

"""simple docstring"""




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       lowercase__						(       snake_case_   :Dataset			,		snake_case_   :Dict[str, str]    ):
	__UpperCAmelCase      		= args.log_outputs
	__UpperCAmelCase      		= '''_'''.join(args.dataset.split('''/'''    ) + [args.config, args.split]    )

	# load metric
	__UpperCAmelCase      		= load_metric('''wer'''    )
	__UpperCAmelCase      		= load_metric('''cer'''    )

	# compute metrics
	__UpperCAmelCase      		= wer.compute(references=result['''target''']			,		predictions=result['''prediction''']    )
	__UpperCAmelCase      		= cer.compute(references=result['''target''']			,		predictions=result['''prediction''']    )

	# print & log results
	__UpperCAmelCase      		= F'''WER: {wer_result}\nCER: {cer_result}'''
	print(snake_case_    )

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

	# log all results in text file. Possibly interesting for analysis
	if log_outputs is not None:
		__UpperCAmelCase      		= F'''log_{dataset_id}_predictions.txt'''
		__UpperCAmelCase      		= F'''log_{dataset_id}_targets.txt'''

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

			result.map(snake_case_			,		with_indices=snake_case_    )





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

	__UpperCAmelCase      		= re.sub(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!
	__UpperCAmelCase      		= ['''\n\n''', '''\n''', '''   ''', '''  ''']

	for t in token_sequences_to_ignore:
		__UpperCAmelCase      		= ''' '''.join(text.split(snake_case_    )    )

	return text





def       lowercase__						(       snake_case_   :Dict    ):
	# load dataset
	__UpperCAmelCase      		= load_dataset(args.dataset			,		args.config			,		split=args.split			,		use_auth_token=snake_case_    )

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

	# load processor
	__UpperCAmelCase      		= AutoFeatureExtractor.from_pretrained(args.model_id    )
	__UpperCAmelCase      		= feature_extractor.sampling_rate

	# resample audio
	__UpperCAmelCase      		= dataset.cast_column('''audio'''			,		Audio(sampling_rate=snake_case_    )    )

	# load eval pipeline
	if args.device is None:
		__UpperCAmelCase      		= 0 if torch.cuda.is_available() else -1
	__UpperCAmelCase      		= pipeline('''automatic-speech-recognition'''			,		model=args.model_id			,		device=args.device    )

	# map function to decode audio
	def map_to_pred(snake_case_   :Dict    ):
		__UpperCAmelCase      		= asr(
		    batch['''audio''']['''array''']			,		chunk_length_s=args.chunk_length_s			,		stride_length_s=args.stride_length_s    )

		__UpperCAmelCase      		= prediction['''text''']
		__UpperCAmelCase      		= normalize_text(batch['''sentence''']    )
		return batch

	# run inference on all examples
	__UpperCAmelCase      		= dataset.map(snake_case_			,		remove_columns=dataset.column_names    )

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


if __name__ == "__main__":
				_lowercase   :	str												=						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.',
				)
				_lowercase   :	List[Any]												=						parser.parse_args()

				main(args)








'''simple docstring'''
from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import (
     TFDeiTForImageClassification,
     TFDeiTForImageClassificationWithTeacher,
     TFDeiTForMaskedImageModeling,
     TFDeiTModel,
 )
 from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import DeiTImageProcessor


class 							_UpperCAmelCase    :




    def __init__(      self	:							int	,  a	:							Dict	,  a	:							Optional[Any]=1_3	,  a	:							str=3_0	,  a	:							Dict=2	,  a	:							List[Any]=3	,  a	:							Optional[int]=True	,  a	:							Tuple=True	,  a	:							List[Any]=3_2	,  a	:							int=2	,  a	:							List[str]=4	,  a	:							Dict=3_7	,  a	:							Optional[Any]="gelu"	,  a	:							List[str]=0.1	,  a	:							Dict=0.1	,  a	:							List[Any]=1_0	,  a	:							Union[str, Any]=0.02	,  a	:							Union[str, Any]=3	,  a	:							int=None	,  a	:							Any=2	,  ):






