






import json
import os
import re
import unittest

from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class 						lowerCamelCase_				(      snake_case__			,				unittest.TestCase ):
				a__					       =  CodeGenTokenizer
				a__					       =  CodeGenTokenizerFast
				a__					       =  True
				a__					       =  {"""add_prefix_space""": True}
				a__					       =  False







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

								# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
								__magic_name__		:Union[str, Any]        =      [
								    '''l''',
								    '''o''',
								    '''w''',
								    '''e''',
								    '''r''',
								    '''s''',
								    '''t''',
								    '''i''',
								    '''d''',
								    '''n''',
								    '''\u0120''',
								    '''\u0120l''',
								    '''\u0120n''',
								    '''\u0120lo''',
								    '''\u0120low''',
								    '''er''',
								    '''\u0120lowest''',
								    '''\u0120newer''',
								    '''\u0120wider''',
								    '''<unk>''',
								    '''<|endoftext|>''',
								]
								__magic_name__		:List[Any]        =      dict(zip(UpperCAmelCase_       ,					range(len(UpperCAmelCase_     )     )     )     )
								__magic_name__		:Dict        =      ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
								__magic_name__		:Dict        =      {'''unk_token''': '''<unk>'''}

								__magic_name__		:Optional[int]        =      os.path.join(self.tmpdirname       ,					VOCAB_FILES_NAMES['''vocab_file''']     )
								__magic_name__		:List[str]        =      os.path.join(self.tmpdirname       ,					VOCAB_FILES_NAMES['''merges_file''']     )
								with open(self.vocab_file       ,					'''w'''       ,					encoding='''utf-8'''     ) as fp:
												fp.write(json.dumps(UpperCAmelCase_     ) + '''\n'''     )
								with open(self.merges_file       ,					'''w'''       ,					encoding='''utf-8'''     ) as fp:
												fp.write('''\n'''.join(UpperCAmelCase_     )     )







				def 			A	(						self       ,					**__lowerCAmelCase     ):
								"""simple docstring"""
								kwargs.update(self.special_tokens_map     )
								return CodeGenTokenizer.from_pretrained(self.tmpdirname       ,					**UpperCAmelCase_     )







				def 			A	(						self       ,					**__lowerCAmelCase     ):
								"""simple docstring"""
								kwargs.update(self.special_tokens_map     )
								return CodeGenTokenizerFast.from_pretrained(self.tmpdirname       ,					**UpperCAmelCase_     )







				def 			A	(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:Any        =      '''lower newer'''
								__magic_name__		:List[Any]        =      '''lower newer'''
								return input_text, output_text







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Dict        =      CodeGenTokenizer(self.vocab_file       ,					self.merges_file       ,					**self.special_tokens_map     )
								__magic_name__		:List[Any]        =      '''lower newer'''
								__magic_name__		:int        =      ['''\u0120low''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
								__magic_name__		:Dict        =      tokenizer.tokenize(UpperCAmelCase_       ,					add_prefix_space=UpperCAmelCase_     )
								self.assertListEqual(UpperCAmelCase_       ,					UpperCAmelCase_     )

								__magic_name__		:int        =      tokens + [tokenizer.unk_token]
								__magic_name__		:List[str]        =      [1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_     )       ,					UpperCAmelCase_     )







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



								if not self.test_rust_tokenizer:
												return

								__magic_name__		:int        =      self.get_tokenizer()
								__magic_name__		:Any        =      self.get_rust_tokenizer(add_prefix_space=UpperCAmelCase_     )

								__magic_name__		:Optional[int]        =      '''lower newer'''

								# Testing tokenization
								__magic_name__		:Tuple        =      tokenizer.tokenize(UpperCAmelCase_       ,					add_prefix_space=UpperCAmelCase_     )
								__magic_name__		:Optional[Any]        =      rust_tokenizer.tokenize(UpperCAmelCase_     )
								self.assertListEqual(UpperCAmelCase_       ,					UpperCAmelCase_     )

								# Testing conversion to ids without special tokens
								__magic_name__		:List[str]        =      tokenizer.encode(UpperCAmelCase_       ,					add_special_tokens=UpperCAmelCase_       ,					add_prefix_space=UpperCAmelCase_     )
								__magic_name__		:List[str]        =      rust_tokenizer.encode(UpperCAmelCase_       ,					add_special_tokens=UpperCAmelCase_     )
								self.assertListEqual(UpperCAmelCase_       ,					UpperCAmelCase_     )

