



"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip 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 OwlViTImageProcessor, OwlViTProcessor




@require_vision
class 			snake_case_						(						unittest.TestCase				):






							def snake_case_  (   self      ):
									a_     :      Tuple 						=     tempfile.mkdtemp()

									# fmt: off
									a_     :      str 						=     ["", "l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"]
									# fmt: on
									a_     :      Any 						=     dict(zip(a_			,     range(len(a_      )      )      )      )
									a_     :      List[str] 						=     ["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
									a_     :      Tuple 						=     {"unk_token": "<unk>"}

									a_     :      Union[str, Any] 						=     os.path.join(self.tmpdirname			,     VOCAB_FILES_NAMES["vocab_file"]      )
									a_     :      List[Any] 						=     os.path.join(self.tmpdirname			,     VOCAB_FILES_NAMES["merges_file"]      )
									with open(self.vocab_file			,     "w"			,     encoding="utf-8"      ) as fp:
											fp.write(json.dumps(a_      ) + "\n"      )
									with open(self.merges_file			,     "w"			,     encoding="utf-8"      ) as fp:
											fp.write("\n".join(a_      )      )

									a_     :      Tuple 						=     {
									    "do_resize": True,
									    "size": 2_0,
									    "do_center_crop": True,
									    "crop_size": 1_8,
									    "do_normalize": True,
									    "image_mean": [0.48_145_466, 0.4_578_275, 0.40_821_073],
									    "image_std": [0.26_862_954, 0.26_130_258, 0.27_577_711],
									}
									a_     :      str 						=     os.path.join(self.tmpdirname			,     a_      )
									with open(self.image_processor_file			,     "w"			,     encoding="utf-8"      ) as fp:
											json.dump(a_			,     a_      )






							def snake_case_  (   self			,     **a_      ):
									return CLIPTokenizer.from_pretrained(self.tmpdirname			,     pad_token="!"			,     **a_      )






							def snake_case_  (   self			,     **a_      ):
									return CLIPTokenizerFast.from_pretrained(self.tmpdirname			,     pad_token="!"			,     **a_      )






							def snake_case_  (   self			,     **a_      ):
									return OwlViTImageProcessor.from_pretrained(self.tmpdirname			,     **a_      )






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






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

									a_     :      List[str] 						=     [Image.fromarray(np.moveaxis(a_			,     0			,     -1      )      ) for x in image_inputs]

									return image_inputs






							def snake_case_  (   self      ):
									a_     :      Any 						=     self.get_tokenizer()
									a_     :      str 						=     self.get_rust_tokenizer()
									a_     :      Optional[Any] 						=     self.get_image_processor()

									a_     :      List[Any] 						=     OwlViTProcessor(tokenizer=a_			,     image_processor=a_      )
									processor_slow.save_pretrained(self.tmpdirname      )
									a_     :      List[str] 						=     OwlViTProcessor.from_pretrained(self.tmpdirname			,     use_fast=a_      )

									a_     :      List[Any] 						=     OwlViTProcessor(tokenizer=a_			,     image_processor=a_      )
									processor_fast.save_pretrained(self.tmpdirname      )
									a_     :      Dict 						=     OwlViTProcessor.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			,     a_      )
									self.assertIsInstance(processor_fast.tokenizer			,     a_      )

									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			,     a_      )
									self.assertIsInstance(processor_fast.image_processor			,     a_      )






							def snake_case_  (   self      ):
									a_     :      Tuple 						=     OwlViTProcessor(tokenizer=self.get_tokenizer()			,     image_processor=self.get_image_processor()      )
									processor.save_pretrained(self.tmpdirname      )

									a_     :      str 						=     self.get_tokenizer(bos_token="(BOS)"			,     eos_token="(EOS)"      )
									a_     :      Union[str, Any] 						=     self.get_image_processor(do_normalize=a_      )

									a_     :      Any 						=     OwlViTProcessor.from_pretrained(
									    self.tmpdirname			,     bos_token="(BOS)"			,     eos_token="(EOS)"			,     do_normalize=a_      )

