



import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE__   			=   get_tests_dir('''fixtures/test_sentencepiece_with_bytefallback.model''')


@require_sentencepiece
@require_tokenizers
class   __lowerCAmelCase  (		UpperCAmelCase_				,  unittest.TestCase   ):
					"""simple docstring"""






					A__					:		Union[str, Any]      							=     GPTSwaTokenizer
					A__					:		Dict      							=     False
					A__					:		List[str]      							=     True
					A__					:		Optional[Any]      							=     False



					def      _a				(  self       :							str       ):






								"""simple docstring"""


								super().setUp()

								# We have a SentencePiece fixture for testing
								A__	  =     GPTSwaTokenizer(_snake_case					,       eos_token='<unk>'					,       bos_token='<unk>'					,       pad_token='<unk>'       )

								tokenizer.save_pretrained(self.tmpdirname       )



					def      _a				(  self       :							Any					,       _snake_case       :							Tuple       ):






								"""simple docstring"""


								A__	  =     'This is a test'
								A__	  =     'This is a test'
								return input_text, output_text



					def      _a				(  self       :							Any       ):






								"""simple docstring"""


								A__	  =     '<s>'
								A__	  =     1

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



					def      _a				(  self       :							Optional[Any]       ):






								"""simple docstring"""


								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(_snake_case       )					,       20_00       )



					def      _a				(  self       :							Any       ):






								"""simple docstring"""


								self.assertEqual(self.get_tokenizer().vocab_size					,       20_00       )



					def      _a				(  self       :							str       ):






								"""simple docstring"""


								A__	  =     GPTSwaTokenizer(_snake_case       )

								A__	  =     tokenizer.tokenize('This is a test'       )
								self.assertListEqual(_snake_case					,       ['▁This', '▁is', '▁a', '▁t', 'est']       )

								self.assertListEqual(tokenizer.convert_tokens_to_ids(_snake_case       )					,       [4_65, 2_87, 2_65, 6_31, 8_42]       )

								A__	  =     tokenizer.tokenize('I was born in 92000, and this is falsé.'       )
								# fmt: off
								self.assertListEqual(
								    _snake_case					,       ['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']					,       )
								# fmt: on

								A__	  =     tokenizer.convert_tokens_to_ids(_snake_case       )
								self.assertListEqual(
								    _snake_case					,       [2_62, 2_72, 15_25, 2_86, 2_71, 2_68, 60, 9_16, 6_33, 6_33, 6_33, 2_59, 2_66, 3_01, 2_87, 3_84, 3_67, 2_63, 1_98, 1_72, 2_60]					,       )

								A__	  =     tokenizer.convert_ids_to_tokens(_snake_case       )
								# fmt: off
								self.assertListEqual(
								    _snake_case					,       ['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']       )
								# fmt: on



					def      _a				(  self       :							Tuple       ):






								"""simple docstring"""


								A__	  =     GPTSwaTokenizer(_snake_case       )
								A__	  =     ['This is a test', 'I was born in 92000, and this is falsé.']
								A__	  =     [
								    [4_65, 2_87, 2_65, 6_31, 8_42],
								    [2_62, 2_72, 15_25, 2_86, 2_71, 2_68, 60, 9_16, 6_33, 6_33, 6_33, 2_59, 2_66, 3_01, 2_87, 3_84, 3_67, 2_63, 1_98, 1_72, 2_60],
								]

								# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
								for text, expected_ids in zip(_snake_case					,       _snake_case       ):
											self.assertListEqual(tokenizer.encode_fast(_snake_case       )					,       _snake_case       )

								# Test that decode_fast returns the input text
								for text, token_ids in zip(_snake_case					,       _snake_case       ):
											self.assertEqual(tokenizer.decode_fast(_snake_case       )					,       _snake_case       )



					@slow
					def      _a				(  self       :							List[Any]       ):






								"""simple docstring"""


								A__	  =     [
								    '<|python|>def fibonacci(n)\n    if n < 0:\n        print(\'Incorrect input\')',
								    'Hey there, how are you doing this fine day?',
								    'This is a text with a trailing spaces followed by a dot     .',
								    'Häj sväjs lillebrör! =)',
								    'Det är inget fel på Mr. Cool',
								]

								# fmt: off
								A__	  =     {'input_ids': [[6_34_23, 5, 68_11, 1_49_54, 2_82, 8_16, 38_21, 6_34_66, 6_34_25, 6_34_62, 18, 6_39_78, 6_78, 3_01, 13_20, 6_34_23, 6_34_55, 6_34_58, 18, 6_39_82, 42_46, 39_40, 19_01, 4_77_89, 55_47, 1_89_94], [1_96_30, 11_00, 6_34_46, 13_42, 6_33, 5_44, 44_88, 5_93, 51_02, 24_16, 6_34_95, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [16_52, 4_28, 2_68, 19_36, 5_15, 2_68, 5_85_93, 2_24_13, 91_06, 5_46, 2_68, 3_32_13, 6_39_79, 6_98, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_51_30, 6_34_50, 9_24, 6_34_49, 22_49, 40_62, 15_58, 3_18, 6_35_04, 2_14_98, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_09, 3_77, 28_27, 25_59, 3_32, 65_75, 6_34_43, 2_68_01, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
								# fmt: on
								self.tokenizer_integration_test_util(
								    expected_encoding=_snake_case					,       model_name='AI-Sweden/gpt-sw3-126m'					,       sequences=_snake_case					,       )









import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   int:
			A__	  =     OmegaConf.load(__UpperCamelCase       )
			A__	  =     torch.load(__UpperCamelCase			,						map_location='cpu'       )['model']
			A__	  =     list(state_dict.keys()       )

			# extract state_dict for VQVAE
			A__	  =     {}
			A__	  =     'first_stage_model.'
			for key in keys:
						if key.startswith(__UpperCamelCase       ):
									A__	  =     state_dict[key]

    # extract state_dict for UNetLDM
			A__	  =     {}
			A__	  =     'model.diffusion_model.'
			for key in keys:
						if key.startswith(__UpperCamelCase       ):
									A__	  =     state_dict[key]

			A__	  =     config.model.params.first_stage_config.params
			A__	  =     config.model.params.unet_config.params

			A__	  =     VQModel(**__UpperCamelCase       ).eval()
			vqvae.load_state_dict(__UpperCamelCase       )

			A__	  =     UNetLDMModel(**__UpperCamelCase       ).eval()
			unet.load_state_dict(__UpperCamelCase       )

			A__	  =     DDIMScheduler(
			    timesteps=config.model.params.timesteps			,						beta_schedule='scaled_linear'			,						beta_start=config.model.params.linear_start			,						beta_end=config.model.params.linear_end			,						clip_sample=__UpperCamelCase			,						)

			A__	  =     LDMPipeline(__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )
			pipeline.save_pretrained(__UpperCamelCase       )


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__   			=   argparse.ArgumentParser()
							parser.add_argument('''--checkpoint_path''', type=str, required=True)
							parser.add_argument('''--config_path''', type=str, required=True)
							parser.add_argument('''--output_path''', type=str, required=True)
							SCREAMING_SNAKE_CASE__   			=   parser.parse_args()

							convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)




