


"""simple docstring"""







import json
import os
import unittest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class  UpperCamelCase_	( UpperCamelCase			,  unittest.TestCase):

	"""simple docstring"""





	snake_case__				:  Any    					=			CLIPTokenizer
	snake_case__				:  Dict    					=			CLIPTokenizerFast
	snake_case__				:  List[Any]    					=			True
	snake_case__				:  Optional[Any]    					=			{}
	snake_case__				:  Dict    					=			False




	def   UpperCAmelCase_      ( self			:				Any   )       ->	Any:
						super().setUp()

						# fmt: off
						__SCREAMING_SNAKE_CASE						   =				["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"]
						# fmt: on
						__SCREAMING_SNAKE_CASE						   =				dict(zip(UpperCAmelCase__			,			range(len(UpperCAmelCase__   )   )   )   )
						__SCREAMING_SNAKE_CASE						   =				["#version: 0.2", "l o", "lo w</w>", "e r</w>"]
						__SCREAMING_SNAKE_CASE						   =				{"unk_token": "<unk>"}

						__SCREAMING_SNAKE_CASE						   =				os.path.join(self.tmpdirname			,			VOCAB_FILES_NAMES["vocab_file"]   )
						__SCREAMING_SNAKE_CASE						   =				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   UpperCAmelCase_      ( self			:				List[Any]			,			**UpperCAmelCase__			:				Tuple   )       ->	List[Any]:
						kwargs.update(self.special_tokens_map   )
						return CLIPTokenizer.from_pretrained(self.tmpdirname			,			**UpperCAmelCase__   )




	def   UpperCAmelCase_      ( self			:				Any			,			**UpperCAmelCase__			:				Optional[Any]   )       ->	List[str]:
						kwargs.update(self.special_tokens_map   )
						return CLIPTokenizerFast.from_pretrained(self.tmpdirname			,			**UpperCAmelCase__   )




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




	def   UpperCAmelCase_      ( self			:				int   )       ->	List[str]:
						__SCREAMING_SNAKE_CASE						   =				CLIPTokenizer(self.vocab_file			,			self.merges_file			,			**self.special_tokens_map   )
						__SCREAMING_SNAKE_CASE						   =				"lower newer"
						__SCREAMING_SNAKE_CASE						   =				["lo", "w", "er</w>", "n", "e", "w", "er</w>"]
						__SCREAMING_SNAKE_CASE						   =				tokenizer.tokenize(UpperCAmelCase__   )
						self.assertListEqual(UpperCAmelCase__			,			UpperCAmelCase__   )

						__SCREAMING_SNAKE_CASE						   =				tokens + [tokenizer.unk_token]
						__SCREAMING_SNAKE_CASE						   =				[1_0, 2, 1_6, 9, 3, 2, 1_6, 2_0]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase__   )			,			UpperCAmelCase__   )




	@require_ftfy
	def   UpperCAmelCase_      ( self			:				Optional[Any]   )       ->	int:
						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""   ):
																__SCREAMING_SNAKE_CASE						   =				self.tokenizer_class.from_pretrained(UpperCAmelCase__			,			**UpperCAmelCase__   )
																__SCREAMING_SNAKE_CASE						   =				self.rust_tokenizer_class.from_pretrained(UpperCAmelCase__			,			**UpperCAmelCase__   )

																__SCREAMING_SNAKE_CASE						   =				"A\n'll 11p223RF☆ho!!to?'d'd''d of a cat to-$''d."
																__SCREAMING_SNAKE_CASE						   =				tokenizer_s.tokenize(UpperCAmelCase__   )
																__SCREAMING_SNAKE_CASE						   =				tokenizer_r.tokenize(UpperCAmelCase__   )

																self.assertListEqual(UpperCAmelCase__			,			UpperCAmelCase__   )

																# Test that the tokenization is identical on an example containing a character (Latin Small Letter A
																# with Tilde) encoded in 2 different ways
																__SCREAMING_SNAKE_CASE						   =				"xa\u0303y" + " " + "x\xe3y"
																__SCREAMING_SNAKE_CASE						   =				tokenizer_s.tokenize(UpperCAmelCase__   )
																__SCREAMING_SNAKE_CASE						   =				tokenizer_r.tokenize(UpperCAmelCase__   )

																self.assertListEqual(UpperCAmelCase__			,			UpperCAmelCase__   )