								# Testing conversion to ids with special tokens
								__magic_name__		:Any        =      self.get_rust_tokenizer(add_prefix_space=UpperCAmelCase_     )
								__magic_name__		:int        =      tokenizer.encode(UpperCAmelCase_       ,					add_prefix_space=UpperCAmelCase_     )
								__magic_name__		:List[Any]        =      rust_tokenizer.encode(UpperCAmelCase_     )
								self.assertListEqual(UpperCAmelCase_       ,					UpperCAmelCase_     )

								# Testing the unknown token
								__magic_name__		:Optional[int]        =      tokens + [rust_tokenizer.unk_token]
								__magic_name__		:List[str]        =      [1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
								self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(UpperCAmelCase_     )       ,					UpperCAmelCase_     )







				def 			A	(						self       ,					*__lowerCAmelCase       ,					**__lowerCAmelCase     ):
								"""simple docstring"""
								pass







				def 			A	(						self       ,					__lowerCAmelCase=1_5     ):
								"""simple docstring"""



								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
												with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''     ):
																__magic_name__		:Optional[int]        =      self.rust_tokenizer_class.from_pretrained(UpperCAmelCase_       ,					**UpperCAmelCase_     )

																# Simple input
																__magic_name__		:str        =      '''This is a simple input'''
																__magic_name__		:List[Any]        =      ['''This is a simple input 1''', '''This is a simple input 2''']
																__magic_name__		:Dict        =      ('''This is a simple input''', '''This is a pair''')
																__magic_name__		:List[str]        =      [
																    ('''This is a simple input 1''', '''This is a simple input 2'''),
																    ('''This is a simple pair 1''', '''This is a simple pair 2'''),
																]

																# Simple input tests
																self.assertRaises(UpperCAmelCase_       ,					tokenizer_r.encode       ,					UpperCAmelCase_       ,					max_length=UpperCAmelCase_       ,					padding='''max_length'''     )

																# Simple input
																self.assertRaises(UpperCAmelCase_       ,					tokenizer_r.encode_plus       ,					UpperCAmelCase_       ,					max_length=UpperCAmelCase_       ,					padding='''max_length'''     )

																# Simple input
																self.assertRaises(
																    UpperCAmelCase_       ,					tokenizer_r.batch_encode_plus       ,					UpperCAmelCase_       ,					max_length=UpperCAmelCase_       ,					padding='''max_length'''       ,					)

																# Pair input
																self.assertRaises(UpperCAmelCase_       ,					tokenizer_r.encode       ,					UpperCAmelCase_       ,					max_length=UpperCAmelCase_       ,					padding='''max_length'''     )

																# Pair input
																self.assertRaises(UpperCAmelCase_       ,					tokenizer_r.encode_plus       ,					UpperCAmelCase_       ,					max_length=UpperCAmelCase_       ,					padding='''max_length'''     )

																# Pair input
																self.assertRaises(
																    UpperCAmelCase_       ,					tokenizer_r.batch_encode_plus       ,					UpperCAmelCase_       ,					max_length=UpperCAmelCase_       ,					padding='''max_length'''       ,					)







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:List[str]        =      CodeGenTokenizer.from_pretrained(self.tmpdirname       ,					pad_token='''<pad>'''     )

								# Simple input
								__magic_name__		:str        =      '''This is a simple input'''
								__magic_name__		:Any        =      ['''This is a simple input looooooooong''', '''This is a simple input''']
								__magic_name__		:str        =      ('''This is a simple input''', '''This is a pair''')
								__magic_name__		:int        =      [
								    ('''This is a simple input loooooong''', '''This is a simple input'''),
								    ('''This is a simple pair loooooong''', '''This is a simple pair'''),
								]

