


"""simple docstring"""


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


__lowerCamelCase      :Tuple	    =		''


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


    class 			A__	(						tr.AbstractTransform):


         """simple docstring"""

         def __init__(	self:						str     ,		__a:						Dict = " "	)->    str:
               lowerCamelCase					:					str        =	sentence_delimiter

         def 		a__						(	self:						Optional[int]     ,		__a:						str	)->    Optional[Any]:
               return list(__A	)







         def 		a__						(	self:						List[Any]     ,		__a:						Tuple	)->    Any:
               lowerCamelCase					:					Any        =	[]
               for sent_idx, sentence in enumerate(__A	):
                     chars.extend(self.process_string(__A	)	)
                     if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(__A	) - 1:
                           chars.append(self.sentence_delimiter	)
               return chars



    __lowerCamelCase      :List[str]	    =		tr.Compose(
        [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
    )
else:
    __lowerCamelCase      :Union[str, Any]	    =		tr.Compose(
        [
            tr.RemoveMultipleSpaces(),
            tr.Strip(),
            tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
            tr.ReduceToListOfListOfChars(),
        ]
    )


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

__lowerCamelCase      :Union[str, Any]	    =		'\\nCharacter error rate (CER) is a common metric of the performance of an automatic speech recognition system.\n\nCER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.\n\nCharacter error rate can be computed as:\n\nCER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct characters,\nN is the number of characters in the reference (N=S+D+C).\n\nCER\'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\nperformance of the ASR system with a CER of 0 being a perfect score.\n'

__lowerCamelCase      :Optional[Any]	    =		'\nComputes CER score of transcribed segments against references.\nArgs:\n    references: list of references for each speech input.\n    predictions: list of transcribtions to score.\n    concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result.\nReturns:\n    (float): the character error rate\n\nExamples:\n\n    >>> predictions = ["this is the prediction", "there is an other sample"]\n    >>> references = ["this is the reference", "there is another one"]\n    >>> cer = datasets.load_metric("cer")\n    >>> cer_score = cer.compute(predictions=predictions, references=references)\n    >>> print(cer_score)\n    0.34146341463414637\n'


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


     """simple docstring"""

     def 		a__						(	self:						Tuple	)->    Optional[int]:
           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 		a__						(	self:						Optional[Any]     ,		__a:						Tuple     ,		__a:						List[str]     ,		__a:						List[str]=False	)->    str:
           if concatenate_texts:
                 return jiwer.compute_measures(
                     __A     ,		__A     ,		truth_transform=__A     ,		hypothesis_transform=__A     ,		)["wer"]

           lowerCamelCase					:					Any        =	0
           lowerCamelCase					:					Dict        =	0
           for prediction, reference in zip(__A     ,		__A	):
                 lowerCamelCase					:					str        =	jiwer.compute_measures(
                     __A     ,		__A     ,		truth_transform=__A     ,		hypothesis_transform=__A     ,		)
                 incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
                 total += measures["substitutions"] + measures["deletions"] + measures["hits"]

           return incorrect / total








"""simple docstring"""


import inspect
import unittest

from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import ConvNextBackbone, ConvNextForImageClassification, ConvNextModel
    from transformers.models.convnext.modeling_convnext import CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor


class 			A__	:


     """simple docstring"""

     def __init__(	self:						Optional[Any]     ,		__a:						Union[str, Any]     ,		__a:						Optional[Any]=13     ,		__a:						Optional[Any]=32     ,		__a:						Dict=3     ,		__a:						int=4     ,		__a:						Dict=[10, 20, 30, 40]     ,		__a:						int=[2, 2, 3, 2]     ,		__a:						Any=True     ,		__a:						List[Any]=True     ,		__a:						Any=37     ,		__a:						Optional[int]="gelu"     ,		__a:						List[str]=10     ,		__a:						Optional[int]=0.02     ,		__a:						Dict=["stage2", "stage3", "stage4"]     ,		__a:						List[str]=[2, 3, 4]     ,		__a:						List[str]=None     ,		)->    Union[str, Any]:
           lowerCamelCase					:					Optional[int]        =	parent
           lowerCamelCase					:					Optional[int]        =	batch_size
           lowerCamelCase					:					Any        =	image_size
           lowerCamelCase					:					Tuple        =	num_channels
           lowerCamelCase					:					str        =	num_stages
           lowerCamelCase					:					List[str]        =	hidden_sizes
           lowerCamelCase					:					str        =	depths
           lowerCamelCase					:					Dict        =	is_training
           lowerCamelCase					:					Optional[Any]        =	use_labels
           lowerCamelCase					:					List[str]        =	intermediate_size
           lowerCamelCase					:					List[str]        =	hidden_act
           lowerCamelCase					:					List[str]        =	num_labels
           lowerCamelCase					:					Union[str, Any]        =	initializer_range
           lowerCamelCase					:					List[Any]        =	out_features
           lowerCamelCase					:					Optional[Any]        =	out_indices
           lowerCamelCase					:					int        =	scope

