"""simple docstring"""



import itertools
import random
import unittest

import numpy as np

from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
				import torch


_UpperCamelCase								=		random.Random()

def  _A(		lowerCAmelCase			,  lowerCAmelCase=1.0			,  lowerCAmelCase=None			,  lowerCAmelCase=None							):
							if rng is None:
														A__       :     List[str]								=			global_rng

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

							return values
@require_torch
class 						__UpperCAmelCase    (unittest.TestCase     ):







			'''simple docstring'''





			def __init__(			self   ,      snake_case_   ,      snake_case_=7   ,      snake_case_=400   ,      snake_case_=2_000   ,      snake_case_=1   ,      snake_case_=0.0   ,      snake_case_=16_000   ,      snake_case_=True   ,      snake_case_=80   ,      snake_case_=16   ,      snake_case_=64   ,      snake_case_="hann_window"   ,      snake_case_=80   ,      snake_case_=7_600   ,      snake_case_=1E-10   ,      snake_case_=True   ,      ):







										'''simple docstring'''


										A__       :     Any								=			parent
										A__       :     Union[str, Any]								=			batch_size
										A__       :     Any								=			min_seq_length
										A__       :     List[Any]								=			max_seq_length
										A__       :     List[str]								=			(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
										A__       :     Tuple								=			feature_size
										A__       :     str								=			padding_value
										A__       :     Optional[int]								=			sampling_rate
										A__       :     Any								=			do_normalize
										A__       :     List[str]								=			num_mel_bins
										A__       :     Optional[int]								=			hop_length
										A__       :     int								=			win_length
										A__       :     Union[str, Any]								=			win_function
										A__       :     List[str]								=			fmin
										A__       :     Optional[int]								=			fmax
										A__       :     int								=			mel_floor
										A__       :     str								=			return_attention_mask





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										return {
										    "feature_size": self.feature_size,
										    "padding_value": self.padding_value,
										    "sampling_rate": self.sampling_rate,
										    "do_normalize": self.do_normalize,
										    "num_mel_bins": self.num_mel_bins,
										    "hop_length": self.hop_length,
										    "win_length": self.win_length,
										    "win_function": self.win_function,
										    "fmin": self.fmin,
										    "fmax": self.fmax,
										    "mel_floor": self.mel_floor,
										    "return_attention_mask": self.return_attention_mask,
										}





			def 		lowerCamelCase					(			self   ,      snake_case_=False   ,      snake_case_=False							):







										'''simple docstring'''


										def _flatten(snake_case_							):
																	return list(itertools.chain(*__lowerCamelCase							)							)

										if equal_length:
																	A__       :     str								=			floats_list((self.batch_size, self.max_seq_length)							)
										else:
																	# make sure that inputs increase in size
																	A__       :     Dict								=			[
																	    _flatten(floats_list((x, self.feature_size)							)							)
																	    for x in range(self.min_seq_length   ,      self.max_seq_length   ,      self.seq_length_diff							)
																	]

										if numpify:
																	A__       :     Optional[int]								=			[np.asarray(__lowerCamelCase							) for x in speech_inputs]

										return speech_inputs





			def 		lowerCamelCase					(			self   ,      snake_case_=False   ,      snake_case_=False							):







										'''simple docstring'''


										if equal_length:
																	A__       :     Union[str, Any]								=			[floats_list((self.max_seq_length, self.num_mel_bins)							) for _ in range(self.batch_size							)]
										else:
																	# make sure that inputs increase in size
																	A__       :     str								=			[
																	    floats_list((x, self.num_mel_bins)							)
																	    for x in range(self.min_seq_length   ,      self.max_seq_length   ,      self.seq_length_diff							)
																	]

										if numpify:
																	A__       :     int								=			[np.asarray(__lowerCamelCase							) for x in speech_inputs]

										return speech_inputs





@require_torch
class 						__UpperCAmelCase    (_snake_case							,   unittest.TestCase     ):







			'''simple docstring'''
			_UpperCamelCase  :						Any					     =				SpeechTaFeatureExtractor





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Dict								=			SpeechTaFeatureExtractionTester(self							)





			def 		lowerCamelCase					(			self   ,      snake_case_							):







										'''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 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Dict								=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
										# create three inputs of length 800, 1000, and 1200
										A__       :     Dict								=			[floats_list((1, x)							)[0] for x in range(800   ,      1_400   ,      200							)]
										A__       :     str								=			[np.asarray(__lowerCamelCase							) for speech_input in speech_inputs]