								__magic_name__		:str        =      tokenizer.pad_token_id

								__magic_name__		:Dict        =      tokenizer(UpperCAmelCase_       ,					padding='''max_length'''       ,					max_length=3_0       ,					return_tensors='''np'''     )
								__magic_name__		:Optional[int]        =      tokenizer(UpperCAmelCase_       ,					padding=UpperCAmelCase_       ,					truncate=UpperCAmelCase_       ,					return_tensors='''np'''     )
								__magic_name__		:int        =      tokenizer(*UpperCAmelCase_       ,					padding='''max_length'''       ,					max_length=6_0       ,					return_tensors='''np'''     )
								__magic_name__		:Union[str, Any]        =      tokenizer(UpperCAmelCase_       ,					padding=UpperCAmelCase_       ,					truncate=UpperCAmelCase_       ,					return_tensors='''np'''     )

								# s
								# test single string max_length padding
								self.assertEqual(out_s['''input_ids'''].shape[-1]       ,					3_0     )
								self.assertTrue(pad_token_id in out_s['''input_ids''']     )
								self.assertTrue(0 in out_s['''attention_mask''']     )

								# s2
								# test automatic padding
								self.assertEqual(out_sa['''input_ids'''].shape[-1]       ,					3_3     )
								# long slice doesn't have padding
								self.assertFalse(pad_token_id in out_sa['''input_ids'''][0]     )
								self.assertFalse(0 in out_sa['''attention_mask'''][0]     )
								# short slice does have padding
								self.assertTrue(pad_token_id in out_sa['''input_ids'''][1]     )
								self.assertTrue(0 in out_sa['''attention_mask'''][1]     )

								# p
								# test single pair max_length padding
								self.assertEqual(out_p['''input_ids'''].shape[-1]       ,					6_0     )
								self.assertTrue(pad_token_id in out_p['''input_ids''']     )
								self.assertTrue(0 in out_p['''attention_mask''']     )

								# p2
								# test automatic padding pair
								self.assertEqual(out_pa['''input_ids'''].shape[-1]       ,					5_2     )
								# long slice pair doesn't have padding
								self.assertFalse(pad_token_id in out_pa['''input_ids'''][0]     )
								self.assertFalse(0 in out_pa['''attention_mask'''][0]     )
								# short slice pair does have padding
								self.assertTrue(pad_token_id in out_pa['''input_ids'''][1]     )
								self.assertTrue(0 in out_pa['''attention_mask'''][1]     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:int        =      '''$$$'''
								__magic_name__		:Tuple        =      CodeGenTokenizer.from_pretrained(self.tmpdirname       ,					bos_token=UpperCAmelCase_       ,					add_bos_token=UpperCAmelCase_     )

								__magic_name__		:Optional[Any]        =      '''This is a simple input'''
								__magic_name__		:Optional[Any]        =      ['''This is a simple input 1''', '''This is a simple input 2''']

								__magic_name__		:Dict        =      tokenizer.bos_token_id

								__magic_name__		:int        =      tokenizer(UpperCAmelCase_     )
								__magic_name__		:List[str]        =      tokenizer(UpperCAmelCase_     )

								self.assertEqual(out_s.input_ids[0]       ,					UpperCAmelCase_     )
								self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids     )     )

								__magic_name__		:Dict        =      tokenizer.decode(out_s.input_ids     )
								__magic_name__		:Dict        =      tokenizer.batch_decode(out_sa.input_ids     )

								self.assertEqual(decode_s.split()[0]       ,					UpperCAmelCase_     )
								self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa     )     )







				@slow
				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]        =      CodeGenTokenizer.from_pretrained('''Salesforce/codegen-350M-mono'''     )

								__magic_name__		:int        =      '''\nif len_a > len_b:\n    result = a\nelse:\n    result = b\n\n\n\n#'''
								__magic_name__		:Dict        =      '''\nif len_a > len_b:      result = a\nelse:      result = b'''

								__magic_name__		:Optional[int]        =      tokenizer.encode(UpperCAmelCase_     )
								__magic_name__		:str        =      ['''^#''', re.escape('''<|endoftext|>'''     ), '''^\'\'\'''', '''^\"\"\"''', '''\n\n\n''']
								__magic_name__		:Tuple        =      tokenizer.decode(UpperCAmelCase_       ,					truncate_before_pattern=UpperCAmelCase_     )
								self.assertEqual(UpperCAmelCase_       ,					UpperCAmelCase_     )







				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)))






