
'''simple docstring'''

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 				A       (		unittest.TestCase      ):





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[Any]							):


				"""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 ,							__lowercase							)
				with open(self.image_processor_file ,							"w" ,							encoding="utf-8"							) as fp:
							json.dump(__lowercase ,							__lowercase							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[Any] ,							**__magic_name__     :    int							):


				"""simple docstring"""

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





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Any ,							**__magic_name__     :    List[str]							):


				"""simple docstring"""

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





	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[Any] ,							**__magic_name__     :    int							):


				"""simple docstring"""

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





	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[str]							):


				"""simple docstring"""

				shutil.rmtree(self.tmpdirname							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Tuple							):


				"""simple docstring"""

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

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

				return image_inputs





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Union[str, Any]							):


				"""simple docstring"""

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

				lowerCAmelCase__									=							FlavaProcessor(tokenizer=__lowercase ,							image_processor=__lowercase							)
				processor_slow.save_pretrained(self.tmpdirname							)
				lowerCAmelCase__									=							FlavaProcessor.from_pretrained(self.tmpdirname ,							use_fast=__lowercase							)

				lowerCAmelCase__									=							FlavaProcessor(tokenizer=__lowercase ,							image_processor=__lowercase							)
				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 ,							__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 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[int]							):


				"""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=__lowercase ,							padding_value=1.0							)

				lowerCAmelCase__									=							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 							__SCREAMING_SNAKE_CASE					(   self     :    Any							):


				"""simple docstring"""

				lowerCAmelCase__									=							self.get_image_processor()
				lowerCAmelCase__									=							self.get_tokenizer()

				lowerCAmelCase__									=							FlavaProcessor(tokenizer=__lowercase ,							image_processor=__lowercase							)

				lowerCAmelCase__									=							self.prepare_image_inputs()

				lowerCAmelCase__									=							image_processor(__lowercase ,							return_tensors="np"							)
				lowerCAmelCase__									=							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							)
				lowerCAmelCase__									=							image_processor(
				    __lowercase ,							return_image_mask=__lowercase ,							return_codebook_pixels=__lowercase ,							return_tensors="np"							)
				random.seed(1234							)
				lowerCAmelCase__									=							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 							__SCREAMING_SNAKE_CASE					(   self     :    str							):


				"""simple docstring"""

				lowerCAmelCase__									=							self.get_image_processor()
				lowerCAmelCase__									=							self.get_tokenizer()

				lowerCAmelCase__									=							FlavaProcessor(tokenizer=__lowercase ,							image_processor=__lowercase							)

				lowerCAmelCase__									=							"lower newer"

				lowerCAmelCase__									=							processor(text=__lowercase							)

				lowerCAmelCase__									=							tokenizer(__lowercase							)

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





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Any							):


				"""simple docstring"""

				lowerCAmelCase__									=							self.get_image_processor()
				lowerCAmelCase__									=							self.get_tokenizer()

				lowerCAmelCase__									=							FlavaProcessor(tokenizer=__lowercase ,							image_processor=__lowercase							)

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

				lowerCAmelCase__									=							processor(text=__lowercase ,							images=__lowercase							)

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

				# add extra args
				lowerCAmelCase__									=							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 							__SCREAMING_SNAKE_CASE					(   self     :    int							):


				"""simple docstring"""

				lowerCAmelCase__									=							self.get_image_processor()
				lowerCAmelCase__									=							self.get_tokenizer()

				lowerCAmelCase__									=							FlavaProcessor(tokenizer=__lowercase ,							image_processor=__lowercase							)

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

				lowerCAmelCase__									=							processor.batch_decode(__lowercase							)
				lowerCAmelCase__									=							tokenizer.batch_decode(__lowercase							)

				self.assertListEqual(__lowercase ,							__lowercase							)







	def 							__SCREAMING_SNAKE_CASE					(   self     :    Union[str, Any]							):


				"""simple docstring"""

				lowerCAmelCase__									=							self.get_image_processor()
				lowerCAmelCase__									=							self.get_tokenizer()

				lowerCAmelCase__									=							FlavaProcessor(tokenizer=__lowercase ,							image_processor=__lowercase							)

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

				lowerCAmelCase__									=							processor(text=__lowercase ,							images=__lowercase							)

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







'''simple docstring'''

from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


UpperCAmelCase__				:	Tuple					=						logging.get_logger(__name__)  # pylint: disable=invalid-name






class 				A       (		SCREAMING_SNAKE_CASE__      ):
	snake_case__ :Dict				       =       ['safety_checker', 'feature_extractor']





	def __init__(   self     :    Optional[Any] ,							__magic_name__     :    AutoencoderKL ,							__magic_name__     :    CLIPTextModel ,							__magic_name__     :    CLIPTokenizer ,							__magic_name__     :    UNetaDConditionModel ,							__magic_name__     :    Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler] ,							__magic_name__     :    StableDiffusionSafetyChecker ,							__magic_name__     :    CLIPImageProcessor ,							__magic_name__     :    bool = True ,							):


				"""simple docstring"""

				super().__init__()
				if hasattr(scheduler.config ,							"steps_offset"							) and scheduler.config.steps_offset != 1:
							lowerCAmelCase__									=							(
							    f"""The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"""
							    f""" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure """
							    "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
							    " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
							    " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
							    " file"
							)
							deprecate("steps_offset!=1" ,							"1.0.0" ,							__magic_name__ ,							standard_warn=__magic_name__							)
							lowerCAmelCase__									=							dict(scheduler.config							)
							lowerCAmelCase__									=							1
							lowerCAmelCase__									=							FrozenDict(__magic_name__							)

				self.register_modules(
				    vae=__magic_name__ ,							text_encoder=__magic_name__ ,							tokenizer=__magic_name__ ,							unet=__magic_name__ ,							scheduler=__magic_name__ ,							safety_checker=__magic_name__ ,							feature_extractor=__magic_name__ ,							)
				self.register_to_config(requires_safety_checker=__magic_name__							)





