






from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)





class __lowerCamelCase					( snake_case__):





	"""simple docstring"""






	def __init__(				self			,       UpperCAmelCase			,       UpperCAmelCase			,       UpperCAmelCase			,       UpperCAmelCase			,       UpperCAmelCase			,       ):
							"""simple docstring"""







							super().__init__()

							self.register_modules(vae=UpperCAmelCase			,       text_encoder=UpperCAmelCase			,       tokenizer=UpperCAmelCase			,       unet=UpperCAmelCase			,       scheduler=UpperCAmelCase       )

	def 			UpperCamelCase       (				self			,       UpperCAmelCase       ):
							"""simple docstring"""







							with torch.no_grad():
													_UpperCAmelCase							   =     self.vae.encode(tfms.ToTensor()(UpperCAmelCase       ).unsqueeze(0       ).to(self.device       ) * 2 - 1       )
													_UpperCAmelCase							   =     0.1_82_15 * latent.latent_dist.sample()
							return latent

	def 			UpperCamelCase       (				self			,       UpperCAmelCase       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     (1 / 0.1_82_15) * latent
							with torch.no_grad():
													_UpperCAmelCase							   =     self.vae.decode(UpperCAmelCase       ).sample
							_UpperCAmelCase							   =     (img / 2 + 0.5).clamp(0			,       1       )
							_UpperCAmelCase							   =     img.detach().cpu().permute(0			,       2			,       3			,       1       ).numpy()
							_UpperCAmelCase							   =     (img * 255).round().astype('uint8'       )
							return Image.fromarray(img[0]       )

	def 			UpperCamelCase       (				self			,       UpperCAmelCase       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     self.tokenizer(
							    UpperCAmelCase			,       padding='max_length'			,       max_length=self.tokenizer.model_max_length			,       truncation=UpperCAmelCase			,       return_tensors='pt'			,       )

							_UpperCAmelCase							   =     self.text_encoder(text_input.input_ids.to(self.device       )       )[0]

							_UpperCAmelCase							   =     self.tokenizer(
							    ''			,       padding='max_length'			,       max_length=self.tokenizer.model_max_length			,       truncation=UpperCAmelCase			,       return_tensors='pt'			,       )

							_UpperCAmelCase							   =     self.text_encoder(uncond_input.input_ids.to(self.device       )       )[0]

							return torch.cat([uncond_embedding, text_embedding]       )

	def __call__(				self			,       UpperCAmelCase			,       UpperCAmelCase			,       UpperCAmelCase = 0.3			,       UpperCAmelCase = 0.6			,       UpperCAmelCase = 0.5			,       UpperCAmelCase = 42			,       UpperCAmelCase = 50			,       UpperCAmelCase = 7.5			,       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     steps - int(kmin * steps       )
							_UpperCAmelCase							   =     steps - int(kmax * steps       )

							_UpperCAmelCase							   =     self.prep_text(UpperCAmelCase       )

							self.scheduler.set_timesteps(UpperCAmelCase       )

							_UpperCAmelCase   ,					_UpperCAmelCase							   =     img.size
							_UpperCAmelCase							   =     self.encode(UpperCAmelCase       )

							torch.manual_seed(UpperCAmelCase       )
							_UpperCAmelCase							   =     torch.randn(
							    (1, self.unet.config.in_channels, height // 8, width // 8)			,       ).to(self.device       )

							_UpperCAmelCase							   =     self.scheduler.add_noise(
							    UpperCAmelCase			,       UpperCAmelCase			,       timesteps=self.scheduler.timesteps[tmax]			,       )

							_UpperCAmelCase							   =     torch.cat([latents] * 2       )

							_UpperCAmelCase							   =     self.scheduler.scale_model_input(UpperCAmelCase			,       self.scheduler.timesteps[tmax]       )

							with torch.no_grad():
													_UpperCAmelCase							   =     self.unet(
													    UpperCAmelCase			,       self.scheduler.timesteps[tmax]			,       encoder_hidden_states=UpperCAmelCase			,       ).sample

							_UpperCAmelCase   ,					_UpperCAmelCase							   =     pred.chunk(2       )
							_UpperCAmelCase							   =     pred_uncond + guidance_scale * (pred_text - pred_uncond)

							_UpperCAmelCase							   =     self.scheduler.step(UpperCAmelCase			,       self.scheduler.timesteps[tmax]			,       UpperCAmelCase       ).prev_sample

							for i, t in enumerate(tqdm(self.scheduler.timesteps       )       ):
													if i > tmax:
																			if i < tmin:  # layout generation phase
																									_UpperCAmelCase							   =     self.scheduler.add_noise(
																									    UpperCAmelCase			,       UpperCAmelCase			,       timesteps=UpperCAmelCase			,       )

																									_UpperCAmelCase							   =     (mix_factor * latents) + (
																									    1 - mix_factor
																									) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
																									_UpperCAmelCase							   =     torch.cat([input] * 2       )

																			else:  # content generation phase
																									_UpperCAmelCase							   =     torch.cat([latents] * 2       )

																			_UpperCAmelCase							   =     self.scheduler.scale_model_input(UpperCAmelCase			,       UpperCAmelCase       )

																			with torch.no_grad():
																									_UpperCAmelCase							   =     self.unet(
																									    UpperCAmelCase			,       UpperCAmelCase			,       encoder_hidden_states=UpperCAmelCase			,       ).sample

