
'''simple docstring'''
import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowercase__											=get_tests_dir('fixtures/test_sentencepiece.model')







@require_sentencepiece
@require_tokenizers
class   a_ (      UpperCamelCase__		,  unittest.TestCase					):
			lowerCamelCase__	:			str       =    ReformerTokenizer
			lowerCamelCase__	:			Dict       =    ReformerTokenizerFast
			lowerCamelCase__	:			int       =    True
			lowerCamelCase__	:			Any       =    False
			lowerCamelCase__	:			Any       =    True




			def 					lowerCAmelCase__					(       self	):
				super().setUp()

				a_      				=						ReformerTokenizer(UpperCAmelCase					,   keep_accents=UpperCAmelCase	)
				tokenizer.save_pretrained(self.tmpdirname	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						"""<s>"""
				a_      				=						1

				self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase	)					,   UpperCAmelCase	)
				self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase	)					,   UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						list(self.get_tokenizer().get_vocab().keys()	)

				self.assertEqual(vocab_keys[0]					,   """<unk>"""	)
				self.assertEqual(vocab_keys[1]					,   """<s>"""	)
				self.assertEqual(vocab_keys[-1]					,   """j"""	)
				self.assertEqual(len(UpperCAmelCase	)					,   10_00	)




			def 					lowerCAmelCase__					(       self	):
				self.assertEqual(self.get_tokenizer().vocab_size					,   10_00	)




			def 					lowerCAmelCase__					(       self	):
				if not self.test_rust_tokenizer:
					return

				a_      				=						self.get_tokenizer()
				a_      				=						self.get_rust_tokenizer()

				a_      				=						"""I was born in 92000, and this is falsé."""

				a_      				=						tokenizer.tokenize(UpperCAmelCase	)
				a_      				=						rust_tokenizer.tokenize(UpperCAmelCase	)
				self.assertListEqual(UpperCAmelCase					,   UpperCAmelCase	)

				a_      				=						tokenizer.encode(UpperCAmelCase					,   add_special_tokens=UpperCAmelCase	)
				a_      				=						rust_tokenizer.encode(UpperCAmelCase					,   add_special_tokens=UpperCAmelCase	)
				self.assertListEqual(UpperCAmelCase					,   UpperCAmelCase	)

				a_      				=						self.get_rust_tokenizer()
				a_      				=						tokenizer.encode(UpperCAmelCase	)
				a_      				=						rust_tokenizer.encode(UpperCAmelCase	)
				self.assertListEqual(UpperCAmelCase					,   UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase=15	):
				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
					with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''	):
						a_      				=						self.rust_tokenizer_class.from_pretrained(UpperCAmelCase					,   **UpperCAmelCase	)

						# Simple input
						a_      				=						"""This is a simple input"""
						a_      				=						["""This is a simple input 1""", """This is a simple input 2"""]
						a_      				=						("""This is a simple input""", """This is a pair""")
						a_      				=						[
						    ("""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 					lowerCAmelCase__					(       self	):
				pass




			def 					lowerCAmelCase__					(       self	):
				a_      				=						ReformerTokenizer(UpperCAmelCase					,   keep_accents=UpperCAmelCase	)

				a_      				=						tokenizer.tokenize("""This is a test"""	)
				self.assertListEqual(UpperCAmelCase					,   ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]	)

				self.assertListEqual(
				    tokenizer.convert_tokens_to_ids(UpperCAmelCase	)					,   [2_85, 46, 10, 1_70, 3_82]					,   )

				a_      				=						tokenizer.tokenize("""I was born in 92000, and this is falsé."""	)
				self.assertListEqual(
				    UpperCAmelCase					,   [
				        SPIECE_UNDERLINE + """I""",
				        SPIECE_UNDERLINE + """was""",
				        SPIECE_UNDERLINE + """b""",
				        """or""",
				        """n""",
				        SPIECE_UNDERLINE + """in""",
				        SPIECE_UNDERLINE + """""",
				        """9""",
				        """2""",
				        """0""",
				        """0""",
				        """0""",
				        """,""",
				        SPIECE_UNDERLINE + """and""",
				        SPIECE_UNDERLINE + """this""",
				        SPIECE_UNDERLINE + """is""",
				        SPIECE_UNDERLINE + """f""",
				        """al""",
				        """s""",
				        """é""",
				        """.""",
				    ]					,   )
				a_      				=						tokenizer.convert_tokens_to_ids(UpperCAmelCase	)
				self.assertListEqual(
				    UpperCAmelCase					,   [8, 21, 84, 55, 24, 19, 7, 0, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 0, 4]					,   )

				a_      				=						tokenizer.convert_ids_to_tokens(UpperCAmelCase	)
				self.assertListEqual(
				    UpperCAmelCase					,   [
				        SPIECE_UNDERLINE + """I""",
				        SPIECE_UNDERLINE + """was""",
				        SPIECE_UNDERLINE + """b""",
				        """or""",
				        """n""",
				        SPIECE_UNDERLINE + """in""",
				        SPIECE_UNDERLINE + """""",
				        """<unk>""",
				        """2""",
				        """0""",
				        """0""",
				        """0""",
				        """,""",
				        SPIECE_UNDERLINE + """and""",
				        SPIECE_UNDERLINE + """this""",
				        SPIECE_UNDERLINE + """is""",
				        SPIECE_UNDERLINE + """f""",
				        """al""",
				        """s""",
				        """<unk>""",
				        """.""",
				    ]					,   )