										# Test not batched input
										A__       :     Dict								=			feat_extract(speech_inputs[0]   ,      return_tensors="""np"""							).input_values
										A__       :     Tuple								=			feat_extract(np_speech_inputs[0]   ,      return_tensors="""np"""							).input_values
										self.assertTrue(np.allclose(__lowerCamelCase   ,      __lowerCamelCase   ,      atol=1E-3							)							)

										# Test batched
										A__       :     Optional[Any]								=			feat_extract(__lowerCamelCase   ,      return_tensors="""np"""							).input_values
										A__       :     str								=			feat_extract(__lowerCamelCase   ,      return_tensors="""np"""							).input_values
										for enc_seq_a, enc_seq_a in zip(__lowerCamelCase   ,      __lowerCamelCase							):
																	self.assertTrue(np.allclose(__lowerCamelCase   ,      __lowerCamelCase   ,      atol=1E-3							)							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Optional[Any]								=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
										A__       :     Dict								=			[floats_list((1, x)							)[0] for x in range(800   ,      1_400   ,      200							)]

										A__       :     Any								=			['''longest''', '''max_length''', '''do_not_pad''']
										A__       :     List[Any]								=			[None, 1_600, None]
										for max_length, padding in zip(__lowerCamelCase   ,      __lowerCamelCase							):
																	A__       :     Tuple								=			feat_extract(__lowerCamelCase   ,      padding=__lowerCamelCase   ,      max_length=__lowerCamelCase   ,      return_tensors="""np"""							)
																	A__       :     Union[str, Any]								=			processed.input_values

																	self._check_zero_mean_unit_variance(input_values[0][:800]							)
																	self.assertTrue(input_values[0][800:].sum() < 1E-6							)
																	self._check_zero_mean_unit_variance(input_values[1][:1_000]							)
																	self.assertTrue(input_values[0][1_000:].sum() < 1E-6							)
																	self._check_zero_mean_unit_variance(input_values[2][:1_200]							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Optional[Any]								=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
										A__       :     Dict								=			range(800   ,      1_400   ,      200							)
										A__       :     Any								=			[floats_list((1, x)							)[0] for x in lengths]

										A__       :     Dict								=			['''longest''', '''max_length''', '''do_not_pad''']
										A__       :     Optional[int]								=			[None, 1_600, None]

										for max_length, padding in zip(__lowerCamelCase   ,      __lowerCamelCase							):
																	A__       :     Union[str, Any]								=			feat_extract(__lowerCamelCase   ,      max_length=__lowerCamelCase   ,      padding=__lowerCamelCase							)
																	A__       :     str								=			processed.input_values

																	self._check_zero_mean_unit_variance(input_values[0][:800]							)
																	self._check_zero_mean_unit_variance(input_values[1][:1_000]							)
																	self._check_zero_mean_unit_variance(input_values[2][:1_200]							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Optional[int]								=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
										A__       :     List[Any]								=			[floats_list((1, x)							)[0] for x in range(800   ,      1_400   ,      200							)]
										A__       :     Union[str, Any]								=			feat_extract(
										    __lowerCamelCase   ,      truncation=__lowerCamelCase   ,      max_length=1_000   ,      padding="""max_length"""   ,      return_tensors="""np"""							)
										A__       :     Optional[Any]								=			processed.input_values

										self._check_zero_mean_unit_variance(input_values[0, :800]							)
										self._check_zero_mean_unit_variance(input_values[1]							)
										self._check_zero_mean_unit_variance(input_values[2]							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Union[str, Any]								=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
										A__       :     Optional[int]								=			[floats_list((1, x)							)[0] for x in range(800   ,      1_400   ,      200							)]
										A__       :     Tuple								=			feat_extract(
										    __lowerCamelCase   ,      truncation=__lowerCamelCase   ,      max_length=1_000   ,      padding="""longest"""   ,      return_tensors="""np"""							)
										A__       :     int								=			processed.input_values

										self._check_zero_mean_unit_variance(input_values[0, :800]							)
										self._check_zero_mean_unit_variance(input_values[1, :1_000]							)
										self._check_zero_mean_unit_variance(input_values[2]							)

										# make sure that if max_length < longest -> then pad to max_length
										self.assertTrue(input_values.shape == (3, 1_000)							)

