





"""simple docstring"""






import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


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



class 					__UpperCAmelCase							(						_UpperCamelCase  ):
				def __init__(		self    :			Optional[Any]	, a_    :			AutoencoderKL	, a_    :			CLIPTextModel	, a_    :			CLIPTokenizer	, a_    :			UNetaDConditionModel	, a_    :			Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]	, a_    :			StableDiffusionSafetyChecker	, a_    :			CLIPImageProcessor	, )	->	List[Any]:



							'''simple docstring'''



							super().__init__()
							self.register_modules(
							    vae=a_	, text_encoder=a_	, tokenizer=a_	, unet=a_	, scheduler=a_	, safety_checker=a_	, feature_extractor=a_	, )
				def       UpperCAmelCase      (		self    :			Any	, a_    :			Optional[Union[str, int]] = "auto"    )	->	str:



							'''simple docstring'''



							if slice_size == "auto":
										# half the attention head size is usually a good trade-off between
										# speed and memory
										a__  :	Any		 =			self.unet.config.attention_head_dim // 2
							self.unet.set_attention_slice(a_    )
				def       UpperCAmelCase      (		self    :			Optional[int]    )	->	int:



							'''simple docstring'''



							self.enable_attention_slicing(a_    )






				@torch.no_grad()
				def __call__(		self    :			str	, a_    :			Union[str, List[str]]	, a_    :			int = 5_12	, a_    :			int = 5_12	, a_    :			int = 50	, a_    :			float = 7.5	, a_    :			Optional[Union[str, List[str]]] = None	, a_    :			Optional[int] = 1	, a_    :			float = 0.0	, a_    :			Optional[torch.Generator] = None	, a_    :			Optional[torch.FloatTensor] = None	, a_    :			Optional[str] = "pil"	, a_    :			bool = True	, a_    :			Optional[Callable[[int, int, torch.FloatTensor], None]] = None	, a_    :			int = 1	, a_    :			Optional[torch.FloatTensor] = None	, **a_    :			Dict	, )	->	str:



							'''simple docstring'''



							if isinstance(a_	, a_    ):
										a__  :	Union[str, Any]		 =			1
							elif isinstance(a_	, a_    ):
										a__  :	Any		 =			len(a_    )
							else:
										raise ValueError(F"`prompt` has to be of type `str` or `list` but is {type(a_    )}"    )

							if height % 8 != 0 or width % 8 != 0:
										raise ValueError(F"`height` and `width` have to be divisible by 8 but are {height} and {width}."    )

							if (callback_steps is None) or (
							    callback_steps is not None and (not isinstance(a_	, a_    ) or callback_steps <= 0)
							):
										raise ValueError(
										    F"`callback_steps` has to be a positive integer but is {callback_steps} of type"
										    F" {type(a_    )}."    )

							# get prompt text embeddings
							a__  :	Tuple		 =			self.tokenizer(
							    a_	, padding="max_length"	, max_length=self.tokenizer.model_max_length	, return_tensors="pt"	, )
							a__  :	Any		 =			text_inputs.input_ids

							if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
										a__  :	Union[str, Any]		 =			self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]    )
										logger.warning(
										    "The following part of your input was truncated because CLIP can only handle sequences up to"
										    F" {self.tokenizer.model_max_length} tokens: {removed_text}"    )
										a__  :	Optional[int]		 =			text_input_ids[:, : self.tokenizer.model_max_length]

							if text_embeddings is None:
										a__  :	Union[str, Any]		 =			self.text_encoder(text_input_ids.to(self.device    )    )[0]

							# duplicate text embeddings for each generation per prompt, using mps friendly method
							a__ ,	a__ ,	a__  :	int		 =			text_embeddings.shape
							a__  :	int		 =			text_embeddings.repeat(1	, a_	, 1    )
							a__  :	Optional[int]		 =			text_embeddings.view(bs_embed * num_images_per_prompt	, a_	, -1    )

							# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
							# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
							# corresponds to doing no classifier free guidance.
							a__  :	Optional[Any]		 =			guidance_scale > 1.0
							# get unconditional embeddings for classifier free guidance
							if do_classifier_free_guidance:
										a__  :	List[str]
										if negative_prompt is None:
													a__  :	Optional[Any]		 =			[""]
										elif type(a_    ) is not type(a_    ):
													raise TypeError(
													    F"`negative_prompt` should be the same type to `prompt`, but got {type(a_    )} !="
													    F" {type(a_    )}."    )
										elif isinstance(a_	, a_    ):
													a__  :	Optional[Any]		 =			[negative_prompt]
										elif batch_size != len(a_    ):
													raise ValueError(
													    F"`negative_prompt`: {negative_prompt} has batch size {len(a_    )}, but `prompt`:"
													    F" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
													    " the batch size of `prompt`."    )
										else:
													a__  :	Any		 =			negative_prompt

										a__  :	Union[str, Any]		 =			text_input_ids.shape[-1]
										a__  :	Optional[Any]		 =			self.tokenizer(
										    a_	, padding="max_length"	, max_length=a_	, truncation=a_	, return_tensors="pt"	, )
										a__  :	Tuple		 =			self.text_encoder(uncond_input.input_ids.to(self.device    )    )[0]

										# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
										a__  :	str		 =			uncond_embeddings.shape[1]
										a__  :	Tuple		 =			uncond_embeddings.repeat(a_	, a_	, 1    )
										a__  :	int		 =			uncond_embeddings.view(batch_size * num_images_per_prompt	, a_	, -1    )

										# For classifier free guidance, we need to do two forward passes.
										# Here we concatenate the unconditional and text embeddings into a single batch
										# to avoid doing two forward passes
										a__  :	Dict		 =			torch.cat([uncond_embeddings, text_embeddings]    )

							# get the initial random noise unless the user supplied it

							# Unlike in other pipelines, latents need to be generated in the target device
							# for 1-to-1 results reproducibility with the CompVis implementation.
							# However this currently doesn't work in `mps`.
							a__  :	Dict		 =			(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
							a__  :	Union[str, Any]		 =			(batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
							a__  :	Optional[int]		 =			text_embeddings.dtype
							if latents is None:
										if self.device.type == "mps":
													# randn does not exist on mps
													a__  :	List[str]		 =			torch.randn(
													    a_	, generator=a_	, device="cpu"	, dtype=a_    ).to(self.device    )
													a__  :	int		 =			torch.randn(a_	, generator=a_	, device="cpu"	, dtype=a_    ).to(
													    self.device    )
										else:
													a__  :	Optional[Any]		 =			torch.randn(
													    a_	, generator=a_	, device=self.device	, dtype=a_    )
													a__  :	List[Any]		 =			torch.randn(a_	, generator=a_	, device=self.device	, dtype=a_    )
							else:
										if latents_reference.shape != latents_shape:
													raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {latents_shape}"    )
										a__  :	int		 =			latents_reference.to(self.device    )
										a__  :	Tuple		 =			latents.to(self.device    )

							# This is the key part of the pipeline where we
							# try to ensure that the generated images w/ the same seed
							# but different sizes actually result in similar images
							a__  :	Dict		 =			(latents_shape[3] - latents_shape_reference[3]) // 2
							a__  :	Optional[Any]		 =			(latents_shape[2] - latents_shape_reference[2]) // 2
							a__  :	Tuple		 =			latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
							a__  :	str		 =			latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
							a__  :	Optional[Any]		 =			0 if dx < 0 else dx
							a__  :	Optional[int]		 =			0 if dy < 0 else dy
							a__  :	str		 =			max(-dx	, 0    )
							a__  :	List[str]		 =			max(-dy	, 0    )
							# import pdb
							# pdb.set_trace()
							a__  :	Tuple		 =			latents_reference[:, :, dy : dy + h, dx : dx + w]

							# set timesteps
							self.scheduler.set_timesteps(a_    )

							# Some schedulers like PNDM have timesteps as arrays
							# It's more optimized to move all timesteps to correct device beforehand
							a__  :	List[Any]		 =			self.scheduler.timesteps.to(self.device    )

							# scale the initial noise by the standard deviation required by the scheduler
							a__  :	List[str]		 =			latents * self.scheduler.init_noise_sigma

							# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
							# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
							# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
							# and should be between [0, 1]
							a__  :	List[Any]		 =			"eta" in set(inspect.signature(self.scheduler.step    ).parameters.keys()    )
							a__  :	str		 =			{}
							if accepts_eta:
										a__  :	Dict		 =			eta

							for i, t in enumerate(self.progress_bar(a_    )    ):
										# expand the latents if we are doing classifier free guidance
										a__  :	int		 =			torch.cat([latents] * 2    ) if do_classifier_free_guidance else latents
										a__  :	Any		 =			self.scheduler.scale_model_input(a_	, a_    )

										# predict the noise residual
										a__  :	Union[str, Any]		 =			self.unet(a_	, a_	, encoder_hidden_states=a_    ).sample

										# perform guidance
										if do_classifier_free_guidance:
													a__ ,	a__  :	Union[str, Any]		 =			noise_pred.chunk(2    )
													a__  :	Optional[int]		 =			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