     def 		a__						(	self:						str	)->    Optional[Any]:
           lowerCamelCase					:					Union[str, Any]        =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

           lowerCamelCase					:					Dict        =	None
           if self.use_labels:
                 lowerCamelCase					:					Dict        =	ids_tensor([self.batch_size]     ,		self.num_labels	)

           lowerCamelCase					:					Any        =	self.get_config()
           return config, pixel_values, labels

     def 		a__						(	self:						Dict	)->    Union[str, Any]:
           return ConvNextConfig(
               num_channels=self.num_channels     ,		hidden_sizes=self.hidden_sizes     ,		depths=self.depths     ,		num_stages=self.num_stages     ,		hidden_act=self.hidden_act     ,		is_decoder=__a     ,		initializer_range=self.initializer_range     ,		out_features=self.out_features     ,		out_indices=self.out_indices     ,		num_labels=self.num_labels     ,		)

     def 		a__						(	self:						Optional[Any]     ,		__a:						List[Any]     ,		__a:						Any     ,		__a:						int	)->    List[Any]:
           lowerCamelCase					:					Optional[int]        =	ConvNextModel(config=__a	)
           model.to(__a	)
           model.eval()
           lowerCamelCase					:					Any        =	model(__a	)
           # expected last hidden states: B, C, H // 32, W // 32
           self.parent.assertEqual(
               result.last_hidden_state.shape     ,		(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)     ,		)

     def 		a__						(	self:						int     ,		__a:						Union[str, Any]     ,		__a:						List[Any]     ,		__a:						Tuple	)->    Optional[int]:
           lowerCamelCase					:					str        =	ConvNextForImageClassification(__a	)
           model.to(__a	)
           model.eval()
           lowerCamelCase					:					Any        =	model(__a     ,		labels=__a	)
           self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.num_labels)	)

     def 		a__						(	self:						List[Any]     ,		__a:						Any     ,		__a:						Optional[int]     ,		__a:						Tuple	)->    List[str]:
           lowerCamelCase					:					List[str]        =	ConvNextBackbone(config=__a	)
           model.to(__a	)
           model.eval()
           lowerCamelCase					:					int        =	model(__a	)

           # verify hidden states
           self.parent.assertEqual(len(result.feature_maps	)     ,		len(config.out_features	)	)
           self.parent.assertListEqual(list(result.feature_maps[0].shape	)     ,		[self.batch_size, self.hidden_sizes[1], 4, 4]	)

           # verify channels
           self.parent.assertEqual(len(model.channels	)     ,		len(config.out_features	)	)
           self.parent.assertListEqual(model.channels     ,		config.hidden_sizes[1:]	)

           # verify backbone works with out_features=None
           lowerCamelCase					:					Tuple        =	None
           lowerCamelCase					:					List[str]        =	ConvNextBackbone(config=__a	)
           model.to(__a	)
           model.eval()
           lowerCamelCase					:					List[Any]        =	model(__a	)

           # verify feature maps
           self.parent.assertEqual(len(result.feature_maps	)     ,		1	)
           self.parent.assertListEqual(list(result.feature_maps[0].shape	)     ,		[self.batch_size, self.hidden_sizes[-1], 1, 1]	)

           # verify channels
           self.parent.assertEqual(len(model.channels	)     ,		1	)
           self.parent.assertListEqual(model.channels     ,		[config.hidden_sizes[-1]]	)







     def 		a__						(	self:						Optional[Any]	)->    Any:
           lowerCamelCase					:					List[Any]        =	self.prepare_config_and_inputs()
           lowerCamelCase     ,			lowerCamelCase     ,			lowerCamelCase					:					Tuple        =	config_and_inputs
           lowerCamelCase					:					int        =	{"""pixel_values""": pixel_values}
           return config, inputs_dict


@require_torch
class 			A__	(						__lowercase ,     __lowercase ,     unittest.TestCase):


     """simple docstring"""
     snake_case__	:					int													=(
         (
             ConvNextModel,
             ConvNextForImageClassification,
             ConvNextBackbone,
         )
         if is_torch_available()
         else ()
     )
     snake_case__	:					str													=(
         {'''feature-extraction''': ConvNextModel, '''image-classification''': ConvNextForImageClassification}
         if is_torch_available()
         else {}
     )

     snake_case__	:					Union[str, Any]													=True
     snake_case__	:					Optional[int]													=False
     snake_case__	:					Tuple													=False
     snake_case__	:					Union[str, Any]													=False
     snake_case__	:					Tuple													=False

     def 		a__						(	self:						Optional[Any]	)->    Union[str, Any]:
           lowerCamelCase					:					Tuple        =	ConvNextModelTester(self	)
           lowerCamelCase					:					List[Any]        =	ConfigTester(self     ,		config_class=__a     ,		has_text_modality=__a     ,		hidden_size=37	)

     def 		a__						(	self:						Optional[int]	)->    Dict:
           self.create_and_test_config_common_properties()
           self.config_tester.create_and_test_config_to_json_string()
           self.config_tester.create_and_test_config_to_json_file()
           self.config_tester.create_and_test_config_from_and_save_pretrained()
           self.config_tester.create_and_test_config_with_num_labels()
           self.config_tester.check_config_can_be_init_without_params()
           self.config_tester.check_config_arguments_init()