										A__       :     Any								=			[floats_list((1, x)							)[0] for x in range(800   ,      1_400   ,      200							)]
										A__       :     Dict								=			feat_extract(
										    __lowerCamelCase   ,      truncation=__lowerCamelCase   ,      max_length=2_000   ,      padding="""longest"""   ,      return_tensors="""np"""							)
										A__       :     Tuple								=			processed.input_values

										self._check_zero_mean_unit_variance(input_values[0, :800]							)
										self._check_zero_mean_unit_variance(input_values[1, :1_000]							)
										self._check_zero_mean_unit_variance(input_values[2]							)

										# make sure that if max_length > longest -> then pad to longest
										self.assertTrue(input_values.shape == (3, 1_200)							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Union[str, Any]								=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
										A__       :     List[str]								=			np.random.rand(100							).astype(np.floataa							)
										A__       :     Any								=			np_speech_inputs.tolist()

										for inputs in [py_speech_inputs, np_speech_inputs]:
																	A__       :     str								=			feature_extractor.pad([{"""input_values""": inputs}]   ,      return_tensors="""np"""							)
																	self.assertTrue(np_processed.input_values.dtype == np.floataa							)
																	A__       :     Tuple								=			feature_extractor.pad([{"""input_values""": inputs}]   ,      return_tensors="""pt"""							)
																	self.assertTrue(pt_processed.input_values.dtype == torch.floataa							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     List[str]								=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
										# create three inputs of length 800, 1000, and 1200
										A__       :     Optional[int]								=			[floats_list((1, x)							)[0] for x in range(800   ,      1_400   ,      200							)]
										A__       :     Tuple								=			[np.asarray(__lowerCamelCase							) for speech_input in speech_inputs]

										# Test feature size
										A__       :     Union[str, Any]								=			feature_extractor(audio_target=__lowerCamelCase   ,      padding=__lowerCamelCase   ,      return_tensors="""np"""							).input_values
										self.assertTrue(input_values.ndim == 3							)
										self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins							)

										# Test not batched input
										A__       :     Tuple								=			feature_extractor(speech_inputs[0]   ,      return_tensors="""np"""							).input_values
										A__       :     Optional[int]								=			feature_extractor(np_speech_inputs[0]   ,      return_tensors="""np"""							).input_values
										self.assertTrue(np.allclose(__lowerCamelCase   ,      __lowerCamelCase   ,      atol=1E-3							)							)

										# Test batched
										A__       :     List[Any]								=			feature_extractor(__lowerCamelCase   ,      return_tensors="""np"""							).input_values
										A__       :     List[Any]								=			feature_extractor(__lowerCamelCase   ,      return_tensors="""np"""							).input_values
										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.
										A__       :     Tuple								=			[floats_list((1, x)							)[0] for x in (800, 800, 800)]
										A__       :     Any								=			np.asarray(__lowerCamelCase							)
										A__       :     Tuple								=			feature_extractor(__lowerCamelCase   ,      return_tensors="""np"""							).input_values
										A__       :     List[Any]								=			feature_extractor(__lowerCamelCase   ,      return_tensors="""np"""							).input_values
										for enc_seq_a, enc_seq_a in zip(__lowerCamelCase   ,      __lowerCamelCase							):
																	self.assertTrue(np.allclose(__lowerCamelCase   ,      __lowerCamelCase   ,      atol=1E-3							)							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Optional[Any]								=			self.feat_extract_tester.prepare_inputs_for_target()
										A__       :     List[str]								=			self.feature_extraction_class(**self.feat_extract_dict							)
										A__       :     Dict								=			feat_extract.model_input_names[0]

										A__       :     str								=			BatchFeature({input_name: speech_inputs}							)

										self.assertTrue(all(len(__lowerCamelCase							) == len(__lowerCamelCase							) for x, y in zip(__lowerCamelCase   ,      processed_features[input_name]							)							)							)

										A__       :     Dict								=			self.feat_extract_tester.prepare_inputs_for_target(equal_length=__lowerCamelCase							)
										A__       :     Optional[Any]								=			BatchFeature({input_name: speech_inputs}   ,      tensor_type="""np"""							)

										A__       :     Any								=			processed_features[input_name]

										if len(batch_features_input.shape							) < 3:
																	A__       :     Any								=			batch_features_input[:, :, None]

										self.assertTrue(
										    batch_features_input.shape
										    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]							), self.feat_extract_tester.num_mel_bins)							)





			@require_torch
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     List[Any]								=			self.feat_extract_tester.prepare_inputs_for_target(equal_length=__lowerCamelCase							)
										A__       :     Tuple								=			self.feature_extraction_class(**self.feat_extract_dict							)
										A__       :     int								=			feat_extract.model_input_names[0]