										# compute the previous noisy sample x_t -> x_t-1
										a__  :	Any		 =			self.scheduler.step(a_	, a_	, a_	, **a_    ).prev_sample

										# call the callback, if provided
										if callback is not None and i % callback_steps == 0:
													callback(a_	, a_	, a_    )

							a__  :	Tuple		 =			1 / 0.1_8215 * latents
							a__  :	Union[str, Any]		 =			self.vae.decode(a_    ).sample

							a__  :	int		 =			(image / 2 + 0.5).clamp(0	, 1    )

							# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
							a__  :	Any		 =			image.cpu().permute(0	, 2	, 3	, 1    ).float().numpy()

							if self.safety_checker is not None:
										a__  :	Union[str, Any]		 =			self.feature_extractor(self.numpy_to_pil(a_    )	, return_tensors="pt"    ).to(
										    self.device    )
										a__ ,	a__  :	Dict		 =			self.safety_checker(
										    images=a_	, clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype    )    )
							else:
										a__  :	Optional[int]		 =			None

							if output_type == "pil":
										a__  :	Union[str, Any]		 =			self.numpy_to_pil(a_    )

							if not return_dict:
										return (image, has_nsfw_concept)

							return StableDiffusionPipelineOutput(images=a_	, nsfw_content_detected=a_    )






"""simple docstring"""






import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
		import torch


if is_vision_available():
		from PIL import Image

		from transformers import MgpstrProcessor, ViTImageProcessor



@require_torch
@require_vision
class 					__UpperCAmelCase							(						unittest.TestCase  ):
				__lowerCamelCase				:	int				     =				ViTImageProcessor if is_vision_available() else None
				@property
				def       UpperCAmelCase      (		self    :			List[str]    )	->	List[str]:



							'''simple docstring'''



							return self.image_processor_tester.prepare_image_processor_dict()
				def       UpperCAmelCase      (		self    :			Union[str, Any]    )	->	Optional[Any]:



							'''simple docstring'''



							a__  :	Optional[Any]		 =			(3, 32, 1_28)
							a__  :	Any		 =			tempfile.mkdtemp()

							# fmt: off
							a__  :	str		 =			["[GO]", "[s]", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z"]
							# fmt: on
							a__  :	List[Any]		 =			dict(zip(a_	, range(len(a_    )    )    )    )

							a__  :	int		 =			os.path.join(self.tmpdirname	, VOCAB_FILES_NAMES["vocab_file"]    )
							with open(self.vocab_file	, "w"	, encoding="utf-8"    ) as fp:
										fp.write(json.dumps(a_    ) + "\n"    )

							a__  :	Any		 =			{
							    "do_normalize": False,
							    "do_resize": True,
							    "image_processor_type": "ViTImageProcessor",
							    "resample": 3,
							    "size": {"height": 32, "width": 1_28},
							}
							a__  :	Tuple		 =			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    :			List[str]	, **a_    :			List[str]    )	->	Union[str, Any]:



							'''simple docstring'''



							return MgpstrTokenizer.from_pretrained(self.tmpdirname	, **a_    )
				def       UpperCAmelCase      (		self    :			List[Any]	, **a_    :			Union[str, Any]    )	->	Dict:



							'''simple docstring'''



							return ViTImageProcessor.from_pretrained(self.tmpdirname	, **a_    )
				def       UpperCAmelCase      (		self    :			Dict    )	->	int:



							'''simple docstring'''



							shutil.rmtree(self.tmpdirname    )
				def       UpperCAmelCase      (		self    :			List[str]    )	->	Any:



							'''simple docstring'''



							a__  :	Any		 =			np.random.randint(2_55	, size=(3, 30, 4_00)	, dtype=np.uinta    )

							a__  :	List[str]		 =			Image.fromarray(np.moveaxis(a_	, 0	, -1    )    )

							return image_input
				def       UpperCAmelCase      (		self    :			Union[str, Any]    )	->	Optional[Any]:



							'''simple docstring'''



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

							a__  :	Optional[int]		 =			MgpstrProcessor(tokenizer=a_	, image_processor=a_    )
							processor.save_pretrained(self.tmpdirname    )
							a__  :	Optional[Any]		 =			MgpstrProcessor.from_pretrained(self.tmpdirname	, use_fast=a_    )

							self.assertEqual(processor.char_tokenizer.get_vocab()	, tokenizer.get_vocab()    )
							self.assertIsInstance(processor.char_tokenizer	, a_    )

							self.assertEqual(processor.image_processor.to_json_string()	, image_processor.to_json_string()    )
							self.assertIsInstance(processor.image_processor	, a_    )
				def       UpperCAmelCase      (		self    :			Union[str, Any]    )	->	str:



