
'''simple docstring'''


import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
   from transformers import SpeechaTextFeatureExtractor

snake_case_ :  List[str]     =					random.Random()



def  __snake_case     (    _UpperCAmelCase					:       str,      _UpperCAmelCase					:       str=1.0,      _UpperCAmelCase					:       Union[str, Any]=None,      _UpperCAmelCase					:       Dict=None):
   if rng is None:
      UpperCamelCase       =  global_rng

   UpperCamelCase       =  []
   for batch_idx in range(shape[0]):
      values.append([])
      for _ in range(shape[1]):
         values[-1].append(rng.random() * scale)

   return values


@require_torch
@require_torchaudio
class 						lowercase__	(  unittest.TestCase   ):






   '''simple docstring'''







   def __init__(			self ,   lowerCamelCase__ ,   lowerCamelCase__=7 ,   lowerCamelCase__=4_0_0 ,   lowerCamelCase__=2_0_0_0 ,   lowerCamelCase__=2_4 ,   lowerCamelCase__=2_4 ,   lowerCamelCase__=0.0 ,   lowerCamelCase__=1_6_0_0_0 ,   lowerCamelCase__=True ,   lowerCamelCase__=True ,   ):

      '''simple docstring'''







      UpperCamelCase       =  parent
      UpperCamelCase       =  batch_size
      UpperCamelCase       =  min_seq_length
      UpperCamelCase       =  max_seq_length
      UpperCamelCase       =  (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
      UpperCamelCase       =  feature_size
      UpperCamelCase       =  num_mel_bins
      UpperCamelCase       =  padding_value
      UpperCamelCase       =  sampling_rate
      UpperCamelCase       =  return_attention_mask
      UpperCamelCase       =  do_normalize







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return {
          "feature_size": self.feature_size,
          "num_mel_bins": self.num_mel_bins,
          "padding_value": self.padding_value,
          "sampling_rate": self.sampling_rate,
          "return_attention_mask": self.return_attention_mask,
          "do_normalize": self.do_normalize,
      }







   def       UpperCAmelCase				(			self ,   lowerCamelCase__=False ,   lowerCamelCase__=False   ):

      '''simple docstring'''







      def _flatten(lowerCamelCase__   ):
         return list(itertools.chain(*lowerCamelCase__   )   )

      if equal_length:
         UpperCamelCase       =  [floats_list((self.max_seq_length, self.feature_size)   ) for _ in range(self.batch_size   )]
      else:
         # make sure that inputs increase in size
         UpperCamelCase       =  [
             floats_list((x, self.feature_size)   )
             for x in range(self.min_seq_length ,   self.max_seq_length ,   self.seq_length_diff   )
         ]
      if numpify:
         UpperCamelCase       =  [np.asarray(lowerCamelCase__   ) for x in speech_inputs]
      return speech_inputs




@require_torch
@require_torchaudio
class 						lowercase__	(  snake_case_,					unittest.TestCase   ):






   '''simple docstring'''







   _snake_case		   =							SpeechaTextFeatureExtractor if is_speech_available() else None







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  SpeechaTextFeatureExtractionTester(self   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      self.assertTrue(np.all(np.mean(lowerCamelCase__ ,   axis=0   ) < 1e-3   )   )
      self.assertTrue(np.all(np.abs(np.var(lowerCamelCase__ ,   axis=0   ) - 1   ) < 1e-3   )   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
      # create three inputs of length 800, 1000, and 1200
      UpperCamelCase       =  [floats_list((1, x)   )[0] for x in range(8_0_0 ,   1_4_0_0 ,   2_0_0   )]
      UpperCamelCase       =  [np.asarray(lowerCamelCase__   ) for speech_input in speech_inputs]

      # Test feature size
      UpperCamelCase       =  feature_extractor(lowerCamelCase__ ,   padding=lowerCamelCase__ ,   return_tensors='''np'''   ).input_features
      self.assertTrue(input_features.ndim == 3   )
      self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size   )

      # Test not batched input
      UpperCamelCase       =  feature_extractor(speech_inputs[0] ,   return_tensors='''np'''   ).input_features
      UpperCamelCase       =  feature_extractor(np_speech_inputs[0] ,   return_tensors='''np'''   ).input_features
      self.assertTrue(np.allclose(lowerCamelCase__ ,   lowerCamelCase__ ,   atol=1e-3   )   )

