




"""simple docstring"""


from jiwer import compute_measures

import datasets


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_      			=       '''\
Word error rate (WER) is a common metric of the performance of an automatic speech recognition system.

The general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.

This problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.

Word error rate can then be computed as:

WER = (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 words,
N is the number of words in the reference (N=S+D+C).

This value indicates the average number of errors per reference word. The lower the value, the better the
performance of the ASR system with a WER of 0 being a perfect score.
'''

A_      			=       '''
Compute WER score of transcribed segments against references.

Args:
    references: List of references for each speech input.
    predictions: List of transcriptions to score.
    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.

Returns:
    (float): the word error rate

Examples:

    >>> predictions = ["this is the prediction", "there is an other sample"]
    >>> references = ["this is the reference", "there is another one"]
    >>> wer = datasets.load_metric("wer")
    >>> wer_score = wer.compute(predictions=predictions, references=references)
    >>> print(wer_score)
    0.5
'''






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







 '''simple docstring'''





 def 				UpperCamelCase_     (       self:	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""",
       ],   )


 def 				UpperCamelCase_     (       self:	Optional[int],   a_:	Any=None,   a_:	int=None,   a_:	str=False	):




   '''simple docstring'''



   if concatenate_texts:
     return compute_measures(a_,   a_	)["wer"]
   else:
     _snake_case							:	int			=						0
     _snake_case							:	int			=						0
     for prediction, reference in zip(a_,   a_	):
       _snake_case							:	Optional[int]			=						compute_measures(a_,   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 huggingface_hub import hf_hub_download

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

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


if is_torch_available():
     import torch

     from transformers import UperNetForSemanticSegmentation
     from transformers.models.upernet.modeling_upernet import UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import AutoImageProcessor






class       lowercase:







 '''simple docstring'''





 def __init__(       self:	Dict,   a_:	Union[str, Any],   a_:	Tuple=13,   a_:	Dict=32,   a_:	Optional[Any]=3,   a_:	Optional[Any]=4,   a_:	Optional[int]=[10, 20, 30, 40],   a_:	Any=[2, 2, 3, 2],   a_:	Dict=True,   a_:	Dict=True,   a_:	List[str]=37,   a_:	Dict="gelu",   a_:	List[str]=10,   a_:	Union[str, Any]=0.02,   a_:	Any=["stage2", "stage3", "stage4"],   a_:	Optional[int]=3,   a_:	Tuple=None,   ):




   '''simple docstring'''



   _snake_case							:	Dict			=						parent
   _snake_case							:	Dict			=						batch_size
   _snake_case							:	Optional[Any]			=						image_size
   _snake_case							:	int			=						num_channels
   _snake_case							:	Tuple			=						num_stages
   _snake_case							:	int			=						hidden_sizes
   _snake_case							:	List[str]			=						depths
   _snake_case							:	str			=						is_training
   _snake_case							:	Dict			=						use_labels
   _snake_case							:	List[str]			=						intermediate_size
   _snake_case							:	Optional[int]			=						hidden_act
   _snake_case							:	Any			=						type_sequence_label_size
   _snake_case							:	List[str]			=						initializer_range
   _snake_case							:	Union[str, Any]			=						out_features
   _snake_case							:	Dict			=						num_labels
   _snake_case							:	int			=						scope
   _snake_case							:	Dict			=						num_stages


 def 				UpperCamelCase_     (       self:	int	):




   '''simple docstring'''



   _snake_case							:	Optional[Any]			=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

   _snake_case							:	Optional[int]			=						None
   if self.use_labels:
     _snake_case							:	Optional[Any]			=						ids_tensor([self.batch_size],   self.type_sequence_label_size	)

   _snake_case							:	Tuple			=						self.get_config()

   return config, pixel_values, labels


 def 				UpperCamelCase_     (       self:	Tuple	):




   '''simple docstring'''



   return ConvNextConfig(
       num_channels=self.num_channels,   num_stages=self.num_stages,   hidden_sizes=self.hidden_sizes,   depths=self.depths,   is_training=self.is_training,   intermediate_size=self.intermediate_size,   hidden_act=self.hidden_act,   out_features=self.out_features,   )


 def 				UpperCamelCase_     (       self:	List[Any]	):




