




'''simple docstring'''



from jiwer import compute_measures

import datasets


UpperCamelCase_    	=				"\\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"

UpperCamelCase_    	=				"\\nWord error rate (WER) is a common metric of the performance of an automatic speech recognition system.\n\nThe 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.\n\nThis 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.\n\nWord error rate can then be computed as:\n\nWER = (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 words,\nN is the number of words in the reference (N=S+D+C).\n\nThis value indicates the average number of errors per reference word. The lower the value, the better the\nperformance of the ASR system with a WER of 0 being a perfect score.\n"

UpperCamelCase_    	=				"\nCompute WER score of transcribed segments against references.\n\nArgs:\n    references: List of references for each speech input.\n    predictions: List of transcriptions to score.\n    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.\n\nReturns:\n    (float): the word 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    >>> wer = datasets.load_metric(\"wer\")\n    >>> wer_score = wer.compute(predictions=predictions, references=references)\n    >>> print(wer_score)\n    0.5\n"






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





       '''simple docstring'''






       def        UpperCamelCase_							(      self  ):







             '''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,							A=None,							A=None,							A=False  ):







             '''simple docstring'''







             if concatenate_texts:
                   return compute_measures(A,							A  )["wer"]
             else:
                   SCREAMING_SNAKE_CASE   :      Dict    					=						0
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						0
                   for prediction, reference in zip(A,							A  ):
                         SCREAMING_SNAKE_CASE   :      Any    					=						compute_measures(A,							A  )
                         incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
                         total += measures["substitutions"] + measures["deletions"] + measures["hits"]
                   return incorrect / total







import inspect
import unittest

from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, 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 torch import nn

       from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
       from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image



class    UpperCAmelCase__:

   '''simple docstring'''




   def __init__(    self	:      str      ,					lowerCAmelCase	:      Tuple      ,					lowerCAmelCase	:      Optional[int]=13      ,					lowerCAmelCase	:      Optional[int]=64      ,					lowerCAmelCase	:      List[Any]=2      ,					lowerCAmelCase	:      int=3      ,					lowerCAmelCase	:      List[str]=True      ,					lowerCAmelCase	:      Optional[int]=True      ,					lowerCAmelCase	:      Tuple=32      ,					lowerCAmelCase	:      Union[str, Any]=5      ,					lowerCAmelCase	:      Dict=4      ,					lowerCAmelCase	:      Any=37      ,					lowerCAmelCase	:      Tuple="gelu"      ,					lowerCAmelCase	:      int=0.1      ,					lowerCAmelCase	:      Optional[int]=0.1      ,					lowerCAmelCase	:      Optional[Any]=10      ,					lowerCAmelCase	:      List[str]=0.02      ,					lowerCAmelCase	:      Optional[int]=[1, 16, 4, 4]      ,					lowerCAmelCase	:      Optional[int]=None      ,					)						->					Optional[Any]:




         """simple docstring"""

         lowercase__  =	parent
         lowercase__  =	batch_size
         lowercase__  =	image_size
         lowercase__  =	patch_size
         lowercase__  =	num_channels
         lowercase__  =	is_training
         lowercase__  =	use_labels
         lowercase__  =	hidden_size
         lowercase__  =	num_hidden_layers
         lowercase__  =	num_attention_heads
         lowercase__  =	intermediate_size
         lowercase__  =	hidden_act
         lowercase__  =	hidden_dropout_prob
         lowercase__  =	attention_probs_dropout_prob
         lowercase__  =	type_sequence_label_size
         lowercase__  =	initializer_range
         lowercase__  =	scope
         lowercase__  =	backbone_featmap_shape

         # in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
         # the number of patches is based on the feature map of the backbone, which by default uses an output stride
         # of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
         lowercase__  =	(self.image_size // 32) ** 2
         lowercase__  =	num_patches + 1


   def       UpperCAmelCase		(    self	:      Dict)						->					Any:




         """simple docstring"""

         lowercase__  =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

         lowercase__  =	None
         if self.use_labels:
               lowercase__  =	ids_tensor([self.batch_size]      ,					self.type_sequence_label_size)

         lowercase__  =	self.get_config()

         return config, pixel_values, labels


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




         """simple docstring"""

         lowercase__  =	{
             'global_padding': 'same',
             'layer_type': 'bottleneck',
             'depths': [3, 4, 9],
             'out_features': ['stage1', 'stage2', 'stage3'],
             'embedding_dynamic_padding': True,
             'hidden_sizes': [4, 8, 16, 32],
             'num_groups': 2,
         }

         return ViTHybridConfig(
             image_size=self.image_size      ,					patch_size=self.patch_size      ,					num_channels=self.num_channels      ,					hidden_size=self.hidden_size      ,					num_hidden_layers=self.num_hidden_layers      ,					num_attention_heads=self.num_attention_heads      ,					intermediate_size=self.intermediate_size      ,					hidden_act=self.hidden_act      ,					hidden_dropout_prob=self.hidden_dropout_prob      ,					attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,					is_decoder=lowerCAmelCase      ,					initializer_range=self.initializer_range      ,					backbone_featmap_shape=self.backbone_featmap_shape      ,					backbone_config=lowerCAmelCase      ,					)


   def       UpperCAmelCase		(    self	:      Optional[int]      ,					lowerCAmelCase	:      Any      ,					lowerCAmelCase	:      Optional[int]      ,					lowerCAmelCase	:      Union[str, Any])						->					int:




         """simple docstring"""

         lowercase__  =	ViTHybridModel(config=lowerCAmelCase)
         model.to(lowerCAmelCase)
         model.eval()
         lowercase__  =	model(lowerCAmelCase)
         self.parent.assertEqual(result.last_hidden_state.shape      ,					(self.batch_size, self.seq_length, self.hidden_size))


   def       UpperCAmelCase		(    self	:      Dict      ,					lowerCAmelCase	:      Union[str, Any]      ,					lowerCAmelCase	:      int      ,					lowerCAmelCase	:      List[str])						->					Tuple:




         """simple docstring"""

         lowercase__  =	self.type_sequence_label_size
         lowercase__  =	ViTHybridForImageClassification(lowerCAmelCase)
         model.to(lowerCAmelCase)
         model.eval()
         lowercase__  =	model(lowerCAmelCase      ,					labels=lowerCAmelCase)
         self.parent.assertEqual(result.logits.shape      ,					(self.batch_size, self.type_sequence_label_size))



   def       UpperCAmelCase		(    self	:      str)						->					Optional[Any]:




         """simple docstring"""

         lowercase__  =	self.prepare_config_and_inputs()
         lowercase__,						lowercase__,						lowercase__  =	config_and_inputs
         lowercase__  =	{'pixel_values': pixel_values}
         return config, inputs_dict



@require_torch
class    UpperCAmelCase__(				lowerCamelCase  ,				lowerCamelCase  ,				unittest.TestCase						):

   '''simple docstring'''




   A     :	Any												=     (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
   A     :	int												=     (
       {"feature-extraction": ViTHybridModel, "image-classification": ViTHybridForImageClassification}
       if is_torch_available()
       else {}
   )
   A     :	Tuple												=     False
   A     :	Tuple												=     False
   A     :	Union[str, Any]												=     False


   def       UpperCAmelCase		(    self	:      Optional[int])						->					Union[str, Any]:




         """simple docstring"""

         lowercase__  =	ViTHybridModelTester(self)
         lowercase__  =	ConfigTester(self      ,					config_class=lowerCAmelCase      ,					has_text_modality=lowerCAmelCase      ,					hidden_size=37)


   def       UpperCAmelCase		(    self	:      Dict)						->					Any:




         """simple docstring"""

         self.config_tester.run_common_tests()