      # Test batched
      UpperCamelCase       =  feature_extractor(lowerCamelCase__ ,   return_tensors='''np'''   ).input_features
      UpperCamelCase       =  feature_extractor(lowerCamelCase__ ,   return_tensors='''np'''   ).input_features
      for enc_seq_a, enc_seq_a in zip(lowerCamelCase__ ,   lowerCamelCase__   ):
         self.assertTrue(np.allclose(lowerCamelCase__ ,   lowerCamelCase__ ,   atol=1e-3   )   )

      # Test 2-D numpy arrays are batched.
      UpperCamelCase       =  [floats_list((1, x)   )[0] for x in (8_0_0, 8_0_0, 8_0_0)]
      UpperCamelCase       =  np.asarray(lowerCamelCase__   )
      UpperCamelCase       =  feature_extractor(lowerCamelCase__ ,   return_tensors='''np'''   ).input_features
      UpperCamelCase       =  feature_extractor(lowerCamelCase__ ,   return_tensors='''np'''   ).input_features
      for enc_seq_a, enc_seq_a in zip(lowerCamelCase__ ,   lowerCamelCase__   ):
         self.assertTrue(np.allclose(lowerCamelCase__ ,   lowerCamelCase__ ,   atol=1e-3   )   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
      UpperCamelCase       =  [floats_list((1, x)   )[0] for x in range(8_0_0 ,   1_4_0_0 ,   2_0_0   )]

      UpperCamelCase       =  ['''longest''', '''max_length''', '''do_not_pad''']
      UpperCamelCase       =  [None, 1_6, None]
      for max_length, padding in zip(lowerCamelCase__ ,   lowerCamelCase__   ):
         UpperCamelCase       =  feature_extractor(
             lowerCamelCase__ ,   padding=lowerCamelCase__ ,   max_length=lowerCamelCase__ ,   return_attention_mask=lowerCamelCase__   )
         UpperCamelCase       =  inputs.input_features
         UpperCamelCase       =  inputs.attention_mask
         UpperCamelCase       =  [np.sum(lowerCamelCase__   ) for x in attention_mask]

         self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]   )
         self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]   )
         self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
      UpperCamelCase       =  [floats_list((1, x)   )[0] for x in range(8_0_0 ,   1_4_0_0 ,   2_0_0   )]

      UpperCamelCase       =  ['''longest''', '''max_length''', '''do_not_pad''']
      UpperCamelCase       =  [None, 1_6, None]
      for max_length, padding in zip(lowerCamelCase__ ,   lowerCamelCase__   ):
         UpperCamelCase       =  feature_extractor(
             lowerCamelCase__ ,   max_length=lowerCamelCase__ ,   padding=lowerCamelCase__ ,   return_tensors='''np''' ,   return_attention_mask=lowerCamelCase__   )
         UpperCamelCase       =  inputs.input_features
         UpperCamelCase       =  inputs.attention_mask
         UpperCamelCase       =  [np.sum(lowerCamelCase__   ) for x in attention_mask]

         self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]   )
         self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1e-6   )
         self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]   )
         self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1e-6   )
         self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
      UpperCamelCase       =  [floats_list((1, x)   )[0] for x in range(8_0_0 ,   1_4_0_0 ,   2_0_0   )]
      UpperCamelCase       =  feature_extractor(
          lowerCamelCase__ ,   padding='''max_length''' ,   max_length=4 ,   truncation=lowerCamelCase__ ,   return_tensors='''np''' ,   return_attention_mask=lowerCamelCase__ ,   )
      UpperCamelCase       =  inputs.input_features
      UpperCamelCase       =  inputs.attention_mask
      UpperCamelCase       =  np.sum(attention_mask == 1 ,   axis=1   )

      self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]   )
      self._check_zero_mean_unit_variance(input_features[1]   )
      self._check_zero_mean_unit_variance(input_features[2]   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
      UpperCamelCase       =  [floats_list((1, x)   )[0] for x in range(8_0_0 ,   1_4_0_0 ,   2_0_0   )]
      UpperCamelCase       =  feature_extractor(
          lowerCamelCase__ ,   padding='''longest''' ,   max_length=4 ,   truncation=lowerCamelCase__ ,   return_tensors='''np''' ,   return_attention_mask=lowerCamelCase__ ,   )
      UpperCamelCase       =  inputs.input_features
      UpperCamelCase       =  inputs.attention_mask
      UpperCamelCase       =  np.sum(attention_mask == 1 ,   axis=1   )

      self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]   )
      self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]   )
      self._check_zero_mean_unit_variance(input_features[2]   )

