



'''simple docstring'''







from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


SCREAMING_SNAKE_CASE__     : str				=   logging.get_logger(__name__)




class 				__lowerCAmelCase(							lowerCAmelCase__      ):
			__snake_case  :		str		=      ['input_values', 'attention_mask']






			def __init__(   self  :							List[str]      ,       SCREAMING_SNAKE_CASE  :							List[str]=1      ,       SCREAMING_SNAKE_CASE  :							Dict=16_000      ,       SCREAMING_SNAKE_CASE  :							Tuple=128      ,       SCREAMING_SNAKE_CASE  :							List[str]=1_024      ,       SCREAMING_SNAKE_CASE  :							int=0.0      ,       SCREAMING_SNAKE_CASE  :							Optional[int]=True      ,       SCREAMING_SNAKE_CASE  :							Optional[Any]=-4.2_67_73_93      ,       SCREAMING_SNAKE_CASE  :							Dict=4.5_68_99_74      ,       SCREAMING_SNAKE_CASE  :							Optional[Any]=False      ,       **SCREAMING_SNAKE_CASE  :							str      ,       ):




					"""simple docstring"""







					super().__init__(feature_size=SCREAMING_SNAKE_CASE      ,       sampling_rate=SCREAMING_SNAKE_CASE      ,       padding_value=SCREAMING_SNAKE_CASE      ,       **SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      num_mel_bins
					SCREAMING_SNAKE_CASE_						:str										=      max_length
					SCREAMING_SNAKE_CASE_						:int										=      do_normalize
					SCREAMING_SNAKE_CASE_						:Any										=      mean
					SCREAMING_SNAKE_CASE_						:Tuple										=      std
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      return_attention_mask






			def 				_lowercase	(   self  :							Tuple      ,       SCREAMING_SNAKE_CASE  :							np.ndarray      ,       SCREAMING_SNAKE_CASE  :							int      ,       ):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Any										=      torch.from_numpy(SCREAMING_SNAKE_CASE	).unsqueeze(0	)
					SCREAMING_SNAKE_CASE_						:Any										=      ta_kaldi.fbank(
					    SCREAMING_SNAKE_CASE      ,       htk_compat=SCREAMING_SNAKE_CASE      ,       sample_frequency=self.sampling_rate      ,       use_energy=SCREAMING_SNAKE_CASE      ,       window_type='hanning'      ,       num_mel_bins=self.num_mel_bins      ,       dither=0.0      ,       frame_shift=10      ,       )

					SCREAMING_SNAKE_CASE_						:Dict										=      fbank.shape[0]
					SCREAMING_SNAKE_CASE_						:Any										=      max_length - n_frames

					# pad or truncate, depending on difference
					if difference > 0:
							SCREAMING_SNAKE_CASE_						:List[str]										=      torch.nn.ZeroPadad((0, 0, 0, difference)	)
							SCREAMING_SNAKE_CASE_						:str										=      pad_module(SCREAMING_SNAKE_CASE	)
					elif difference < 0:
							SCREAMING_SNAKE_CASE_						:Tuple										=      fbank[0:max_length, :]

					SCREAMING_SNAKE_CASE_						:List[Any]										=      fbank.numpy()

					return fbank






			def 				_lowercase	(   self  :							List[str]      ,       SCREAMING_SNAKE_CASE  :							np.ndarray	):




					"""simple docstring"""







					return (input_values - (self.mean)) / (self.std * 2)






			def __call__(   self  :							Optional[int]      ,       SCREAMING_SNAKE_CASE  :							Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]      ,       SCREAMING_SNAKE_CASE  :							Optional[int] = None      ,       SCREAMING_SNAKE_CASE  :							Optional[Union[str, TensorType]] = None      ,       **SCREAMING_SNAKE_CASE  :							List[Any]      ,       ):




