





import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
				from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
				__lowerCAmelCase 	=      False

if is_vision_available():
				from PIL import Image

				from transformers import (
				    AutoProcessor,
				    PixaStructImageProcessor,
				    PixaStructProcessor,
				    PreTrainedTokenizerFast,
				    TaTokenizer,
				)




@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11			,							reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."			,							)
@require_vision
@require_torch
class 	_UpperCAmelCase						( unittest.TestCase       ):




					def 						snake_case_		(    self							)    ->	str:
							UpperCamelCase__								=						tempfile.mkdtemp()

							UpperCamelCase__								=						PixaStructImageProcessor()
							UpperCamelCase__								=						TaTokenizer.from_pretrained("""t5-small"""							)

							UpperCamelCase__								=						PixaStructProcessor(_UpperCAmelCase     ,   _UpperCAmelCase							)

							processor.save_pretrained(self.tmpdirname							)




					def 						snake_case_		(    self     ,   **__lowerCAmelCase							)    ->	Dict:
							return AutoProcessor.from_pretrained(self.tmpdirname     ,   **_UpperCAmelCase							).tokenizer




					def 						snake_case_		(    self     ,   **__lowerCAmelCase							)    ->	List[Any]:
							return AutoProcessor.from_pretrained(self.tmpdirname     ,   **_UpperCAmelCase							).image_processor




					def 						snake_case_		(    self							)    ->	str:
							shutil.rmtree(self.tmpdirname							)




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

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

							return image_inputs




					def 						snake_case_		(    self							)    ->	List[str]:
							UpperCamelCase__								=						PixaStructProcessor(tokenizer=self.get_tokenizer()     ,   image_processor=self.get_image_processor()							)
							processor.save_pretrained(self.tmpdirname							)

							UpperCamelCase__								=						self.get_tokenizer(bos_token="""(BOS)"""     ,   eos_token="""(EOS)"""							)
							UpperCamelCase__								=						self.get_image_processor(do_normalize=_UpperCAmelCase     ,   padding_value=1.0							)

							UpperCamelCase__								=						PixaStructProcessor.from_pretrained(
							    self.tmpdirname     ,   bos_token="""(BOS)"""     ,   eos_token="""(EOS)"""     ,   do_normalize=_UpperCAmelCase     ,   padding_value=1.0							)

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

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




					def 						snake_case_		(    self							)    ->	Tuple:
							UpperCamelCase__								=						self.get_image_processor()
							UpperCamelCase__								=						self.get_tokenizer()

							UpperCamelCase__								=						PixaStructProcessor(tokenizer=_UpperCAmelCase     ,   image_processor=_UpperCAmelCase							)

							UpperCamelCase__								=						self.prepare_image_inputs()

							UpperCamelCase__								=						image_processor(_UpperCAmelCase     ,   return_tensors="""np"""							)
							UpperCamelCase__								=						processor(images=_UpperCAmelCase     ,   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 						snake_case_		(    self							)    ->	Union[str, Any]:
							UpperCamelCase__								=						self.get_image_processor()
							UpperCamelCase__								=						self.get_tokenizer()

							UpperCamelCase__								=						PixaStructProcessor(tokenizer=_UpperCAmelCase     ,   image_processor=_UpperCAmelCase							)

							UpperCamelCase__								=						"""lower newer"""

							UpperCamelCase__								=						processor(text=_UpperCAmelCase							)

							UpperCamelCase__								=						tokenizer(_UpperCAmelCase     ,   return_token_type_ids=_UpperCAmelCase     ,   add_special_tokens=_UpperCAmelCase							)

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




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

							UpperCamelCase__								=						PixaStructProcessor(tokenizer=_UpperCAmelCase     ,   image_processor=_UpperCAmelCase							)

							UpperCamelCase__								=						"""lower newer"""
							UpperCamelCase__								=						self.prepare_image_inputs()

							UpperCamelCase__								=						processor(text=_UpperCAmelCase     ,   images=_UpperCAmelCase							)

							self.assertListEqual(
							    list(inputs.keys()							)     ,   ["""flattened_patches""", """attention_mask""", """decoder_attention_mask""", """decoder_input_ids"""]							)

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




					def 						snake_case_		(    self							)    ->	Tuple:
							UpperCamelCase__								=						self.get_image_processor()
							UpperCamelCase__								=						self.get_tokenizer()

							UpperCamelCase__								=						PixaStructProcessor(tokenizer=_UpperCAmelCase     ,   image_processor=_UpperCAmelCase							)

							UpperCamelCase__								=						"""lower newer"""
							UpperCamelCase__								=						self.prepare_image_inputs()

							UpperCamelCase__								=						processor(text=_UpperCAmelCase     ,   images=_UpperCAmelCase							)

							UpperCamelCase__								=						[512, 1_024, 2_048, 4_096]
							UpperCamelCase__								=						[770, 770, 770, 770]
							# with text
							for i, max_patch in enumerate(_UpperCAmelCase							):
									UpperCamelCase__								=						processor(text=_UpperCAmelCase     ,   images=_UpperCAmelCase     ,   max_patches=_UpperCAmelCase							)
									self.assertEqual(inputs["""flattened_patches"""][0].shape[0]     ,   _UpperCAmelCase							)
									self.assertEqual(inputs["""flattened_patches"""][0].shape[1]     ,   expected_hidden_size[i]							)