       '''simple docstring'''






       lowercase_						:     Any							     =  parent
       lowercase_						:     Union[str, Any]							     =  batch_size
       lowercase_						:     Any							     =  image_size
       lowercase_						:     str							     =  patch_size
       lowercase_						:     int							     =  num_channels
       lowercase_						:     str							     =  is_training
       lowercase_						:     str							     =  use_labels
       lowercase_						:     List[Any]							     =  hidden_size
       lowercase_						:     List[str]							     =  num_hidden_layers
       lowercase_						:     Dict							     =  num_attention_heads
       lowercase_						:     Optional[int]							     =  intermediate_size
       lowercase_						:     str							     =  hidden_act
       lowercase_						:     Union[str, Any]							     =  hidden_dropout_prob
       lowercase_						:     List[str]							     =  attention_probs_dropout_prob
       lowercase_						:     List[str]							     =  type_sequence_label_size
       lowercase_						:     List[Any]							     =  initializer_range
       lowercase_						:     str							     =  scope
       lowercase_						:     Tuple							     =  encoder_stride

       # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
       lowercase_						:     Any							     =  (image_size // patch_size) ** 2
       lowercase_						:     str							     =  num_patches + 2




    def 					lowerCAmelCase__					(      self	:							Dict      ):






       '''simple docstring'''






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

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

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

       return config, pixel_values, labels




    def 					lowerCAmelCase__					(      self	:							Dict      ):






       '''simple docstring'''






       return DeiTConfig(
           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=a	,  initializer_range=self.initializer_range	,  encoder_stride=self.encoder_stride	,  )




    def 					lowerCAmelCase__					(      self	:							Any	,  a	:							List[str]	,  a	:							str	,  a	:							Tuple      ):






       '''simple docstring'''






       lowercase_						:     List[str]							     =  TFDeiTModel(config=a      )
       lowercase_						:     int							     =  model(a      )
       self.parent.assertEqual(result.last_hidden_state.shape	,  (self.batch_size, self.seq_length, self.hidden_size)      )




    def 					lowerCAmelCase__					(      self	:							Dict	,  a	:							Optional[int]	,  a	:							List[Any]	,  a	:							Union[str, Any]      ):






       '''simple docstring'''






       lowercase_						:     List[str]							     =  TFDeiTForMaskedImageModeling(config=a      )
       lowercase_						:     Union[str, Any]							     =  model(a      )
       self.parent.assertEqual(
           result.reconstruction.shape	,  (self.batch_size, self.num_channels, self.image_size, self.image_size)      )

       # test greyscale images
       lowercase_						:     Dict							     =  1
       lowercase_						:     Tuple							     =  TFDeiTForMaskedImageModeling(a      )

       lowercase_						:     Tuple							     =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
       lowercase_						:     Dict							     =  model(a      )
       self.parent.assertEqual(result.reconstruction.shape	,  (self.batch_size, 1, self.image_size, self.image_size)      )




    def 					lowerCAmelCase__					(      self	:							int	,  a	:							List[str]	,  a	:							List[str]	,  a	:							Optional[int]      ):






       '''simple docstring'''






       lowercase_						:     str							     =  self.type_sequence_label_size
       lowercase_						:     Union[str, Any]							     =  TFDeiTForImageClassification(a      )
       lowercase_						:     int							     =  model(a	,  labels=a      )
       self.parent.assertEqual(result.logits.shape	,  (self.batch_size, self.type_sequence_label_size)      )

       # test greyscale images
       lowercase_						:     Dict							     =  1
       lowercase_						:     str							     =  TFDeiTForImageClassification(a      )

       lowercase_						:     Tuple							     =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
       lowercase_						:     Any							     =  model(a	,  labels=a      )
       self.parent.assertEqual(result.logits.shape	,  (self.batch_size, self.type_sequence_label_size)      )





    def 					lowerCAmelCase__					(      self	:							Tuple      ):






       '''simple docstring'''






       lowercase_						:     Dict							     =  self.prepare_config_and_inputs()
       lowercase_     ,		lowercase_     ,		lowercase_						:     Optional[int]							     =  config_and_inputs
       lowercase_						:     Optional[Any]							     =  {"pixel_values": pixel_values}
       return config, inputs_dict


@require_tf
class 							_UpperCAmelCase    (		snake_case					,       snake_case					,       unittest.TestCase      ):
    __lowerCamelCase: Tuple									=     (
        (
            TFDeiTModel,
            TFDeiTForImageClassification,
            TFDeiTForImageClassificationWithTeacher,
            TFDeiTForMaskedImageModeling,
        )
        if is_tf_available()
        else ()
    )
    __lowerCamelCase: Tuple									=     (
        {
            'feature-extraction': TFDeiTModel,
            'image-classification': (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
        }
        if is_tf_available()
        else {}
    )