					"""simple docstring"""







					if sampling_rate is not None:
							if sampling_rate != self.sampling_rate:
									raise ValueError(
									    f'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
									    f' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'
									    f' {self.sampling_rate} and not {sampling_rate}.'	)
					else:
							logger.warning(
							    'It is strongly recommended to pass the `sampling_rate` argument to this function. '
							    'Failing to do so can result in silent errors that might be hard to debug.'	)

					SCREAMING_SNAKE_CASE_						:List[Any]										=      isinstance(SCREAMING_SNAKE_CASE      ,       np.ndarray	) and len(raw_speech.shape	) > 1
					if is_batched_numpy and len(raw_speech.shape	) > 2:
							raise ValueError(f'Only mono-channel audio is supported for input to {self}'	)
					SCREAMING_SNAKE_CASE_						:Tuple										=      is_batched_numpy or (
					    isinstance(SCREAMING_SNAKE_CASE      ,       (list, tuple)	) and (isinstance(raw_speech[0]      ,       (np.ndarray, tuple, list)	))
					)

					if is_batched:
							SCREAMING_SNAKE_CASE_						:Optional[Any]										=      [np.asarray(SCREAMING_SNAKE_CASE      ,       dtype=np.floataa	) for speech in raw_speech]
					elif not is_batched and not isinstance(SCREAMING_SNAKE_CASE      ,       np.ndarray	):
							SCREAMING_SNAKE_CASE_						:Dict										=      np.asarray(SCREAMING_SNAKE_CASE      ,       dtype=np.floataa	)
					elif isinstance(SCREAMING_SNAKE_CASE      ,       np.ndarray	) and raw_speech.dtype is np.dtype(np.floataa	):
							SCREAMING_SNAKE_CASE_						:str										=      raw_speech.astype(np.floataa	)

					# always return batch
					if not is_batched:
							SCREAMING_SNAKE_CASE_						:Tuple										=      [raw_speech]

					# extract fbank features and pad/truncate to max_length
					SCREAMING_SNAKE_CASE_						:str										=      [self._extract_fbank_features(SCREAMING_SNAKE_CASE      ,       max_length=self.max_length	) for waveform in raw_speech]

					# convert into BatchFeature
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      BatchFeature({'input_values': features}	)

					# make sure list is in array format
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      padded_inputs.get('input_values'	)
					if isinstance(input_values[0]      ,       SCREAMING_SNAKE_CASE	):
							SCREAMING_SNAKE_CASE_						:Tuple										=      [np.asarray(SCREAMING_SNAKE_CASE      ,       dtype=np.floataa	) for feature in input_values]

					# normalization
					if self.do_normalize:
							SCREAMING_SNAKE_CASE_						:List[str]										=      [self.normalize(SCREAMING_SNAKE_CASE	) for feature in input_values]

					if return_tensors is not None:
							SCREAMING_SNAKE_CASE_						:Tuple										=      padded_inputs.convert_to_tensors(SCREAMING_SNAKE_CASE	)

					return padded_inputs








"""simple docstring"""



import json
import os
import random
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
    from PIL import Image

    from transformers import FlavaImageProcessor, FlavaProcessor
    from transformers.models.flava.image_processing_flava import (
        FLAVA_CODEBOOK_MEAN,
        FLAVA_CODEBOOK_STD,
        FLAVA_IMAGE_MEAN,
        FLAVA_IMAGE_STD,
    )

@require_vision
class 			a	(  unittest.TestCase      ):







    def      UpperCamelCase_ (     self			):
      lowercase				       =			tempfile.mkdtemp()

