


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


__a     =			""""""


if version.parse(importlib_metadata.version("""jiwer""")) < version.parse("""2.3.0"""):






 class   __lowercase				(  tr.AbstractTransform  ):





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






          """simple docstring"""







          UpperCAmelCase							=     sentence_delimiter





       def        _lowercase				(		self						:					List[Any]					,       __lowerCamelCase						:					str       )    ->   int:






          """simple docstring"""







          return list(__lowerCamelCase       )





       def        _lowercase				(		self						:					str					,       __lowerCamelCase						:					List[str]       )    ->   List[str]:






          """simple docstring"""







          UpperCAmelCase							=     []
          for sent_idx, sentence in enumerate(__lowerCamelCase       ):
             chars.extend(self.process_string(__lowerCamelCase       )       )
             if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(__lowerCamelCase       ) - 1:
                chars.append(self.sentence_delimiter       )
          return chars

 __a     =			tr.Compose(
     [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
 )
else:
 __a     =			tr.Compose(
     [
         tr.RemoveMultipleSpaces(),
         tr.Strip(),
         tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
         tr.ReduceToListOfListOfChars(),
     ]
 )


__a     =			"""\
@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.}
}
"""

__a     =			"""\
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.
"""

__a     =			"""
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   __lowercase				(  datasets.Metric  ):





      def        _lowercase				(		self						:					Tuple       )    ->   int:






         """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        _lowercase				(		self						:					Union[str, Any]					,       __lowerCamelCase						:					Union[str, Any]					,       __lowerCamelCase						:					str					,       __lowerCamelCase						:					Optional[Any]=False       )    ->   List[str]:






         """simple docstring"""







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

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

         return incorrect / total







import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
 import torch


if is_vision_available():
 from PIL import Image

 from transformers import MgpstrProcessor, ViTImageProcessor






@require_torch
@require_vision
class   __lowercase				(  unittest.TestCase  ):
      UpperCamelCase								=       ViTImageProcessor if is_vision_available() else None





      @property
      def        _lowercase				(		self						:					Tuple       )    ->   int:






         """simple docstring"""







         return self.image_processor_tester.prepare_image_processor_dict()





      def        _lowercase				(		self						:					str       )    ->   Union[str, Any]:






         """simple docstring"""







         UpperCAmelCase							=     (3, 3_2, 1_2_8)
         UpperCAmelCase							=     tempfile.mkdtemp()

         # fmt: off
         UpperCAmelCase							=     ["""[GO]""", """[s]""", """0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9""", """a""", """b""", """c""", """d""", """e""", """f""", """g""", """h""", """i""", """j""", """k""", """l""", """m""", """n""", """o""", """p""", """q""", """r""", """s""", """t""", """u""", """v""", """w""", """x""", """y""", """z"""]
         # fmt: on
         UpperCAmelCase							=     dict(zip(__lowerCamelCase					,       range(len(__lowerCamelCase       )       )       )       )

         UpperCAmelCase							=     os.path.join(self.tmpdirname					,       VOCAB_FILES_NAMES["""vocab_file"""]       )
         with open(self.vocab_file					,       """w"""					,       encoding="""utf-8"""       ) as fp:
            fp.write(json.dumps(__lowerCamelCase       ) + """\n"""       )

         UpperCAmelCase							=     {
             """do_normalize""": False,
             """do_resize""": True,
             """image_processor_type""": """ViTImageProcessor""",
             """resample""": 3,
             """size""": {"""height""": 3_2, """width""": 1_2_8},
         }
         UpperCAmelCase							=     os.path.join(self.tmpdirname					,       __lowerCamelCase       )
         with open(self.image_processor_file					,       """w"""					,       encoding="""utf-8"""       ) as fp:
            json.dump(__lowerCamelCase					,       __lowerCamelCase       )





      def        _lowercase				(		self						:					Optional[Any]					,       **__lowerCamelCase						:					Union[str, Any]       )    ->   int:






