






import json
import os
import unittest

from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
    VOCAB_FILES_NAMES,
    GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class 				_UpperCamelCase      (a__				,		unittest.TestCase      ):
      snake_case_	=				GPTSanJapaneseTokenizer
      snake_case_	=				False
      snake_case_	=				{"""do_clean_text""": False, """add_prefix_space""": False}


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

           # fmt: off
           __lowerCAmelCase             =       ["""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>""", """<|bagoftoken|>""", """<|endoftext|>"""]
           # fmt: on
           __lowerCAmelCase             =       {"""emoji""": {"""\ud83d\ude00""": """<|emoji1|>"""}, """emoji_inv""": {"""<|emoji1|>""": """\ud83d\ude00"""}}  # 😀
           __lowerCAmelCase             =       {"""unk_token""": """<unk>"""}

           __lowerCAmelCase             =       os.path.join(self.tmpdirname       ,    VOCAB_FILES_NAMES["vocab_file"]						)
           __lowerCAmelCase             =       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(__a						)						)


      def 			__UpperCAmelCase		(						self       ,    **__UpperCamelCase						)->						Dict:
           kwargs.update(self.special_tokens_map						)
           return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname       ,    **__a						)


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase						)->						List[str]:
           __lowerCAmelCase             =       """こんにちは、世界。 \nこんばんは、㔺界。😀"""
           __lowerCAmelCase             =       """こんにちは、世界。 \nこんばんは、世界。😀"""
           return input_text, output_text


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase						)->						Union[str, Any]:
           __lowerCAmelCase             =       self.get_input_output_texts(__a						)
           __lowerCAmelCase             =       tokenizer.encode(__a       ,    add_special_tokens=__a						)
           __lowerCAmelCase             =       tokenizer.decode(__a       ,    clean_up_tokenization_spaces=__a						)
           return text, ids


      def 			__UpperCAmelCase		(						self						)->						Union[str, Any]:
           pass  # TODO add if relevant


      def 			__UpperCAmelCase		(						self						)->						str:
           pass  # TODO add if relevant


      def 			__UpperCAmelCase		(						self						)->						int:
           pass  # TODO add if relevant


      def 			__UpperCAmelCase		(						self						)->						Dict:
           __lowerCAmelCase             =       self.get_tokenizer()

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

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

           # Testing conversion to ids with special tokens
           __lowerCAmelCase             =       tokens + [tokenizer.unk_token]
           __lowerCAmelCase             =       [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 1_9]
           __lowerCAmelCase             =       tokenizer.convert_tokens_to_ids(__a						)
           self.assertListEqual(__a       ,    __a						)


      def 			__UpperCAmelCase		(						self						)->						Optional[int]:
           __lowerCAmelCase             =       self.get_tokenizer()

           # Testing tokenization
           __lowerCAmelCase             =       """こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。"""
           __lowerCAmelCase             =       """こんにちは、、、、世界。こんばんは、、、、世界。"""
           __lowerCAmelCase             =       tokenizer.encode(__a						)
           __lowerCAmelCase             =       tokenizer.decode(__a						)
           self.assertEqual(__a       ,    __a						)


      @slow
      def 			__UpperCAmelCase		(						self						)->						List[Any]:
           __lowerCAmelCase             =       self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese"						)

           # Testing tokenization
           __lowerCAmelCase             =       """こんにちは、世界。"""
           __lowerCAmelCase             =       """こんばんは、㔺界。😀"""
           __lowerCAmelCase             =       """こんにちは、世界。こんばんは、世界。😀"""
           __lowerCAmelCase             =       tokenizer.encode(prefix_text + input_text						)
           __lowerCAmelCase             =       tokenizer.encode(""       ,    prefix_text=prefix_text + input_text						)
           __lowerCAmelCase             =       tokenizer.encode(__a       ,    prefix_text=__a						)
           __lowerCAmelCase             =       tokenizer.decode(__a						)
           __lowerCAmelCase             =       tokenizer.decode(__a						)
           __lowerCAmelCase             =       tokenizer.decode(__a						)
           self.assertEqual(__a       ,    __a						)
           self.assertEqual(__a       ,    __a						)
           self.assertEqual(__a       ,    __a						)


      @slow
      def 			__UpperCAmelCase		(						self						)->						Optional[int]:
           __lowerCAmelCase             =       self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese"						)

