



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 	snake_case_			(unittest.TestCase	):




       """simple docstring"""






       def 			A_ (  self):

             """simple docstring"""





             UpperCAmelCase_	:						Optional[int]					   =		tempfile.mkdtemp()

             # fmt: off
             UpperCAmelCase_	:						Optional[Any]					   =		["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]
             # fmt: on
             UpperCAmelCase_	:						Optional[Any]					   =		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]))

             UpperCAmelCase_	:						Optional[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,
             }

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



       def 			A_ (  self  ,**lowercase):

             """simple docstring"""





             return BertTokenizer.from_pretrained(self.tmpdirname  ,**lowercase)



       def 			A_ (  self  ,**lowercase):

             """simple docstring"""





             return BertTokenizerFast.from_pretrained(self.tmpdirname  ,**lowercase)



       def 			A_ (  self  ,**lowercase):

             """simple docstring"""





             return FlavaImageProcessor.from_pretrained(self.tmpdirname  ,**lowercase)



       def 			A_ (  self):

             """simple docstring"""





             shutil.rmtree(self.tmpdirname)



       def 			A_ (  self):

             """simple docstring"""





             UpperCAmelCase_	:						Union[str, Any]					   =		[np.random.randint(255  ,size=(3, 30, 400)  ,dtype=np.uinta)]

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

             return image_inputs



       def 			A_ (  self):

             """simple docstring"""





             UpperCAmelCase_	:						List[Any]					   =		self.get_tokenizer()
             UpperCAmelCase_	:						List[str]					   =		self.get_rust_tokenizer()
             UpperCAmelCase_	:						str					   =		self.get_image_processor()

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

             UpperCAmelCase_	:						Union[str, Any]					   =		FlavaProcessor(tokenizer=lowercase  ,image_processor=lowercase)
             processor_fast.save_pretrained(self.tmpdirname)
             UpperCAmelCase_	:						Union[str, Any]					   =		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 			A_ (  self):

             """simple docstring"""





             UpperCAmelCase_	:						Optional[int]					   =		FlavaProcessor(tokenizer=self.get_tokenizer()  ,image_processor=self.get_image_processor())
             processor.save_pretrained(self.tmpdirname)

             UpperCAmelCase_	:						List[Any]					   =		self.get_tokenizer(bos_token="(BOS)"  ,eos_token="(EOS)")
             UpperCAmelCase_	:						Tuple					   =		self.get_image_processor(do_normalize=lowercase  ,padding_value=1.0)

             UpperCAmelCase_	:						str					   =		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 			A_ (  self):

             """simple docstring"""





             UpperCAmelCase_	:						Tuple					   =		self.get_image_processor()
             UpperCAmelCase_	:						Dict					   =		self.get_tokenizer()

             UpperCAmelCase_	:						Dict					   =		FlavaProcessor(tokenizer=lowercase  ,image_processor=lowercase)

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

             UpperCAmelCase_	:						str					   =		image_processor(lowercase  ,return_tensors="np")
             UpperCAmelCase_	:						Union[str, 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(1234)
             UpperCAmelCase_	:						Union[str, Any]					   =		image_processor(
                 lowercase  ,return_image_mask=lowercase  ,return_codebook_pixels=lowercase  ,return_tensors="np")
             random.seed(1234)
             UpperCAmelCase_	:						Tuple					   =		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 			A_ (  self):

             """simple docstring"""





             UpperCAmelCase_	:						int					   =		self.get_image_processor()
             UpperCAmelCase_	:						int					   =		self.get_tokenizer()

             UpperCAmelCase_	:						Any					   =		FlavaProcessor(tokenizer=lowercase  ,image_processor=lowercase)

             UpperCAmelCase_	:						Tuple					   =		"lower newer"

             UpperCAmelCase_	:						str					   =		processor(text=lowercase)

             UpperCAmelCase_	:						Optional[Any]					   =		tokenizer(lowercase)

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



       def 			A_ (  self):

             """simple docstring"""





             UpperCAmelCase_	:						Any					   =		self.get_image_processor()
             UpperCAmelCase_	:						Optional[Any]					   =		self.get_tokenizer()

             UpperCAmelCase_	:						Any					   =		FlavaProcessor(tokenizer=lowercase  ,image_processor=lowercase)

             UpperCAmelCase_	:						Optional[int]					   =		"lower newer"
             UpperCAmelCase_	:						Union[str, Any]					   =		self.prepare_image_inputs()

             UpperCAmelCase_	:						Dict					   =		processor(text=lowercase  ,images=lowercase)

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

             # add extra args
             UpperCAmelCase_	:						Union[str, Any]					   =		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 			A_ (  self):

             """simple docstring"""





             UpperCAmelCase_	:						int					   =		self.get_image_processor()
             UpperCAmelCase_	:						Dict					   =		self.get_tokenizer()

             UpperCAmelCase_	:						int					   =		FlavaProcessor(tokenizer=lowercase  ,image_processor=lowercase)

             UpperCAmelCase_	:						List[Any]					   =		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

             UpperCAmelCase_	:						Tuple					   =		processor.batch_decode(lowercase)
             UpperCAmelCase_	:						Union[str, Any]					   =		tokenizer.batch_decode(lowercase)

             self.assertListEqual(lowercase  ,lowercase)



       def 			A_ (  self):

             """simple docstring"""





             UpperCAmelCase_	:						List[str]					   =		self.get_image_processor()
             UpperCAmelCase_	:						List[Any]					   =		self.get_tokenizer()

             UpperCAmelCase_	:						Tuple					   =		FlavaProcessor(tokenizer=lowercase  ,image_processor=lowercase)

             UpperCAmelCase_	:						List[Any]					   =		"lower newer"
             UpperCAmelCase_	:						Optional[Any]					   =		self.prepare_image_inputs()

             UpperCAmelCase_	:						List[Any]					   =		processor(text=lowercase  ,images=lowercase)

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





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


__lowerCamelCase								=      logging.get_logger(__name__)

__lowerCamelCase								=      {
    '''MCG-NJU/videomae-base''': '''https://huggingface.co/MCG-NJU/videomae-base/resolve/main/config.json''',
}


class 	snake_case_			(lowercase__	):




       """simple docstring"""






       _lowerCamelCase           =					"""videomae"""



       def __init__(  self  ,lowercase=224  ,lowercase=16  ,lowercase=3  ,lowercase=16  ,lowercase=2  ,lowercase=768  ,lowercase=12  ,lowercase=12  ,lowercase=3072  ,lowercase="gelu"  ,lowercase=0.0  ,lowercase=0.0  ,lowercase=0.02  ,lowercase=1E-12  ,lowercase=True  ,lowercase=True  ,lowercase=6  ,lowercase=384  ,lowercase=4  ,lowercase=1536  ,lowercase=True  ,**lowercase  ,):

             """simple docstring"""





             super().__init__(**lowercase)

             UpperCAmelCase_	:						Optional[int]					   =		image_size
             UpperCAmelCase_	:						Optional[Any]					   =		patch_size
             UpperCAmelCase_	:						Tuple					   =		num_channels
             UpperCAmelCase_	:						Optional[int]					   =		num_frames
             UpperCAmelCase_	:						str					   =		tubelet_size

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

             UpperCAmelCase_	:						List[Any]					   =		decoder_num_attention_heads
             UpperCAmelCase_	:						Optional[int]					   =		decoder_hidden_size
             UpperCAmelCase_	:						Any					   =		decoder_num_hidden_layers
             UpperCAmelCase_	:						Dict					   =		decoder_intermediate_size
             UpperCAmelCase_	:						str					   =		norm_pix_loss