         """simple docstring"""







         return MgpstrTokenizer.from_pretrained(self.tmpdirname					,       **__lowerCamelCase       )





      def        _lowercase				(		self						:					int					,       **__lowerCamelCase						:					Optional[int]       )    ->   Optional[int]:






         """simple docstring"""







         return ViTImageProcessor.from_pretrained(self.tmpdirname					,       **__lowerCamelCase       )





      def        _lowercase				(		self						:					Optional[Any]       )    ->   Dict:






         """simple docstring"""







         shutil.rmtree(self.tmpdirname       )





      def        _lowercase				(		self						:					Any       )    ->   str:






         """simple docstring"""







         UpperCAmelCase							=     np.random.randint(2_5_5					,       size=(3, 3_0, 4_0_0)					,       dtype=np.uinta       )

         UpperCAmelCase							=     Image.fromarray(np.moveaxis(__lowerCamelCase					,       0					,       -1       )       )

         return image_input





      def        _lowercase				(		self						:					Tuple       )    ->   Tuple:






         """simple docstring"""







         UpperCAmelCase							=     self.get_tokenizer()
         UpperCAmelCase							=     self.get_image_processor()

         UpperCAmelCase							=     MgpstrProcessor(tokenizer=__lowerCamelCase					,       image_processor=__lowerCamelCase       )
         processor.save_pretrained(self.tmpdirname       )
         UpperCAmelCase							=     MgpstrProcessor.from_pretrained(self.tmpdirname					,       use_fast=__lowerCamelCase       )

         self.assertEqual(processor.char_tokenizer.get_vocab()					,       tokenizer.get_vocab()       )
         self.assertIsInstance(processor.char_tokenizer					,       __lowerCamelCase       )

         self.assertEqual(processor.image_processor.to_json_string()					,       image_processor.to_json_string()       )
         self.assertIsInstance(processor.image_processor					,       __lowerCamelCase       )





      def        _lowercase				(		self						:					int       )    ->   Optional[Any]:






         """simple docstring"""







         UpperCAmelCase							=     self.get_tokenizer()
         UpperCAmelCase							=     self.get_image_processor()

         UpperCAmelCase							=     MgpstrProcessor(tokenizer=__lowerCamelCase					,       image_processor=__lowerCamelCase       )
         processor.save_pretrained(self.tmpdirname       )

         UpperCAmelCase							=     self.get_tokenizer(bos_token="""(BOS)"""					,       eos_token="""(EOS)"""       )
         UpperCAmelCase							=     self.get_image_processor(do_normalize=__lowerCamelCase					,       padding_value=1.0       )

         UpperCAmelCase							=     MgpstrProcessor.from_pretrained(
             self.tmpdirname					,       bos_token="""(BOS)"""					,       eos_token="""(EOS)"""					,       do_normalize=__lowerCamelCase					,       padding_value=1.0       )

         self.assertEqual(processor.char_tokenizer.get_vocab()					,       tokenizer_add_kwargs.get_vocab()       )
         self.assertIsInstance(processor.char_tokenizer					,       __lowerCamelCase       )

         self.assertEqual(processor.image_processor.to_json_string()					,       image_processor_add_kwargs.to_json_string()       )
         self.assertIsInstance(processor.image_processor					,       __lowerCamelCase       )





      def        _lowercase				(		self						:					int       )    ->   Any:






         """simple docstring"""







         UpperCAmelCase							=     self.get_image_processor()
         UpperCAmelCase							=     self.get_tokenizer()

         UpperCAmelCase							=     MgpstrProcessor(tokenizer=__lowerCamelCase					,       image_processor=__lowerCamelCase       )

         UpperCAmelCase							=     self.prepare_image_inputs()

         UpperCAmelCase							=     image_processor(__lowerCamelCase					,       return_tensors="""np"""       )
         UpperCAmelCase							=     processor(images=__lowerCamelCase					,       return_tensors="""np"""       )

         for key in input_image_proc.keys():
            self.assertAlmostEqual(input_image_proc[key].sum()					,       input_processor[key].sum()					,       delta=1e-2       )





      def        _lowercase				(		self						:					str       )    ->   int:






         """simple docstring"""







         UpperCAmelCase							=     self.get_image_processor()
         UpperCAmelCase							=     self.get_tokenizer()

         UpperCAmelCase							=     MgpstrProcessor(tokenizer=__lowerCamelCase					,       image_processor=__lowerCamelCase       )

         UpperCAmelCase							=     """test"""

         UpperCAmelCase							=     processor(text=__lowerCamelCase       )

         UpperCAmelCase							=     tokenizer(__lowerCamelCase       )
         for key in encoded_tok.keys():
            self.assertListEqual(encoded_tok[key]					,       encoded_processor[key]       )





      def        _lowercase				(		self						:					Optional[int]       )    ->   Dict:






         """simple docstring"""







         UpperCAmelCase							=     self.get_image_processor()
         UpperCAmelCase							=     self.get_tokenizer()

         UpperCAmelCase							=     MgpstrProcessor(tokenizer=__lowerCamelCase					,       image_processor=__lowerCamelCase       )

         UpperCAmelCase							=     """test"""
         UpperCAmelCase							=     self.prepare_image_inputs()

         UpperCAmelCase							=     processor(text=__lowerCamelCase					,       images=__lowerCamelCase       )

         self.assertListEqual(list(inputs.keys()       )					,       ["""pixel_values""", """labels"""]       )

         # test if it raises when no input is passed
         with pytest.raises(__lowerCamelCase       ):
            processor()





      def        _lowercase				(		self						:					str       )    ->   Tuple:






         """simple docstring"""







         UpperCAmelCase							=     self.get_image_processor()
         UpperCAmelCase							=     self.get_tokenizer()

         UpperCAmelCase							=     MgpstrProcessor(tokenizer=__lowerCamelCase					,       image_processor=__lowerCamelCase       )

         UpperCAmelCase							=     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

         UpperCAmelCase							=     processor.char_decode(__lowerCamelCase       )
         UpperCAmelCase							=     tokenizer.batch_decode(__lowerCamelCase       )
         UpperCAmelCase							=     [seq.replace(""" """					,       """"""       ) for seq in decoded_tok]

         self.assertListEqual(__lowerCamelCase					,       __lowerCamelCase       )





      def        _lowercase				(		self						:					Optional[Any]       )    ->   Tuple:






         """simple docstring"""







         UpperCAmelCase							=     self.get_image_processor()
         UpperCAmelCase							=     self.get_tokenizer()

         UpperCAmelCase							=     MgpstrProcessor(tokenizer=__lowerCamelCase					,       image_processor=__lowerCamelCase       )

         UpperCAmelCase							=     None
         UpperCAmelCase							=     self.prepare_image_inputs()

         UpperCAmelCase							=     processor(text=__lowerCamelCase					,       images=__lowerCamelCase       )

         self.assertListEqual(list(inputs.keys()       )					,       processor.model_input_names       )





      def        _lowercase				(		self						:					Dict       )    ->   Optional[Any]:






         """simple docstring"""







         UpperCAmelCase							=     self.get_image_processor()
         UpperCAmelCase							=     self.get_tokenizer()

         UpperCAmelCase							=     MgpstrProcessor(tokenizer=__lowerCamelCase					,       image_processor=__lowerCamelCase       )

         UpperCAmelCase							=     torch.randn(1					,       2_7					,       3_8       )
         UpperCAmelCase							=     torch.randn(1					,       2_7					,       5_0_2_5_7       )
         UpperCAmelCase							=     torch.randn(1					,       2_7					,       3_0_5_2_2       )

         UpperCAmelCase							=     processor.batch_decode([char_input, bpe_input, wp_input]       )

         self.assertListEqual(list(results.keys()       )					,       ["""generated_text""", """scores""", """char_preds""", """bpe_preds""", """wp_preds"""]       )