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

     @unittest.skip(reason="""ConvNext does not use inputs_embeds"""	)
     def 		a__						(	self:						int	)->    Dict:
           pass

     @unittest.skip(reason="""ConvNext does not support input and output embeddings"""	)
     def 		a__						(	self:						Dict	)->    Optional[Any]:
           pass

     @unittest.skip(reason="""ConvNext does not use feedforward chunking"""	)
     def 		a__						(	self:						int	)->    List[Any]:
           pass

     def 		a__						(	self:						Union[str, Any]	)->    int:
           lowerCamelCase     ,			lowerCamelCase					:					List[str]        =	self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 lowerCamelCase					:					Any        =	model_class(__a	)
                 lowerCamelCase					:					str        =	inspect.signature(model.forward	)
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 lowerCamelCase					:					Optional[Any]        =	[*signature.parameters.keys()]

                 lowerCamelCase					:					List[str]        =	["""pixel_values"""]
                 self.assertListEqual(arg_names[:1]     ,		__a	)

     def 		a__						(	self:						Optional[int]	)->    str:
           lowerCamelCase					:					Any        =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__a	)

     def 		a__						(	self:						str	)->    int:
           lowerCamelCase					:					int        =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_backbone(*__a	)

     def 		a__						(	self:						int	)->    Optional[int]:
           def check_hidden_states_output(__a:						Tuple     ,		__a:						int     ,		__a:						Tuple	):
                 lowerCamelCase					:					str        =	model_class(__a	)
                 model.to(__a	)
                 model.eval()

                 with torch.no_grad():
                       lowerCamelCase					:					Tuple        =	model(**self._prepare_for_class(__a     ,		__a	)	)

                 lowerCamelCase					:					int        =	outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                 lowerCamelCase					:					Optional[int]        =	self.model_tester.num_stages
                 self.assertEqual(len(__a	)     ,		expected_num_stages + 1	)

                 # ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
                 self.assertListEqual(
                     list(hidden_states[0].shape[-2:]	)     ,		[self.model_tester.image_size // 4, self.model_tester.image_size // 4]     ,		)

           lowerCamelCase     ,			lowerCamelCase					:					List[Any]        =	self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 lowerCamelCase					:					List[Any]        =	True
                 check_hidden_states_output(__a     ,		__a     ,		__a	)

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 lowerCamelCase					:					Tuple        =	True

                 check_hidden_states_output(__a     ,		__a     ,		__a	)

     def 		a__						(	self:						Dict	)->    Optional[Any]:
           lowerCamelCase					:					List[str]        =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*__a	)







     @slow
     def 		a__						(	self:						Optional[Any]	)->    Tuple:
           for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 lowerCamelCase					:					str        =	ConvNextModel.from_pretrained(__a	)
                 self.assertIsNotNone(__a	)




def   snake_case		(     )   ->      Optional[int]:
      lowerCamelCase					:					int        =	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    )
      return image


@require_torch
@require_vision
class 			A__	(						unittest.TestCase):


     """simple docstring"""

     @cached_property
     def 		a__						(	self:						Dict	)->    Union[str, Any]:
           return AutoImageProcessor.from_pretrained("""facebook/convnext-tiny-224"""	) if is_vision_available() else None







     @slow
     def 		a__						(	self:						List[str]	)->    Dict:
           lowerCamelCase					:					Tuple        =	ConvNextForImageClassification.from_pretrained("""facebook/convnext-tiny-224"""	).to(__a	)

           lowerCamelCase					:					Dict        =	self.default_image_processor
           lowerCamelCase					:					Union[str, Any]        =	prepare_img()
           lowerCamelCase					:					Optional[Any]        =	image_processor(images=__a     ,		return_tensors="""pt"""	).to(__a	)

           # forward pass
           with torch.no_grad():
                 lowerCamelCase					:					Any        =	model(**__a	)

           # verify the logits
           lowerCamelCase					:					Union[str, Any]        =	torch.Size((1, 1_000)	)
           self.assertEqual(outputs.logits.shape     ,		__a	)

           lowerCamelCase					:					Tuple        =	torch.tensor([-0.02_60, -0.47_39, 0.19_11]	).to(__a	)

           self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,		__a     ,		atol=1e-4	)	)


@require_torch
class 			A__	(						unittest.TestCase ,     __lowercase):


     """simple docstring"""
     snake_case__	:					Union[str, Any]													=(ConvNextBackbone,) if is_torch_available() else ()
     snake_case__	:					Optional[Any]													=ConvNextConfig

     snake_case__	:					Optional[Any]													=False

     def 		a__						(	self:						List[str]	)->    int:
           lowerCamelCase					:					Dict        =	ConvNextModelTester(self	)