      # make sure that if max_length < longest -> then pad to max_length
      self.assertEqual(input_features.shape ,   (3, 4, 2_4)   )

      UpperCamelCase       =  [floats_list((1, x)   )[0] for x in range(8_0_0 ,   1_4_0_0 ,   2_0_0   )]
      UpperCamelCase       =  feature_extractor(
          lowerCamelCase__ ,   padding='''longest''' ,   max_length=1_6 ,   truncation=lowerCamelCase__ ,   return_tensors='''np''' ,   return_attention_mask=lowerCamelCase__ ,   )
      UpperCamelCase       =  inputs.input_features
      UpperCamelCase       =  inputs.attention_mask
      UpperCamelCase       =  np.sum(attention_mask == 1 ,   axis=1   )

      self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]   )
      self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]   )
      self._check_zero_mean_unit_variance(input_features[2]   )

      # make sure that if max_length < longest -> then pad to max_length
      self.assertEqual(input_features.shape ,   (3, 6, 2_4)   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      import torch

      UpperCamelCase       =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
      UpperCamelCase       =  np.random.rand(1_0_0 ,   3_2   ).astype(np.floataa   )
      UpperCamelCase       =  np_speech_inputs.tolist()

      for inputs in [py_speech_inputs, np_speech_inputs]:
         UpperCamelCase       =  feature_extractor.pad([{'''input_features''': inputs}] ,   return_tensors='''np'''   )
         self.assertTrue(np_processed.input_features.dtype == np.floataa   )
         UpperCamelCase       =  feature_extractor.pad([{'''input_features''': inputs}] ,   return_tensors='''pt'''   )
         self.assertTrue(pt_processed.input_features.dtype == torch.floataa   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      from datasets import load_dataset

      UpperCamelCase       =  load_dataset('''hf-internal-testing/librispeech_asr_dummy''' ,   '''clean''' ,   split='''validation'''   )
      # automatic decoding with librispeech
      UpperCamelCase       =  ds.sort('''id'''   ).select(range(lowerCamelCase__   )   )[:num_samples]['''audio''']

      return [x["array"] for x in speech_samples]







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  np.array([
          -1.5745, -1.7713, -1.7020, -1.6069, -1.2250, -1.1105, -0.9072, -0.8241,
          -1.2310, -0.8098, -0.3320, -0.4101, -0.7985, -0.4996, -0.8213, -0.9128,
          -1.0420, -1.1286, -1.0440, -0.7999, -0.8405, -1.2275, -1.5443, -1.4625,
      ]   )
      # fmt: on

      UpperCamelCase       =  self._load_datasamples(1   )
      UpperCamelCase       =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
      UpperCamelCase       =  feature_extractor(lowerCamelCase__ ,   return_tensors='''pt'''   ).input_features
      self.assertEquals(input_features.shape ,   (1, 5_8_4, 2_4)   )
      self.assertTrue(np.allclose(input_features[0, 0, :3_0] ,   lowerCamelCase__ ,   atol=1e-4   )   )







'''simple docstring'''


import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer


class 						lowercase__	(  snake_case_   ):






   '''simple docstring'''







   _snake_case		   =							['''image_processor''', '''tokenizer''']
   _snake_case		   =							'''BlipImageProcessor'''
   _snake_case		   =							'''AutoTokenizer'''







   def __init__(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__   ):

      '''simple docstring'''







      super().__init__(lowerCamelCase__ ,   lowerCamelCase__   )

      # add QFormer tokenizer
      UpperCamelCase       =  qformer_tokenizer







   def __call__(			self ,   lowerCamelCase__ = None ,   lowerCamelCase__ = None ,   lowerCamelCase__ = True ,   lowerCamelCase__ = False ,   lowerCamelCase__ = None ,   lowerCamelCase__ = None ,   lowerCamelCase__ = 0 ,   lowerCamelCase__ = None ,   lowerCamelCase__ = None ,   lowerCamelCase__ = False ,   lowerCamelCase__ = False ,   lowerCamelCase__ = False ,   lowerCamelCase__ = False ,   lowerCamelCase__ = False ,   lowerCamelCase__ = True ,   lowerCamelCase__ = None ,   **lowerCamelCase__ ,   ):

