
import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list


class   __lowerCAmelCase		(		unittest.TestCase ):



      """simple docstring"""







      def 						snake_case_     ( self						:    Union[str, Any]			):
          __lowercase						:     Any		      =    '''<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K \' X J Q Z'''.split(''' '''			)
          __lowercase						:     str		      =    dict(zip(_snake_case			,    range(len(_snake_case			)			)			)			)

          __lowercase						:     Tuple		      =    {
              '''pad_token''': '''<pad>''',
              '''unk_token''': '''<unk>''',
              '''bos_token''': '''<s>''',
              '''eos_token''': '''</s>''',
          }
          __lowercase						:     List[str]		      =    {
              '''feature_size''': 1,
              '''padding_value''': 0.0,
              '''sampling_rate''': 1_6000,
              '''return_attention_mask''': False,
              '''do_normalize''': True,
          }

          __lowercase						:     Any		      =    tempfile.mkdtemp()
          __lowercase						:     str		      =    os.path.join(self.tmpdirname			,    VOCAB_FILES_NAMES['''vocab_file''']			)
          __lowercase						:     Union[str, Any]		      =    os.path.join(self.tmpdirname			,    _snake_case			)
          with open(self.vocab_file			,    '''w'''			,    encoding='''utf-8'''			) as fp:
              fp.write(json.dumps(_snake_case			) + '''\n'''			)

          with open(self.feature_extraction_file			,    '''w'''			,    encoding='''utf-8'''			) as fp:
              fp.write(json.dumps(_snake_case			) + '''\n'''			)




      def 						snake_case_     ( self						:    Dict			,    **_snake_case						:    List[str]			):
          __lowercase						:     Dict		      =    self.add_kwargs_tokens_map.copy()
          kwargs.update(_snake_case			)
          return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname			,    **_snake_case			)




      def 						snake_case_     ( self						:    Optional[int]			,    **_snake_case						:    Any			):
          return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname			,    **_snake_case			)




      def 						snake_case_     ( self						:    Dict			):
          shutil.rmtree(self.tmpdirname			)




      def 						snake_case_     ( self						:    Any			):
          __lowercase						:     Tuple		      =    self.get_tokenizer()
          __lowercase						:     Tuple		      =    self.get_feature_extractor()

          __lowercase						:     str		      =    WavaVecaProcessor(tokenizer=_snake_case			,    feature_extractor=_snake_case			)

          processor.save_pretrained(self.tmpdirname			)
          __lowercase						:     List[Any]		      =    WavaVecaProcessor.from_pretrained(self.tmpdirname			)

          self.assertEqual(processor.tokenizer.get_vocab()			,    tokenizer.get_vocab()			)
          self.assertIsInstance(processor.tokenizer			,    _snake_case			)

          self.assertEqual(processor.feature_extractor.to_json_string()			,    feature_extractor.to_json_string()			)
          self.assertIsInstance(processor.feature_extractor			,    _snake_case			)




      def 						snake_case_     ( self						:    List[Any]			):
          __lowercase						:     Union[str, Any]		      =    WavaVecaProcessor(tokenizer=self.get_tokenizer()			,    feature_extractor=self.get_feature_extractor()			)
          processor.save_pretrained(self.tmpdirname			)

          __lowercase						:     str		      =    self.get_tokenizer(bos_token='''(BOS)'''			,    eos_token='''(EOS)'''			)
          __lowercase						:     Union[str, Any]		      =    self.get_feature_extractor(do_normalize=_snake_case			,    padding_value=1.0			)

          __lowercase						:     Tuple		      =    WavaVecaProcessor.from_pretrained(
              self.tmpdirname			,    bos_token='''(BOS)'''			,    eos_token='''(EOS)'''			,    do_normalize=_snake_case			,    padding_value=1.0			)

          self.assertEqual(processor.tokenizer.get_vocab()			,    tokenizer_add_kwargs.get_vocab()			)
          self.assertIsInstance(processor.tokenizer			,    _snake_case			)

          self.assertEqual(processor.feature_extractor.to_json_string()			,    feature_extractor_add_kwargs.to_json_string()			)
          self.assertIsInstance(processor.feature_extractor			,    _snake_case			)




      def 						snake_case_     ( self						:    Optional[Any]			):
          __lowercase						:     Union[str, Any]		      =    self.get_feature_extractor()
          __lowercase						:     List[Any]		      =    self.get_tokenizer()