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

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






							def snake_case_  (   self      ):
									a_     :      int 						=     self.get_image_processor()
									a_     :      List[Any] 						=     self.get_tokenizer()

									a_     :      Dict 						=     OwlViTProcessor(tokenizer=a_			,     image_processor=a_      )

									a_     :      int 						=     self.prepare_image_inputs()

									a_     :      Any 						=     image_processor(a_			,     return_tensors="np"      )
									a_     :      Union[str, Any] 						=     processor(images=a_			,     return_tensors="np"      )

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






							def snake_case_  (   self      ):
									a_     :      List[Any] 						=     self.get_image_processor()
									a_     :      Tuple 						=     self.get_tokenizer()

									a_     :      Tuple 						=     OwlViTProcessor(tokenizer=a_			,     image_processor=a_      )

									a_     :      Any 						=     "lower newer"

									a_     :      Dict 						=     processor(text=a_			,     return_tensors="np"      )

									a_     :      Tuple 						=     tokenizer(a_			,     return_tensors="np"      )

									for key in encoded_tok.keys():
											self.assertListEqual(encoded_tok[key][0].tolist()			,     encoded_processor[key][0].tolist()      )






							def snake_case_  (   self      ):
									a_     :      List[str] 						=     self.get_image_processor()
									a_     :      Dict 						=     self.get_tokenizer()

									a_     :      Tuple 						=     OwlViTProcessor(tokenizer=a_			,     image_processor=a_      )

									a_     :      str 						=     "lower newer"
									a_     :      Dict 						=     self.prepare_image_inputs()

									a_     :      int 						=     processor(text=a_			,     images=a_      )

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

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






							def snake_case_  (   self      ):
									a_     :      int 						=     "google/owlvit-base-patch32"
									a_     :      Union[str, Any] 						=     OwlViTProcessor.from_pretrained(a_      )

									a_     :      Tuple 						=     ["cat", "nasa badge"]
									a_     :      int 						=     processor(text=a_      )

									a_     :      List[Any] 						=     1_6
									self.assertListEqual(list(inputs.keys()      )			,     ["input_ids", "attention_mask"]      )
									self.assertEqual(inputs["input_ids"].shape			,     (2, seq_length)      )

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






							def snake_case_  (   self      ):
									a_     :      Dict 						=     "google/owlvit-base-patch32"
									a_     :      Dict 						=     OwlViTProcessor.from_pretrained(a_      )

									a_     :      str 						=     [["cat", "nasa badge"], ["person"]]
									a_     :      Union[str, Any] 						=     processor(text=a_      )

									a_     :      Dict 						=     1_6
									a_     :      str 						=     len(a_      )
									a_     :      Any 						=     max([len(a_      ) for texts in input_texts]      )

									self.assertListEqual(list(inputs.keys()      )			,     ["input_ids", "attention_mask"]      )
									self.assertEqual(inputs["input_ids"].shape			,     (batch_size * num_max_text_queries, seq_length)      )

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






							def snake_case_  (   self      ):
									a_     :      str 						=     "google/owlvit-base-patch32"
									a_     :      Optional[Any] 						=     OwlViTProcessor.from_pretrained(a_      )

									a_     :      Union[str, Any] 						=     ["cat", "nasa badge"]
									a_     :      List[str] 						=     processor(text=a_      )

									a_     :      List[Any] 						=     1_6
									a_     :      str 						=     inputs["input_ids"]
									a_     :      List[Any] 						=     [
									    [4_9_4_0_6, 2_3_6_8, 4_9_4_0_7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
									    [4_9_4_0_6, 6_8_4_1, 1_1_3_0_1, 4_9_4_0_7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
									]