							# without text input
							for i, max_patch in enumerate(_UpperCAmelCase							):
									UpperCamelCase__								=						processor(images=_UpperCAmelCase     ,   max_patches=_UpperCAmelCase							)
									self.assertEqual(inputs["""flattened_patches"""][0].shape[0]     ,   _UpperCAmelCase							)
									self.assertEqual(inputs["""flattened_patches"""][0].shape[1]     ,   expected_hidden_size[i]							)




					def 						snake_case_		(    self							)    ->	Dict:
							UpperCamelCase__								=						self.get_image_processor()
							UpperCamelCase__								=						self.get_tokenizer()

							UpperCamelCase__								=						PixaStructProcessor(tokenizer=_UpperCAmelCase     ,   image_processor=_UpperCAmelCase							)

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

							UpperCamelCase__								=						processor.batch_decode(_UpperCAmelCase							)
							UpperCamelCase__								=						tokenizer.batch_decode(_UpperCAmelCase							)

							self.assertListEqual(_UpperCAmelCase     ,   _UpperCAmelCase							)




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

							UpperCamelCase__								=						PixaStructProcessor(tokenizer=_UpperCAmelCase     ,   image_processor=_UpperCAmelCase							)

							UpperCamelCase__								=						"""lower newer"""
							UpperCamelCase__								=						self.prepare_image_inputs()

							UpperCamelCase__								=						processor(text=_UpperCAmelCase     ,   images=_UpperCAmelCase							)

							# For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
							self.assertListEqual(
							    list(inputs.keys()							)     ,   ["""flattened_patches""", """attention_mask""", """decoder_attention_mask""", """decoder_input_ids"""]							)

							UpperCamelCase__								=						processor(text=_UpperCAmelCase							)

							# For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
							self.assertListEqual(list(inputs.keys()							)     ,   ["""input_ids""", """attention_mask"""]							)













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


_UpperCAmelCase					:						Any         =     logging.get_logger(__name__)

_UpperCAmelCase					:						Dict         =     {
    'ZinengTang/tvlt-base': 'https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json',
}


class 		lowerCAmelCase_				(  UpperCamelCase ):
     __lowerCamelCase       :    Any 	=     '''tvlt'''





     def __init__(	self							, _UpperCAmelCase=224							, _UpperCAmelCase=2048							, _UpperCAmelCase=128							, _UpperCAmelCase=[16, 16]							, _UpperCAmelCase=[16, 16]							, _UpperCAmelCase=3							, _UpperCAmelCase=1							, _UpperCAmelCase=8							, _UpperCAmelCase=768							, _UpperCAmelCase=12							, _UpperCAmelCase=12							, _UpperCAmelCase=3072							, _UpperCAmelCase="gelu"							, _UpperCAmelCase=0.0							, _UpperCAmelCase=0.0							, _UpperCAmelCase=0.02							, _UpperCAmelCase=1E-6							, _UpperCAmelCase=True							, _UpperCAmelCase=False							, _UpperCAmelCase=16							, _UpperCAmelCase=512							, _UpperCAmelCase=8							, _UpperCAmelCase=2048							, _UpperCAmelCase=0.75							, _UpperCAmelCase=0.15							, _UpperCAmelCase="frame-level"							, _UpperCAmelCase=True							, _UpperCAmelCase=True							, _UpperCAmelCase="classification"							, **_UpperCAmelCase							, )		->				List[Any]:
        super().__init__(**_UpperCAmelCase  )

        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}'''  )

        lowerCamelCase									=image_size
        lowerCamelCase									=spectrogram_length
        lowerCamelCase									=frequency_length
        lowerCamelCase									=image_patch_size
        lowerCamelCase									=audio_patch_size
        lowerCamelCase									=num_image_channels
        lowerCamelCase									=num_audio_channels
        lowerCamelCase									=num_frames

        lowerCamelCase									=hidden_size
        lowerCamelCase									=num_hidden_layers
        lowerCamelCase									=num_attention_heads
        lowerCamelCase									=intermediate_size
        lowerCamelCase									=hidden_act
        lowerCamelCase									=hidden_dropout_prob
        lowerCamelCase									=attention_probs_dropout_prob
        lowerCamelCase									=initializer_range
        lowerCamelCase									=layer_norm_eps
        lowerCamelCase									=qkv_bias
        lowerCamelCase									=use_mean_pooling

        lowerCamelCase									=decoder_num_attention_heads
        lowerCamelCase									=decoder_hidden_size
        lowerCamelCase									=decoder_num_hidden_layers
        lowerCamelCase									=decoder_intermediate_size
        lowerCamelCase									=pixel_mask_ratio
        lowerCamelCase									=audio_mask_ratio
        lowerCamelCase									=audio_mask_type

        lowerCamelCase									=task_matching
        lowerCamelCase									=task_mae
        lowerCamelCase									=loss_type