
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 							lowercase   (						unittest.TestCase  ):



		def __UpperCamelCase					(					self			)    ->   Dict:
				"""simple docstring"""
				UpperCamelCase            =	tempfile.mkdtemp()

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



		def __UpperCamelCase					(					self							, **A_			)    ->   int:
				"""simple docstring"""
				return BertTokenizer.from_pretrained(self.tmpdirname							, **A_			)



		def __UpperCamelCase					(					self							, **A_			)    ->   List[Any]:
				"""simple docstring"""
				return BertTokenizerFast.from_pretrained(self.tmpdirname							, **A_			)



		def __UpperCamelCase					(					self							, **A_			)    ->   List[str]:
				"""simple docstring"""
				return FlavaImageProcessor.from_pretrained(self.tmpdirname							, **A_			)



		def __UpperCamelCase					(					self			)    ->   List[str]:
				"""simple docstring"""
				shutil.rmtree(self.tmpdirname			)



		def __UpperCamelCase					(					self			)    ->   Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase            =	[np.random.randint(255							, size=(3, 30, 400)							, dtype=np.uinta			)]

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

				return image_inputs



		def __UpperCamelCase					(					self			)    ->   int:
				"""simple docstring"""
				UpperCamelCase            =	self.get_tokenizer()
				UpperCamelCase            =	self.get_rust_tokenizer()
				UpperCamelCase            =	self.get_image_processor()

				UpperCamelCase            =	FlavaProcessor(tokenizer=A_							, image_processor=A_			)
				processor_slow.save_pretrained(self.tmpdirname			)
				UpperCamelCase            =	FlavaProcessor.from_pretrained(self.tmpdirname							, use_fast=A_			)

				UpperCamelCase            =	FlavaProcessor(tokenizer=A_							, image_processor=A_			)
				processor_fast.save_pretrained(self.tmpdirname			)
				UpperCamelCase            =	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 __UpperCamelCase					(					self			)    ->   Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase            =	FlavaProcessor(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=A_							, padding_value=1.0			)

				UpperCamelCase            =	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 __UpperCamelCase					(					self			)    ->   int:
				"""simple docstring"""
				UpperCamelCase            =	self.get_image_processor()
				UpperCamelCase            =	self.get_tokenizer()

				UpperCamelCase            =	FlavaProcessor(tokenizer=A_							, image_processor=A_			)

				UpperCamelCase            =	self.prepare_image_inputs()

				UpperCamelCase            =	image_processor(A_							, return_tensors='np'			)
				UpperCamelCase            =	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(1_234			)
				UpperCamelCase            =	image_processor(
				    A_							, return_image_mask=A_							, return_codebook_pixels=A_							, return_tensors='np'			)
				random.seed(1_234			)
				UpperCamelCase            =	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 __UpperCamelCase					(					self			)    ->   List[str]:
				"""simple docstring"""
				UpperCamelCase            =	self.get_image_processor()
				UpperCamelCase            =	self.get_tokenizer()

				UpperCamelCase            =	FlavaProcessor(tokenizer=A_							, image_processor=A_			)

				UpperCamelCase            =	'lower newer'

				UpperCamelCase            =	processor(text=A_			)

				UpperCamelCase            =	tokenizer(A_			)

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



		def __UpperCamelCase					(					self			)    ->   str:
				"""simple docstring"""
				UpperCamelCase            =	self.get_image_processor()
				UpperCamelCase            =	self.get_tokenizer()

				UpperCamelCase            =	FlavaProcessor(tokenizer=A_							, image_processor=A_			)

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

				UpperCamelCase            =	processor(text=A_							, images=A_			)

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

				# add extra args
				UpperCamelCase            =	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 __UpperCamelCase					(					self			)    ->   str:
				"""simple docstring"""
				UpperCamelCase            =	self.get_image_processor()
				UpperCamelCase            =	self.get_tokenizer()

				UpperCamelCase            =	FlavaProcessor(tokenizer=A_							, image_processor=A_			)

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

				UpperCamelCase            =	processor.batch_decode(A_			)
				UpperCamelCase            =	tokenizer.batch_decode(A_			)

				self.assertListEqual(A_							, A_			)



		def __UpperCamelCase					(					self			)    ->   int:
				"""simple docstring"""
				UpperCamelCase            =	self.get_image_processor()
				UpperCamelCase            =	self.get_tokenizer()

				UpperCamelCase            =	FlavaProcessor(tokenizer=A_							, image_processor=A_			)

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

				UpperCamelCase            =	processor(text=A_							, images=A_			)

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


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

import numpy as np
import PIL

from ...utils import BaseOutput, is_invisible_watermark_available, is_torch_available, is_transformers_available


@dataclass
class 							lowercase   (						_SCREAMING_SNAKE_CASE  ):
		__lowercase			:			Union[List[PIL.Image.Image], np.ndarray]


if is_transformers_available() and is_torch_available() and is_invisible_watermark_available():
					from .pipeline_stable_diffusion_xl import StableDiffusionXLPipeline
					from .pipeline_stable_diffusion_xl_imgaimg import StableDiffusionXLImgaImgPipeline