   @unittest.skip(reason='ViT does not use inputs_embeds')
   def       UpperCAmelCase		(    self	:      Dict)						->					Dict:




         """simple docstring"""

         pass


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




         """simple docstring"""

         lowercase__,						lowercase__  =	self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
               lowercase__  =	model_class(lowerCAmelCase)
               self.assertIsInstance(model.get_input_embeddings()      ,					(nn.Module))
               lowercase__  =	model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(lowerCAmelCase      ,					nn.Linear))


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




         """simple docstring"""

         lowercase__,						lowercase__  =	self.model_tester.prepare_config_and_inputs_for_common()

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

               lowercase__  =	['pixel_values']
               self.assertListEqual(arg_names[:1]      ,					lowerCAmelCase)


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




         """simple docstring"""

         lowercase__  =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowerCAmelCase)


   def       UpperCAmelCase		(    self	:      int)						->					List[Any]:




         """simple docstring"""

         lowercase__  =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase)


   def       UpperCAmelCase		(    self	:      str)						->					Tuple:




         """simple docstring"""

         lowercase__,						lowercase__  =	self.model_tester.prepare_config_and_inputs_for_common()

         lowercase__  =	_config_zero_init(lowerCAmelCase)
         for model_class in self.all_model_classes:
               lowercase__  =	model_class(config=lowerCAmelCase)
               # Skip the check for the backbone
               for name, module in model.named_modules():
                     if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
                           lowercase__  =	[f'''{name}.{key}''' for key in module.state_dict().keys()]
                           break

               for name, param in model.named_parameters():
                     if param.requires_grad:
                           if name in backbone_params:
                                 continue
                           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'''      ,					)



   @slow
   def       UpperCAmelCase		(    self	:      Any)						->					Optional[int]:




         """simple docstring"""

         for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               lowercase__  =	ViTHybridModel.from_pretrained(lowerCAmelCase)
               self.assertIsNotNone(lowerCAmelCase)

def 	_lowerCAmelCase (    ):
      lowercase__  =	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
      return image



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

   '''simple docstring'''




   @cached_property
   def       UpperCAmelCase		(    self	:      int)						->					Union[str, Any]:




         """simple docstring"""

         return (
             ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0])
             if is_vision_available()
             else None
         )


   @slow
   def       UpperCAmelCase		(    self	:      Union[str, Any])						->					Optional[Any]:




         """simple docstring"""

         lowercase__  =	ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(
             lowerCAmelCase)

         lowercase__  =	self.default_image_processor
         lowercase__  =	prepare_img()
         lowercase__  =	image_processor(images=lowerCAmelCase      ,					return_tensors='pt').to(lowerCAmelCase)

         # forward pass
         with torch.no_grad():
               lowercase__  =	model(**lowerCAmelCase)

         # verify the logits
         lowercase__  =	torch.Size((1, 10_00))
         self.assertEqual(outputs.logits.shape      ,					lowerCAmelCase)

         lowercase__  =	torch.tensor([-1.90_90, -0.49_93, -0.23_89]).to(lowerCAmelCase)

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



   @slow
   @require_accelerate
   def       UpperCAmelCase		(    self	:      int)						->					Dict:




         """simple docstring"""

         lowercase__  =	ViTHybridImageProcessor.from_pretrained('google/vit-hybrid-base-bit-384')
         lowercase__  =	ViTHybridForImageClassification.from_pretrained('google/vit-hybrid-base-bit-384'      ,					device_map='auto')

         lowercase__  =	prepare_img()

         lowercase__  =	image_processor(images=lowerCAmelCase      ,					return_tensors='pt')
         lowercase__  =	model(**lowerCAmelCase)
         lowercase__  =	outputs.logits
         # model predicts one of the 1000 ImageNet classes
         lowercase__  =	logits.argmax(-1).item()

         self.assertTrue(model.config.idalabel[predicted_class_idx]      ,					'tabby, tabby cat')