      # fmt: off
      lowercase				       =			['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
      # fmt: on
      lowercase				       =			os.path.join(self.tmpdirname				,						VOCAB_FILES_NAMES['vocab_file']			)

      with open(self.vocab_file				,						'w'				,						encoding='utf-8'			) as fp:
        fp.write(''.join([x + '\n' for x in vocab_tokens]			)			)

      lowercase				       =			{
          'image_mean': FLAVA_IMAGE_MEAN,
          'image_std': FLAVA_IMAGE_STD,
          'do_normalize': True,
          'do_resize': True,
          'size': 2_2_4,
          'do_center_crop': True,
          'crop_size': 2_2_4,
          'input_size_patches': 1_4,
          'total_mask_patches': 7_5,
          'mask_group_max_patches': None,
          'mask_group_min_patches': 1_6,
          'mask_group_min_aspect_ratio': 0.3,
          'mask_group_max_aspect_ratio': None,
          'codebook_do_resize': True,
          'codebook_size': 1_1_2,
          'codebook_do_center_crop': True,
          'codebook_crop_size': 1_1_2,
          'codebook_do_map_pixels': True,
          'codebook_do_normalize': True,
          'codebook_image_mean': FLAVA_CODEBOOK_MEAN,
          'codebook_image_std': FLAVA_CODEBOOK_STD,
      }

      lowercase				       =			os.path.join(self.tmpdirname				,						_lowerCamelCase			)
      with open(self.image_processor_file				,						'w'				,						encoding='utf-8'			) as fp:
        json.dump(_lowerCamelCase				,						_lowerCamelCase			)







    def      UpperCamelCase_ (     self				,						**_lowerCamelCase			):
      return BertTokenizer.from_pretrained(self.tmpdirname				,						**_lowerCamelCase			)







    def      UpperCamelCase_ (     self				,						**_lowerCamelCase			):
      return BertTokenizerFast.from_pretrained(self.tmpdirname				,						**_lowerCamelCase			)







    def      UpperCamelCase_ (     self				,						**_lowerCamelCase			):
      return FlavaImageProcessor.from_pretrained(self.tmpdirname				,						**_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      shutil.rmtree(self.tmpdirname			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			[np.random.randint(2_5_5				,						size=(3, 3_0, 4_0_0)				,						dtype=np.uinta			)]

      lowercase				       =			[Image.fromarray(np.moveaxis(_lowerCamelCase				,						0				,						-1			)			) for x in image_inputs]

      return image_inputs







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_tokenizer()
      lowercase				       =			self.get_rust_tokenizer()
      lowercase				       =			self.get_image_processor()

      lowercase				       =			FlavaProcessor(tokenizer=_lowerCamelCase				,						image_processor=_lowerCamelCase			)
      processor_slow.save_pretrained(self.tmpdirname			)
      lowercase				       =			FlavaProcessor.from_pretrained(self.tmpdirname				,						use_fast=_lowerCamelCase			)

      lowercase				       =			FlavaProcessor(tokenizer=_lowerCamelCase				,						image_processor=_lowerCamelCase			)
      processor_fast.save_pretrained(self.tmpdirname			)
      lowercase				       =			FlavaProcessor.from_pretrained(self.tmpdirname			)

      self.assertEqual(processor_slow.tokenizer.get_vocab()				,						tokenizer_slow.get_vocab()			)
      self.assertEqual(processor_fast.tokenizer.get_vocab()				,						tokenizer_fast.get_vocab()			)
      self.assertEqual(tokenizer_slow.get_vocab()				,						tokenizer_fast.get_vocab()			)
      self.assertIsInstance(processor_slow.tokenizer				,						_lowerCamelCase			)
      self.assertIsInstance(processor_fast.tokenizer				,						_lowerCamelCase			)

      self.assertEqual(processor_slow.image_processor.to_json_string()				,						image_processor.to_json_string()			)
      self.assertEqual(processor_fast.image_processor.to_json_string()				,						image_processor.to_json_string()			)
      self.assertIsInstance(processor_slow.image_processor				,						_lowerCamelCase			)
      self.assertIsInstance(processor_fast.image_processor				,						_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			FlavaProcessor(tokenizer=self.get_tokenizer()				,						image_processor=self.get_image_processor()			)
      processor.save_pretrained(self.tmpdirname			)

      lowercase				       =			self.get_tokenizer(bos_token='(BOS)'				,						eos_token='(EOS)'			)
      lowercase				       =			self.get_image_processor(do_normalize=_lowerCamelCase				,						padding_value=1.0			)

      lowercase				       =			FlavaProcessor.from_pretrained(
          self.tmpdirname				,						bos_token='(BOS)'				,						eos_token='(EOS)'				,						do_normalize=_lowerCamelCase				,						padding_value=1.0			)

