
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path

import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized

from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available

from ..wavaveca.test_feature_extraction_wavaveca import floats_list


if is_pyctcdecode_available():
	from huggingface_hub import snapshot_download
	from pyctcdecode import BeamSearchDecoderCTC

	from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
	from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput

if is_torch_available():
	from transformers import WavaVecaForCTC







@require_pyctcdecode
class        A_   (			unittest.TestCase   ):




	'''simple docstring'''


	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    '| <pad> <unk> <s> </s> a b c d e f g h i j k'.split()
		lowercase       =    dict(zip(snake_case		,    range(len(snake_case       )       )       )       )

		lowercase       =    {
		    'unk_token': '<unk>',
		    'bos_token': '<s>',
		    'eos_token': '</s>',
		}
		lowercase       =    {
		    'feature_size': 1,
		    'padding_value': 0.0,
		    'sampling_rate': 1_6000,
		    'return_attention_mask': False,
		    'do_normalize': True,
		}

		lowercase       =    tempfile.mkdtemp()
		lowercase       =    os.path.join(self.tmpdirname		,    VOCAB_FILES_NAMES['vocab_file']       )
		lowercase       =    os.path.join(self.tmpdirname		,    snake_case       )
		with open(self.vocab_file		,    'w'		,    encoding='utf-8'       ) as fp:
			fp.write(json.dumps(snake_case       ) + '\n'       )

		with open(self.feature_extraction_file		,    'w'		,    encoding='utf-8'       ) as fp:
			fp.write(json.dumps(snake_case       ) + '\n'       )

		# load decoder from hub
		lowercase       =    'hf-internal-testing/ngram-beam-search-decoder'





	def 	SCREAMING_SNAKE_CASE__						(       self		,    **snake_case       ):
		lowercase       =    self.add_kwargs_tokens_map.copy()
		kwargs.update(snake_case       )
		return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname		,    **snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    **snake_case       ):
		return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname		,    **snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    **snake_case       ):
		return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name		,    **snake_case       )





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





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.get_tokenizer()
		lowercase       =    self.get_feature_extractor()
		lowercase       =    self.get_decoder()

		lowercase       =    WavaVecaProcessorWithLM(tokenizer=snake_case		,    feature_extractor=snake_case		,    decoder=snake_case       )

		processor.save_pretrained(self.tmpdirname       )
		lowercase       =    WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname       )

		# tokenizer
		self.assertEqual(processor.tokenizer.get_vocab()		,    tokenizer.get_vocab()       )
		self.assertIsInstance(processor.tokenizer		,    snake_case       )

		# feature extractor
		self.assertEqual(processor.feature_extractor.to_json_string()		,    feature_extractor.to_json_string()       )
		self.assertIsInstance(processor.feature_extractor		,    snake_case       )

		# decoder
		self.assertEqual(processor.decoder._alphabet.labels		,    decoder._alphabet.labels       )
		self.assertEqual(
		    processor.decoder.model_container[decoder._model_key]._unigram_set		,    decoder.model_container[decoder._model_key]._unigram_set		,    )
		self.assertIsInstance(processor.decoder		,    snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    WavaVecaProcessorWithLM(
		    tokenizer=self.get_tokenizer()		,    feature_extractor=self.get_feature_extractor()		,    decoder=self.get_decoder()       )
		processor.save_pretrained(self.tmpdirname       )

		# make sure that error is thrown when decoder alphabet doesn't match
		lowercase       =    WavaVecaProcessorWithLM.from_pretrained(
		    self.tmpdirname		,    alpha=5.0		,    beta=3.0		,    score_boundary=-7.0		,    unk_score_offset=3       )

		# decoder
		self.assertEqual(processor.language_model.alpha		,    5.0       )
		self.assertEqual(processor.language_model.beta		,    3.0       )
		self.assertEqual(processor.language_model.score_boundary		,    -7.0       )
		self.assertEqual(processor.language_model.unk_score_offset		,    3       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.get_tokenizer()
		# add token to trigger raise
		tokenizer.add_tokens(['xx']       )
		with self.assertRaisesRegex(snake_case		,    'include'       ):
			WavaVecaProcessorWithLM(
			    tokenizer=snake_case		,    feature_extractor=self.get_feature_extractor()		,    decoder=self.get_decoder()       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.get_feature_extractor()
		lowercase       =    self.get_tokenizer()
		lowercase       =    self.get_decoder()

		lowercase       =    WavaVecaProcessorWithLM(tokenizer=snake_case		,    feature_extractor=snake_case		,    decoder=snake_case       )

		lowercase       =    floats_list((3, 1000)       )

		lowercase       =    feature_extractor(snake_case		,    return_tensors='np'       )
		lowercase       =    processor(snake_case		,    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 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.get_feature_extractor()
		lowercase       =    self.get_tokenizer()
		lowercase       =    self.get_decoder()

		lowercase       =    WavaVecaProcessorWithLM(tokenizer=snake_case		,    feature_extractor=snake_case		,    decoder=snake_case       )

		lowercase       =    'This is a test string'

		lowercase       =    processor(text=snake_case       )

		lowercase       =    tokenizer(snake_case       )