										A__       :     int								=			BatchFeature({input_name: speech_inputs}   ,      tensor_type="""pt"""							)

										A__       :     Tuple								=			processed_features[input_name]

										if len(batch_features_input.shape							) < 3:
																	A__       :     Dict								=			batch_features_input[:, :, None]

										self.assertTrue(
										    batch_features_input.shape
										    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]							), self.feat_extract_tester.num_mel_bins)							)





			@require_torch
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Union[str, Any]								=			self.feature_extraction_class(**self.feat_extract_dict							)
										A__       :     List[Any]								=			self.feat_extract_tester.prepare_inputs_for_target()
										A__       :     Optional[Any]								=			feat_extract.model_input_names[0]

										A__       :     Any								=			BatchFeature({input_name: speech_inputs}							)

										A__       :     str								=			feat_extract.num_mel_bins  # hack!

										A__       :     Optional[Any]								=			feat_extract.pad(__lowerCamelCase   ,      padding="""longest"""   ,      return_tensors="""np"""							)[input_name]
										A__       :     Any								=			feat_extract.pad(__lowerCamelCase   ,      padding="""longest"""   ,      return_tensors="""pt"""							)[input_name]

										self.assertTrue(abs(input_np.astype(np.floataa							).sum() - input_pt.numpy().astype(np.floataa							).sum()							) < 1E-2							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     List[Any]								=			self.feat_extract_dict
										A__       :     Dict								=			True
										A__       :     List[str]								=			self.feature_extraction_class(**__lowerCamelCase							)
										A__       :     str								=			self.feat_extract_tester.prepare_inputs_for_target()
										A__       :     Union[str, Any]								=			[len(__lowerCamelCase							) for x in speech_inputs]
										A__       :     List[Any]								=			feat_extract.model_input_names[0]

										A__       :     Dict								=			BatchFeature({input_name: speech_inputs}							)

										A__       :     Tuple								=			feat_extract.num_mel_bins  # hack!

										A__       :     Tuple								=			feat_extract.pad(__lowerCamelCase   ,      padding="""longest"""   ,      return_tensors="""np"""							)
										self.assertIn("""attention_mask"""   ,      __lowerCamelCase							)
										self.assertListEqual(list(processed.attention_mask.shape							)   ,      list(processed[input_name].shape[:2]							)							)
										self.assertListEqual(processed.attention_mask.sum(-1							).tolist()   ,      __lowerCamelCase							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     List[Any]								=			self.feat_extract_dict
										A__       :     Optional[int]								=			True
										A__       :     int								=			self.feature_extraction_class(**__lowerCamelCase							)
										A__       :     List[Any]								=			self.feat_extract_tester.prepare_inputs_for_target()
										A__       :     Any								=			[len(__lowerCamelCase							) for x in speech_inputs]
										A__       :     List[str]								=			feat_extract.model_input_names[0]

										A__       :     str								=			BatchFeature({input_name: speech_inputs}							)
										A__       :     int								=			min(__lowerCamelCase							)

										A__       :     Optional[int]								=			feat_extract.num_mel_bins  # hack!

										A__       :     Optional[int]								=			feat_extract.pad(
										    __lowerCamelCase   ,      padding="""max_length"""   ,      max_length=__lowerCamelCase   ,      truncation=__lowerCamelCase   ,      return_tensors="""np"""							)
										self.assertIn("""attention_mask"""   ,      __lowerCamelCase							)
										self.assertListEqual(
										    list(processed_pad.attention_mask.shape							)   ,      [processed_pad[input_name].shape[0], max_length]							)
										self.assertListEqual(
										    processed_pad.attention_mask[:, :max_length].sum(-1							).tolist()   ,      [max_length for x in speech_inputs]							)





			def 		lowerCamelCase					(			self   ,      snake_case_							):







										'''simple docstring'''


										from datasets import load_dataset

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

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





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Union[str, Any]								=			torch.tensor(
										    [2.3804E-03, 2.0752E-03, 1.9836E-03, 2.1057E-03, 1.6174E-03,
										     3.0518E-04, 9.1553E-05, 3.3569E-04, 9.7656E-04, 1.8311E-03,
										     2.0142E-03, 2.1057E-03, 1.7395E-03, 4.5776E-04, -3.9673E-04,
										     4.5776E-04, 1.0071E-03, 9.1553E-05, 4.8828E-04, 1.1597E-03,
										     7.3242E-04, 9.4604E-04, 1.8005E-03, 1.8311E-03, 8.8501E-04,
										     4.2725E-04, 4.8828E-04, 7.3242E-04, 1.0986E-03, 2.1057E-03]							)
										# fmt: on

