




'''simple docstring'''


import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class 					lowercase						(  __lowerCAmelCase,					unittest.TestCase ):
							_lowerCamelCase		:      Union[str, Any]=   RobertaTokenizer
							_lowerCamelCase		:      str=   RobertaTokenizerFast
							_lowerCamelCase		:      Dict=   True
							_lowerCamelCase		:      Optional[Any]=   {'''cls_token''': '''<s>'''}


							def 						_snake_case							(			self)							->				Union[str, Any]:
								super().setUp()

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

								UpperCAmelCase_		:      List[Any]       	=					os.path.join(self.tmpdirname    ,			VOCAB_FILES_NAMES['vocab_file'])
								UpperCAmelCase_		:      Dict       	=					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(lowerCAmelCase_) + '\n')
								with open(self.merges_file    ,			'w'    ,			encoding='utf-8') as fp:
									fp.write('\n'.join(lowerCAmelCase_))


							def 						_snake_case							(			self    ,			**_snake_case)							->				List[str]:
								kwargs.update(self.special_tokens_map)
								return self.tokenizer_class.from_pretrained(self.tmpdirname    ,			**lowerCAmelCase_)


							def 						_snake_case							(			self    ,			**_snake_case)							->				List[str]:
								kwargs.update(self.special_tokens_map)
								return RobertaTokenizerFast.from_pretrained(self.tmpdirname    ,			**lowerCAmelCase_)


							def 						_snake_case							(			self    ,			_snake_case)							->				List[str]:
								UpperCAmelCase_		:      int       	=					'lower newer'
								UpperCAmelCase_		:      Union[str, Any]       	=					'lower newer'
								return input_text, output_text


							def 						_snake_case							(			self)							->				Optional[Any]:
								UpperCAmelCase_		:      List[str]       	=					self.tokenizer_class(self.vocab_file    ,			self.merges_file    ,			**self.special_tokens_map)
								UpperCAmelCase_		:      Union[str, Any]       	=					'lower newer'
								UpperCAmelCase_		:      Dict       	=					['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
								UpperCAmelCase_		:      Tuple       	=					tokenizer.tokenize(lowerCAmelCase_)  # , add_prefix_space=True)
								self.assertListEqual(lowerCAmelCase_    ,			lowerCAmelCase_)

								UpperCAmelCase_		:      Tuple       	=					tokens + [tokenizer.unk_token]
								UpperCAmelCase_		:      Tuple       	=					[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase_)    ,			lowerCAmelCase_)


							def 						_snake_case							(			self)							->				Tuple:
								UpperCAmelCase_		:      List[str]       	=					self.get_tokenizer()

								self.assertListEqual(tokenizer.encode('Hello world!'    ,			add_special_tokens=lowerCAmelCase_)    ,			[0, 3_1414, 232, 328, 2])
								self.assertListEqual(
								    tokenizer.encode('Hello world! cécé herlolip 418'    ,			add_special_tokens=lowerCAmelCase_)    ,			[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]    ,			)


							@slow
							def 						_snake_case							(			self)							->				List[Any]:
								UpperCAmelCase_		:      int       	=					self.tokenizer_class.from_pretrained('roberta-base')

								UpperCAmelCase_		:      Optional[Any]       	=					tokenizer.encode('sequence builders'    ,			add_special_tokens=lowerCAmelCase_)
								UpperCAmelCase_		:      List[Any]       	=					tokenizer.encode('multi-sequence build'    ,			add_special_tokens=lowerCAmelCase_)

								UpperCAmelCase_		:      List[str]       	=					tokenizer.encode(
								    'sequence builders'    ,			add_special_tokens=lowerCAmelCase_    ,			add_prefix_space=lowerCAmelCase_)
								UpperCAmelCase_		:      Optional[int]       	=					tokenizer.encode(
								    'sequence builders'    ,			'multi-sequence build'    ,			add_special_tokens=lowerCAmelCase_    ,			add_prefix_space=lowerCAmelCase_)

								UpperCAmelCase_		:      Tuple       	=					tokenizer.build_inputs_with_special_tokens(lowerCAmelCase_)
								UpperCAmelCase_		:      Union[str, Any]       	=					tokenizer.build_inputs_with_special_tokens(lowerCAmelCase_    ,			lowerCAmelCase_)

								assert encoded_sentence == encoded_text_from_decode
								assert encoded_pair == encoded_pair_from_decode


							def 						_snake_case							(			self)							->				Any:
								UpperCAmelCase_		:      Optional[Any]       	=					self.get_tokenizer()

