






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      lowercase__     (			_UpperCAmelCase,					unittest.TestCase	):
	a_         =MvpTokenizer
	a_         =MvpTokenizerFast
	a_         =True
	a_         =filter_roberta_detectors

	def 		UpperCAmelCase  (							self       )->					Optional[Any]:







							'''simple docstring'''





							super().setUp()
							lowerCAmelCase__       =						[
							    "l",
							    "o",
							    "w",
							    "e",
							    "r",
							    "s",
							    "t",
							    "i",
							    "d",
							    "n",
							    "\u0120",
							    "\u0120l",
							    "\u0120n",
							    "\u0120lo",
							    "\u0120low",
							    "er",
							    "\u0120lowest",
							    "\u0120newer",
							    "\u0120wider",
							    "<unk>",
							]
							lowerCAmelCase__       =						dict(zip(__UpperCAmelCase		,    range(len(__UpperCAmelCase       )       )       )       )
							lowerCAmelCase__       =						["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
							lowerCAmelCase__       =						{"unk_token": "<unk>"}

							lowerCAmelCase__       =						os.path.join(self.tmpdirname		,    VOCAB_FILES_NAMES["vocab_file"]       )
							lowerCAmelCase__       =						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		,    **__UpperCAmelCase       )->					Any:







							'''simple docstring'''





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

	def 		UpperCAmelCase  (							self		,    **__UpperCAmelCase       )->					List[Any]:







							'''simple docstring'''





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

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase       )->					List[Any]:







							'''simple docstring'''





							return "lower newer", "lower newer"

	@cached_property
	def 		UpperCAmelCase  (							self       )->					Tuple:







							'''simple docstring'''





							return MvpTokenizer.from_pretrained("RUCAIBox/mvp"       )

	@cached_property
	def 		UpperCAmelCase  (							self       )->					Tuple:







							'''simple docstring'''





							return MvpTokenizerFast.from_pretrained("RUCAIBox/mvp"       )

	@require_torch
	def 		UpperCAmelCase  (							self       )->					Dict:







							'''simple docstring'''





							lowerCAmelCase__       =						["A long paragraph for summarization.", "Another paragraph for summarization."]
							lowerCAmelCase__       =						[0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													lowerCAmelCase__       =						tokenizer(__UpperCAmelCase		,    max_length=len(__UpperCAmelCase       )		,    padding=__UpperCAmelCase		,    return_tensors="pt"       )
													self.assertIsInstance(__UpperCAmelCase		,    __UpperCAmelCase       )

													self.assertEqual((2, 9)		,    batch.input_ids.shape       )
													self.assertEqual((2, 9)		,    batch.attention_mask.shape       )
													lowerCAmelCase__       =						batch.input_ids.tolist()[0]
													self.assertListEqual(__UpperCAmelCase		,    __UpperCAmelCase       )
													# Test that special tokens are reset

	@require_torch
	def 		UpperCAmelCase  (							self       )->					Dict:







							'''simple docstring'''





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

	@require_torch
	def 		UpperCAmelCase  (							self       )->					int:







							'''simple docstring'''





							lowerCAmelCase__       =						[
							    "Summary of the text.",
							    "Another summary.",
							]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													lowerCAmelCase__       =						tokenizer(text_target=__UpperCAmelCase		,    max_length=32		,    padding="max_length"		,    return_tensors="pt"       )
													self.assertEqual(32		,    targets["input_ids"].shape[1]       )

	@require_torch
	def 		UpperCAmelCase  (							self       )->					Dict:







							'''simple docstring'''





							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													lowerCAmelCase__       =						tokenizer(
													    ["I am a small frog" * 1024, "I am a small frog"]		,    padding=__UpperCAmelCase		,    truncation=__UpperCAmelCase		,    return_tensors="pt"       )
													self.assertIsInstance(__UpperCAmelCase		,    __UpperCAmelCase       )
													self.assertEqual(batch.input_ids.shape		,    (2, 1024)       )

	@require_torch
	def 		UpperCAmelCase  (							self       )->					str:







							'''simple docstring'''





							lowerCAmelCase__       =						["A long paragraph for summarization."]
							lowerCAmelCase__       =						[
							    "Summary of the text.",
							]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													lowerCAmelCase__       =						tokenizer(__UpperCAmelCase		,    text_target=__UpperCAmelCase		,    return_tensors="pt"       )
													lowerCAmelCase__       =						inputs["input_ids"]
													lowerCAmelCase__       =						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 		UpperCAmelCase  (							self       )->					Union[str, Any]:







							'''simple docstring'''





							pass


	def 		UpperCAmelCase  (							self       )->					List[str]:







							'''simple docstring'''





							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"       ):
																			lowerCAmelCase__       =						self.rust_tokenizer_class.from_pretrained(__UpperCAmelCase		,    **__UpperCAmelCase       )
																			lowerCAmelCase__       =						self.tokenizer_class.from_pretrained(__UpperCAmelCase		,    **__UpperCAmelCase       )
																			lowerCAmelCase__       =						"A, <mask> AllenNLP sentence."
																			lowerCAmelCase__       =						tokenizer_r.encode_plus(__UpperCAmelCase		,    add_special_tokens=__UpperCAmelCase		,    return_token_type_ids=__UpperCAmelCase       )
																			lowerCAmelCase__       =						tokenizer_p.encode_plus(__UpperCAmelCase		,    add_special_tokens=__UpperCAmelCase		,    return_token_type_ids=__UpperCAmelCase       )

																			# 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"]       )		,    )

																			lowerCAmelCase__       =						tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]       )
																			lowerCAmelCase__       =						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, 50264, 3823, 487, 21992, 3645, 4, 2]       )
																			self.assertSequenceEqual(tokens_r["input_ids"]		,    [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]       )

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














import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


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

	a_							=    parser.parse_args()

	a_							=    UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

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

	a_							=    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)






