
import argparse
import json
import os

from tensorflow.core.protobuf.saved_model_pba import SavedModel


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
__lowerCAmelCase					:			int			      =		"."

# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
__lowerCAmelCase					:			List[Any]			      =		[
    "Assert",
    "AssignVariableOp",
    "EmptyTensorList",
    "MergeV2Checkpoints",
    "ReadVariableOp",
    "ResourceGather",
    "RestoreV2",
    "SaveV2",
    "ShardedFilename",
    "StatefulPartitionedCall",
    "StaticRegexFullMatch",
    "VarHandleOp",
]


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	) ->						int:
    __lowercase		:			Dict	    =       SavedModel()
    __lowercase		:			int	    =       []

    with open(os.path.join(__lowerCAmelCase	,					'''utils'''	,					'''tf_ops'''	,					'''onnx.json'''	)	) as f:
        __lowercase		:			Tuple	    =       json.load(__lowerCAmelCase	)['''opsets''']

    for i in range(1	,					opset + 1	):
        onnx_ops.extend(onnx_opsets[str(__lowerCAmelCase	)]	)

    with open(__lowerCAmelCase	,					'''rb'''	) as f:
        saved_model.ParseFromString(f.read()	)

    __lowercase		:			str	    =       set()

    # Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs)
    for meta_graph in saved_model.meta_graphs:
        # Add operations in the graph definition
        model_op_names.update(node.op for node in meta_graph.graph_def.node	)

        # Go through the functions in the graph definition
        for func in meta_graph.graph_def.library.function:
            # Add operations in each function
            model_op_names.update(node.op for node in func.node_def	)

    # Convert to list, sorted if you want
    __lowercase		:			List[Any]	    =       sorted(__lowerCAmelCase	)
    __lowercase		:			Optional[Any]	    =       []

    for op in model_op_names:
        if op not in onnx_ops and op not in INTERNAL_OPS:
            incompatible_ops.append(__lowerCAmelCase	)

    if strict and len(__lowerCAmelCase	) > 0:
        raise Exception(F'Found the following incompatible ops for the opset {opset}:\n' + incompatible_ops	)
    elif len(__lowerCAmelCase	) > 0:
        print(F'Found the following incompatible ops for the opset {opset}:'	)
        print(*__lowerCAmelCase	,					sep='''\n'''	)
    else:
        print(F'The saved model {saved_model_path} can properly be converted with ONNX.'	)


if __name__ == "__main__":
 __lowerCAmelCase					:			int			      =		argparse.ArgumentParser()
 parser.add_argument("--saved_model_path", help="Path of the saved model to check (the .pb file).")
 parser.add_argument(
     "--opset", default=12, type=int, help="The ONNX opset against which the model has to be tested."
 )
 parser.add_argument(
     "--framework", choices=["onnx"], default="onnx", help="Frameworks against which to test the saved model."
 )
 parser.add_argument(
     "--strict", action="store_true", help="Whether make the checking strict (raise errors) or not (raise warnings)"
 )
 __lowerCAmelCase					:			List[Any]			      =		parser.parse_args()

 if args.framework == "onnx":
  onnx_compliancy(args.saved_model_path, args.strict, args.opset)




import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


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

__lowerCAmelCase					:			Dict			      =		"\\nWIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU\nIt can be used to evaluate the quality of machine-generated texts.\n"


__lowerCAmelCase					:			Optional[int]			      =		"\nCalculates sari score (between 0 and 100) given a list of source and predicted\nsentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.\nArgs:\n    sources: list of source sentences where each sentence should be a string.\n    predictions: list of predicted sentences where each sentence should be a string.\n    references: list of lists of reference sentences where each sentence should be a string.\nReturns:\n    sari: sari score\n    sacrebleu: sacrebleu score\n    exact: exact score\n\nExamples:\n    >>> sources=[\"About 95 species are currently accepted .\"]\n    >>> predictions=[\"About 95 you now get in .\"]\n    >>> references=[[\"About 95 species are currently known .\"]]\n    >>> wiki_split = datasets.load_metric(\"wiki_split\")\n    >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)\n    >>> print(results)\n    {'sari': 21.805555555555557, 'sacrebleu': 14.535768424205482, 'exact': 0.0}\n"


def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						str:

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

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

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

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

    return white_space_fix(remove_articles(remove_punc(lower(__lowerCAmelCase	)	)	)	)


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase	) ->						List[str]:
    return int(normalize_answer(__lowerCAmelCase	) == normalize_answer(__lowerCAmelCase	)	)


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase	) ->						Optional[int]:
    __lowercase		:			Union[str, Any]	    =       [any(compute_exact(__lowerCAmelCase	,					__lowerCAmelCase	) for ref in refs	) for pred, refs in zip(__lowerCAmelCase	,					__lowerCAmelCase	)]
    return (sum(__lowerCAmelCase	) / len(__lowerCAmelCase	)) * 100


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	) ->						Optional[int]:
    __lowercase		:			Union[str, Any]	    =       [rgram for rgrams in rgramslist for rgram in rgrams]
    __lowercase		:			Dict	    =       Counter(__lowerCAmelCase	)

    __lowercase		:			Union[str, Any]	    =       Counter(__lowerCAmelCase	)
    __lowercase		:			str	    =       Counter()
    for sgram, scount in sgramcounter.items():
        __lowercase		:			List[Any]	    =       scount * numref

    __lowercase		:			Optional[Any]	    =       Counter(__lowerCAmelCase	)
    __lowercase		:			Union[str, Any]	    =       Counter()
    for cgram, ccount in cgramcounter.items():
        __lowercase		:			Union[str, Any]	    =       ccount * numref