			@cached_property
			def 					lowerCAmelCase__					(       self	):
				return ReformerTokenizer.from_pretrained("""google/reformer-crime-and-punishment"""	)




			@slow
			def 					lowerCAmelCase__					(       self	):
				a_      				=						"""Hello World!"""
				a_      				=						[1_26, 32, 2_62, 1_52, 38, 72, 2_87]

				self.assertListEqual(UpperCAmelCase					,   self.big_tokenizer.encode(UpperCAmelCase	)	)




			@slow
			def 					lowerCAmelCase__					(       self	):
				a_      				=						(
				    """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
				    """ add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
				)
				a_      				=						[
				    1_08,
				    2_65,
				    24,
				    1_11,
				    4,
				    2_58,
				    1_56,
				    35,
				    28,
				    2_75,
				    3,
				    2_59,
				    2_97,
				    2_60,
				    84,
				    4,
				    35,
				    1_10,
				    44,
				    8,
				    2_59,
				    91,
				    2_68,
				    21,
				    11,
				    2_09,
				    2_74,
				    1_09,
				    2_66,
				    2_77,
				    1_17,
				    86,
				    93,
				    3_15,
				    2_58,
				    2_78,
				    2_58,
				    2_77,
				    2_58,
				    0,
				    2_58,
				    2_88,
				    2_58,
				    3_19,
				    2_58,
				    0,
				    2_58,
				    0,
				    2_58,
				    0,
				    2_58,
				    0,
				    2_58,
				    2_87,
				    2_58,
				    3_15,
				    2_58,
				    2_89,
				    2_58,
				    2_78,
				    99,
				    2_69,
				    2_66,
				    2_62,
				    8,
				    2_59,
				    2_41,
				    4,
				    2_17,
				    2_30,
				    2_68,
				    2_66,
				    55,
				    1_68,
				    1_06,
				    75,
				    1_93,
				    2_66,
				    2_23,
				    27,
				    49,
				    26,
				    2_82,
				    25,
				    2_64,
				    2_99,
				    19,
				    26,
				    0,
				    2_58,
				    2_77,
				    1_17,
				    86,
				    93,
				    1_76,
				    1_83,
				    2_70,
				    11,
				    2_62,
				    42,
				    61,
				    2_65,
				]

				self.assertListEqual(UpperCAmelCase					,   self.big_tokenizer.encode(UpperCAmelCase	)	)




			@require_torch
			@slow
			def 					lowerCAmelCase__					(       self	):
				import torch

				from transformers import ReformerConfig, ReformerModel

				# Build sequence
				a_      				=						list(self.big_tokenizer.get_vocab().keys()	)[:10]
				a_      				=						""" """.join(UpperCAmelCase	)
				a_      				=						self.big_tokenizer.encode_plus(UpperCAmelCase					,   return_tensors="""pt"""	)
				a_      				=						self.big_tokenizer.batch_encode_plus([sequence, sequence]					,   return_tensors="""pt"""	)

				a_      				=						ReformerConfig()
				# The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
				a_      				=						encoded_sequence["""input_ids"""].shape
				a_      				=						ReformerModel(UpperCAmelCase	)

				# Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
				assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

				with torch.no_grad():
					model(**UpperCAmelCase	)
					model(**UpperCAmelCase	)






			@slow
			def 					lowerCAmelCase__					(       self	):
				# fmt: off
				a_      				=						{"""input_ids""": [[1_08, 2_65, 24, 1_11, 4, 2_58, 1_56, 7, 51, 2_79, 58, 7, 76, 25, 69, 2_78], [1_40, 2_43, 2_64, 1_34, 17, 2_67, 77, 2_63, 22, 2_62, 2_97, 2_58, 3_04, 1_77, 2_79, 2_66, 14, 89, 13, 35, 2_61, 2_99, 2_72, 1_37, 2_75, 2_78]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
				# fmt: on

				# This tokenizer does not know some characters like ")".
				# That is the reason why we use very simple texts here.
				# Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
				a_      				=						[
				    """This is a very simple sentence.""",
				    """The quick brown fox jumps over the lazy dog.""",
				]

				self.tokenizer_integration_test_util(
				    expected_encoding=UpperCAmelCase					,   model_name="""google/reformer-crime-and-punishment"""					,   revision="""0e6c3decb8211d49bf881013425dc8b0448b3f5a"""					,   padding=UpperCAmelCase					,   sequences=UpperCAmelCase					,   )


'''simple docstring'''
import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
		lowercase__											=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.',
		)

		lowercase__											=parser.parse_args()

		lowercase__											=UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

		lowercase__											=CLIPImageProcessor()
		lowercase__											=CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')

		lowercase__											=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)