   '''simple docstring'''



   return UperNetConfig(
       backbone_config=self.get_backbone_config(),   hidden_size=512,   pool_scales=[1, 2, 3, 6],   use_auxiliary_head=a_,   auxiliary_loss_weight=0.4,   auxiliary_in_channels=40,   auxiliary_channels=256,   auxiliary_num_convs=1,   auxiliary_concat_input=a_,   loss_ignore_index=255,   num_labels=self.num_labels,   )


 def 				UpperCamelCase_     (       self:	Tuple,   a_:	List[Any],   a_:	Dict,   a_:	Tuple	):




   '''simple docstring'''



   _snake_case							:	List[Any]			=						UperNetForSemanticSegmentation(config=a_	)
   model.to(a_	)
   model.eval()
   _snake_case							:	Tuple			=						model(a_	)
   self.parent.assertEqual(
       result.logits.shape,   (self.batch_size, self.num_labels, self.image_size, self.image_size)	)


 def 				UpperCamelCase_     (       self:	Optional[Any]	):




   '''simple docstring'''



   _snake_case							:	Any			=						self.prepare_config_and_inputs()
   (
       (
       _snake_case
   )	,      (
       _snake_case
   )	,      (
       _snake_case
   )	,      
   )							:	List[Any]			=						config_and_inputs
   _snake_case							:	Any			=						{"""pixel_values""": pixel_values}
   return config, inputs_dict







@require_torch
class       lowercase(  __a ,    __a ,    unittest.TestCase				):







 '''simple docstring'''





 lowercase__				      =      (UperNetForSemanticSegmentation,) if is_torch_available() else ()
 lowercase__				      =      {"image-segmentation": UperNetForSemanticSegmentation} if is_torch_available() else {}
 lowercase__				      =      False
 lowercase__				      =      False
 lowercase__				      =      False
 lowercase__				      =      False
 lowercase__				      =      False
 lowercase__				      =      False


 def 				UpperCamelCase_     (       self:	Optional[Any]	):




   '''simple docstring'''



   _snake_case							:	List[str]			=						UperNetModelTester(self	)
   _snake_case							:	Dict			=						ConfigTester(self,   config_class=a_,   has_text_modality=a_,   hidden_size=37	)


 def 				UpperCamelCase_     (       self:	Any	):




   '''simple docstring'''



   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 				UpperCamelCase_     (       self:	List[Any]	):




   '''simple docstring'''



   return


 def 				UpperCamelCase_     (       self:	Union[str, Any]	):




   '''simple docstring'''



   _snake_case	,      _snake_case							:	Any			=						self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     _snake_case							:	Dict			=						model_class(a_	)
     _snake_case							:	Optional[int]			=						inspect.signature(model.forward	)
     # signature.parameters is an OrderedDict => so arg_names order is deterministic
     _snake_case							:	Tuple			=						[*signature.parameters.keys()]

     _snake_case							:	Any			=						["""pixel_values"""]
     self.assertListEqual(arg_names[:1],   a_	)


 def 				UpperCamelCase_     (       self:	Any	):




   '''simple docstring'''



   _snake_case							:	Union[str, Any]			=						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_semantic_segmentation(*a_	)


 @unittest.skip(reason="""UperNet does not use inputs_embeds"""	)
 def 				UpperCamelCase_     (       self:	Any	):




   '''simple docstring'''



   pass


 @unittest.skip(reason="""UperNet does not support input and output embeddings"""	)
 def 				UpperCamelCase_     (       self:	Dict	):




   '''simple docstring'''



   pass


 @unittest.skip(reason="""UperNet does not have a base model"""	)
 def 				UpperCamelCase_     (       self:	Optional[Any]	):




   '''simple docstring'''



   pass


 @unittest.skip(reason="""UperNet does not have a base model"""	)
 def 				UpperCamelCase_     (       self:	int	):




   '''simple docstring'''



   pass


 @require_torch_multi_gpu
 @unittest.skip(reason="""UperNet has some layers using `add_module` which doesn't work well with `nn.DataParallel`"""	)
 def 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   pass


 @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""	)
 def 				UpperCamelCase_     (       self:	Dict	):




   '''simple docstring'''



   pass


 def 				UpperCamelCase_     (       self:	str	):




   '''simple docstring'''



   def check_hidden_states_output(a_:	Dict,   a_:	List[str],   a_:	Optional[int]	):
     _snake_case							:	Optional[Any]			=						model_class(a_	)
     model.to(a_	)
     model.eval()

     with torch.no_grad():
       _snake_case							:	Any			=						model(**self._prepare_for_class(a_,   a_	)	)