																			_UpperCAmelCase   ,					_UpperCAmelCase							   =     pred.chunk(2       )
																			_UpperCAmelCase							   =     pred_uncond + guidance_scale * (pred_text - pred_uncond)

																			_UpperCAmelCase							   =     self.scheduler.step(UpperCAmelCase			,       UpperCAmelCase			,       UpperCAmelCase       ).prev_sample

							return self.decode(UpperCAmelCase       )








import json
import os
import unittest

from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import (
    VOCAB_FILES_NAMES,
    GPTNeoXJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class __lowerCamelCase					( snake_case__	,			unittest.TestCase):





	"""simple docstring"""






	UpperCamelCase__								= GPTNeoXJapaneseTokenizer
	UpperCamelCase__								= False
	UpperCamelCase__								= {"do_clean_text": False, "add_prefix_space": False}

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							super().setUp()

							_UpperCAmelCase							   =     [
							    'こん',
							    'こんに',
							    'にちは',
							    'ばんは',
							    '世界,㔺界',
							    '、',
							    '。',
							    '<BR>',
							    '<SP>',
							    '<TAB>',
							    '<URL>',
							    '<EMAIL>',
							    '<TEL>',
							    '<DATE>',
							    '<PRICE>',
							    '<BLOCK>',
							    '<KIGOU>',
							    '<U2000U2BFF>',
							    '<|emoji1|>',
							    '<unk>',
							    '<|startoftext|>',
							    '<|endoftext|>',
							]
							_UpperCAmelCase							   =     {'emoji': {'\ud83d\ude00': '<|emoji1|>'}, 'emoji_inv': {'<|emoji1|>': '\ud83d\ude00'}}  # 😀
							_UpperCAmelCase							   =     {'unk_token': '<unk>'}

							_UpperCAmelCase							   =     os.path.join(self.tmpdirname			,       VOCAB_FILES_NAMES['vocab_file']       )
							_UpperCAmelCase							   =     os.path.join(self.tmpdirname			,       VOCAB_FILES_NAMES['emoji_file']       )
							with open(self.vocab_file			,       'w'			,       encoding='utf-8'       ) as vocab_writer:
													vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]       )       )
							with open(self.emoji_file			,       'w'       ) as emoji_writer:
													emoji_writer.write(json.dumps(UpperCAmelCase       )       )

	def 			UpperCamelCase       (				self			,       **UpperCAmelCase       ):
							"""simple docstring"""







							kwargs.update(self.special_tokens_map       )
							return GPTNeoXJapaneseTokenizer.from_pretrained(self.tmpdirname			,       **UpperCAmelCase       )

	def 			UpperCamelCase       (				self			,       UpperCAmelCase       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     'こんにちは、世界。 \nこんばんは、㔺界。😀'
							_UpperCAmelCase							   =     'こんにちは、世界。 \nこんばんは、世界。😀'
							return input_text, output_text

	def 			UpperCamelCase       (				self			,       UpperCAmelCase       ):
							"""simple docstring"""







							_UpperCAmelCase   ,					_UpperCAmelCase							   =     self.get_input_output_texts(UpperCAmelCase       )
							_UpperCAmelCase							   =     tokenizer.encode(UpperCAmelCase			,       add_special_tokens=UpperCAmelCase       )
							_UpperCAmelCase							   =     tokenizer.decode(UpperCAmelCase			,       clean_up_tokenization_spaces=UpperCAmelCase       )
							return text, ids

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							pass  # TODO add if relevant

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							pass  # TODO add if relevant

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							pass  # TODO add if relevant

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     self.get_tokenizer()

							# Testing tokenization
							_UpperCAmelCase							   =     'こんにちは、世界。　こんばんは、㔺界。'
							_UpperCAmelCase							   =     ['こん', 'にちは', '、', '世界', '。', '<SP>', 'こん', 'ばんは', '、', '㔺界', '。']
							_UpperCAmelCase							   =     tokenizer.tokenize(UpperCAmelCase       )
							self.assertListEqual(UpperCAmelCase			,       UpperCAmelCase       )

							# Testing conversion to ids without special tokens
							_UpperCAmelCase							   =     [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
							_UpperCAmelCase							   =     tokenizer.convert_tokens_to_ids(UpperCAmelCase       )
							self.assertListEqual(UpperCAmelCase			,       UpperCAmelCase       )

							# Testing conversion to ids with special tokens
							_UpperCAmelCase							   =     tokens + [tokenizer.unk_token]
							_UpperCAmelCase							   =     [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
							_UpperCAmelCase							   =     tokenizer.convert_tokens_to_ids(UpperCAmelCase       )
							self.assertListEqual(UpperCAmelCase			,       UpperCAmelCase       )

	@slow
	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     self.tokenizer_class.from_pretrained('abeja/gpt-neox-japanese-2.7b'       )

							_UpperCAmelCase							   =     tokenizer.encode('ありがとう。'			,       add_special_tokens=UpperCAmelCase       )
							_UpperCAmelCase							   =     tokenizer.encode('どういたしまして。'			,       add_special_tokens=UpperCAmelCase       )

							_UpperCAmelCase							   =     tokenizer.build_inputs_with_special_tokens(UpperCAmelCase       )
							_UpperCAmelCase							   =     tokenizer.build_inputs_with_special_tokens(UpperCAmelCase			,       UpperCAmelCase       )

							assert encoded_sentence == ids_a
							assert encoded_pair == ids_a + ids_a

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							pass

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							pass