          __lowercase						:     Dict		      =    WavaVecaProcessor(tokenizer=_snake_case			,    feature_extractor=_snake_case			)

          __lowercase						:     Union[str, Any]		      =    floats_list((3, 1000)			)

          __lowercase						:     Dict		      =    feature_extractor(_snake_case			,    return_tensors='''np'''			)
          __lowercase						:     Union[str, Any]		      =    processor(_snake_case			,    return_tensors='''np'''			)

          for key in input_feat_extract.keys():
              self.assertAlmostEqual(input_feat_extract[key].sum()			,    input_processor[key].sum()			,    delta=1E-2			)




      def 						snake_case_     ( self						:    Tuple			):
          __lowercase						:     int		      =    self.get_feature_extractor()
          __lowercase						:     Optional[int]		      =    self.get_tokenizer()

          __lowercase						:     Optional[Any]		      =    WavaVecaProcessor(tokenizer=_snake_case			,    feature_extractor=_snake_case			)

          __lowercase						:     List[str]		      =    '''This is a test string'''

          __lowercase						:     Union[str, Any]		      =    processor(text=_snake_case			)

          __lowercase						:     Any		      =    tokenizer(_snake_case			)

          for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]			,    encoded_processor[key]			)




      def 						snake_case_     ( self						:    Dict			):
          __lowercase						:     Union[str, Any]		      =    self.get_feature_extractor()
          __lowercase						:     List[str]		      =    self.get_tokenizer()

          __lowercase						:     Union[str, Any]		      =    WavaVecaProcessor(tokenizer=_snake_case			,    feature_extractor=_snake_case			)

          __lowercase						:     Union[str, Any]		      =    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          __lowercase						:     Tuple		      =    processor.batch_decode(_snake_case			)
          __lowercase						:     Union[str, Any]		      =    tokenizer.batch_decode(_snake_case			)

          self.assertListEqual(_snake_case			,    _snake_case			)




      def 						snake_case_     ( self						:    Optional[Any]			):
          __lowercase						:     List[Any]		      =    self.get_feature_extractor()
          __lowercase						:     Optional[int]		      =    self.get_tokenizer()

          __lowercase						:     int		      =    WavaVecaProcessor(tokenizer=_snake_case			,    feature_extractor=_snake_case			)

          self.assertListEqual(
              processor.model_input_names			,    feature_extractor.model_input_names			,    msg='''`processor` and `feature_extractor` model input names do not match'''			,    )




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowerCAmelCase   :     Dict      =						logging.get_logger(__name__)


class   __lowerCAmelCase		(		lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     Union[str, Any]       				=      ['''pixel_values''']




      def __init__( self						:    int			,    _snake_case						:    bool = True			,    _snake_case						:    Optional[Dict[str, int]] = None			,    _snake_case						:    PILImageResampling = PILImageResampling.BILINEAR			,    _snake_case						:    bool = True			,    _snake_case						:    Union[int, float] = 1 / 255			,    _snake_case						:    bool = True			,    _snake_case						:    Optional[Union[float, List[float]]] = None			,    _snake_case						:    Optional[Union[float, List[float]]] = None			,    **_snake_case						:    List[str]			,    ):
          super().__init__(**_snake_case			)
          __lowercase						:     Tuple		      =    size if size is not None else {'''height''': 224, '''width''': 224}
          __lowercase						:     List[str]		      =    get_size_dict(_snake_case			)
          __lowercase						:     Union[str, Any]		      =    do_resize
          __lowercase						:     int		      =    do_rescale
          __lowercase						:     List[Any]		      =    do_normalize
          __lowercase						:     Dict		      =    size
          __lowercase						:     Any		      =    resample
          __lowercase						:     int		      =    rescale_factor
          __lowercase						:     Optional[Any]		      =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          __lowercase						:     Optional[Any]		      =    image_std if image_std is not None else IMAGENET_STANDARD_STD