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

      self.assertEqual(processor.image_processor.to_json_string()				,						image_processor_add_kwargs.to_json_string()			)
      self.assertIsInstance(processor.image_processor				,						_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_image_processor()
      lowercase				       =			self.get_tokenizer()

      lowercase				       =			FlavaProcessor(tokenizer=_lowerCamelCase				,						image_processor=_lowerCamelCase			)

      lowercase				       =			self.prepare_image_inputs()

      lowercase				       =			image_processor(_lowerCamelCase				,						return_tensors='np'			)
      lowercase				       =			processor(images=_lowerCamelCase				,						return_tensors='np'			)

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

      # With rest of the args
      random.seed(1_2_3_4			)
      lowercase				       =			image_processor(
          _lowerCamelCase				,						return_image_mask=_lowerCamelCase				,						return_codebook_pixels=_lowerCamelCase				,						return_tensors='np'			)
      random.seed(1_2_3_4			)
      lowercase				       =			processor(
          images=_lowerCamelCase				,						return_image_mask=_lowerCamelCase				,						return_codebook_pixels=_lowerCamelCase				,						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      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_image_processor()
      lowercase				       =			self.get_tokenizer()

      lowercase				       =			FlavaProcessor(tokenizer=_lowerCamelCase				,						image_processor=_lowerCamelCase			)

      lowercase				       =			'lower newer'

      lowercase				       =			processor(text=_lowerCamelCase			)

      lowercase				       =			tokenizer(_lowerCamelCase			)

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







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_image_processor()
      lowercase				       =			self.get_tokenizer()

      lowercase				       =			FlavaProcessor(tokenizer=_lowerCamelCase				,						image_processor=_lowerCamelCase			)

      lowercase				       =			'lower newer'
      lowercase				       =			self.prepare_image_inputs()

      lowercase				       =			processor(text=_lowerCamelCase				,						images=_lowerCamelCase			)

      self.assertListEqual(list(inputs.keys()			)				,						['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values']			)

      # add extra args
      lowercase				       =			processor(text=_lowerCamelCase				,						images=_lowerCamelCase				,						return_codebook_pixels=_lowerCamelCase				,						return_image_mask=_lowerCamelCase			)

      self.assertListEqual(
          list(inputs.keys()			)				,						[
              'input_ids',
              'token_type_ids',
              'attention_mask',
              'pixel_values',
              'codebook_pixel_values',
              'bool_masked_pos',
          ]				,						)

      # test if it raises when no input is passed
      with pytest.raises(_lowerCamelCase			):
        processor()







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_image_processor()
      lowercase				       =			self.get_tokenizer()

      lowercase				       =			FlavaProcessor(tokenizer=_lowerCamelCase				,						image_processor=_lowerCamelCase			)

      lowercase				       =			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

      lowercase				       =			processor.batch_decode(_lowerCamelCase			)
      lowercase				       =			tokenizer.batch_decode(_lowerCamelCase			)

      self.assertListEqual(_lowerCamelCase				,						_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_image_processor()
      lowercase				       =			self.get_tokenizer()

      lowercase				       =			FlavaProcessor(tokenizer=_lowerCamelCase				,						image_processor=_lowerCamelCase			)

      lowercase				       =			'lower newer'
      lowercase				       =			self.prepare_image_inputs()

      lowercase				       =			processor(text=_lowerCamelCase				,						images=_lowerCamelCase			)

      self.assertListEqual(list(inputs.keys()			)				,						processor.model_input_names			)