    # KEEP
    __lowercase		:			Optional[Any]	    =       sgramcounter_rep & cgramcounter_rep
    __lowercase		:			Dict	    =       keepgramcounter_rep & rgramcounter
    __lowercase		:			str	    =       sgramcounter_rep & rgramcounter

    __lowercase		:			Tuple	    =       0
    __lowercase		:			int	    =       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		:			Any	    =       1
    __lowercase		:			Union[str, Any]	    =       1
    if len(__lowerCAmelCase	) > 0:
        __lowercase		:			List[str]	    =       keeptmpscorea / len(__lowerCAmelCase	)
    if len(__lowerCAmelCase	) > 0:
        # Fix an alleged bug [2] in the keep score computation.
        # keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
        __lowercase		:			Dict	    =       keeptmpscorea / sum(keepgramcounterall_rep.values()	)
    __lowercase		:			Dict	    =       0
    if keepscore_precision > 0 or keepscore_recall > 0:
        __lowercase		:			str	    =       2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

    # DELETION
    __lowercase		:			Tuple	    =       sgramcounter_rep - cgramcounter_rep
    __lowercase		:			str	    =       delgramcounter_rep - rgramcounter
    __lowercase		:			int	    =       sgramcounter_rep - rgramcounter
    __lowercase		:			Dict	    =       0
    __lowercase		:			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.
    __lowercase		:			Union[str, Any]	    =       1
    if len(__lowerCAmelCase	) > 0:
        __lowercase		:			Union[str, Any]	    =       deltmpscorea / len(__lowerCAmelCase	)

    # ADDITION
    __lowercase		:			int	    =       set(__lowerCAmelCase	) - set(__lowerCAmelCase	)
    __lowercase		:			Tuple	    =       set(__lowerCAmelCase	) & set(__lowerCAmelCase	)
    __lowercase		:			Any	    =       set(__lowerCAmelCase	) - set(__lowerCAmelCase	)

    __lowercase		:			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.
    __lowercase		:			int	    =       1
    __lowercase		:			Any	    =       1
    if len(__lowerCAmelCase	) > 0:
        __lowercase		:			Any	    =       addtmpscore / len(__lowerCAmelCase	)
    if len(__lowerCAmelCase	) > 0:
        __lowercase		:			str	    =       addtmpscore / len(__lowerCAmelCase	)
    __lowercase		:			int	    =       0
    if addscore_precision > 0 or addscore_recall > 0:
        __lowercase		:			Any	    =       2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

    return (keepscore, delscore_precision, addscore)


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	) ->						Union[str, Any]:
    __lowercase		:			Tuple	    =       len(__lowerCAmelCase	)

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

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

    for i in range(0	,					len(__lowerCAmelCase	) - 1	):
        if i < len(__lowerCAmelCase	) - 1:
            __lowercase		:			Dict	    =       sagrams[i] + ''' ''' + sagrams[i + 1]
            sagrams.append(__lowerCAmelCase	)
        if i < len(__lowerCAmelCase	) - 2:
            __lowercase		:			int	    =       sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2]
            sagrams.append(__lowerCAmelCase	)
        if i < len(__lowerCAmelCase	) - 3:
            __lowercase		:			str	    =       sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2] + ''' ''' + sagrams[i + 3]
            sagrams.append(__lowerCAmelCase	)

    for i in range(0	,					len(__lowerCAmelCase	) - 1	):
        if i < len(__lowerCAmelCase	) - 1:
            __lowercase		:			int	    =       cagrams[i] + ''' ''' + cagrams[i + 1]
            cagrams.append(__lowerCAmelCase	)
        if i < len(__lowerCAmelCase	) - 2:
            __lowercase		:			Any	    =       cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2]
            cagrams.append(__lowerCAmelCase	)
        if i < len(__lowerCAmelCase	) - 3:
            __lowercase		:			Optional[int]	    =       cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2] + ''' ''' + cagrams[i + 3]
            cagrams.append(__lowerCAmelCase	)

    ((__lowercase)    ,      (__lowercase)    ,      (__lowercase))		:			List[str]	    =       SARIngram(__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	)
    ((__lowercase)    ,      (__lowercase)    ,      (__lowercase))		:			Optional[int]	    =       SARIngram(__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	)
    ((__lowercase)    ,      (__lowercase)    ,      (__lowercase))		:			Optional[Any]	    =       SARIngram(__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	)
    ((__lowercase)    ,      (__lowercase)    ,      (__lowercase))		:			Any	    =       SARIngram(__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	)
    __lowercase		:			List[Any]	    =       sum([keepascore, keepascore, keepascore, keepascore]	) / 4
    __lowercase		:			Any	    =       sum([delascore, delascore, delascore, delascore]	) / 4
    __lowercase		:			int	    =       sum([addascore, addascore, addascore, addascore]	) / 4
    __lowercase		:			str	    =       (avgkeepscore + avgdelscore + avgaddscore) / 3
    return finalscore


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase = True	,					__lowerCAmelCase = "13a"	,					__lowerCAmelCase = True	) ->						List[str]:
    # 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		:			List[Any]	    =       sentence.lower()

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

    if not return_str:
        __lowercase		:			List[Any]	    =       normalized_sent.split()

    return normalized_sent


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


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





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



      """simple docstring"""







      def 		snake_case_			(      self		:       Dict				):
          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 		snake_case_			(      self		:       Optional[int]				,       _snake_case		:       Optional[Any]				,       _snake_case		:       List[Any]				,       _snake_case		:       List[Any]				):
          __lowercase		:			List[Any]	    =       {}
          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