      '''simple docstring'''







      if images is None and text is None:
         raise ValueError('''You have to specify at least images or text.'''   )

      UpperCamelCase       =  BatchFeature()

      if text is not None:
         UpperCamelCase       =  self.tokenizer(
             text=lowerCamelCase__ ,   add_special_tokens=lowerCamelCase__ ,   padding=lowerCamelCase__ ,   truncation=lowerCamelCase__ ,   max_length=lowerCamelCase__ ,   stride=lowerCamelCase__ ,   pad_to_multiple_of=lowerCamelCase__ ,   return_attention_mask=lowerCamelCase__ ,   return_overflowing_tokens=lowerCamelCase__ ,   return_special_tokens_mask=lowerCamelCase__ ,   return_offsets_mapping=lowerCamelCase__ ,   return_token_type_ids=lowerCamelCase__ ,   return_length=lowerCamelCase__ ,   verbose=lowerCamelCase__ ,   return_tensors=lowerCamelCase__ ,   **lowerCamelCase__ ,   )
         encoding.update(lowerCamelCase__   )
         UpperCamelCase       =  self.qformer_tokenizer(
             text=lowerCamelCase__ ,   add_special_tokens=lowerCamelCase__ ,   padding=lowerCamelCase__ ,   truncation=lowerCamelCase__ ,   max_length=lowerCamelCase__ ,   stride=lowerCamelCase__ ,   pad_to_multiple_of=lowerCamelCase__ ,   return_attention_mask=lowerCamelCase__ ,   return_overflowing_tokens=lowerCamelCase__ ,   return_special_tokens_mask=lowerCamelCase__ ,   return_offsets_mapping=lowerCamelCase__ ,   return_token_type_ids=lowerCamelCase__ ,   return_length=lowerCamelCase__ ,   verbose=lowerCamelCase__ ,   return_tensors=lowerCamelCase__ ,   **lowerCamelCase__ ,   )
         UpperCamelCase       =  qformer_text_encoding.pop('''input_ids'''   )
         UpperCamelCase       =  qformer_text_encoding.pop('''attention_mask'''   )

      if images is not None:
         UpperCamelCase       =  self.image_processor(lowerCamelCase__ ,   return_tensors=lowerCamelCase__   )
         encoding.update(lowerCamelCase__   )

      return encoding







   def       UpperCAmelCase				(			self ,   *lowerCamelCase__ ,   **lowerCamelCase__   ):

      '''simple docstring'''







      return self.tokenizer.batch_decode(*lowerCamelCase__ ,   **lowerCamelCase__   )







   def       UpperCAmelCase				(			self ,   *lowerCamelCase__ ,   **lowerCamelCase__   ):

      '''simple docstring'''







      return self.tokenizer.decode(*lowerCamelCase__ ,   **lowerCamelCase__   )







   @property
   # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.tokenizer.model_input_names
      UpperCamelCase       =  self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   **lowerCamelCase__   ):

      '''simple docstring'''







      if os.path.isfile(lowerCamelCase__   ):
         raise ValueError(f'Provided path ({save_directory}) should be a directory, not a file'   )
      os.makedirs(lowerCamelCase__ ,   exist_ok=lowerCamelCase__   )
      UpperCamelCase       =  os.path.join(lowerCamelCase__ ,   '''qformer_tokenizer'''   )
      self.qformer_tokenizer.save_pretrained(lowerCamelCase__   )
      return super().save_pretrained(lowerCamelCase__ ,   **lowerCamelCase__   )







   @classmethod
   def       UpperCAmelCase				(			cls ,   lowerCamelCase__ ,   **lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  AutoTokenizer.from_pretrained(lowerCamelCase__ ,   subfolder='''qformer_tokenizer'''   )
      UpperCamelCase       =  cls._get_arguments_from_pretrained(lowerCamelCase__ ,   **lowerCamelCase__   )
      args.append(lowerCamelCase__   )
      return cls(*lowerCamelCase__   )





