





from typing import List

import jiwer
import jiwer.transforms as tr
from packaging import version

import datasets
from datasets.config import PY_VERSION


if PY_VERSION < version.parse("""3.8"""):
						import importlib_metadata
else:
						import importlib.metadata as importlib_metadata


_SCREAMING_SNAKE_CASE 		=						""""""


if version.parse(importlib_metadata.version("""jiwer""")) < version.parse("""2.3.0"""):
						class SCREAMING_SNAKE_CASE_  (   tr.AbstractTransform   ):






												def __init__( self : List[str]						,				_A : str = " "		)							->      List[Any]:







															"""simple docstring"""


															snake_case_						:     List[Any]     =		sentence_delimiter






												def UpperCAmelCase_			( self : List[Any]						,				_A : Union[str, Any]		)							->      str:







															"""simple docstring"""


															return list(lowercase_		)






												def UpperCAmelCase_			( self : Tuple						,				_A : Union[str, Any]		)							->      int:







															"""simple docstring"""


															snake_case_						:     Union[str, Any]     =		[]
															for sent_idx, sentence in enumerate(lowercase_		):
																		chars.extend(self.process_string(lowercase_		)		)
																		if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(lowercase_		) - 1:
																					chars.append(self.sentence_delimiter		)
															return chars
						_SCREAMING_SNAKE_CASE 		=						tr.Compose(
						    [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
						)
else:
						_SCREAMING_SNAKE_CASE 		=						tr.Compose(
						    [
						        tr.RemoveMultipleSpaces(),
						        tr.Strip(),
						        tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
						        tr.ReduceToListOfListOfChars(),
						    ]
						)


_SCREAMING_SNAKE_CASE 		=						"""\
@inproceedings{inproceedings,
    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},
    year = {2004},
    month = {01},
    pages = {},
    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}
}
"""

_SCREAMING_SNAKE_CASE 		=						"""\
Character error rate (CER) is a common metric of the performance of an automatic speech recognition system.

CER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.

Character error rate can be computed as:

CER = (S + D + I) / N = (S + D + I) / (S + D + C)

where

S is the number of substitutions,
D is the number of deletions,
I is the number of insertions,
C is the number of correct characters,
N is the number of characters in the reference (N=S+D+C).

CER's output is not always a number between 0 and 1, in particular when there is a high number of insertions. This value is often associated to the percentage of characters that were incorrectly predicted. The lower the value, the better the
performance of the ASR system with a CER of 0 being a perfect score.
"""

_SCREAMING_SNAKE_CASE 		=						"""
Computes CER score of transcribed segments against references.
Args:
    references: list of references for each speech input.
    predictions: list of transcribtions to score.
    concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result.
Returns:
    (float): the character error rate

Examples:

    >>> predictions = [\"this is the prediction\", \"there is an other sample\"]
    >>> references = [\"this is the reference\", \"there is another one\"]
    >>> cer = datasets.load_metric(\"cer\")
    >>> cer_score = cer.compute(predictions=predictions, references=references)
    >>> print(cer_score)
    0.34146341463414637
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,			_KWARGS_DESCRIPTION   )
class SCREAMING_SNAKE_CASE_  (   datasets.Metric   ):






						def UpperCAmelCase_			( self : List[str]		)							->      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.Value('string'						,				id='sequence'		),
									        }		)						,				codebase_urls=['https://github.com/jitsi/jiwer/']						,				reference_urls=[
									        'https://en.wikipedia.org/wiki/Word_error_rate',
									        'https://sites.google.com/site/textdigitisation/qualitymeasures/computingerrorrates',
									    ]						,				)






						def UpperCAmelCase_			( self : List[Any]						,				_A : Tuple						,				_A : int						,				_A : Tuple=False		)							->      Any:







									"""simple docstring"""


									if concatenate_texts:
												return jiwer.compute_measures(
												    lowercase_						,				lowercase_						,				truth_transform=lowercase_						,				hypothesis_transform=lowercase_						,				)["wer"]

									snake_case_						:     int     =		0
									snake_case_						:     Optional[int]     =		0
									for prediction, reference in zip(lowercase_						,				lowercase_		):
												snake_case_						:     int     =		jiwer.compute_measures(
												    lowercase_						,				lowercase_						,				truth_transform=lowercase_						,				hypothesis_transform=lowercase_						,				)
												incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
												total += measures["substitutions"] + measures["deletions"] + measures["hits"]

									return incorrect / total








import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				,	__a				,	__a				,	__a				):
	# Initialise PyTorch model.
	# If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
	# TapasConfig to False.

