any]=False       ,      )      ->				Tuple:
   __snake_case							       =   len(references[0]       )
   if any(len(_lowercase       ) != references_per_prediction for refs in references       ):
      raise ValueError('''Sacrebleu requires the same number of references for each prediction'''       )
   __snake_case							       =   [[refs[i] for refs in references] for i in range(_lowercase       )]
   __snake_case							       =   sacrebleu.corpus_bleu(
       _lowercase       ,      _lowercase       ,      smooth_method=_lowercase       ,      smooth_value=_lowercase       ,      force=_lowercase       ,      lowercase=_lowercase       ,      use_effective_order=_lowercase       ,      )
   return output.score







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


      def    a (self       :      List[Any]      ):





         """simple docstring"""



         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    a (self       :      Dict				,			a__       :      Any				,			a__       :      str				,			a__       :      Tuple      ):





         """simple docstring"""



         __snake_case							       =   {}
         result.update({'''sari''': compute_sari(sources=a__				,			predictions=a__				,			references=a__      )}      )
         result.update({'''sacrebleu''': compute_sacrebleu(predictions=a__				,			references=a__      )}      )
         result.update({'''exact''': compute_em(predictions=a__				,			references=a__      )}      )
         return result






'''simple docstring'''




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
a   :			List[Any]					  =    logging.get_logger(__name__)

a   :			Union[str, Any]					  =    torch.device("""cpu""")



def       __lowerCamelCase  (					)      ->			Any:
							UpperCAmelCase     :		Optional[Any]							     =		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
							UpperCAmelCase     :		Dict							     =		Image.open(requests.get(_lowercase	,				stream=_lowercase		).raw		)
							return im



def       __lowerCamelCase  (					_lowercase		)      ->			Dict:
							if swiftformer_name == "swiftformer_xs":
														return torch.tensor([-2.1_703e00, 2.1_107e00, -2.0_811e00, 8.8_685e-01, 2.4_360e-01]		)

							elif swiftformer_name == "swiftformer_s":
														return torch.tensor([3.9_636e-01, 2.3_478e-01, -1.6_963e00, -1.7_381e00, -8.6_337e-01]		)

							elif swiftformer_name == "swiftformer_l1":
														return torch.tensor([-4.2_768e-01, -4.7_429e-01, -1.0_897e00, -1.0_248e00, 3.5_523e-02]		)

							elif swiftformer_name == "swiftformer_l3":
														return torch.tensor([-2.5_330e-01, 2.4_211e-01, -6.0_185e-01, -8.2_789e-01, -6.0_446e-02]		)



def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase		)      ->			str:
							UpperCAmelCase     :		Union[str, Any]							     =		dct.pop(_lowercase		)
							UpperCAmelCase     :		str							     =		val



def       __lowerCamelCase  (					_lowercase		)      ->			List[str]:
							UpperCAmelCase     :		Tuple							     =		[]
							for k in state_dict.keys():
														UpperCAmelCase     :		Dict							     =		k
														if ".pwconv" in k:
																					UpperCAmelCase     :		Union[str, Any]							     =		k_new.replace(""".pwconv"""	,				""".point_wise_conv"""		)
														if ".dwconv" in k:
																					UpperCAmelCase     :		Dict							     =		k_new.replace(""".dwconv"""	,				""".depth_wise_conv"""		)
														if ".Proj." in k:
																					UpperCAmelCase     :		str							     =		k_new.replace(""".Proj."""	,				""".proj."""		)
														if "patch_embed" in k_new:
																					UpperCAmelCase     :		Dict							     =		k_new.replace("""patch_embed"""	,				"""swiftformer.patch_embed.patch_embedding"""		)
														if "network" in k_new:
																					UpperCAmelCase     :		Optional[Any]							     =		k_new.split("""."""		)
																					if ls[2].isdigit():
																												UpperCAmelCase     :		Any							     =		"""swiftformer.encoder.network.""" + ls[1] + """.blocks.""" + ls[2] + """.""" + """.""".join(ls[3:]		)
																					else:
																												UpperCAmelCase     :		Any							     =		k_new.replace("""network"""	,				"""swiftformer.encoder.network"""		)
														rename_keys.append((k, k_new)		)
							return rename_keys



@torch.no_grad()
def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase		)      ->			Optional[int]:
							UpperCAmelCase     :		Optional[Any]							     =		SwiftFormerConfig()

							# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
							UpperCAmelCase     :		List[Any]							     =		1_0_0_0
							UpperCAmelCase     :		List[str]							     =		"""huggingface/label-files"""
							UpperCAmelCase     :		Tuple							     =		"""imagenet-1k-id2label.json"""
							UpperCAmelCase     :		Dict							     =		json.load(open(hf_hub_download(_lowercase	,				_lowercase	,				repo_type="""dataset"""		)	,				"""r"""		)		)
							UpperCAmelCase     :		Tuple							     =		{int(_lowercase		): v for k, v in idalabel.items()}
							UpperCAmelCase     :		Tuple							     =		idalabel
							UpperCAmelCase     :		List[Any]							     =		{v: k for k, v in idalabel.items()}

							# size of the architecture
							if swiftformer_name == "swiftformer_xs":
														UpperCAmelCase     :		List[Any]							     =		[3, 3, 6, 4]
														UpperCAmelCase     :		int							     =		[4_8, 5_6, 1_1_2, 2_2_0]

							elif swiftformer_name == "swiftformer_s":
														UpperCAmelCase     :		str							     =		[3, 3, 9, 6]
														UpperCAmelCase     :		str							     =		[4_8, 6_4, 1_6_8, 2_2_4]

							elif swiftformer_name == "swiftformer_l1":
														UpperCAmelCase     :		List[Any]							     =		[4, 3, 1_0, 5]
														UpperCAmelCase     :		Union[str, Any]							     =		[4_8, 9_6, 1_9_2, 3_8_4]

							elif swiftformer_name == "swiftformer_l3":
														UpperCAmelCase     :		Any							     =		[4, 4, 1_2, 6]
														UpperCAmelCase     :		List[Any]							     =		[6_4, 1_2_8, 3_2_0, 5_1_2]

							# load state_dict of original model, remove and rename some keys
							if original_ckpt:
														if original_ckpt.startswith("""https"""		):
																					UpperCAmelCase     :		Dict							     =		torch.hub.load_state_dict_from_url(_lowercase	,				map_location="""cpu"""	,				check_hash=_lowercase		)
														else:
																					UpperCAmelCase     :		Dict							     =		torch.load(_lowercase	,				map_location="""cpu"""		)
							UpperCAmelCase     :		str							     =		checkpoint

							UpperCAmelCase     :		Tuple							     =		create_rename_keys(_lowercase		)
							for rename_key_src, rename_key_dest in rename_keys:
														rename_key(_lowercase	,				_lowercase	,				_lowercase		)

							# load HuggingFace model
							UpperCAmelCase     :		str							     =		SwiftFormerForImageClassification(_lowercase		).eval()
							hf_model.load_state_dict(_lowercase		)

							# prepare test inputs
							UpperCAmelCase     :		Any							     =		prepare_img()
							UpperCAmelCase     :		List[Any]							     =		ViTImageProcessor.from_pretrained("""preprocessor_config"""		)
							UpperCAmelCase     :		List[str]							     =		processor(images=_lowercase	,				return_tensors="""pt"""		)

							# compare outputs from both models
							UpperCAmelCase     :		List[str]							     =		get_expected_output(_lowercase		)
							UpperCAmelCase     :		Dict							     =		hf_model(inputs["""pixel_values"""]		).logits

							assert hf_logits.shape == torch.Size([1, 1_0_0_0]		)
							assert torch.allclose(hf_logits[0, 0:5]	,				_lowercase	,				atol=1e-3		)

							Path(_lowercase		).mkdir(exist_ok=_lowercase		)
							print(F'''Saving model {swiftformer_name} to {pytorch_dump_folder_path}'''		)
							hf_model.save_pretrained(_lowercase		)


if __name__ == "__main__":
							a   :			Tuple					  =    argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--swiftformer_name""",
							    default="""swiftformer_xs""",
							    choices=["""swiftformer_xs""", """swiftformer_s""", """swiftformer_l1""", """swiftformer_l3"""],
							    type=str,
							    help="""Name of the SwiftFormer model you'd like to convert.""",
							)
							parser.add_argument(
							    """--pytorch_dump_folder_path""",
							    default="""./converted_outputs/""",
							    type=str,
							    help="""Path to the output PyTorch model directory.""",
							)
							parser.add_argument("""--original_ckpt""", default=None, type=str, help="""Path to the original model checkpoint.""")

							a   :			Union[str, Any]					  =    parser.parse_args()
							convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)