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





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case=(2, 10, 16)		,    snake_case=77       ):
		np.random.seed(snake_case       )
		return np.random.rand(*snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.get_feature_extractor()
		lowercase       =    self.get_tokenizer()
		lowercase       =    self.get_decoder()

		lowercase       =    WavaVecaProcessorWithLM(tokenizer=snake_case		,    feature_extractor=snake_case		,    decoder=snake_case       )

		lowercase       =    self._get_dummy_logits(shape=(10, 16)		,    seed=13       )

		lowercase       =    processor.decode(snake_case       )

		lowercase       =    decoder.decode_beams(snake_case       )[0]

		self.assertEqual(decoded_decoder[0]		,    decoded_processor.text       )
		self.assertEqual('</s> <s> </s>'		,    decoded_processor.text       )
		self.assertEqual(decoded_decoder[-2]		,    decoded_processor.logit_score       )
		self.assertEqual(decoded_decoder[-1]		,    decoded_processor.lm_score       )





	@parameterized.expand([[None], ['fork'], ['spawn']]       )
	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case       ):
		lowercase       =    self.get_feature_extractor()
		lowercase       =    self.get_tokenizer()
		lowercase       =    self.get_decoder()

		lowercase       =    WavaVecaProcessorWithLM(tokenizer=snake_case		,    feature_extractor=snake_case		,    decoder=snake_case       )

		lowercase       =    self._get_dummy_logits()

		# note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
		#       otherwise, the LM won't be available to the pool's sub-processes.
		# manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
		if pool_context is None:
			lowercase       =    processor.batch_decode(snake_case       )
		else:
			with get_context(snake_case       ).Pool() as pool:
				lowercase       =    processor.batch_decode(snake_case		,    snake_case       )

		lowercase       =    list(snake_case       )

		with get_context('fork'       ).Pool() as p:
			lowercase       =    decoder.decode_beams_batch(snake_case		,    snake_case       )

		lowercase  ,					lowercase  ,					lowercase       =    [], [], []
		for beams in decoded_beams:
			texts_decoder.append(beams[0][0]       )
			logit_scores_decoder.append(beams[0][-2]       )
			lm_scores_decoder.append(beams[0][-1]       )

		self.assertListEqual(snake_case		,    decoded_processor.text       )
		self.assertListEqual(['<s> <s> </s>', '<s> <s> <s>']		,    decoded_processor.text       )
		self.assertListEqual(snake_case		,    decoded_processor.logit_score       )
		self.assertListEqual(snake_case		,    decoded_processor.lm_score       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.get_feature_extractor()
		lowercase       =    self.get_tokenizer()
		lowercase       =    self.get_decoder()

		lowercase       =    WavaVecaProcessorWithLM(tokenizer=snake_case		,    feature_extractor=snake_case		,    decoder=snake_case       )

		lowercase       =    self._get_dummy_logits()

		lowercase       =    15
		lowercase       =    -20.0
		lowercase       =    -4.0

		lowercase       =    processor.batch_decode(
		    snake_case		,    beam_width=snake_case		,    beam_prune_logp=snake_case		,    token_min_logp=snake_case		,    )
		lowercase       =    decoded_processor_out.text

		lowercase       =    list(snake_case       )

		with get_context('fork'       ).Pool() as pool:
			lowercase       =    decoder.decode_beams_batch(
			    snake_case		,    snake_case		,    beam_width=snake_case		,    beam_prune_logp=snake_case		,    token_min_logp=snake_case		,    )

		lowercase       =    [d[0][0] for d in decoded_decoder_out]
		lowercase       =    [d[0][2] for d in decoded_decoder_out]
		lowercase       =    [d[0][3] for d in decoded_decoder_out]

		self.assertListEqual(snake_case		,    snake_case       )
		self.assertListEqual(['</s> <s> <s>', '<s> <s> <s>']		,    snake_case       )

