

import json
import os
import random
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 IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
	from PIL import Image

	from transformers import FlavaImageProcessor, FlavaProcessor
	from transformers.models.flava.image_processing_flava import (
	    FLAVA_CODEBOOK_MEAN,
	    FLAVA_CODEBOOK_STD,
	    FLAVA_IMAGE_MEAN,
	    FLAVA_IMAGE_STD,
	)



@require_vision
class    A__(unittest.TestCase    ):
						"""simple docstring"""






						def 	UpperCamelCase__			(  self  )      ->				str:
									a_		:		Optional[int]				    =				tempfile.mkdtemp()

									# fmt: off
									a_		:		List[str]				    =				["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing""", """,""", """low""", """lowest"""]
									# fmt: on
									a_		:		int				    =				os.path.join(self.tmpdirname       ,     VOCAB_FILES_NAMES["""vocab_file"""]  )

									with open(self.vocab_file       ,     """w"""       ,     encoding="""utf-8"""  ) as fp:
												fp.write("""""".join([x + """\n""" for x in vocab_tokens]  )  )

									a_		:		List[Any]				    =				{
									    """image_mean""": FLAVA_IMAGE_MEAN,
									    """image_std""": FLAVA_IMAGE_STD,
									    """do_normalize""": True,
									    """do_resize""": True,
									    """size""": 224,
									    """do_center_crop""": True,
									    """crop_size""": 224,
									    """input_size_patches""": 14,
									    """total_mask_patches""": 75,
									    """mask_group_max_patches""": None,
									    """mask_group_min_patches""": 16,
									    """mask_group_min_aspect_ratio""": 0.3,
									    """mask_group_max_aspect_ratio""": None,
									    """codebook_do_resize""": True,
									    """codebook_size""": 112,
									    """codebook_do_center_crop""": True,
									    """codebook_crop_size""": 112,
									    """codebook_do_map_pixels""": True,
									    """codebook_do_normalize""": True,
									    """codebook_image_mean""": FLAVA_CODEBOOK_MEAN,
									    """codebook_image_std""": FLAVA_CODEBOOK_STD,
									}

									a_		:		Tuple				    =				os.path.join(self.tmpdirname       ,     _lowercase  )
									with open(self.image_processor_file       ,     """w"""       ,     encoding="""utf-8"""  ) as fp:
												json.dump(_lowercase       ,     _lowercase  )





						def 	UpperCamelCase__			(  self       ,     **_lowercase  )      ->				Any:
									return BertTokenizer.from_pretrained(self.tmpdirname       ,     **_lowercase  )





						def 	UpperCamelCase__			(  self       ,     **_lowercase  )      ->				Dict:
									return BertTokenizerFast.from_pretrained(self.tmpdirname       ,     **_lowercase  )





						def 	UpperCamelCase__			(  self       ,     **_lowercase  )      ->				List[Any]:
									return FlavaImageProcessor.from_pretrained(self.tmpdirname       ,     **_lowercase  )





						def 	UpperCamelCase__			(  self  )      ->				Union[str, Any]:
									shutil.rmtree(self.tmpdirname  )





						def 	UpperCamelCase__			(  self  )      ->				Union[str, Any]:
									a_		:		int				    =				[np.random.randint(255       ,     size=(3, 30, 400)       ,     dtype=np.uinta  )]

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

									return image_inputs





						def 	UpperCamelCase__			(  self  )      ->				Tuple:
									a_		:		List[Any]				    =				self.get_tokenizer()
									a_		:		int				    =				self.get_rust_tokenizer()
									a_		:		int				    =				self.get_image_processor()

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

									a_		:		Optional[Any]				    =				FlavaProcessor(tokenizer=_lowercase       ,     image_processor=_lowercase  )
									processor_fast.save_pretrained(self.tmpdirname  )
									a_		:		Tuple				    =				FlavaProcessor.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       ,     _lowercase  )
									self.assertIsInstance(processor_fast.tokenizer       ,     _lowercase  )

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





						def 	UpperCamelCase__			(  self  )      ->				Union[str, Any]:
									a_		:		List[str]				    =				FlavaProcessor(tokenizer=self.get_tokenizer()       ,     image_processor=self.get_image_processor()  )
									processor.save_pretrained(self.tmpdirname  )

									a_		:		int				    =				self.get_tokenizer(bos_token="""(BOS)"""       ,     eos_token="""(EOS)"""  )
									a_		:		List[Any]				    =				self.get_image_processor(do_normalize=_lowercase       ,     padding_value=1.0  )

									a_		:		Tuple				    =				FlavaProcessor.from_pretrained(
									    self.tmpdirname       ,     bos_token="""(BOS)"""       ,     eos_token="""(EOS)"""       ,     do_normalize=_lowercase       ,     padding_value=1.0  )

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

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





						def 	UpperCamelCase__			(  self  )      ->				Dict:
									a_		:		Tuple				    =				self.get_image_processor()
									a_		:		Optional[int]				    =				self.get_tokenizer()

									a_		:		int				    =				FlavaProcessor(tokenizer=_lowercase       ,     image_processor=_lowercase  )

									a_		:		Optional[int]				    =				self.prepare_image_inputs()

