


import argparse
import re
from typing import Dict

import torch
from datasets import Audio, Dataset, load_dataset, load_metric

from transformers import AutoFeatureExtractor, pipeline


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  A_      :				Any		     =					args.log_outputs
  A_      :				str		     =					'''_'''.join(args.dataset.split('''/'''       ) + [args.config, args.split]       )

  # load metric
  A_      :				Tuple		     =					load_metric('''wer'''       )
  A_      :				Tuple		     =					load_metric('''cer'''       )

  # compute metrics
  A_      :				Tuple		     =					wer.compute(references=result['''target'''] ,			predictions=result['''prediction''']       )
  A_      :				str		     =					cer.compute(references=result['''target'''] ,			predictions=result['''prediction''']       )

  # print & log results
  A_      :				Union[str, Any]		     =					f'''WER: {wer_result}\nCER: {cer_result}'''
  print(SCREAMING_SNAKE_CASE       )

  with open(f'''{dataset_id}_eval_results.txt''' ,			'''w'''       ) as f:
    f.write(SCREAMING_SNAKE_CASE       )

  # log all results in text file. Possibly interesting for analysis
  if log_outputs is not None:
    A_      :				Optional[int]		     =					f'''log_{dataset_id}_predictions.txt'''
    A_      :				List[str]		     =					f'''log_{dataset_id}_targets.txt'''

    with open(SCREAMING_SNAKE_CASE ,			'''w'''       ) as p, open(SCREAMING_SNAKE_CASE ,			'''w'''       ) as t:
      # mapping function to write output
      def write_to_file(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
        p.write(f'''{i}''' + '''\n'''       )
        p.write(batch['''prediction'''] + '''\n'''       )
        t.write(f'''{i}''' + '''\n'''       )
        t.write(batch['''target'''] + '''\n'''       )

      result.map(SCREAMING_SNAKE_CASE ,			with_indices=SCREAMING_SNAKE_CASE       )


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  A_      :				Any		     =					'''[,?.!\-\;\:"“%‘”�—’…–]'''  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

  A_      :				List[str]		     =					re.sub(SCREAMING_SNAKE_CASE ,			'''''' ,			text.lower()       )

  # In addition, we can normalize the target text, e.g. removing new lines characters etc...
  # note that order is important here!
  A_      :				int		     =					['''\n\n''', '''\n''', '''   ''', '''  ''']

  for t in token_sequences_to_ignore:
    A_      :				str		     =					''' '''.join(text.split(SCREAMING_SNAKE_CASE       )       )

  return text


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  # load dataset
  A_      :				List[Any]		     =					load_dataset(args.dataset ,			args.config ,			split=args.split ,			use_auth_token=SCREAMING_SNAKE_CASE       )

  # for testing: only process the first two examples as a test
  # dataset = dataset.select(range(10))

  # load processor
  A_      :				List[Any]		     =					AutoFeatureExtractor.from_pretrained(args.model_id       )
  A_      :				Any		     =					feature_extractor.sampling_rate

  # resample audio
  A_      :				Union[str, Any]		     =					dataset.cast_column('''audio''' ,			Audio(sampling_rate=SCREAMING_SNAKE_CASE       )       )

  # load eval pipeline
  if args.device is None:
    A_      :				Tuple		     =					0 if torch.cuda.is_available() else -1
  A_      :				Union[str, Any]		     =					pipeline('''automatic-speech-recognition''' ,			model=args.model_id ,			device=args.device       )

  # map function to decode audio
  def map_to_pred(SCREAMING_SNAKE_CASE       ):
    A_      :				Any		     =					asr(
        batch['''audio''']['''array'''] ,			chunk_length_s=args.chunk_length_s ,			stride_length_s=args.stride_length_s       )

    A_      :				Union[str, Any]		     =					prediction['''text''']
    A_      :				Any		     =					normalize_text(batch['''sentence''']       )
    return batch

  # run inference on all examples
  A_      :				List[Any]		     =					dataset.map(SCREAMING_SNAKE_CASE ,			remove_columns=dataset.column_names       )