     _snake_case							:	Any			=						outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

     _snake_case							:	List[str]			=						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],   )

   _snake_case	,      _snake_case							:	str			=						self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     _snake_case							:	int			=						True
     check_hidden_states_output(a_,   a_,   a_	)

     # check that output_hidden_states also work using config
     del inputs_dict["output_hidden_states"]
     _snake_case							:	Optional[int]			=						True

     check_hidden_states_output(a_,   a_,   a_	)


 def 				UpperCamelCase_     (       self:	str	):




   '''simple docstring'''



   _snake_case	,      _snake_case							:	int			=						self.model_tester.prepare_config_and_inputs_for_common()

   _snake_case							:	Tuple			=						_config_zero_init(a_	)
   _snake_case							:	Dict			=						_config_zero_init(configs_no_init.backbone_config	)
   for model_class in self.all_model_classes:
     _snake_case							:	Optional[int]			=						model_class(config=a_	)
     for name, param in model.named_parameters():
       if param.requires_grad:
         self.assertIn(
             ((param.data.mean() * 1E9).round() / 1E9).item(),   [0.0, 1.0],   msg=f"Parameter {name} of model {model_class} seems not properly initialized",   )


 @unittest.skip(reason="""UperNet does not have tied weights"""	)
 def 				UpperCamelCase_     (       self:	Union[str, Any]	):




   '''simple docstring'''



   pass


 @slow
 def 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     _snake_case							:	int			=						UperNetForSemanticSegmentation.from_pretrained(a_	)
     self.assertIsNotNone(a_	)







def       UpperCAmelCase__ ():
  """simple docstring"""

  _snake_case							:	Union[str, Any]			=						hf_hub_download(
      repo_id="""hf-internal-testing/fixtures_ade20k"""	,     repo_type="""dataset"""	,     filename="""ADE_val_00000001.jpg"""					)
  _snake_case							:	List[Any]			=						Image.open(snake_case__					).convert("""RGB"""					)
  return image






@require_torch
@require_vision
@slow
class       lowercase(  unittest.TestCase				):







 '''simple docstring'''





 def 				UpperCamelCase_     (       self:	Tuple	):




   '''simple docstring'''



   _snake_case							:	str			=						AutoImageProcessor.from_pretrained("""openmmlab/upernet-swin-tiny"""	)
   _snake_case							:	Any			=						UperNetForSemanticSegmentation.from_pretrained("""openmmlab/upernet-swin-tiny"""	).to(a_	)

   _snake_case							:	Dict			=						prepare_img()
   _snake_case							:	str			=						processor(images=a_,   return_tensors="""pt"""	).to(a_	)

   with torch.no_grad():
     _snake_case							:	Tuple			=						model(**a_	)

   _snake_case							:	Tuple			=						torch.Size((1, model.config.num_labels, 512, 512)	)
   self.assertEqual(outputs.logits.shape,   a_	)

   _snake_case							:	int			=						torch.tensor(
       [[-7.5_958, -7.5_958, -7.4_302], [-7.5_958, -7.5_958, -7.4_302], [-7.4_797, -7.4_797, -7.3_068]]	).to(a_	)
   self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3],   a_,   atol=1E-4	)	)


 def 				UpperCamelCase_     (       self:	int	):




   '''simple docstring'''



   _snake_case							:	Optional[Any]			=						AutoImageProcessor.from_pretrained("""openmmlab/upernet-convnext-tiny"""	)
   _snake_case							:	Optional[int]			=						UperNetForSemanticSegmentation.from_pretrained("""openmmlab/upernet-convnext-tiny"""	).to(a_	)

   _snake_case							:	List[str]			=						prepare_img()
   _snake_case							:	Tuple			=						processor(images=a_,   return_tensors="""pt"""	).to(a_	)

   with torch.no_grad():
     _snake_case							:	Optional[Any]			=						model(**a_	)

   _snake_case							:	Union[str, Any]			=						torch.Size((1, model.config.num_labels, 512, 512)	)
   self.assertEqual(outputs.logits.shape,   a_	)

   _snake_case							:	Optional[Any]			=						torch.tensor(
       [[-8.8_110, -8.8_110, -8.6_521], [-8.8_110, -8.8_110, -8.6_521], [-8.7_746, -8.7_746, -8.6_130]]	).to(a_	)
   self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3],   a_,   atol=1E-4	)	)