								UpperCAmelCase_		:      str       	=					'Encode this sequence.'
								UpperCAmelCase_		:      Optional[int]       	=					tokenizer.byte_encoder[' '.encode('utf-8')[0]]

								# Testing encoder arguments
								UpperCAmelCase_		:      Dict       	=					tokenizer.encode(lowerCAmelCase_    ,			add_special_tokens=lowerCAmelCase_    ,			add_prefix_space=lowerCAmelCase_)
								UpperCAmelCase_		:      str       	=					tokenizer.convert_ids_to_tokens(encoded[0])[0]
								self.assertNotEqual(lowerCAmelCase_    ,			lowerCAmelCase_)

								UpperCAmelCase_		:      int       	=					tokenizer.encode(lowerCAmelCase_    ,			add_special_tokens=lowerCAmelCase_    ,			add_prefix_space=lowerCAmelCase_)
								UpperCAmelCase_		:      int       	=					tokenizer.convert_ids_to_tokens(encoded[0])[0]
								self.assertEqual(lowerCAmelCase_    ,			lowerCAmelCase_)

								tokenizer.add_special_tokens({'bos_token': '<s>'})
								UpperCAmelCase_		:      Any       	=					tokenizer.encode(lowerCAmelCase_    ,			add_special_tokens=lowerCAmelCase_)
								UpperCAmelCase_		:      Any       	=					tokenizer.convert_ids_to_tokens(encoded[1])[0]
								self.assertNotEqual(lowerCAmelCase_    ,			lowerCAmelCase_)

								# Testing spaces after special tokens
								UpperCAmelCase_		:      Tuple       	=					'<mask>'
								tokenizer.add_special_tokens(
								    {'mask_token': AddedToken(lowerCAmelCase_    ,			lstrip=lowerCAmelCase_    ,			rstrip=lowerCAmelCase_)})  # mask token has a left space
								UpperCAmelCase_		:      int       	=					tokenizer.convert_tokens_to_ids(lowerCAmelCase_)

								UpperCAmelCase_		:      Optional[int]       	=					'Encode <mask> sequence'
								UpperCAmelCase_		:      Optional[int]       	=					'Encode <mask>sequence'

								UpperCAmelCase_		:      int       	=					tokenizer.encode(lowerCAmelCase_)
								UpperCAmelCase_		:      Any       	=					encoded.index(lowerCAmelCase_)
								UpperCAmelCase_		:      Dict       	=					tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1])[0]
								self.assertEqual(lowerCAmelCase_    ,			lowerCAmelCase_)

								UpperCAmelCase_		:      Optional[int]       	=					tokenizer.encode(lowerCAmelCase_)
								UpperCAmelCase_		:      List[str]       	=					encoded.index(lowerCAmelCase_)
								UpperCAmelCase_		:      Any       	=					tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1])[0]
								self.assertNotEqual(lowerCAmelCase_    ,			lowerCAmelCase_)


							def 						_snake_case							(			self)							->				List[str]:
								pass


							def 						_snake_case							(			self)							->				str:
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
										UpperCAmelCase_		:      List[Any]       	=					self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_    ,			**lowerCAmelCase_)
										UpperCAmelCase_		:      str       	=					self.tokenizer_class.from_pretrained(lowerCAmelCase_    ,			**lowerCAmelCase_)
										UpperCAmelCase_		:      Optional[Any]       	=					'A, <mask> AllenNLP sentence.'
										UpperCAmelCase_		:      List[str]       	=					tokenizer_r.encode_plus(lowerCAmelCase_    ,			add_special_tokens=lowerCAmelCase_    ,			return_token_type_ids=lowerCAmelCase_)
										UpperCAmelCase_		:      Any       	=					tokenizer_p.encode_plus(lowerCAmelCase_    ,			add_special_tokens=lowerCAmelCase_    ,			return_token_type_ids=lowerCAmelCase_)

										# token_type_ids should put 0 everywhere
										self.assertEqual(sum(tokens_r['token_type_ids'])    ,			sum(tokens_p['token_type_ids']))

										# attention_mask should put 1 everywhere, so sum over length should be 1
										self.assertEqual(
										    sum(tokens_r['attention_mask']) / len(tokens_r['attention_mask'])    ,			sum(tokens_p['attention_mask']) / len(tokens_p['attention_mask'])    ,			)

										UpperCAmelCase_		:      Dict       	=					tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids'])
										UpperCAmelCase_		:      int       	=					tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids'])