           # Testing tokenization
           __lowerCAmelCase             =       """こんにちは、世界。"""
           __lowerCAmelCase             =       """こんばんは、㔺界。😀"""

           __lowerCAmelCase             =       len(tokenizer.encode(__a						)						) - 2
           __lowerCAmelCase             =       len(tokenizer.encode(__a						)						) - 2

           __lowerCAmelCase             =       [1] + [0] * (len_prefix + len_text + 1)
           __lowerCAmelCase             =       [1] * (len_prefix + len_text + 1) + [0]
           __lowerCAmelCase             =       [1] + [1] * (len_prefix) + [0] * (len_text + 1)

           __lowerCAmelCase             =       tokenizer(prefix_text + input_text						).token_type_ids
           __lowerCAmelCase             =       tokenizer(""       ,    prefix_text=prefix_text + input_text						).token_type_ids
           __lowerCAmelCase             =       tokenizer(__a       ,    prefix_text=__a						).token_type_ids
           self.assertListEqual(__a       ,    __a						)
           self.assertListEqual(__a       ,    __a						)
           self.assertListEqual(__a       ,    __a						)


      @slow
      def 			__UpperCAmelCase		(						self						)->						str:
           __lowerCAmelCase             =       self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese"						)

           __lowerCAmelCase             =       tokenizer.encode("あンいワ"						)
           __lowerCAmelCase             =       tokenizer.encode(""       ,    prefix_text="あンいワ"						)
           __lowerCAmelCase             =       tokenizer.encode("いワ"       ,    prefix_text="あン"						)

           self.assertEqual(tokenizer.decode(__a						)       ,    tokenizer.decode(__a						)						)
           self.assertEqual(tokenizer.decode(__a						)       ,    tokenizer.decode(__a						)						)
           self.assertNotEqual(__a       ,    __a						)
           self.assertNotEqual(__a       ,    __a						)
           self.assertEqual(x_token_a[1]       ,    x_token_a[-1]						)  # SEG token
           self.assertEqual(x_token_a[1]       ,    x_token_a[3]						)  # SEG token


      @slow
      def 			__UpperCAmelCase		(						self						)->						Union[str, Any]:
           __lowerCAmelCase             =       self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese"						)

           __lowerCAmelCase             =       [["""武田信玄""", """は、"""], ["""織田信長""", """の配下の、"""]]
           __lowerCAmelCase             =       tokenizer(__a       ,    padding=__a						)
           __lowerCAmelCase             =       tokenizer.batch_encode_plus(__a       ,    padding=__a						)

           # fmt: off
           __lowerCAmelCase             =       [[3_5_9_9_3, 8_6_4_0, 2_5_9_4_8, 3_5_9_9_8, 3_0_6_4_7, 3_5_6_7_5, 3_5_9_9_9, 3_5_9_9_9], [3_5_9_9_3, 1_0_3_8_2, 9_8_6_8, 3_5_9_9_8, 3_0_6_4_6, 9_4_5_9, 3_0_6_4_6, 3_5_6_7_5]]
           __lowerCAmelCase             =       [[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
           __lowerCAmelCase             =       [[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
           # fmt: on
           self.assertListEqual(x_token.input_ids       ,    __a						)
           self.assertListEqual(x_token.token_type_ids       ,    __a						)
           self.assertListEqual(x_token.attention_mask       ,    __a						)
           self.assertListEqual(x_token_a.input_ids       ,    __a						)
           self.assertListEqual(x_token_a.token_type_ids       ,    __a						)
           self.assertListEqual(x_token_a.attention_mask       ,    __a						)


      def 			__UpperCAmelCase		(						self						)->						Union[str, Any]:
           # Intentionally convert some words to accommodate character fluctuations unique to Japanese
           pass


      def 			__UpperCAmelCase		(						self						)->						List[str]:
           # tokenizer has no padding token
           pass










import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
      snake_case__				:	Optional[int]					       =					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.',
      )

      snake_case__				:	Tuple					       =					parser.parse_args()

      snake_case__				:	Dict					       =					UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

      snake_case__				:	Optional[int]					       =					CLIPImageProcessor()
      snake_case__				:	Optional[Any]					       =					CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')

      snake_case__				:	Dict					       =					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)