										A__       :     Dict								=			self._load_datasamples(1							)
										A__       :     List[str]								=			SpeechTaFeatureExtractor()
										A__       :     Optional[Any]								=			feature_extractor(__lowerCamelCase   ,      return_tensors="""pt"""							).input_values
										self.assertEquals(input_values.shape   ,      (1, 93_680)							)
										self.assertTrue(torch.allclose(input_values[0, :30]   ,      __lowerCamelCase   ,      atol=1E-6							)							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Union[str, Any]								=			torch.tensor(
										    [-2.68_70, -3.01_04, -3.13_56, -3.53_52, -3.00_44, -3.03_53, -3.47_19, -3.67_77,
										     -3.15_20, -2.94_35, -2.65_53, -2.87_95, -2.99_44, -2.59_21, -3.02_79, -3.03_86,
										     -3.08_64, -3.12_91, -3.23_53, -2.74_44, -2.68_31, -2.72_87, -3.17_61, -3.15_71,
										     -3.27_26, -3.05_82, -3.10_07, -3.45_33, -3.46_95, -3.09_98]							)
										# fmt: on

										A__       :     Optional[Any]								=			self._load_datasamples(1							)
										A__       :     Union[str, Any]								=			SpeechTaFeatureExtractor()
										A__       :     List[Any]								=			feature_extractor(audio_target=__lowerCamelCase   ,      return_tensors="""pt"""							).input_values
										self.assertEquals(input_values.shape   ,      (1, 366, 80)							)
										self.assertTrue(torch.allclose(input_values[0, 0, :30]   ,      __lowerCamelCase   ,      atol=1E-4							)							)









'''simple docstring'''





import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available




class 				UpperCAmelCase			(			_snake_case						):
 UpperCAmelCase				=					["image_processor", "tokenizer"]
 UpperCAmelCase				=					"OwlViTImageProcessor"
 UpperCAmelCase				=					("CLIPTokenizer", "CLIPTokenizerFast")


 def __init__(      self							:	List[str]			,       __lowerCamelCase							:	Optional[int]=None			,       __lowerCamelCase							:	Optional[Any]=None			,       **__lowerCamelCase							:	str ):
    UpperCAmelCase__	:int      =      None
    if "feature_extractor" in kwargs:
       warnings.warn(
           '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
           ''' instead.'''			,       __lowerCamelCase			,       )
       UpperCAmelCase__	:Union[str, Any]      =      kwargs.pop('''feature_extractor''' )

    UpperCAmelCase__	:List[str]      =      image_processor if image_processor is not None else feature_extractor
    if image_processor is None:
       raise ValueError('''You need to specify an `image_processor`.''' )
    if tokenizer is None:
       raise ValueError('''You need to specify a `tokenizer`.''' )

    super().__init__(__lowerCamelCase			,       __lowerCamelCase )


 def __call__(      self							:	int			,       __lowerCamelCase							:	str=None			,       __lowerCamelCase							:	List[Any]=None			,       __lowerCamelCase							:	Dict=None			,       __lowerCamelCase							:	str="max_length"			,       __lowerCamelCase							:	Any="np"			,       **__lowerCamelCase							:	Tuple ):

    if text is None and query_images is None and images is None:
       raise ValueError(
           '''You have to specify at least one text or query image or image. All three cannot be none.''' )

    if text is not None:
       if isinstance(__lowerCamelCase			,       __lowerCamelCase ) or (isinstance(__lowerCamelCase			,       __lowerCamelCase ) and not isinstance(text[0]			,       __lowerCamelCase )):
          UpperCAmelCase__	:Any      =      [self.tokenizer(__lowerCamelCase			,       padding=__lowerCamelCase			,       return_tensors=__lowerCamelCase			,       **__lowerCamelCase )]

       elif isinstance(__lowerCamelCase			,       __lowerCamelCase ) and isinstance(text[0]			,       __lowerCamelCase ):
          UpperCAmelCase__	:Tuple      =      []

          # Maximum number of queries across batch
          UpperCAmelCase__	:List[str]      =      max([len(__lowerCamelCase ) for t in text] )

