
'''simple docstring'''







import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
						import torch

if is_datasets_available():
						from datasets import load_dataset

_snake_case				:   Optional[int]     =				random.Random()



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Optional[Any]	,			_SCREAMING_SNAKE_CASE		:   Union[str, Any]=1.0	,			_SCREAMING_SNAKE_CASE		:   str=None	,			_SCREAMING_SNAKE_CASE		:   Tuple=None  ):
						if rng is None:
												_SCREAMING_SNAKE_CASE				= global_rng

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

						return values


class 			lowerCAmelCase (				unittest.TestCase			):




		def __init__(				self					,						UpperCamelCase					,						UpperCamelCase=7					,						UpperCamelCase=400					,						UpperCamelCase=2_000					,						UpperCamelCase=2_048					,						UpperCamelCase=128					,						UpperCamelCase=1					,						UpperCamelCase=512					,						UpperCamelCase=30					,						UpperCamelCase=44_100					,						):
								_SCREAMING_SNAKE_CASE				= parent
								_SCREAMING_SNAKE_CASE				= batch_size
								_SCREAMING_SNAKE_CASE				= min_seq_length
								_SCREAMING_SNAKE_CASE				= max_seq_length
								_SCREAMING_SNAKE_CASE				= (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
								_SCREAMING_SNAKE_CASE				= spectrogram_length
								_SCREAMING_SNAKE_CASE				= feature_size
								_SCREAMING_SNAKE_CASE				= num_audio_channels
								_SCREAMING_SNAKE_CASE				= hop_length
								_SCREAMING_SNAKE_CASE				= chunk_length
								_SCREAMING_SNAKE_CASE				= sampling_rate




		def     lowercase       (				self      ):
								return {
								    "spectrogram_length": self.spectrogram_length,
								    "feature_size": self.feature_size,
								    "num_audio_channels": self.num_audio_channels,
								    "hop_length": self.hop_length,
								    "chunk_length": self.chunk_length,
								    "sampling_rate": self.sampling_rate,
								}




		def     lowercase       (				self					,						UpperCamelCase=False					,						UpperCamelCase=False      ):
								def _flatten(UpperCamelCase      ):
														return list(itertools.chain(*UpperCamelCase      )      )

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


@require_torch
@require_torchaudio
class 			lowerCAmelCase (				__UpperCAmelCase		,     unittest.TestCase			):
		a							:					List[Any]       =					TvltFeatureExtractor




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= TvltFeatureExtractionTester(self      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.feature_extraction_class(**self.feat_extract_dict      )
								self.assertTrue(hasattr(UpperCamelCase					,						"spectrogram_length"      )      )
								self.assertTrue(hasattr(UpperCamelCase					,						"feature_size"      )      )
								self.assertTrue(hasattr(UpperCamelCase					,						"num_audio_channels"      )      )
								self.assertTrue(hasattr(UpperCamelCase					,						"hop_length"      )      )
								self.assertTrue(hasattr(UpperCamelCase					,						"chunk_length"      )      )
								self.assertTrue(hasattr(UpperCamelCase					,						"sampling_rate"      )      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.feature_extraction_class(**self.feat_extract_dict      )

								with tempfile.TemporaryDirectory() as tmpdirname:
														_SCREAMING_SNAKE_CASE				= feat_extract_first.save_pretrained(UpperCamelCase      )[0]
														check_json_file_has_correct_format(UpperCamelCase      )
														_SCREAMING_SNAKE_CASE				= self.feature_extraction_class.from_pretrained(UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= feat_extract_first.to_dict()
								_SCREAMING_SNAKE_CASE				= feat_extract_second.to_dict()
								_SCREAMING_SNAKE_CASE				= dict_first.pop("mel_filters"      )
								_SCREAMING_SNAKE_CASE				= dict_second.pop("mel_filters"      )
								self.assertTrue(np.allclose(UpperCamelCase					,						UpperCamelCase      )      )
								self.assertEqual(UpperCamelCase					,						UpperCamelCase      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.feature_extraction_class(**self.feat_extract_dict      )

								with tempfile.TemporaryDirectory() as tmpdirname:
														_SCREAMING_SNAKE_CASE				= os.path.join(UpperCamelCase					,						"feat_extract.json"      )
														feat_extract_first.to_json_file(UpperCamelCase      )
														_SCREAMING_SNAKE_CASE				= self.feature_extraction_class.from_json_file(UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= feat_extract_first.to_dict()
								_SCREAMING_SNAKE_CASE				= feat_extract_second.to_dict()
								_SCREAMING_SNAKE_CASE				= dict_first.pop("mel_filters"      )
								_SCREAMING_SNAKE_CASE				= dict_second.pop("mel_filters"      )
								self.assertTrue(np.allclose(UpperCamelCase					,						UpperCamelCase      )      )
								self.assertEqual(UpperCamelCase					,						UpperCamelCase      )




		def     lowercase       (				self      ):
								# Initialize feature_extractor
								_SCREAMING_SNAKE_CASE				= self.feature_extraction_class(**self.feat_extract_dict      )