    __lowerCamelCase: Union[str, Any]									=     False
    __lowerCamelCase: Any									=     False
    __lowerCamelCase: List[str]									=     False
    __lowerCamelCase: Optional[Any]									=     False




    def 					lowerCAmelCase__					(      self	:							int      ):






       '''simple docstring'''






       lowercase_						:     int							     =  TFDeiTModelTester(self      )
       lowercase_						:     Any							     =  ConfigTester(self	,  config_class=a	,  has_text_modality=a	,  hidden_size=3_7      )




    def 					lowerCAmelCase__					(      self	:							int      ):






       '''simple docstring'''






       self.config_tester.run_common_tests()




    @unittest.skip(reason="DeiT does not use inputs_embeds"      )
    def 					lowerCAmelCase__					(      self	:							Optional[int]      ):






       '''simple docstring'''






       pass




    def 					lowerCAmelCase__					(      self	:							int      ):






       '''simple docstring'''






       lowercase_     ,		lowercase_						:     Tuple							     =  self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
          lowercase_						:     Dict							     =  model_class(a      )
          self.assertIsInstance(model.get_input_embeddings()	,  (tf.keras.layers.Layer)      )
          lowercase_						:     Optional[int]							     =  model.get_output_embeddings()
          self.assertTrue(x is None or isinstance(a	,  tf.keras.layers.Dense      )      )




    def 					lowerCAmelCase__					(      self	:							str      ):






       '''simple docstring'''






       lowercase_     ,		lowercase_						:     Union[str, Any]							     =  self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
          lowercase_						:     Any							     =  model_class(a      )
          lowercase_						:     str							     =  inspect.signature(model.call      )
          # signature.parameters is an OrderedDict => so arg_names order is deterministic
          lowercase_						:     int							     =  [*signature.parameters.keys()]

          lowercase_						:     Dict							     =  ["pixel_values"]
          self.assertListEqual(arg_names[:1]	,  a      )




    def 					lowerCAmelCase__					(      self	:							Optional[Any]      ):






       '''simple docstring'''






       lowercase_						:     int							     =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*a      )




    def 					lowerCAmelCase__					(      self	:							str      ):






       '''simple docstring'''






       lowercase_						:     int							     =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_image_modeling(*a      )




    def 					lowerCAmelCase__					(      self	:							List[Any]      ):






       '''simple docstring'''






       lowercase_						:     int							     =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*a      )




    def 					lowerCAmelCase__					(      self	:							Any	,  a	:							int	,  a	:							Union[str, Any]	,  a	:							Any=False      ):






       '''simple docstring'''






       lowercase_						:     Union[str, Any]							     =  super()._prepare_for_class(a	,  a	,  return_labels=a      )

       if return_labels:
          if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call      ).parameters:
             del inputs_dict["labels"]

       return inputs_dict





    @slow
    def 					lowerCAmelCase__					(      self	:							Optional[Any]      ):






       '''simple docstring'''






       for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          lowercase_						:     List[Any]							     =  TFDeiTModel.from_pretrained(a      )
          self.assertIsNotNone(a      )




def 		__SCREAMING_SNAKE_CASE       (	):


   """simple docstring"""




   lowercase_						:     str							     =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
   return image





@require_tf
@require_vision
class 							_UpperCAmelCase    (		unittest.TestCase      ):




    @cached_property
    def 					lowerCAmelCase__					(      self	:							List[Any]      ):






       '''simple docstring'''






       return (
           DeiTImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224"      )
           if is_vision_available()
           else None
       )





    @slow
    def 					lowerCAmelCase__					(      self	:							List[Any]      ):






       '''simple docstring'''






       lowercase_						:     Optional[Any]							     =  TFDeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224"      )

       lowercase_						:     Union[str, Any]							     =  self.default_image_processor
       lowercase_						:     Tuple							     =  prepare_img()
       lowercase_						:     Optional[Any]							     =  image_processor(images=a	,  return_tensors="tf"      )

       # forward pass
       lowercase_						:     int							     =  model(**a      )

       # verify the logits
       lowercase_						:     Union[str, Any]							     =  tf.TensorShape((1, 1_0_0_0)      )
       self.assertEqual(outputs.logits.shape	,  a      )

       lowercase_						:     Optional[Any]							     =  tf.constant([-1.0266, 0.1912, -1.2861]      )

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