          # Pad all batch samples to max number of text queries
          for t in text:
             if len(__lowerCamelCase ) != max_num_queries:
                UpperCAmelCase__	:str      =      t + [''' '''] * (max_num_queries - len(__lowerCamelCase ))

             UpperCAmelCase__	:Tuple      =      self.tokenizer(__lowerCamelCase			,       padding=__lowerCamelCase			,       return_tensors=__lowerCamelCase			,       **__lowerCamelCase )
             encodings.append(__lowerCamelCase )
       else:
          raise TypeError('''Input text should be a string, a list of strings or a nested list of strings''' )

       if return_tensors == "np":
          UpperCAmelCase__	:List[Any]      =      np.concatenate([encoding['''input_ids'''] for encoding in encodings]			,       axis=0 )
          UpperCAmelCase__	:Any      =      np.concatenate([encoding['''attention_mask'''] for encoding in encodings]			,       axis=0 )

       elif return_tensors == "jax" and is_flax_available():
          import jax.numpy as jnp

          UpperCAmelCase__	:List[Any]      =      jnp.concatenate([encoding['''input_ids'''] for encoding in encodings]			,       axis=0 )
          UpperCAmelCase__	:Union[str, Any]      =      jnp.concatenate([encoding['''attention_mask'''] for encoding in encodings]			,       axis=0 )

       elif return_tensors == "pt" and is_torch_available():
          import torch

          UpperCAmelCase__	:Any      =      torch.cat([encoding['''input_ids'''] for encoding in encodings]			,       dim=0 )
          UpperCAmelCase__	:List[str]      =      torch.cat([encoding['''attention_mask'''] for encoding in encodings]			,       dim=0 )

       elif return_tensors == "tf" and is_tf_available():
          import tensorflow as tf

          UpperCAmelCase__	:Optional[Any]      =      tf.stack([encoding['''input_ids'''] for encoding in encodings]			,       axis=0 )
          UpperCAmelCase__	:int      =      tf.stack([encoding['''attention_mask'''] for encoding in encodings]			,       axis=0 )

       else:
          raise ValueError('''Target return tensor type could not be returned''' )

       UpperCAmelCase__	:List[Any]      =      BatchEncoding()
       UpperCAmelCase__	:Union[str, Any]      =      input_ids
       UpperCAmelCase__	:Dict      =      attention_mask

    if query_images is not None:
       UpperCAmelCase__	:Tuple      =      BatchEncoding()
       UpperCAmelCase__	:int      =      self.image_processor(
           __lowerCamelCase			,       return_tensors=__lowerCamelCase			,       **__lowerCamelCase ).pixel_values
       UpperCAmelCase__	:Optional[int]      =      query_pixel_values

    if images is not None:
       UpperCAmelCase__	:str      =      self.image_processor(__lowerCamelCase			,       return_tensors=__lowerCamelCase			,       **__lowerCamelCase )

    if text is not None and images is not None:
       UpperCAmelCase__	:Dict      =      image_features.pixel_values
       return encoding
    elif query_images is not None and images is not None:
       UpperCAmelCase__	:Tuple      =      image_features.pixel_values
       return encoding
    elif text is not None or query_images is not None:
       return encoding
    else:
       return BatchEncoding(data=dict(**__lowerCamelCase )			,       tensor_type=__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	int			,       *__lowerCamelCase							:	Optional[int]			,       **__lowerCamelCase							:	int ):
    return self.image_processor.post_process(*__lowerCamelCase			,       **__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Dict			,       *__lowerCamelCase							:	Any			,       **__lowerCamelCase							:	Tuple ):
    return self.image_processor.post_process_object_detection(*__lowerCamelCase			,       **__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any]			,       *__lowerCamelCase							:	Tuple			,       **__lowerCamelCase							:	str ):
    return self.image_processor.post_process_image_guided_detection(*__lowerCamelCase			,       **__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	int			,       *__lowerCamelCase							:	Optional[Any]			,       **__lowerCamelCase							:	Union[str, Any] ):
    return self.tokenizer.batch_decode(*__lowerCamelCase			,       **__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[Any]			,       *__lowerCamelCase							:	Union[str, Any]			,       **__lowerCamelCase							:	Dict ):
    return self.tokenizer.decode(*__lowerCamelCase			,       **__lowerCamelCase )


 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any] ):
    warnings.warn(
        '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''			,       __lowerCamelCase			,       )
    return self.image_processor_class





 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any] ):
    warnings.warn(
        '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''			,       __lowerCamelCase			,       )
    return self.image_processor