  # compute and log_results
  # do not change function below
  log_results(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
     UpperCamelCase								=	argparse.ArgumentParser()

     parser.add_argument(
         """--model_id""", type=str, required=True, help="""Model identifier. Should be loadable with 🤗 Transformers"""
     )
     parser.add_argument(
         """--dataset""",
         type=str,
         required=True,
         help="""Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets""",
     )
     parser.add_argument(
         """--config""", type=str, required=True, help="""Config of the dataset. *E.g.* `'en'`  for Common Voice"""
     )
     parser.add_argument("""--split""", type=str, required=True, help="""Split of the dataset. *E.g.* `'test'`""")
     parser.add_argument(
         """--chunk_length_s""", type=float, default=None, help="""Chunk length in seconds. Defaults to 5 seconds."""
     )
     parser.add_argument(
         """--stride_length_s""", type=float, default=None, help="""Stride of the audio chunks. Defaults to 1 second."""
     )
     parser.add_argument(
         """--log_outputs""", action="""store_true""", help="""If defined, write outputs to log file for analysis."""
     )
     parser.add_argument(
         """--device""",
         type=int,
         default=None,
         help="""The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.""",
     )
     UpperCamelCase								=	parser.parse_args()

     main(args)








import inspect
import unittest

from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 torch import nn

     from transformers import RegNetForImageClassification, RegNetModel
     from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import AutoImageProcessor






class   _lowerCamelCase :




       """simple docstring"""



       def __init__(      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=3				,       _SCREAMING_SNAKE_CASE=32				,       _SCREAMING_SNAKE_CASE=3				,       _SCREAMING_SNAKE_CASE=10				,       _SCREAMING_SNAKE_CASE=[10, 20, 30, 40]				,       _SCREAMING_SNAKE_CASE=[1, 1, 2, 1]				,       _SCREAMING_SNAKE_CASE=True				,       _SCREAMING_SNAKE_CASE=True				,       _SCREAMING_SNAKE_CASE="relu"				,       _SCREAMING_SNAKE_CASE=3				,       _SCREAMING_SNAKE_CASE=None				,       )->List[str]:




         '''simple docstring'''





         A_      :				str		     =					parent
         A_      :				int		     =					batch_size
         A_      :				List[str]		     =					image_size
         A_      :				Dict		     =					num_channels
         A_      :				Tuple		     =					embeddings_size
         A_      :				Union[str, Any]		     =					hidden_sizes
         A_      :				Dict		     =					depths
         A_      :				str		     =					is_training
         A_      :				Union[str, Any]		     =					use_labels
         A_      :				Union[str, Any]		     =					hidden_act
         A_      :				Optional[Any]		     =					num_labels
         A_      :				Tuple		     =					scope
         A_      :				Optional[int]		     =					len(_SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self		)->Optional[Any]:




         '''simple docstring'''





         A_      :				List[str]		     =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

         A_      :				str		     =					None
         if self.use_labels:
           A_      :				Union[str, Any]		     =					ids_tensor([self.batch_size]				,       self.num_labels		)

         A_      :				Optional[Any]		     =					self.get_config()

         return config, pixel_values, labels



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




         '''simple docstring'''





         return RegNetConfig(
             num_channels=self.num_channels				,       embeddings_size=self.embeddings_size				,       hidden_sizes=self.hidden_sizes				,       depths=self.depths				,       hidden_act=self.hidden_act				,       num_labels=self.num_labels				,       )



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




         '''simple docstring'''





         A_      :				Dict		     =					RegNetModel(config=_SCREAMING_SNAKE_CASE		)
         model.to(_SCREAMING_SNAKE_CASE		)
         model.eval()
         A_      :				Any		     =					model(_SCREAMING_SNAKE_CASE		)
         # 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        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)->Union[str, Any]:




         '''simple docstring'''





         A_      :				Union[str, Any]		     =					self.num_labels
         A_      :				Dict		     =					RegNetForImageClassification(_SCREAMING_SNAKE_CASE		)
         model.to(_SCREAMING_SNAKE_CASE		)
         model.eval()
         A_      :				int		     =					model(_SCREAMING_SNAKE_CASE				,       labels=_SCREAMING_SNAKE_CASE		)
         self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.num_labels)		)







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




         '''simple docstring'''





         A_      :				Tuple		     =					self.prepare_config_and_inputs()
         A_     ,       A_     ,       A_      :				str		     =					config_and_inputs
         A_      :				Any		     =					{'''pixel_values''': pixel_values}
         return config, inputs_dict






@require_torch
class   _lowerCamelCase (					UpperCamelCase					,							UpperCamelCase					,							unittest.TestCase			):