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

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

									# With rest of the args
									random.seed(1_234  )
									a_		:		Union[str, Any]				    =				image_processor(
									    _lowercase       ,     return_image_mask=_lowercase       ,     return_codebook_pixels=_lowercase       ,     return_tensors="""np"""  )
									random.seed(1_234  )
									a_		:		Dict				    =				processor(
									    images=_lowercase       ,     return_image_mask=_lowercase       ,     return_codebook_pixels=_lowercase       ,     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 	UpperCamelCase__			(  self  )      ->				List[Any]:
									a_		:		Union[str, Any]				    =				self.get_image_processor()
									a_		:		Optional[int]				    =				self.get_tokenizer()

									a_		:		int				    =				FlavaProcessor(tokenizer=_lowercase       ,     image_processor=_lowercase  )

									a_		:		Optional[int]				    =				"""lower newer"""

									a_		:		Dict				    =				processor(text=_lowercase  )

									a_		:		Tuple				    =				tokenizer(_lowercase  )

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





						def 	UpperCamelCase__			(  self  )      ->				str:
									a_		:		List[Any]				    =				self.get_image_processor()
									a_		:		Dict				    =				self.get_tokenizer()

									a_		:		Any				    =				FlavaProcessor(tokenizer=_lowercase       ,     image_processor=_lowercase  )

									a_		:		Union[str, Any]				    =				"""lower newer"""
									a_		:		Tuple				    =				self.prepare_image_inputs()

									a_		:		Union[str, Any]				    =				processor(text=_lowercase       ,     images=_lowercase  )

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

									# add extra args
									a_		:		List[str]				    =				processor(text=_lowercase       ,     images=_lowercase       ,     return_codebook_pixels=_lowercase       ,     return_image_mask=_lowercase  )

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

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





						def 	UpperCamelCase__			(  self  )      ->				str:
									a_		:		Tuple				    =				self.get_image_processor()
									a_		:		List[Any]				    =				self.get_tokenizer()

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

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

									a_		:		List[Any]				    =				processor.batch_decode(_lowercase  )
									a_		:		Union[str, Any]				    =				tokenizer.batch_decode(_lowercase  )

									self.assertListEqual(_lowercase       ,     _lowercase  )





						def 	UpperCamelCase__			(  self  )      ->				Optional[Any]:
									a_		:		Optional[Any]				    =				self.get_image_processor()
									a_		:		Union[str, Any]				    =				self.get_tokenizer()

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

									a_		:		Union[str, Any]				    =				"""lower newer"""
									a_		:		Dict				    =				self.prepare_image_inputs()

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

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





from ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case							:     Optional[Any] 						=	logging.get_logger(__name__)

__snake_case							:     str 						=	{
    """ZinengTang/tvlt-base""": """https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json""",
}



class    A__(a_    ):
						"""simple docstring"""






						_A  :							Any   			=	'''tvlt'''





						def __init__(  self       ,     _lowercase=224       ,     _lowercase=2_048       ,     _lowercase=128       ,     _lowercase=[16, 16]       ,     _lowercase=[16, 16]       ,     _lowercase=3       ,     _lowercase=1       ,     _lowercase=8       ,     _lowercase=768       ,     _lowercase=12       ,     _lowercase=12       ,     _lowercase=3_072       ,     _lowercase="gelu"       ,     _lowercase=0.0       ,     _lowercase=0.0       ,     _lowercase=0.0_2       ,     _lowercase=1e-6       ,     _lowercase=True       ,     _lowercase=False       ,     _lowercase=16       ,     _lowercase=512       ,     _lowercase=8       ,     _lowercase=2_048       ,     _lowercase=0.7_5       ,     _lowercase=0.1_5       ,     _lowercase="frame-level"       ,     _lowercase=True       ,     _lowercase=True       ,     _lowercase="classification"       ,     **_lowercase       ,     )      ->				Tuple:
									super().__init__(**_lowercase  )

									if audio_mask_type not in ("frame-level", "patch_level"):
												raise ValueError(
												    """audio_mask_type must be one of two acceptable strategies - {'frame_level', 'patch-level') """
												    F'''got {audio_mask_type}'''  )

									a_		:		int				    =				image_size
									a_		:		str				    =				spectrogram_length
									a_		:		int				    =				frequency_length
									a_		:		str				    =				image_patch_size
									a_		:		int				    =				audio_patch_size
									a_		:		Any				    =				num_image_channels
									a_		:		str				    =				num_audio_channels
									a_		:		int				    =				num_frames

									a_		:		Optional[int]				    =				hidden_size
									a_		:		Tuple				    =				num_hidden_layers
									a_		:		Any				    =				num_attention_heads
									a_		:		Union[str, Any]				    =				intermediate_size
									a_		:		Tuple				    =				hidden_act
									a_		:		Union[str, Any]				    =				hidden_dropout_prob
									a_		:		int				    =				attention_probs_dropout_prob
									a_		:		Any				    =				initializer_range
									a_		:		int				    =				layer_norm_eps
									a_		:		Union[str, Any]				    =				qkv_bias
									a_		:		str				    =				use_mean_pooling

									a_		:		List[Any]				    =				decoder_num_attention_heads
									a_		:		Optional[Any]				    =				decoder_hidden_size
									a_		:		Dict				    =				decoder_num_hidden_layers
									a_		:		Optional[Any]				    =				decoder_intermediate_size
									a_		:		Dict				    =				pixel_mask_ratio
									a_		:		int				    =				audio_mask_ratio
									a_		:		Optional[Any]				    =				audio_mask_type

									a_		:		Union[str, Any]				    =				task_matching
									a_		:		Dict				    =				task_mae
									a_		:		Union[str, Any]				    =				loss_type