      def 						snake_case_     ( self						:    int			,    _snake_case						:    np.ndarray			,    _snake_case						:    Dict[str, int]			,    _snake_case						:    PILImageResampling = PILImageResampling.BILINEAR			,    _snake_case						:    Optional[Union[str, ChannelDimension]] = None			,    **_snake_case						:    Optional[int]			,    ):
          __lowercase						:     List[Any]		      =    get_size_dict(_snake_case			)
          if "height" not in size or "width" not in size:
              raise ValueError(F'The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'			)
          return resize(
              _snake_case			,    size=(size['''height'''], size['''width'''])			,    resample=_snake_case			,    data_format=_snake_case			,    **_snake_case			)




      def 						snake_case_     ( self						:    Union[str, Any]			,    _snake_case						:    np.ndarray			,    _snake_case						:    float			,    _snake_case						:    Optional[Union[str, ChannelDimension]] = None			,    **_snake_case						:    Dict			):
          return rescale(_snake_case			,    scale=_snake_case			,    data_format=_snake_case			,    **_snake_case			)




      def 						snake_case_     ( self						:    Any			,    _snake_case						:    np.ndarray			,    _snake_case						:    Union[float, List[float]]			,    _snake_case						:    Union[float, List[float]]			,    _snake_case						:    Optional[Union[str, ChannelDimension]] = None			,    **_snake_case						:    Any			,    ):
          return normalize(_snake_case			,    mean=_snake_case			,    std=_snake_case			,    data_format=_snake_case			,    **_snake_case			)




      def 						snake_case_     ( self						:    Dict			,    _snake_case						:    ImageInput			,    _snake_case						:    Optional[bool] = None			,    _snake_case						:    Dict[str, int] = None			,    _snake_case						:    PILImageResampling = None			,    _snake_case						:    Optional[bool] = None			,    _snake_case						:    Optional[float] = None			,    _snake_case						:    Optional[bool] = None			,    _snake_case						:    Optional[Union[float, List[float]]] = None			,    _snake_case						:    Optional[Union[float, List[float]]] = None			,    _snake_case						:    Optional[Union[str, TensorType]] = None			,    _snake_case						:    Union[str, ChannelDimension] = ChannelDimension.FIRST			,    **_snake_case						:    str			,    ):
          __lowercase						:     List[Any]		      =    do_resize if do_resize is not None else self.do_resize
          __lowercase						:     Any		      =    do_rescale if do_rescale is not None else self.do_rescale
          __lowercase						:     Optional[Any]		      =    do_normalize if do_normalize is not None else self.do_normalize
          __lowercase						:     Optional[Any]		      =    resample if resample is not None else self.resample
          __lowercase						:     Tuple		      =    rescale_factor if rescale_factor is not None else self.rescale_factor
          __lowercase						:     Dict		      =    image_mean if image_mean is not None else self.image_mean
          __lowercase						:     Any		      =    image_std if image_std is not None else self.image_std

          __lowercase						:     Optional[int]		      =    size if size is not None else self.size
          __lowercase						:     Tuple		      =    get_size_dict(_snake_case			)

          __lowercase						:     Optional[int]		      =    make_list_of_images(_snake_case			)

          if not valid_images(_snake_case			):
              raise ValueError(
                  '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                  '''torch.Tensor, tf.Tensor or jax.ndarray.'''			)

          if do_resize and size is None:
              raise ValueError('''Size must be specified if do_resize is True.'''			)

          if do_rescale and rescale_factor is None:
              raise ValueError('''Rescale factor must be specified if do_rescale is True.'''			)

          # All transformations expect numpy arrays.
          __lowercase						:     Optional[Any]		      =    [to_numpy_array(_snake_case			) for image in images]

          if do_resize:
              __lowercase						:     List[Any]		      =    [self.resize(image=_snake_case			,    size=_snake_case			,    resample=_snake_case			) for image in images]

          if do_rescale:
              __lowercase						:     Tuple		      =    [self.rescale(image=_snake_case			,    scale=_snake_case			) for image in images]

          if do_normalize:
              __lowercase						:     Union[str, Any]		      =    [self.normalize(image=_snake_case			,    mean=_snake_case			,    std=_snake_case			) for image in images]

          __lowercase						:     Tuple		      =    [to_channel_dimension_format(_snake_case			,    _snake_case			) for image in images]

          __lowercase						:     List[str]		      =    {'''pixel_values''': images}
          return BatchFeature(data=_snake_case			,    tensor_type=_snake_case			)