		self.assertTrue(np.array_equal(snake_case		,    decoded_processor_out.logit_score       )       )
		self.assertTrue(np.allclose([-20.054, -18.447]		,    snake_case		,    atol=1E-3       )       )

		self.assertTrue(np.array_equal(snake_case		,    decoded_processor_out.lm_score       )       )
		self.assertTrue(np.allclose([-15.554, -13.9_474]		,    snake_case		,    atol=1E-3       )       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.get_feature_extractor()
		lowercase       =    self.get_tokenizer()
		lowercase       =    self.get_decoder()

		lowercase       =    WavaVecaProcessorWithLM(tokenizer=snake_case		,    feature_extractor=snake_case		,    decoder=snake_case       )

		lowercase       =    self._get_dummy_logits()

		lowercase       =    2.0
		lowercase       =    5.0
		lowercase       =    -20.0
		lowercase       =    True

		lowercase       =    processor.batch_decode(
		    snake_case		,    alpha=snake_case		,    beta=snake_case		,    unk_score_offset=snake_case		,    lm_score_boundary=snake_case		,    )
		lowercase       =    decoded_processor_out.text

		lowercase       =    list(snake_case       )
		decoder.reset_params(
		    alpha=snake_case		,    beta=snake_case		,    unk_score_offset=snake_case		,    lm_score_boundary=snake_case		,    )

		with get_context('fork'       ).Pool() as pool:
			lowercase       =    decoder.decode_beams_batch(
			    snake_case		,    snake_case		,    )

		lowercase       =    [d[0][0] for d in decoded_decoder_out]

		self.assertListEqual(snake_case		,    snake_case       )
		self.assertListEqual(['<s> </s> <s> </s> </s>', '</s> </s> <s> </s> </s>']		,    snake_case       )
		lowercase       =    processor.decoder.model_container[processor.decoder._model_key]
		self.assertEqual(lm_model.alpha		,    2.0       )
		self.assertEqual(lm_model.beta		,    5.0       )
		self.assertEqual(lm_model.unk_score_offset		,    -20.0       )
		self.assertEqual(lm_model.score_boundary		,    snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm'       )

		lowercase       =    processor.decoder.model_container[processor.decoder._model_key]
		lowercase       =    Path(language_model._kenlm_model.path.decode('utf-8'       )       ).parent.parent.absolute()

		lowercase       =    os.listdir(snake_case       )
		lowercase       =    ['alphabet.json', 'language_model']

		downloaded_decoder_files.sort()
		expected_decoder_files.sort()

		# test that only decoder relevant files from
		# https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
		# are downloaded and none of the rest (e.g. README.md, ...)
		self.assertListEqual(snake_case		,    snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    snapshot_download('hf-internal-testing/processor_with_lm'       )

		lowercase       =    WavaVecaProcessorWithLM.from_pretrained(snake_case       )

		lowercase       =    processor.decoder.model_container[processor.decoder._model_key]
		lowercase       =    Path(language_model._kenlm_model.path.decode('utf-8'       )       ).parent.parent.absolute()

		lowercase       =    os.listdir(snake_case       )
		lowercase       =    os.listdir(snake_case       )

		local_decoder_files.sort()
		expected_decoder_files.sort()

		# test that both decoder form hub and local files in cache are the same
		self.assertListEqual(snake_case		,    snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm'       )
		lowercase       =    AutoProcessor.from_pretrained('hf-internal-testing/processor_with_lm'       )

		lowercase       =    floats_list((3, 1000)       )

		lowercase       =    processor_wavaveca(snake_case		,    return_tensors='np'       )
		lowercase       =    processor_auto(snake_case		,    return_tensors='np'       )

		for key in input_wavaveca.keys():
			self.assertAlmostEqual(input_wavaveca[key].sum()		,    input_auto[key].sum()		,    delta=1E-2       )

		lowercase       =    self._get_dummy_logits()

		lowercase       =    processor_wavaveca.batch_decode(snake_case       )
		lowercase       =    processor_auto.batch_decode(snake_case       )

		self.assertListEqual(decoded_wavaveca.text		,    decoded_auto.text       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.get_feature_extractor()
		lowercase       =    self.get_tokenizer()
		lowercase       =    self.get_decoder()

		lowercase       =    WavaVecaProcessorWithLM(tokenizer=snake_case		,    feature_extractor=snake_case		,    decoder=snake_case       )

		self.assertListEqual(
		    processor.model_input_names		,    feature_extractor.model_input_names		,    msg='`processor` and `feature_extractor` model input names do not match'		,    )





