





import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors



@require_tokenizers
class 					__lowerCAmelCase       (			_a,     unittest.TestCase     ):
			lowerCamelCase_     :     Union[str, Any]       			=      MvpTokenizer
			lowerCamelCase_     :     List[Any]       			=      MvpTokenizerFast
			lowerCamelCase_     :     Dict       			=      True
			lowerCamelCase_     :     Union[str, Any]       			=      filter_roberta_detectors






			def 			lowerCamelCase  (self    )     ->   List[Any]:


							'''simple docstring'''



							super().setUp()
							snake_case_						:    int				=  [
							    '''l''',
							    '''o''',
							    '''w''',
							    '''e''',
							    '''r''',
							    '''s''',
							    '''t''',
							    '''i''',
							    '''d''',
							    '''n''',
							    '''\u0120''',
							    '''\u0120l''',
							    '''\u0120n''',
							    '''\u0120lo''',
							    '''\u0120low''',
							    '''er''',
							    '''\u0120lowest''',
							    '''\u0120newer''',
							    '''\u0120wider''',
							    '''<unk>''',
							]
							snake_case_						:    List[str]				=  dict(zip(__magic_name__					,							range(len(__magic_name__    )    )    )    )
							snake_case_						:    Any				=  ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
							snake_case_						:    Dict				=  {'''unk_token''': '''<unk>'''}

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






			def 			lowerCamelCase  (self					,							**__magic_name__    )     ->   Optional[Any]:


							'''simple docstring'''



							kwargs.update(self.special_tokens_map    )
							return self.tokenizer_class.from_pretrained(self.tmpdirname					,							**__magic_name__    )






			def 			lowerCamelCase  (self					,							**__magic_name__    )     ->   List[str]:


							'''simple docstring'''



							kwargs.update(self.special_tokens_map    )
							return self.rust_tokenizer_class.from_pretrained(self.tmpdirname					,							**__magic_name__    )






			def 			lowerCamelCase  (self					,							__magic_name__    )     ->   Tuple:


							'''simple docstring'''



							return "lower newer", "lower newer"






			@cached_property
			def 			lowerCamelCase  (self    )     ->   Optional[int]:


							'''simple docstring'''



							return MvpTokenizer.from_pretrained('''RUCAIBox/mvp'''    )






			@cached_property
			def 			lowerCamelCase  (self    )     ->   List[str]:


							'''simple docstring'''



							return MvpTokenizerFast.from_pretrained('''RUCAIBox/mvp'''    )






			@require_torch
			def 			lowerCamelCase  (self    )     ->   List[str]:


							'''simple docstring'''



							snake_case_						:    List[str]				=  ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
							snake_case_						:    List[str]				=  [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											snake_case_						:    List[Any]				=  tokenizer(__magic_name__					,							max_length=len(__magic_name__    )					,							padding=__magic_name__					,							return_tensors='''pt'''    )
											self.assertIsInstance(__magic_name__					,							__magic_name__    )

											self.assertEqual((2, 9)					,							batch.input_ids.shape    )
											self.assertEqual((2, 9)					,							batch.attention_mask.shape    )
											snake_case_						:    Optional[int]				=  batch.input_ids.tolist()[0]
											self.assertListEqual(__magic_name__					,							__magic_name__    )
											# Test that special tokens are reset






			@require_torch
			def 			lowerCamelCase  (self    )     ->   Union[str, Any]:


							'''simple docstring'''



							snake_case_						:    Tuple				=  ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											snake_case_						:    Tuple				=  tokenizer(__magic_name__					,							padding=__magic_name__					,							return_tensors='''pt'''    )
											# check if input_ids are returned and no labels
											self.assertIn('''input_ids'''					,							__magic_name__    )
											self.assertIn('''attention_mask'''					,							__magic_name__    )
											self.assertNotIn('''labels'''					,							__magic_name__    )
											self.assertNotIn('''decoder_attention_mask'''					,							__magic_name__    )






			@require_torch
			def 			lowerCamelCase  (self    )     ->   Any:


							'''simple docstring'''



							snake_case_						:    List[Any]				=  [
							    '''Summary of the text.''',
							    '''Another summary.''',
							]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											snake_case_						:    Tuple				=  tokenizer(text_target=__magic_name__					,							max_length=32					,							padding='''max_length'''					,							return_tensors='''pt'''    )
											self.assertEqual(32					,							targets['''input_ids'''].shape[1]    )






			@require_torch
			def 			lowerCamelCase  (self    )     ->   List[Any]:


							'''simple docstring'''



							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											snake_case_						:    Union[str, Any]				=  tokenizer(
											    ['''I am a small frog''' * 1024, '''I am a small frog''']					,							padding=__magic_name__					,							truncation=__magic_name__					,							return_tensors='''pt'''    )
											self.assertIsInstance(__magic_name__					,							__magic_name__    )
											self.assertEqual(batch.input_ids.shape					,							(2, 1024)    )






			@require_torch
			def 			lowerCamelCase  (self    )     ->   List[str]:


							'''simple docstring'''



							snake_case_						:    Dict				=  ['''A long paragraph for summarization.''']
							snake_case_						:    List[Any]				=  [
							    '''Summary of the text.''',
							]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											snake_case_						:    str				=  tokenizer(__magic_name__					,							text_target=__magic_name__					,							return_tensors='''pt'''    )
											snake_case_						:    Tuple				=  inputs['''input_ids''']
											snake_case_						:    Tuple				=  inputs['''labels''']
											self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()    )
											self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()    )
											self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()    )
											self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()    )






			def 			lowerCamelCase  (self    )     ->   Optional[int]:


							'''simple docstring'''



							pass






			def 			lowerCamelCase  (self    )     ->   List[Any]:


							'''simple docstring'''



							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''    ):
															snake_case_						:    Any				=  self.rust_tokenizer_class.from_pretrained(__magic_name__					,							**__magic_name__    )
															snake_case_						:    Dict				=  self.tokenizer_class.from_pretrained(__magic_name__					,							**__magic_name__    )
															snake_case_						:    Optional[int]				=  '''A, <mask> AllenNLP sentence.'''
															snake_case_						:    Optional[int]				=  tokenizer_r.encode_plus(__magic_name__					,							add_special_tokens=__magic_name__					,							return_token_type_ids=__magic_name__    )
															snake_case_						:    int				=  tokenizer_p.encode_plus(__magic_name__					,							add_special_tokens=__magic_name__					,							return_token_type_ids=__magic_name__    )

															# 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''']    )					,							)

															snake_case_						:    str				=  tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']    )
															snake_case_						:    Optional[Any]				=  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(
															    __magic_name__					,							['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']    )
															self.assertSequenceEqual(
															    __magic_name__					,							['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']    )







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)
