


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   __magic_name__      (							self    ):
								a_     			=   tempfile.mkdtemp()

								# fmt: off
								a_     			=   ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
								# fmt: on
								a_     			=   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_     			=   {
								    '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_     			=   os.path.join(self.tmpdirname			,       _a    )
								with open(self.image_processor_file			,       """w"""			,       encoding="""utf-8"""    ) as fp:
									json.dump(_a			,       _a    )





							def   __magic_name__      (							self			,       **_SCREAMING_SNAKE_CASE    ):
								return BertTokenizer.from_pretrained(self.tmpdirname			,       **_a    )





							def   __magic_name__      (							self			,       **_SCREAMING_SNAKE_CASE    ):
								return BertTokenizerFast.from_pretrained(self.tmpdirname			,       **_a    )





							def   __magic_name__      (							self			,       **_SCREAMING_SNAKE_CASE    ):
								return FlavaImageProcessor.from_pretrained(self.tmpdirname			,       **_a    )





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





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

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

								return image_inputs





							def   __magic_name__      (							self    ):
								a_     			=   self.get_tokenizer()
								a_     			=   self.get_rust_tokenizer()
								a_     			=   self.get_image_processor()

								a_     			=   FlavaProcessor(tokenizer=_a			,       image_processor=_a    )
								processor_slow.save_pretrained(self.tmpdirname    )
								a_     			=   FlavaProcessor.from_pretrained(self.tmpdirname			,       use_fast=_a    )

								a_     			=   FlavaProcessor(tokenizer=_a			,       image_processor=_a    )
								processor_fast.save_pretrained(self.tmpdirname    )
								a_     			=   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			,       _a    )
								self.assertIsInstance(processor_fast.tokenizer			,       _a    )

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





							def   __magic_name__      (							self    ):
								a_     			=   FlavaProcessor(tokenizer=self.get_tokenizer()			,       image_processor=self.get_image_processor()    )
								processor.save_pretrained(self.tmpdirname    )

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

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

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

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





							def   __magic_name__      (							self    ):
								a_     			=   self.get_image_processor()
								a_     			=   self.get_tokenizer()

								a_     			=   FlavaProcessor(tokenizer=_a			,       image_processor=_a    )

								a_     			=   self.prepare_image_inputs()

								a_     			=   image_processor(_a			,       return_tensors="""np"""    )
								a_     			=   processor(images=_a			,       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    )
								a_     			=   image_processor(
								    _a			,       return_image_mask=_a			,       return_codebook_pixels=_a			,       return_tensors="""np"""    )
								random.seed(1234    )
								a_     			=   processor(
								    images=_a			,       return_image_mask=_a			,       return_codebook_pixels=_a			,       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   __magic_name__      (							self    ):
								a_     			=   self.get_image_processor()
								a_     			=   self.get_tokenizer()

								a_     			=   FlavaProcessor(tokenizer=_a			,       image_processor=_a    )

								a_     			=   'lower newer'

								a_     			=   processor(text=_a    )

								a_     			=   tokenizer(_a    )

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





							def   __magic_name__      (							self    ):
								a_     			=   self.get_image_processor()
								a_     			=   self.get_tokenizer()

								a_     			=   FlavaProcessor(tokenizer=_a			,       image_processor=_a    )

								a_     			=   'lower newer'
								a_     			=   self.prepare_image_inputs()

								a_     			=   processor(text=_a			,       images=_a    )

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

								# add extra args
								a_     			=   processor(text=_a			,       images=_a			,       return_codebook_pixels=_a			,       return_image_mask=_a    )

								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(_a    ):
									processor()





							def   __magic_name__      (							self    ):
								a_     			=   self.get_image_processor()
								a_     			=   self.get_tokenizer()

								a_     			=   FlavaProcessor(tokenizer=_a			,       image_processor=_a    )

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

								a_     			=   processor.batch_decode(_a    )
								a_     			=   tokenizer.batch_decode(_a    )

								self.assertListEqual(_a			,       _a    )





							def   __magic_name__      (							self    ):
								a_     			=   self.get_image_processor()
								a_     			=   self.get_tokenizer()

								a_     			=   FlavaProcessor(tokenizer=_a			,       image_processor=_a    )

								a_     			=   'lower newer'
								a_     			=   self.prepare_image_inputs()

								a_     			=   processor(text=_a			,       images=_a    )

								self.assertListEqual(list(inputs.keys()    )			,       processor.model_input_names    )
'''simple docstring'''




from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available


@dataclass
# Copied from diffusers.pipelines.stable_diffusion.__init__.StableDiffusionPipelineOutput with Stable->Alt
class    UpperCAmelCase__	(							_snake_case      ):






   """simple docstring"""
   A				:							Union[List[PIL.Image.Image], np.ndarray]
   A				:							Optional[List[bool]]


if is_transformers_available() and is_torch_available():
 from .modeling_roberta_series import RobertaSeriesModelWithTransformation
 from .pipeline_alt_diffusion import AltDiffusionPipeline
 from .pipeline_alt_diffusion_imgaimg import AltDiffusionImgaImgPipeline