										# Rust correctly handles the space before the mask while python doesnt
										self.assertSequenceEqual(tokens_p['input_ids']    ,			[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2])
										self.assertSequenceEqual(tokens_r['input_ids']    ,			[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2])

										self.assertSequenceEqual(
										    lowerCAmelCase_    ,			['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'])
										self.assertSequenceEqual(
										    lowerCAmelCase_    ,			['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'])


							def 						_snake_case							(			self)							->				int:
								for trim_offsets, add_prefix_space in itertools.product([True, False]    ,			repeat=2):
									UpperCAmelCase_		:      List[str]       	=					self.rust_tokenizer_class.from_pretrained(
									    self.tmpdirname    ,			use_fast=lowerCAmelCase_    ,			add_prefix_space=lowerCAmelCase_    ,			trim_offsets=lowerCAmelCase_)

									UpperCAmelCase_		:      Union[str, Any]       	=					json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__())
									UpperCAmelCase_		:      str       	=					json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__())

									self.assertEqual(pre_tokenizer_state['add_prefix_space']    ,			lowerCAmelCase_)

									self.assertEqual(post_processor_state['add_prefix_space']    ,			lowerCAmelCase_)
									self.assertEqual(post_processor_state['trim_offsets']    ,			lowerCAmelCase_)





							def 						_snake_case							(			self)							->				Optional[Any]:
								# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
								# `trim_offsets`
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
										UpperCAmelCase_		:      Optional[int]       	=					'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
										UpperCAmelCase_		:      List[str]       	=					F"""{text_of_1_token} {text_of_1_token}"""

										UpperCAmelCase_		:      Optional[int]       	=					self.rust_tokenizer_class.from_pretrained(
										    lowerCAmelCase_    ,			use_fast=lowerCAmelCase_    ,			add_prefix_space=lowerCAmelCase_    ,			trim_offsets=lowerCAmelCase_)
										UpperCAmelCase_		:      Dict       	=					tokenizer_r(lowerCAmelCase_    ,			return_offsets_mapping=lowerCAmelCase_    ,			add_special_tokens=lowerCAmelCase_)
										self.assertEqual(encoding.offset_mapping[0]    ,			(0, len(lowerCAmelCase_)))
										self.assertEqual(
										    encoding.offset_mapping[1]    ,			(len(lowerCAmelCase_) + 1, len(lowerCAmelCase_) + 1 + len(lowerCAmelCase_))    ,			)

										UpperCAmelCase_		:      Union[str, Any]       	=					self.rust_tokenizer_class.from_pretrained(
										    lowerCAmelCase_    ,			use_fast=lowerCAmelCase_    ,			add_prefix_space=lowerCAmelCase_    ,			trim_offsets=lowerCAmelCase_)
										UpperCAmelCase_		:      int       	=					tokenizer_r(lowerCAmelCase_    ,			return_offsets_mapping=lowerCAmelCase_    ,			add_special_tokens=lowerCAmelCase_)
										self.assertEqual(encoding.offset_mapping[0]    ,			(0, len(lowerCAmelCase_)))
										self.assertEqual(
										    encoding.offset_mapping[1]    ,			(len(lowerCAmelCase_) + 1, len(lowerCAmelCase_) + 1 + len(lowerCAmelCase_))    ,			)

										UpperCAmelCase_		:      List[Any]       	=					self.rust_tokenizer_class.from_pretrained(
										    lowerCAmelCase_    ,			use_fast=lowerCAmelCase_    ,			add_prefix_space=lowerCAmelCase_    ,			trim_offsets=lowerCAmelCase_)
										UpperCAmelCase_		:      List[str]       	=					tokenizer_r(lowerCAmelCase_    ,			return_offsets_mapping=lowerCAmelCase_    ,			add_special_tokens=lowerCAmelCase_)
										self.assertEqual(encoding.offset_mapping[0]    ,			(0, len(lowerCAmelCase_)))
										self.assertEqual(
										    encoding.offset_mapping[1]    ,			(len(lowerCAmelCase_), len(lowerCAmelCase_) + 1 + len(lowerCAmelCase_))    ,			)