	@staticmethod
	def 	SCREAMING_SNAKE_CASE__						(       snake_case		,    snake_case       ):
		lowercase       =    [d[key] for d in offsets]
		return retrieved_list





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm'       )
		lowercase       =    self._get_dummy_logits()[0]

		lowercase       =    processor.decode(snake_case		,    output_word_offsets=snake_case       )
		# check Wav2Vec2CTCTokenizerOutput keys for word
		self.assertEqual(len(outputs.keys()       )		,    4       )
		self.assertTrue('text' in outputs       )
		self.assertTrue('word_offsets' in outputs       )
		self.assertTrue(isinstance(snake_case		,    snake_case       )       )

		self.assertEqual(' '.join(self.get_from_offsets(outputs['word_offsets']		,    'word'       )       )		,    outputs.text       )
		self.assertListEqual(self.get_from_offsets(outputs['word_offsets']		,    'word'       )		,    ['<s>', '<s>', '</s>']       )
		self.assertListEqual(self.get_from_offsets(outputs['word_offsets']		,    'start_offset'       )		,    [0, 2, 4]       )
		self.assertListEqual(self.get_from_offsets(outputs['word_offsets']		,    'end_offset'       )		,    [1, 3, 5]       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm'       )
		lowercase       =    self._get_dummy_logits()

		lowercase       =    processor.batch_decode(snake_case		,    output_word_offsets=snake_case       )

		# check Wav2Vec2CTCTokenizerOutput keys for word
		self.assertEqual(len(outputs.keys()       )		,    4       )
		self.assertTrue('text' in outputs       )
		self.assertTrue('word_offsets' in outputs       )
		self.assertTrue(isinstance(snake_case		,    snake_case       )       )

		self.assertListEqual(
		    [' '.join(self.get_from_offsets(snake_case		,    'word'       )       ) for o in outputs['word_offsets']]		,    outputs.text       )
		self.assertListEqual(self.get_from_offsets(outputs['word_offsets'][0]		,    'word'       )		,    ['<s>', '<s>', '</s>']       )
		self.assertListEqual(self.get_from_offsets(outputs['word_offsets'][0]		,    'start_offset'       )		,    [0, 2, 4]       )
		self.assertListEqual(self.get_from_offsets(outputs['word_offsets'][0]		,    'end_offset'       )		,    [1, 3, 5]       )





	@slow
	@require_torch
	@require_torchaudio
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		import torch

		lowercase       =    load_dataset('common_voice'		,    'en'		,    split='train'		,    streaming=snake_case       )
		lowercase       =    ds.cast_column('audio'		,    datasets.Audio(sampling_rate=1_6000       )       )
		lowercase       =    iter(snake_case       )
		lowercase       =    next(snake_case       )

		lowercase       =    AutoProcessor.from_pretrained('patrickvonplaten/wav2vec2-base-100h-with-lm'       )
		lowercase       =    WavaVecaForCTC.from_pretrained('patrickvonplaten/wav2vec2-base-100h-with-lm'       )

		# compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
		lowercase       =    processor(sample['audio']['array']		,    return_tensors='pt'       ).input_values

		with torch.no_grad():
			lowercase       =    model(snake_case       ).logits.cpu().numpy()

		lowercase       =    processor.decode(logits[0]		,    output_word_offsets=snake_case       )

		lowercase       =    model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
		lowercase       =    [
		    {
		        'start_time': d['start_offset'] * time_offset,
		        'end_time': d['end_offset'] * time_offset,
		        'word': d['word'],
		    }
		    for d in output['word_offsets']
		]

		lowercase       =    'WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL'

		# output words
		self.assertEqual(' '.join(self.get_from_offsets(snake_case		,    'word'       )       )		,    snake_case       )
		self.assertEqual(' '.join(self.get_from_offsets(snake_case		,    'word'       )       )		,    output.text       )

		# output times
		lowercase       =    torch.tensor(self.get_from_offsets(snake_case		,    'start_time'       )       )
		lowercase       =    torch.tensor(self.get_from_offsets(snake_case		,    'end_time'       )       )