							'''simple docstring'''



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

							a__  :	Optional[int]		 =			MgpstrProcessor(tokenizer=a_	, image_processor=a_    )
							processor.save_pretrained(self.tmpdirname    )

							a__  :	Union[str, Any]		 =			self.get_tokenizer(bos_token="(BOS)"	, eos_token="(EOS)"    )
							a__  :	Any		 =			self.get_image_processor(do_normalize=a_	, padding_value=1.0    )

							a__  :	Dict		 =			MgpstrProcessor.from_pretrained(
							    self.tmpdirname	, bos_token="(BOS)"	, eos_token="(EOS)"	, do_normalize=a_	, padding_value=1.0    )

							self.assertEqual(processor.char_tokenizer.get_vocab()	, tokenizer_add_kwargs.get_vocab()    )
							self.assertIsInstance(processor.char_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    :			Optional[int]    )	->	Dict:



							'''simple docstring'''



							a__  :	str		 =			self.get_image_processor()
							a__  :	Union[str, Any]		 =			self.get_tokenizer()

							a__  :	Any		 =			MgpstrProcessor(tokenizer=a_	, image_processor=a_    )

							a__  :	List[str]		 =			self.prepare_image_inputs()

							a__  :	List[Any]		 =			image_processor(a_	, return_tensors="np"    )
							a__  :	Optional[Any]		 =			processor(images=a_	, return_tensors="np"    )

							for key in input_image_proc.keys():
										self.assertAlmostEqual(input_image_proc[key].sum()	, input_processor[key].sum()	, delta=1E-2    )
				def       UpperCAmelCase      (		self    :			Tuple    )	->	int:



							'''simple docstring'''



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

							a__  :	int		 =			MgpstrProcessor(tokenizer=a_	, image_processor=a_    )

							a__  :	List[str]		 =			"test"

							a__  :	Any		 =			processor(text=a_    )

							a__  :	Tuple		 =			tokenizer(a_    )
							for key in encoded_tok.keys():
										self.assertListEqual(encoded_tok[key]	, encoded_processor[key]    )
				def       UpperCAmelCase      (		self    :			Optional[int]    )	->	Optional[int]:



							'''simple docstring'''



							a__  :	Tuple		 =			self.get_image_processor()
							a__  :	List[str]		 =			self.get_tokenizer()

							a__  :	List[Any]		 =			MgpstrProcessor(tokenizer=a_	, image_processor=a_    )

							a__  :	str		 =			"test"
							a__  :	str		 =			self.prepare_image_inputs()

							a__  :	Any		 =			processor(text=a_	, images=a_    )

							self.assertListEqual(list(inputs.keys()    )	, ["pixel_values", "labels"]    )

							# test if it raises when no input is passed
							with pytest.raises(a_    ):
										processor()
				def       UpperCAmelCase      (		self    :			List[str]    )	->	int:



							'''simple docstring'''



							a__  :	Any		 =			self.get_image_processor()
							a__  :	Tuple		 =			self.get_tokenizer()

							a__  :	Dict		 =			MgpstrProcessor(tokenizer=a_	, image_processor=a_    )

							a__  :	Optional[Any]		 =			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

							a__  :	Any		 =			processor.char_decode(a_    )
							a__  :	str		 =			tokenizer.batch_decode(a_    )
							a__  :	Union[str, Any]		 =			[seq.replace(" "	, ""    ) for seq in decoded_tok]

							self.assertListEqual(a_	, a_    )
				def       UpperCAmelCase      (		self    :			List[Any]    )	->	Dict:



							'''simple docstring'''



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

							a__  :	Any		 =			MgpstrProcessor(tokenizer=a_	, image_processor=a_    )

							a__  :	str		 =			None
							a__  :	Optional[Any]		 =			self.prepare_image_inputs()

							a__  :	Optional[int]		 =			processor(text=a_	, images=a_    )

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






				def       UpperCAmelCase      (		self    :			Optional[int]    )	->	List[Any]:



							'''simple docstring'''



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

							a__  :	Optional[Any]		 =			MgpstrProcessor(tokenizer=a_	, image_processor=a_    )

							a__  :	List[str]		 =			torch.randn(1	, 27	, 38    )
							a__  :	Tuple		 =			torch.randn(1	, 27	, 5_02_57    )
							a__  :	List[Any]		 =			torch.randn(1	, 27	, 3_05_22    )

							a__  :	Optional[Any]		 =			processor.batch_decode([char_input, bpe_input, wp_input]    )

							self.assertListEqual(list(results.keys()    )	, ["generated_text", "scores", "char_preds", "bpe_preds", "wp_preds"]    )