	# initialize configuration from json file
	snake_case_						:     Optional[int]     =		TapasConfig.from_json_file(__a				)
	# set absolute/relative position embeddings parameter
	snake_case_						:     List[Any]     =		reset_position_index_per_cell

	# set remaining parameters of TapasConfig as well as the model based on the task
	if task == "SQA":
		snake_case_						:     int     =		TapasForQuestionAnswering(config=__a				)
	elif task == "WTQ":
		# run_task_main.py hparams
		snake_case_						:     Optional[int]     =		4
		snake_case_						:     List[str]     =		True
		# hparam_utils.py hparams
		snake_case_						:     Optional[Any]     =		0.664694
		snake_case_						:     Dict     =		0.207951
		snake_case_						:     Tuple     =		0.121194
		snake_case_						:     Dict     =		True
		snake_case_						:     int     =		True
		snake_case_						:     int     =		False
		snake_case_						:     str     =		0.0352513

		snake_case_						:     int     =		TapasForQuestionAnswering(config=__a				)
	elif task == "WIKISQL_SUPERVISED":
		# run_task_main.py hparams
		snake_case_						:     int     =		4
		snake_case_						:     Optional[int]     =		False
		# hparam_utils.py hparams
		snake_case_						:     str     =		36.4519
		snake_case_						:     Optional[Any]     =		0.903421
		snake_case_						:     List[Any]     =		222.088
		snake_case_						:     Optional[int]     =		True
		snake_case_						:     Optional[Any]     =		True
		snake_case_						:     str     =		True
		snake_case_						:     int     =		0.763141

		snake_case_						:     str     =		TapasForQuestionAnswering(config=__a				)
	elif task == "TABFACT":
		snake_case_						:     List[Any]     =		TapasForSequenceClassification(config=__a				)
	elif task == "MLM":
		snake_case_						:     Optional[int]     =		TapasForMaskedLM(config=__a				)
	elif task == "INTERMEDIATE_PRETRAINING":
		snake_case_						:     Tuple     =		TapasModel(config=__a				)
	else:
		raise ValueError(f"""Task {task} not supported."""				)

	print(f"""Building PyTorch model from configuration: {config}"""				)
	# Load weights from tf checkpoint
	load_tf_weights_in_tapas(__a				,	__a				,	__a				)

	# Save pytorch-model (weights and configuration)
	print(f"""Save PyTorch model to {pytorch_dump_path}"""				)
	model.save_pretrained(__a				)

	# Save tokenizer files
	print(f"""Save tokenizer files to {pytorch_dump_path}"""				)
	snake_case_						:     str     =		TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + 'vocab.txt'				,	model_max_length=5_12				)
	tokenizer.save_pretrained(__a				)

	print('Used relative position embeddings:'				,	model.config.reset_position_index_per_cell				)


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE 		=						argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--task""", default="""SQA""", type=str, help="""Model task for which to convert a checkpoint. Defaults to SQA."""
						)
						parser.add_argument(
						    """--reset_position_index_per_cell""",
						    default=False,
						    action="""store_true""",
						    help="""Whether to use relative position embeddings or not. Defaults to True.""",
						)
						parser.add_argument(
						    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
						)
						parser.add_argument(
						    """--tapas_config_file""",
						    default=None,
						    type=str,
						    required=True,
						    help=(
						        """The config json file corresponding to the pre-trained TAPAS model. \n"""
						        """This specifies the model architecture."""
						    ),
						)
						parser.add_argument(
						    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
						)
						_SCREAMING_SNAKE_CASE 		=						parser.parse_args()
						convert_tf_checkpoint_to_pytorch(
						    args.task,
						    args.reset_position_index_per_cell,
						    args.tf_checkpoint_path,
						    args.tapas_config_file,
						    args.pytorch_dump_path,
						)