																# Test that the tokenization is identical on unicode of space type
																__SCREAMING_SNAKE_CASE						   =				[
																    "\u0009",  # (horizontal tab, '\t')
																    "\u000B",  # (vertical tab)
																    "\u000C",  # (form feed)
																    "\u0020",  # (space, ' ')
																    "\u200E",  # (left-to-right mark):w
																    "\u200F",  # (right-to-left mark)
																]
																for unicode_seq in spaces_unicodes:
																					__SCREAMING_SNAKE_CASE						   =				tokenizer_s.tokenize(UpperCAmelCase__   )
																					__SCREAMING_SNAKE_CASE						   =				tokenizer_r.tokenize(UpperCAmelCase__   )

																					self.assertListEqual(UpperCAmelCase__			,			UpperCAmelCase__   )

																# Test that the tokenization is identical on unicode of line break type
																__SCREAMING_SNAKE_CASE						   =				[
																    "\u000A",  # (line feed, '\n')
																    "\r\n",  # (carriage return and line feed, '\r\n')
																    "\u000D",  # (carriage return, '\r')
																    "\r",  # (carriage return, '\r')
																    "\u000D",  # (carriage return, '\r')
																    "\u2028",  # (line separator)
																    "\u2029",  # (paragraph separator)
																    # "\u0085", # (next line)
																]

																# The tokenization is not identical for the character "\u0085" (next line). The slow version using ftfy transforms
																# it into the Horizontal Ellipsis character "…" ("\u2026") while the fast version transforms it into a
																# space (and thus into an empty list).

																for unicode_seq in line_break_unicodes:
																					__SCREAMING_SNAKE_CASE						   =				tokenizer_s.tokenize(UpperCAmelCase__   )
																					__SCREAMING_SNAKE_CASE						   =				tokenizer_r.tokenize(UpperCAmelCase__   )

																					self.assertListEqual(UpperCAmelCase__			,			UpperCAmelCase__   )




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

																__SCREAMING_SNAKE_CASE						   =				self.rust_tokenizer_class.from_pretrained(
																    UpperCAmelCase__			,			use_fast=UpperCAmelCase__			,			)
																__SCREAMING_SNAKE_CASE						   =				tokenizer_r(UpperCAmelCase__			,			return_offsets_mapping=UpperCAmelCase__			,			add_special_tokens=UpperCAmelCase__   )
																self.assertEqual(encoding.offset_mapping[0]			,			(0, len(UpperCAmelCase__   ))   )
																self.assertEqual(
																    encoding.offset_mapping[1]			,			(len(UpperCAmelCase__   ) + 1, len(UpperCAmelCase__   ) + 1 + len(UpperCAmelCase__   ))			,			)

																__SCREAMING_SNAKE_CASE						   =				F""" {text}"""

																__SCREAMING_SNAKE_CASE						   =				self.rust_tokenizer_class.from_pretrained(
																    UpperCAmelCase__			,			use_fast=UpperCAmelCase__			,			)
																__SCREAMING_SNAKE_CASE						   =				tokenizer_r(UpperCAmelCase__			,			return_offsets_mapping=UpperCAmelCase__			,			add_special_tokens=UpperCAmelCase__   )
																self.assertEqual(encoding.offset_mapping[0]			,			(1, 1 + len(UpperCAmelCase__   ))   )
																self.assertEqual(
																    encoding.offset_mapping[1]			,			(1 + len(UpperCAmelCase__   ) + 1, 1 + len(UpperCAmelCase__   ) + 1 + len(UpperCAmelCase__   ))			,			)




	def   UpperCAmelCase_      ( self			:				str   )       ->	Optional[int]:
						# Test related to the breaking change introduced in transformers v4.17.0
						# We need to check that an error in raised when the user try to load a previous version of the tokenizer.
						with self.assertRaises(UpperCAmelCase__   ) as context:
											self.rust_tokenizer_class.from_pretrained("robot-test/old-clip-tokenizer"   )

						self.assertTrue(
						    context.exception.args[0].startswith(
						        "The `backend_tokenizer` provided does not match the expected format."   )   )




	@require_ftfy
	def   UpperCAmelCase_      ( self			:				Optional[int]   )       ->	int:
						super().test_tokenization_python_rust_equals()




	def   UpperCAmelCase_      ( self			:				Optional[int]   )       ->	Optional[Any]:
						# CLIP always lower cases letters
						pass



'''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)