	@property
	def 							__SCREAMING_SNAKE_CASE					(   self     :    Union[str, Any]							):


				"""simple docstring"""

				return {k: getattr(self ,							__magic_name__							) for k in self.config.keys() if not k.startswith("_"							)}





	def 							__SCREAMING_SNAKE_CASE					(   self     :    int ,							__magic_name__     :    Optional[Union[str, int]] = "auto"							):


				"""simple docstring"""

				if slice_size == "auto":
							# half the attention head size is usually a good trade-off between
							# speed and memory
							lowerCAmelCase__									=							self.unet.config.attention_head_dim // 2
				self.unet.set_attention_slice(__magic_name__							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Union[str, Any]							):


				"""simple docstring"""

				self.enable_attention_slicing(__magic_name__							)





	@torch.no_grad()
	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[Any] ,							__magic_name__     :    Union[str, List[str]] ,							__magic_name__     :    Union[torch.FloatTensor, PIL.Image.Image] ,							__magic_name__     :    Union[torch.FloatTensor, PIL.Image.Image] ,							__magic_name__     :    float = 0.8 ,							__magic_name__     :    Optional[int] = 50 ,							__magic_name__     :    Optional[float] = 7.5 ,							__magic_name__     :    Optional[Union[str, List[str]]] = None ,							__magic_name__     :    Optional[int] = 1 ,							__magic_name__     :    Optional[float] = 0.0 ,							__magic_name__     :    Optional[torch.Generator] = None ,							__magic_name__     :    Optional[str] = "pil" ,							__magic_name__     :    bool = True ,							__magic_name__     :    Optional[Callable[[int, int, torch.FloatTensor], None]] = None ,							__magic_name__     :    int = 1 ,							):


				"""simple docstring"""

				return StableDiffusionInpaintPipelineLegacy(**self.components							)(
				    prompt=__magic_name__ ,							image=__magic_name__ ,							mask_image=__magic_name__ ,							strength=__magic_name__ ,							num_inference_steps=__magic_name__ ,							guidance_scale=__magic_name__ ,							negative_prompt=__magic_name__ ,							num_images_per_prompt=__magic_name__ ,							eta=__magic_name__ ,							generator=__magic_name__ ,							output_type=__magic_name__ ,							return_dict=__magic_name__ ,							callback=__magic_name__ ,							)





	@torch.no_grad()
	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[Any] ,							__magic_name__     :    Union[str, List[str]] ,							__magic_name__     :    Union[torch.FloatTensor, PIL.Image.Image] ,							__magic_name__     :    float = 0.8 ,							__magic_name__     :    Optional[int] = 50 ,							__magic_name__     :    Optional[float] = 7.5 ,							__magic_name__     :    Optional[Union[str, List[str]]] = None ,							__magic_name__     :    Optional[int] = 1 ,							__magic_name__     :    Optional[float] = 0.0 ,							__magic_name__     :    Optional[torch.Generator] = None ,							__magic_name__     :    Optional[str] = "pil" ,							__magic_name__     :    bool = True ,							__magic_name__     :    Optional[Callable[[int, int, torch.FloatTensor], None]] = None ,							__magic_name__     :    int = 1 ,							**__magic_name__     :    List[str] ,							):


				"""simple docstring"""

				return StableDiffusionImgaImgPipeline(**self.components							)(
				    prompt=__magic_name__ ,							image=__magic_name__ ,							strength=__magic_name__ ,							num_inference_steps=__magic_name__ ,							guidance_scale=__magic_name__ ,							negative_prompt=__magic_name__ ,							num_images_per_prompt=__magic_name__ ,							eta=__magic_name__ ,							generator=__magic_name__ ,							output_type=__magic_name__ ,							return_dict=__magic_name__ ,							callback=__magic_name__ ,							callback_steps=__magic_name__ ,							)







	@torch.no_grad()
	def 							__SCREAMING_SNAKE_CASE					(   self     :    Tuple ,							__magic_name__     :    Union[str, List[str]] ,							__magic_name__     :    int = 512 ,							__magic_name__     :    int = 512 ,							__magic_name__     :    int = 50 ,							__magic_name__     :    float = 7.5 ,							__magic_name__     :    Optional[Union[str, List[str]]] = None ,							__magic_name__     :    Optional[int] = 1 ,							__magic_name__     :    float = 0.0 ,							__magic_name__     :    Optional[torch.Generator] = None ,							__magic_name__     :    Optional[torch.FloatTensor] = None ,							__magic_name__     :    Optional[str] = "pil" ,							__magic_name__     :    bool = True ,							__magic_name__     :    Optional[Callable[[int, int, torch.FloatTensor], None]] = None ,							__magic_name__     :    int = 1 ,							):


				"""simple docstring"""

				return StableDiffusionPipeline(**self.components							)(
				    prompt=__magic_name__ ,							height=__magic_name__ ,							width=__magic_name__ ,							num_inference_steps=__magic_name__ ,							guidance_scale=__magic_name__ ,							negative_prompt=__magic_name__ ,							num_images_per_prompt=__magic_name__ ,							eta=__magic_name__ ,							generator=__magic_name__ ,							latents=__magic_name__ ,							output_type=__magic_name__ ,							return_dict=__magic_name__ ,							callback=__magic_name__ ,							callback_steps=__magic_name__ ,							)