										UpperCAmelCase_		:      Optional[Any]       	=					self.rust_tokenizer_class.from_pretrained(
										    lowerCAmelCase_    ,			use_fast=lowerCAmelCase_    ,			add_prefix_space=lowerCAmelCase_    ,			trim_offsets=lowerCAmelCase_)
										UpperCAmelCase_		:      Optional[Any]       	=					tokenizer_r(lowerCAmelCase_    ,			return_offsets_mapping=lowerCAmelCase_    ,			add_special_tokens=lowerCAmelCase_)
										self.assertEqual(encoding.offset_mapping[0]    ,			(0, len(lowerCAmelCase_)))
										self.assertEqual(
										    encoding.offset_mapping[1]    ,			(len(lowerCAmelCase_), len(lowerCAmelCase_) + 1 + len(lowerCAmelCase_))    ,			)

										UpperCAmelCase_		:      Union[str, Any]       	=					F""" {text}"""

										# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
										#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
										# )
										# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
										# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
										# self.assertEqual(
										#     encoding.offset_mapping[1],
										#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
										# )

										UpperCAmelCase_		:      int       	=					self.rust_tokenizer_class.from_pretrained(
										    lowerCAmelCase_    ,			use_fast=lowerCAmelCase_    ,			add_prefix_space=lowerCAmelCase_    ,			trim_offsets=lowerCAmelCase_)
										UpperCAmelCase_		:      List[str]       	=					tokenizer_r(lowerCAmelCase_    ,			return_offsets_mapping=lowerCAmelCase_    ,			add_special_tokens=lowerCAmelCase_)
										self.assertEqual(encoding.offset_mapping[0]    ,			(1, 1 + len(lowerCAmelCase_)))
										self.assertEqual(
										    encoding.offset_mapping[1]    ,			(1 + len(lowerCAmelCase_) + 1, 1 + len(lowerCAmelCase_) + 1 + len(lowerCAmelCase_))    ,			)

										UpperCAmelCase_		:      Union[str, Any]       	=					self.rust_tokenizer_class.from_pretrained(
										    lowerCAmelCase_    ,			use_fast=lowerCAmelCase_    ,			add_prefix_space=lowerCAmelCase_    ,			trim_offsets=lowerCAmelCase_)
										UpperCAmelCase_		:      Optional[int]       	=					tokenizer_r(lowerCAmelCase_    ,			return_offsets_mapping=lowerCAmelCase_    ,			add_special_tokens=lowerCAmelCase_)
										self.assertEqual(encoding.offset_mapping[0]    ,			(0, 1 + len(lowerCAmelCase_)))
										self.assertEqual(
										    encoding.offset_mapping[1]    ,			(1 + len(lowerCAmelCase_), 1 + len(lowerCAmelCase_) + 1 + len(lowerCAmelCase_))    ,			)

										UpperCAmelCase_		:      str       	=					self.rust_tokenizer_class.from_pretrained(
										    lowerCAmelCase_    ,			use_fast=lowerCAmelCase_    ,			add_prefix_space=lowerCAmelCase_    ,			trim_offsets=lowerCAmelCase_)
										UpperCAmelCase_		:      List[Any]       	=					tokenizer_r(lowerCAmelCase_    ,			return_offsets_mapping=lowerCAmelCase_    ,			add_special_tokens=lowerCAmelCase_)
										self.assertEqual(encoding.offset_mapping[0]    ,			(0, 1 + len(lowerCAmelCase_)))
										self.assertEqual(
										    encoding.offset_mapping[1]    ,			(1 + len(lowerCAmelCase_), 1 + len(lowerCAmelCase_) + 1 + len(lowerCAmelCase_))    ,			)









'''simple docstring'''


import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
						lowerCAmelCase__										= argparse.ArgumentParser()

						parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")

						parser.add_argument(
						    "--txt2img_unclip",
						    default="kakaobrain/karlo-v1-alpha",
						    type=str,
						    required=False,
						    help="The pretrained txt2img unclip.",
						)

						lowerCAmelCase__										= parser.parse_args()

						lowerCAmelCase__										= UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

						lowerCAmelCase__										= CLIPImageProcessor()
						lowerCAmelCase__										= CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14")

						lowerCAmelCase__										= UnCLIPImageVariationPipeline(
						    decoder=txtaimg.decoder,
						    text_encoder=txtaimg.text_encoder,
						    tokenizer=txtaimg.tokenizer,
						    text_proj=txtaimg.text_proj,
						    feature_extractor=feature_extractor,
						    image_encoder=image_encoder,
						    super_res_first=txtaimg.super_res_first,
						    super_res_last=txtaimg.super_res_last,
						    decoder_scheduler=txtaimg.decoder_scheduler,
						    super_res_scheduler=txtaimg.super_res_scheduler,
						)

						imgaimg.save_pretrained(args.dump_path)