									self.assertListEqual(list(inputs.keys()      )			,     ["input_ids", "attention_mask"]      )
									self.assertEqual(inputs["input_ids"].shape			,     (2, seq_length)      )
									self.assertListEqual(list(input_ids[0]      )			,     predicted_ids[0]      )
									self.assertListEqual(list(input_ids[1]      )			,     predicted_ids[1]      )






							def snake_case_  (   self      ):
									a_     :      Any 						=     self.get_image_processor()
									a_     :      Tuple 						=     self.get_tokenizer()

									a_     :      List[Any] 						=     OwlViTProcessor(tokenizer=a_			,     image_processor=a_      )

									a_     :      Dict 						=     self.prepare_image_inputs()
									a_     :      int 						=     self.prepare_image_inputs()

									a_     :      Optional[Any] 						=     processor(images=a_			,     query_images=a_      )

									self.assertListEqual(list(inputs.keys()      )			,     ["query_pixel_values", "pixel_values"]      )

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






							def snake_case_  (   self      ):
									a_     :      Union[str, Any] 						=     self.get_image_processor()
									a_     :      Union[str, Any] 						=     self.get_tokenizer()

									a_     :      List[Any] 						=     OwlViTProcessor(tokenizer=a_			,     image_processor=a_      )

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

									a_     :      Any 						=     processor.batch_decode(a_      )
									a_     :      Optional[Any] 						=     tokenizer.batch_decode(a_      )

									self.assertListEqual(a_			,     a_      )




"""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

SCREAMING_SNAKE_CASE_				= random.Random()



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__=1.0, SCREAMING_SNAKE_CASE__=None, SCREAMING_SNAKE_CASE__=None	)			->      Tuple:
		if rng is None:
				a_     :      Any 						=     global_rng

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

		return values




class 			snake_case_						(						unittest.TestCase				):






							def __init__(   self			,     a_			,     a_=7			,     a_=4_0_0			,     a_=2_0_0_0			,     a_=2_0_4_8			,     a_=1_2_8			,     a_=1			,     a_=5_1_2			,     a_=3_0			,     a_=4_4_1_0_0			,     ):
									a_     :      Optional[Any] 						=     parent
									a_     :      Tuple 						=     batch_size
									a_     :      Union[str, Any] 						=     min_seq_length
									a_     :      Dict 						=     max_seq_length
									a_     :      Union[str, Any] 						=     (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
									a_     :      int 						=     spectrogram_length
									a_     :      Optional[Any] 						=     feature_size
									a_     :      Optional[int] 						=     num_audio_channels
									a_     :      List[str] 						=     hop_length
									a_     :      Optional[int] 						=     chunk_length
									a_     :      List[str] 						=     sampling_rate






							def snake_case_  (   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 snake_case_  (   self			,     a_=False			,     a_=False      ):
									def _flatten(a_      ):
											return list(itertools.chain(*a_      )      )

									if equal_length:
											a_     :      Tuple 						=     [floats_list((self.max_seq_length, self.feature_size)      ) for _ in range(self.batch_size      )]
									else:
											# make sure that inputs increase in size
											a_     :      Optional[Any] 						=     [
											    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_     :      Tuple 						=     [np.asarray(a_      ) for x in speech_inputs]
									return speech_inputs




@require_torch
@require_torchaudio
class 			snake_case_						(						a_     ,unittest.TestCase				):
							__lowerCAmelCase    					=					TvltFeatureExtractor






							def snake_case_  (   self      ):
									a_     :      Dict 						=     TvltFeatureExtractionTester(self      )






							def snake_case_  (   self      ):
									a_     :      Optional[Any] 						=     self.feature_extraction_class(**self.feat_extract_dict      )
									self.assertTrue(hasattr(a_			,     "spectrogram_length"      )      )
									self.assertTrue(hasattr(a_			,     "feature_size"      )      )
									self.assertTrue(hasattr(a_			,     "num_audio_channels"      )      )
									self.assertTrue(hasattr(a_			,     "hop_length"      )      )
									self.assertTrue(hasattr(a_			,     "chunk_length"      )      )
									self.assertTrue(hasattr(a_			,     "sampling_rate"      )      )






