






import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class 						lowerCamelCase_				(      __snake_case			,				unittest.TestCase ):
				a__					       =  MgpstrTokenizer
				a__					       =  False
				a__					       =  {}
				a__					       =  False







				def 			A	(						self     ):
								"""simple docstring"""
								super().setUp()

								# fmt: off
								__magic_name__		:Any        =      ['''[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
								__magic_name__		:int        =      dict(zip(__UpperCamelCase       ,					range(len(__UpperCamelCase     )     )     )     )

								__magic_name__		:Optional[Any]        =      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(__UpperCamelCase     ) + '''\n'''     )







				def 			A	(						self       ,					**__lowerCAmelCase     ):
								"""simple docstring"""
								return MgpstrTokenizer.from_pretrained(self.tmpdirname       ,					**__UpperCamelCase     )







				def 			A	(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:Dict        =      '''tester'''
								__magic_name__		:int        =      '''tester'''
								return input_text, output_text







				@unittest.skip('''MGP-STR always lower cases letters.'''     )
				def 			A	(						self     ):
								"""simple docstring"""
								pass







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:int        =      self.get_tokenizers(do_lower_case=__UpperCamelCase     )
								for tokenizer in tokenizers:
												with self.subTest(F'''{tokenizer.__class__.__name__}'''     ):
																__magic_name__		:Union[str, Any]        =      '''[SPECIAL_TOKEN]'''

																tokenizer.add_special_tokens({'''cls_token''': special_token}     )
																__magic_name__		:Tuple        =      tokenizer.encode([special_token]       ,					add_special_tokens=__UpperCamelCase     )
																self.assertEqual(len(__UpperCamelCase     )       ,					1     )

																__magic_name__		:Union[str, Any]        =      tokenizer.decode(__UpperCamelCase       ,					skip_special_tokens=__UpperCamelCase     )
																self.assertTrue(special_token not in decoded     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Optional[int]        =      self.get_tokenizers()
								for tokenizer in tokenizers:
												with self.subTest(F'''{tokenizer.__class__.__name__}'''     ):
																__magic_name__     ,				__magic_name__		:Optional[Any]        =      self.get_input_output_texts(__UpperCamelCase     )

																__magic_name__		:Union[str, Any]        =      tokenizer.tokenize(__UpperCamelCase     )
																__magic_name__		:Any        =      tokenizer.convert_tokens_to_ids(__UpperCamelCase     )
																__magic_name__		:Optional[int]        =      tokenizer.encode(__UpperCamelCase       ,					add_special_tokens=__UpperCamelCase     )
																self.assertListEqual(__UpperCamelCase       ,					__UpperCamelCase     )

																__magic_name__		:Union[str, Any]        =      tokenizer.convert_ids_to_tokens(__UpperCamelCase     )
																self.assertNotEqual(len(__UpperCamelCase     )       ,					0     )
																__magic_name__		:Any        =      tokenizer.decode(__UpperCamelCase     )
																self.assertIsInstance(__UpperCamelCase       ,					__UpperCamelCase     )

																self.assertEqual(text_a.replace(''' '''       ,					''''''     )       ,					__UpperCamelCase     )







				@unittest.skip('''MGP-STR tokenizer only handles one sequence.'''     )
				def 			A	(						self     ):
								"""simple docstring"""
								pass







				@unittest.skip('''inputs cannot be pretokenized in MgpstrTokenizer'''     )
				def 			A	(						self     ):
								"""simple docstring"""
								pass








import argparse
import math
import os

import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel

def      __lowercase					( ):







				"""simple docstring"""






				__magic_name__		:Optional[int]        =      argparse.ArgumentParser()
				parser.add_argument(
				    '''-m''',  '''--pretrained_model_name_or_path''',  type=snake_case,  default=snake_case,  required=snake_case,  help='''Path to pretrained model or model identifier from huggingface.co/models.''',  )
				parser.add_argument(
				    '''-c''',  '''--caption''',  type=snake_case,  default='''robotic cat with wings''',  help='''Text used to generate images.''',  )
				parser.add_argument(
				    '''-n''',  '''--images_num''',  type=snake_case,  default=4,  help='''How much images to generate.''',  )
				parser.add_argument(
				    '''-s''',  '''--seed''',  type=snake_case,  default=4_2,  help='''Seed for random process.''',  )
				parser.add_argument(
				    '''-ci''',  '''--cuda_id''',  type=snake_case,  default=0,  help='''cuda_id.''',  )
				__magic_name__		:Optional[Any]        =      parser.parse_args()
				return args

def      __lowercase					( snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				if not len(snake_case ) == rows * cols:
								raise ValueError('''The specified number of rows and columns are not correct.''' )

				__magic_name__     ,				__magic_name__		:Tuple        =      imgs[0].size
				__magic_name__		:List[Any]        =      Image.new('''RGB''',  size=(cols * w, rows * h) )
				__magic_name__     ,				__magic_name__		:List[Any]        =      grid.size

				for i, img in enumerate(snake_case ):
								grid.paste(snake_case,  box=(i % cols * w, i // cols * h) )
				return grid

def      __lowercase					( snake_case,  snake_case="robotic cat with wings",  snake_case=7.5,  snake_case=5_0,  snake_case=1,  snake_case=4_2,  ):







				"""simple docstring"""






				__magic_name__		:List[Any]        =      torch.Generator(pipeline.device ).manual_seed(snake_case )
				__magic_name__		:str        =      pipeline(
				    snake_case,  guidance_scale=snake_case,  num_inference_steps=snake_case,  generator=snake_case,  num_images_per_prompt=snake_case,  ).images
				__magic_name__		:Tuple        =      int(math.sqrt(snake_case ) )
				__magic_name__		:Union[str, Any]        =      image_grid(snake_case,  rows=_rows,  cols=num_images_per_prompt // _rows )
				return grid, images


SCREAMING_SNAKE_CASE__      :			Any							=		parse_args()
# Load models and create wrapper for stable diffusion
SCREAMING_SNAKE_CASE__      :			int							=		CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="""tokenizer""")
SCREAMING_SNAKE_CASE__      :			str							=		CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="""text_encoder""")
SCREAMING_SNAKE_CASE__      :			Optional[Any]							=		AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="""vae""")
SCREAMING_SNAKE_CASE__      :			int							=		UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="""unet""")

SCREAMING_SNAKE_CASE__      :			Union[str, Any]							=		StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
SCREAMING_SNAKE_CASE__      :			str							=		lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, """best_model.pt""")):
					SCREAMING_SNAKE_CASE__      :			str							=		load(args.pretrained_model_name_or_path, model=unet)
					unet.eval()
					setattr(pipeline, """unet""", unet)
else:
					SCREAMING_SNAKE_CASE__      :			Dict							=		unet.to(torch.device("""cuda""", args.cuda_id))
SCREAMING_SNAKE_CASE__      :			Any							=		pipeline.to(unet.device)
SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      :			Dict							=		generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
grid.save(os.path.join(args.pretrained_model_name_or_path, """{}.png""".format("""_""".join(args.caption.split()))))
SCREAMING_SNAKE_CASE__      :			int							=		os.path.join(args.pretrained_model_name_or_path, """_""".join(args.caption.split()))
os.makedirs(dirname, exist_ok=True)
for idx, image in enumerate(images):
					image.save(os.path.join(dirname, """{}.png""".format(idx + 1)))