       """simple docstring"""
       snake_case      =    (RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
       snake_case      =    (
           {"feature-extraction": RegNetModel, "image-classification": RegNetForImageClassification}
           if is_torch_available()
           else {}
       )

       snake_case      =    False
       snake_case      =    False
       snake_case      =    False
       snake_case      =    False



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




         '''simple docstring'''





         A_      :				Union[str, Any]		     =					RegNetModelTester(self		)
         A_      :				Union[str, Any]		     =					ConfigTester(self				,       config_class=_SCREAMING_SNAKE_CASE				,       has_text_modality=_SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self		)->Dict:




         '''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        _snake_case       (      self		)->Tuple:




         '''simple docstring'''





         return



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




         '''simple docstring'''





         pass



       @unittest.skip(reason='''RegNet does not support input and output embeddings'''		)
       def        _snake_case       (      self		)->str:




         '''simple docstring'''





         pass



       def        _snake_case       (      self		)->List[Any]:




         '''simple docstring'''





         A_     ,       A_      :				Optional[Any]		     =					self.model_tester.prepare_config_and_inputs_for_common()

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

           A_      :				Any		     =					['''pixel_values''']
           self.assertListEqual(arg_names[:1]				,       _SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self		)->Any:




         '''simple docstring'''





         A_      :				Dict		     =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self		)->Optional[Any]:




         '''simple docstring'''





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

         for model_class in self.all_model_classes:
           A_      :				Union[str, Any]		     =					model_class(config=_SCREAMING_SNAKE_CASE		)
           for name, module in model.named_modules():
             if isinstance(_SCREAMING_SNAKE_CASE				,       (nn.BatchNormad, nn.GroupNorm)		):
               self.assertTrue(
                   torch.all(module.weight == 1		)				,       msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''				,       )
               self.assertTrue(
                   torch.all(module.bias == 0		)				,       msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''				,       )



       def        _snake_case       (      self		)->List[Any]:




         '''simple docstring'''





         def check_hidden_states_output(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		):
           A_      :				str		     =					model_class(_SCREAMING_SNAKE_CASE		)
           model.to(_SCREAMING_SNAKE_CASE		)
           model.eval()

           with torch.no_grad():
             A_      :				Tuple		     =					model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)		)

           A_      :				Union[str, Any]		     =					outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

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

           # RegNet'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 // 2, self.model_tester.image_size // 2]				,       )

         A_     ,       A_      :				Tuple		     =					self.model_tester.prepare_config_and_inputs_for_common()
         A_      :				int		     =					['''basic''', '''bottleneck''']
         for model_class in self.all_model_classes:
           for layer_type in layers_type:
             A_      :				int		     =					layer_type
             A_      :				List[Any]		     =					True
             check_hidden_states_output(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)

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

             check_hidden_states_output(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self		)->Dict:




         '''simple docstring'''





         A_      :				List[str]		     =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE		)







       @slow
       def        _snake_case       (      self		)->str:




         '''simple docstring'''





         for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           A_      :				Dict		     =					RegNetModel.from_pretrained(_SCREAMING_SNAKE_CASE		)
           self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)




def   _SCREAMING_SNAKE_CASE	( ):
  A_      :				int		     =					Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
  return image







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




       """simple docstring"""



       @cached_property
       def        _snake_case       (      self		)->List[str]:




         '''simple docstring'''





         return (
             AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)
             if is_vision_available()
             else None
         )







       @slow
       def        _snake_case       (      self		)->Tuple:




         '''simple docstring'''





         A_      :				List[Any]		     =					RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(_SCREAMING_SNAKE_CASE		)

         A_      :				Optional[Any]		     =					self.default_image_processor
         A_      :				Any		     =					prepare_img()
         A_      :				Optional[Any]		     =					image_processor(images=_SCREAMING_SNAKE_CASE				,       return_tensors='''pt'''		).to(_SCREAMING_SNAKE_CASE		)

         # forward pass
         with torch.no_grad():
           A_      :				Union[str, Any]		     =					model(**_SCREAMING_SNAKE_CASE		)

         # verify the logits
         A_      :				Union[str, Any]		     =					torch.Size((1, 1000)		)
         self.assertEqual(outputs.logits.shape				,       _SCREAMING_SNAKE_CASE		)

         A_      :				Optional[int]		     =					torch.tensor([-0.4_1_8_0, -1.5_0_5_1, -3.4_8_3_6]		).to(_SCREAMING_SNAKE_CASE		)

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