							def snake_case_  (   self      ):
									a_     :      List[Any] 						=     self.feature_extraction_class(**self.feat_extract_dict      )

									with tempfile.TemporaryDirectory() as tmpdirname:
											a_     :      List[Any] 						=     feat_extract_first.save_pretrained(a_      )[0]
											check_json_file_has_correct_format(a_      )
											a_     :      Tuple 						=     self.feature_extraction_class.from_pretrained(a_      )

									a_     :      Dict 						=     feat_extract_first.to_dict()
									a_     :      List[str] 						=     feat_extract_second.to_dict()
									a_     :      List[Any] 						=     dict_first.pop("mel_filters"      )
									a_     :      str 						=     dict_second.pop("mel_filters"      )
									self.assertTrue(np.allclose(a_			,     a_      )      )
									self.assertEqual(a_			,     a_      )






							def snake_case_  (   self      ):
									a_     :      List[str] 						=     self.feature_extraction_class(**self.feat_extract_dict      )

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

									a_     :      Dict 						=     feat_extract_first.to_dict()
									a_     :      str 						=     feat_extract_second.to_dict()
									a_     :      int 						=     dict_first.pop("mel_filters"      )
									a_     :      List[Any] 						=     dict_second.pop("mel_filters"      )
									self.assertTrue(np.allclose(a_			,     a_      )      )
									self.assertEqual(a_			,     a_      )






							def snake_case_  (   self      ):
									# Initialize feature_extractor
									a_     :      str 						=     self.feature_extraction_class(**self.feat_extract_dict      )

									# create three inputs of length 800, 1000, and 1200
									a_     :      Optional[Any] 						=     [floats_list((1, x)      )[0] for x in range(8_0_0			,     1_4_0_0			,     2_0_0      )]
									a_     :      Union[str, Any] 						=     [np.asarray(a_      ) for speech_input in speech_inputs]

									# Test not batched input
									a_     :      Dict 						=     feature_extractor(np_speech_inputs[0]			,     return_tensors="np"			,     sampling_rate=4_4_1_0_0      ).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
									a_     :      int 						=     feature_extractor(a_			,     return_tensors="np"			,     sampling_rate=4_4_1_0_0      ).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
									a_     :      Optional[int] 						=     feature_extractor(
									    a_			,     return_tensors="np"			,     sampling_rate=4_4_1_0_0			,     mask_audio=a_      ).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.
									a_     :      List[str] 						=     [floats_list((1, x)      )[0] for x in (8_0_0, 8_0_0, 8_0_0)]
									a_     :      Union[str, Any] 						=     np.asarray(a_      )
									a_     :      Dict 						=     feature_extractor(a_			,     return_tensors="np"			,     sampling_rate=4_4_1_0_0      ).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 snake_case_  (   self			,     a_      ):
									a_     :      Optional[int] 						=     load_dataset("hf-internal-testing/librispeech_asr_dummy"			,     "clean"			,     split="validation"      )
									# automatic decoding with librispeech
									a_     :      List[str] 						=     ds.sort("id"      ).select(range(a_      )      )[:num_samples]["audio"]

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






							def snake_case_  (   self      ):
									a_     :      List[str] 						=     self._load_datasamples(1      )
									a_     :      Any 						=     TvltFeatureExtractor()
									a_     :      Union[str, Any] 						=     feature_extractor(a_			,     return_tensors="pt"      ).audio_values

									self.assertEquals(audio_values.shape			,     (1, 1, 1_9_2, 1_2_8)      )

									a_     :      Dict 						=     torch.tensor([[-0.3_032, -0.2_708], [-0.4_434, -0.4_007]]      )
									self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]			,     a_			,     atol=1e-4      )      )