								# create three inputs of length 800, 1000, and 1200
								_SCREAMING_SNAKE_CASE				= [floats_list((1, x)      )[0] for x in range(800					,						1_400					,						200      )]
								_SCREAMING_SNAKE_CASE				= [np.asarray(UpperCamelCase      ) for speech_input in speech_inputs]

								# Test not batched input
								_SCREAMING_SNAKE_CASE				= feature_extractor(np_speech_inputs[0]					,						return_tensors="np"					,						sampling_rate=44_100      ).audio_values

								self.assertTrue(encoded_audios.ndim == 4      )
								self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size      )
								self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length      )
								self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels      )

								# Test batched
								_SCREAMING_SNAKE_CASE				= feature_extractor(UpperCamelCase					,						return_tensors="np"					,						sampling_rate=44_100      ).audio_values

								self.assertTrue(encoded_audios.ndim == 4      )
								self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size      )
								self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length      )
								self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels      )

								# Test audio masking
								_SCREAMING_SNAKE_CASE				= feature_extractor(
								    UpperCamelCase					,						return_tensors="np"					,						sampling_rate=44_100					,						mask_audio=UpperCamelCase      ).audio_values

								self.assertTrue(encoded_audios.ndim == 4      )
								self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size      )
								self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length      )
								self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels      )

								# Test 2-D numpy arrays are batched.
								_SCREAMING_SNAKE_CASE				= [floats_list((1, x)      )[0] for x in (800, 800, 800)]
								_SCREAMING_SNAKE_CASE				= np.asarray(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= feature_extractor(UpperCamelCase					,						return_tensors="np"					,						sampling_rate=44_100      ).audio_values
								self.assertTrue(encoded_audios.ndim == 4      )
								self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size      )
								self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length      )
								self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels      )




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

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




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self._load_datasamples(1      )
								_SCREAMING_SNAKE_CASE				= TvltFeatureExtractor()
								_SCREAMING_SNAKE_CASE				= feature_extractor(UpperCamelCase					,						return_tensors="pt"      ).audio_values

								self.assertEquals(audio_values.shape					,						(1, 1, 192, 128)      )

								_SCREAMING_SNAKE_CASE				= torch.tensor([[-0.30_32, -0.27_08], [-0.44_34, -0.40_07]]      )
								self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]					,						UpperCamelCase					,						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 			lowerCAmelCase (				__UpperCAmelCase			):
		a							:					Optional[int]       =					["""image_processor""", """tokenizer"""]
		a							:					Optional[int]       =					"""BlipImageProcessor"""
		a							:					Optional[int]       =					"""AutoTokenizer"""




		def __init__(				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase      ):
								super().__init__(UpperCamelCase					,						UpperCamelCase      )

								# add QFormer tokenizer
								_SCREAMING_SNAKE_CASE				= qformer_tokenizer




		def __call__(				self					,						UpperCamelCase = None					,						UpperCamelCase = None					,						UpperCamelCase = True					,						UpperCamelCase = False					,						UpperCamelCase = None					,						UpperCamelCase = None					,						UpperCamelCase = 0					,						UpperCamelCase = None					,						UpperCamelCase = None					,						UpperCamelCase = False					,						UpperCamelCase = False					,						UpperCamelCase = False					,						UpperCamelCase = False					,						UpperCamelCase = False					,						UpperCamelCase = True					,						UpperCamelCase = None					,						**UpperCamelCase					,						):
								if images is None and text is None:
														raise ValueError("You have to specify at least images or text."      )

								_SCREAMING_SNAKE_CASE				= BatchFeature()

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

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

								return encoding




		def     lowercase       (				self					,						*UpperCamelCase					,						**UpperCamelCase      ):
								return self.tokenizer.batch_decode(*UpperCamelCase					,						**UpperCamelCase      )




		def     lowercase       (				self					,						*UpperCamelCase					,						**UpperCamelCase      ):
								return self.tokenizer.decode(*UpperCamelCase					,						**UpperCamelCase      )




		@property
		# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.tokenizer.model_input_names
								_SCREAMING_SNAKE_CASE				= self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )




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




		@classmethod
		def     lowercase       (				cls					,						UpperCamelCase					,						**UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= AutoTokenizer.from_pretrained(UpperCamelCase					,						subfolder="qformer_tokenizer"      )
								_SCREAMING_SNAKE_CASE				= cls._get_arguments_from_pretrained(UpperCamelCase					,						**UpperCamelCase      )
								args.append(UpperCamelCase      )
								return cls(*UpperCamelCase      )