



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       __lowerCAmelCase				(  unittest.TestCase ):




    def 							snake_case     (							self	):



         """simple docstring"""





         _lowerCAmelCase      			=					tempfile.mkdtemp()

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

         _lowerCAmelCase      			=					{
             """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,
         }

         _lowerCAmelCase      			=					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 							snake_case     (							self		,       **_snake_case	):



         """simple docstring"""





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




    def 							snake_case     (							self		,       **_snake_case	):



         """simple docstring"""





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




    def 							snake_case     (							self		,       **_snake_case	):



         """simple docstring"""





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




    def 							snake_case     (							self	):



         """simple docstring"""





         shutil.rmtree(self.tmpdirname	)




    def 							snake_case     (							self	):



         """simple docstring"""





         _lowerCAmelCase      			=					[np.random.randint(255		,       size=(3, 30, 400)		,       dtype=np.uinta	)]

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

         return image_inputs




    def 							snake_case     (							self	):



         """simple docstring"""





         _lowerCAmelCase      			=					self.get_tokenizer()
         _lowerCAmelCase      			=					self.get_rust_tokenizer()
         _lowerCAmelCase      			=					self.get_image_processor()

         _lowerCAmelCase      			=					FlavaProcessor(tokenizer=_snake_case		,       image_processor=_snake_case	)
         processor_slow.save_pretrained(self.tmpdirname	)
         _lowerCAmelCase      			=					FlavaProcessor.from_pretrained(self.tmpdirname		,       use_fast=_snake_case	)

         _lowerCAmelCase      			=					FlavaProcessor(tokenizer=_snake_case		,       image_processor=_snake_case	)
         processor_fast.save_pretrained(self.tmpdirname	)
         _lowerCAmelCase      			=					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		,       _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 							snake_case     (							self	):



         """simple docstring"""





         _lowerCAmelCase      			=					FlavaProcessor(tokenizer=self.get_tokenizer()		,       image_processor=self.get_image_processor()	)
         processor.save_pretrained(self.tmpdirname	)

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

         _lowerCAmelCase      			=					FlavaProcessor.from_pretrained(
             self.tmpdirname		,       bos_token="""(BOS)"""		,       eos_token="""(EOS)"""		,       do_normalize=_snake_case		,       padding_value=1.0	)

         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 							snake_case     (							self	):



         """simple docstring"""





         _lowerCAmelCase      			=					self.get_image_processor()
         _lowerCAmelCase      			=					self.get_tokenizer()

         _lowerCAmelCase      			=					FlavaProcessor(tokenizer=_snake_case		,       image_processor=_snake_case	)

         _lowerCAmelCase      			=					self.prepare_image_inputs()

         _lowerCAmelCase      			=					image_processor(_snake_case		,       return_tensors="""np"""	)
         _lowerCAmelCase      			=					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	)

         # With rest of the args
         random.seed(1234	)
         _lowerCAmelCase      			=					image_processor(
             _snake_case		,       return_image_mask=_snake_case		,       return_codebook_pixels=_snake_case		,       return_tensors="""np"""	)
         random.seed(1234	)
         _lowerCAmelCase      			=					processor(
             images=_snake_case		,       return_image_mask=_snake_case		,       return_codebook_pixels=_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 							snake_case     (							self	):



         """simple docstring"""





         _lowerCAmelCase      			=					self.get_image_processor()
         _lowerCAmelCase      			=					self.get_tokenizer()

         _lowerCAmelCase      			=					FlavaProcessor(tokenizer=_snake_case		,       image_processor=_snake_case	)

         _lowerCAmelCase      			=					"""lower newer"""

         _lowerCAmelCase      			=					processor(text=_snake_case	)

         _lowerCAmelCase      			=					tokenizer(_snake_case	)

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




    def 							snake_case     (							self	):



         """simple docstring"""





         _lowerCAmelCase      			=					self.get_image_processor()
         _lowerCAmelCase      			=					self.get_tokenizer()

         _lowerCAmelCase      			=					FlavaProcessor(tokenizer=_snake_case		,       image_processor=_snake_case	)

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

         _lowerCAmelCase      			=					processor(text=_snake_case		,       images=_snake_case	)

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

         # add extra args
         _lowerCAmelCase      			=					processor(text=_snake_case		,       images=_snake_case		,       return_codebook_pixels=_snake_case		,       return_image_mask=_snake_case	)

         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(_snake_case	):
              processor()




    def 							snake_case     (							self	):



         """simple docstring"""





         _lowerCAmelCase      			=					self.get_image_processor()
         _lowerCAmelCase      			=					self.get_tokenizer()

         _lowerCAmelCase      			=					FlavaProcessor(tokenizer=_snake_case		,       image_processor=_snake_case	)

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

         _lowerCAmelCase      			=					processor.batch_decode(_snake_case	)
         _lowerCAmelCase      			=					tokenizer.batch_decode(_snake_case	)

         self.assertListEqual(_snake_case		,       _snake_case	)




    def 							snake_case     (							self	):



         """simple docstring"""





         _lowerCAmelCase      			=					self.get_image_processor()
         _lowerCAmelCase      			=					self.get_tokenizer()

         _lowerCAmelCase      			=					FlavaProcessor(tokenizer=_snake_case		,       image_processor=_snake_case	)

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

         _lowerCAmelCase      			=					processor(text=_snake_case		,       images=_snake_case	)

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








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A__			   = {
    """configuration_videomae""": ["""VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VideoMAEConfig"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A__			   = [
	    """VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """VideoMAEForPreTraining""",
	    """VideoMAEModel""",
	    """VideoMAEPreTrainedModel""",
	    """VideoMAEForVideoClassification""",
	]

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A__			   = ["""VideoMAEFeatureExtractor"""]
	A__			   = ["""VideoMAEImageProcessor"""]

if TYPE_CHECKING:
	from .configuration_videomae import VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP, VideoMAEConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_videomae import (
		    VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST,
		    VideoMAEForPreTraining,
		    VideoMAEForVideoClassification,
		    VideoMAEModel,
		    VideoMAEPreTrainedModel,
		)

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_videomae import VideoMAEFeatureExtractor
		from .image_processing_videomae import VideoMAEImageProcessor

else:
	import sys

	A__			   = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