		# fmt: off
		lowercase       =    torch.tensor([1.4_199, 1.6_599, 2.2_599, 3.0, 3.24, 3.5_999, 3.7_999, 4.0_999, 4.26, 4.94, 5.28, 5.6_599, 5.78, 5.94, 6.32, 6.5_399, 6.6_599]       )
		lowercase       =    torch.tensor([1.5_399, 1.8_999, 2.9, 3.16, 3.5_399, 3.72, 4.0_199, 4.1_799, 4.76, 5.1_599, 5.5_599, 5.6_999, 5.86, 6.1_999, 6.38, 6.6_199, 6.94]       )
		# fmt: on

		self.assertTrue(torch.allclose(snake_case		,    snake_case		,    atol=0.01       )       )
		self.assertTrue(torch.allclose(snake_case		,    snake_case		,    atol=0.01       )       )



import json
import os
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 FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
	from PIL import Image

	from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor







@require_vision
class        A_   (			unittest.TestCase   ):




	'''simple docstring'''


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

		lowercase       =    [
		    '[UNK]',
		    '[CLS]',
		    '[SEP]',
		    '[PAD]',
		    '[MASK]',
		    '的',
		    '价',
		    '格',
		    '是',
		    '15',
		    '便',
		    'alex',
		    '##andra',
		    '，',
		    '。',
		    '-',
		    't',
		    'shirt',
		]
		lowercase       =    os.path.join(self.tmpdirname		,    VOCAB_FILES_NAMES['vocab_file']       )
		with open(self.vocab_file		,    'w'		,    encoding='utf-8'       ) as vocab_writer:
			vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]       )       )

		lowercase       =    {
		    'do_resize': True,
		    'size': {'height': 224, 'width': 224},
		    'do_center_crop': True,
		    'crop_size': {'height': 18, 'width': 18},
		    '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],
		    'do_convert_rgb': True,
		}
		lowercase       =    os.path.join(self.tmpdirname		,    snake_case       )
		with open(self.image_processor_file		,    'w'		,    encoding='utf-8'       ) as fp:
			json.dump(snake_case		,    snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    **snake_case       ):
		return BertTokenizer.from_pretrained(self.tmpdirname		,    **snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    **snake_case       ):
		return BertTokenizerFast.from_pretrained(self.tmpdirname		,    **snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    **snake_case       ):
		return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname		,    **snake_case       )





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





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    [np.random.randint(255		,    size=(3, 30, 400)		,    dtype=np.uinta       )]

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

		return image_inputs





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

		lowercase       =    ChineseCLIPProcessor(tokenizer=snake_case		,    image_processor=snake_case       )
		processor_slow.save_pretrained(self.tmpdirname       )
		lowercase       =    ChineseCLIPProcessor.from_pretrained(self.tmpdirname		,    use_fast=snake_case       )

		lowercase       =    ChineseCLIPProcessor(tokenizer=snake_case		,    image_processor=snake_case       )
		processor_fast.save_pretrained(self.tmpdirname       )
		lowercase       =    ChineseCLIPProcessor.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		,    snake_case       )
		self.assertIsInstance(processor_fast.tokenizer		,    snake_case       )

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





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

		lowercase       =    self.get_tokenizer(cls_token='(CLS)'		,    sep_token='(SEP)'       )
		lowercase       =    self.get_image_processor(do_normalize=snake_case       )

		lowercase       =    ChineseCLIPProcessor.from_pretrained(
		    self.tmpdirname		,    cls_token='(CLS)'		,    sep_token='(SEP)'		,    do_normalize=snake_case       )

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

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





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

		lowercase       =    ChineseCLIPProcessor(tokenizer=snake_case		,    image_processor=snake_case       )

		lowercase       =    self.prepare_image_inputs()

		lowercase       =    image_processor(snake_case		,    return_tensors='np'       )
		lowercase       =    processor(images=snake_case		,    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 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.get_image_processor()
		lowercase       =    self.get_tokenizer()

		lowercase       =    ChineseCLIPProcessor(tokenizer=snake_case		,    image_processor=snake_case       )

		lowercase       =    'Alexandra，T-shirt的价格是15便士。'

		lowercase       =    processor(text=snake_case       )

		lowercase       =    tokenizer(snake_case       )

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





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

		lowercase       =    ChineseCLIPProcessor(tokenizer=snake_case		,    image_processor=snake_case       )

		lowercase       =    'Alexandra，T-shirt的价格是15便士。'
		lowercase       =    self.prepare_image_inputs()

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

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

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





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

		lowercase       =    ChineseCLIPProcessor(tokenizer=snake_case		,    image_processor=snake_case       )

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

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

		self.assertListEqual(snake_case		,    snake_case       )





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

		lowercase       =    ChineseCLIPProcessor(tokenizer=snake_case		,    image_processor=snake_case       )

		lowercase       =    'Alexandra，T-shirt的价格是15便士。'
		lowercase       =    self.prepare_image_inputs()

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

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

