


"""simple docstring"""






import shutil
import tempfile
import unittest

from transformers import (
    SPIECE_UNDERLINE,
    AddedToken,
    BatchEncoding,
    NllbTokenizer,
    NllbTokenizerFast,
    is_torch_available,
)
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase    :     Optional[int]   						=		get_tests_dir('fixtures/test_sentencepiece.model')


if is_torch_available():
							from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

UpperCAmelCase    :     Optional[Any]   						=		25_6047
UpperCAmelCase    :     Dict   						=		25_6145





@require_sentencepiece
@require_tokenizers
class      lowerCamelCase__   (     lowerCamelCase_     ,    unittest.TestCase				):







					"""simple docstring"""


					__a	       =							NllbTokenizer
					__a	       =							NllbTokenizerFast
					__a	       =							True
					__a	       =							True
					__a	       =							{}






					def lowerCamelCase__							(       self    : Tuple  ):
												'''simple docstring'''






												super().setUp()

												# We have a SentencePiece fixture for testing
												__UpperCAmelCase   :						List[Any]      							=					NllbTokenizer(lowerCAmelCase__ ,       keep_accents=lowerCAmelCase__  )
												tokenizer.save_pretrained(self.tmpdirname  )






					def lowerCamelCase__							(       self    : Any  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Dict      							=					NllbTokenizer(lowerCAmelCase__ ,       keep_accents=lowerCAmelCase__  )

												__UpperCAmelCase   :						str      							=					tokenizer.tokenize("""This is a test"""  )
												self.assertListEqual(lowerCAmelCase__ ,       ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]  )

												self.assertListEqual(
												    tokenizer.convert_tokens_to_ids(lowerCAmelCase__  ) ,       [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] ,       )

												__UpperCAmelCase   :						Union[str, Any]      							=					tokenizer.tokenize("""I was born in 92000, and this is falsé."""  )
												self.assertListEqual(
												    lowerCAmelCase__ ,       [
												        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""",
												        """.""",
												    ] ,       )
												__UpperCAmelCase   :						int      							=					tokenizer.convert_tokens_to_ids(lowerCAmelCase__  )
												self.assertListEqual(
												    lowerCAmelCase__ ,       [
												        value + tokenizer.fairseq_offset
												        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
												    ] ,       )

												__UpperCAmelCase   :						Tuple      							=					tokenizer.convert_ids_to_tokens(lowerCAmelCase__  )
												self.assertListEqual(
												    lowerCAmelCase__ ,       [
												        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>""",
												        """.""",
												    ] ,       )






					def lowerCamelCase__							(       self    : Any  ):
												'''simple docstring'''






												__UpperCAmelCase   :						str      							=					(self.rust_tokenizer_class, """hf-internal-testing/tiny-random-nllb""", {})
												for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																			with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''  ):
																										__UpperCAmelCase   :						List[str]      							=					self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__ ,       **lowerCAmelCase__  )
																										__UpperCAmelCase   :						str      							=					self.tokenizer_class.from_pretrained(lowerCAmelCase__ ,       **lowerCAmelCase__  )

																										__UpperCAmelCase   :						List[str]      							=					tempfile.mkdtemp()

																										__UpperCAmelCase   :						List[Any]      							=					tokenizer_r.save_pretrained(lowerCAmelCase__  )
																										__UpperCAmelCase   :						Tuple      							=					tokenizer_p.save_pretrained(lowerCAmelCase__  )

																										# Checks it save with the same files + the tokenizer.json file for the fast one
																										self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files  )  )
																										__UpperCAmelCase   :						str      							=					tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f  )
																										self.assertSequenceEqual(lowerCAmelCase__ ,       lowerCAmelCase__  )

																										# Checks everything loads correctly in the same way
																										__UpperCAmelCase   :						int      							=					tokenizer_r.from_pretrained(lowerCAmelCase__  )
																										__UpperCAmelCase   :						Any      							=					tokenizer_p.from_pretrained(lowerCAmelCase__  )

																										# Check special tokens are set accordingly on Rust and Python
																										for key in tokenizer_pp.special_tokens_map:
																																	self.assertTrue(hasattr(lowerCAmelCase__ ,       lowerCAmelCase__  )  )

																										shutil.rmtree(lowerCAmelCase__  )

																										# Save tokenizer rust, legacy_format=True
																										__UpperCAmelCase   :						List[Any]      							=					tempfile.mkdtemp()

																										__UpperCAmelCase   :						List[Any]      							=					tokenizer_r.save_pretrained(lowerCAmelCase__ ,       legacy_format=lowerCAmelCase__  )
																										__UpperCAmelCase   :						Dict      							=					tokenizer_p.save_pretrained(lowerCAmelCase__  )

																										# Checks it save with the same files
																										self.assertSequenceEqual(lowerCAmelCase__ ,       lowerCAmelCase__  )

																										# Checks everything loads correctly in the same way
																										__UpperCAmelCase   :						int      							=					tokenizer_r.from_pretrained(lowerCAmelCase__  )
																										__UpperCAmelCase   :						Union[str, Any]      							=					tokenizer_p.from_pretrained(lowerCAmelCase__  )

																										# Check special tokens are set accordingly on Rust and Python
																										for key in tokenizer_pp.special_tokens_map:
																																	self.assertTrue(hasattr(lowerCAmelCase__ ,       lowerCAmelCase__  )  )

																										shutil.rmtree(lowerCAmelCase__  )

																										# Save tokenizer rust, legacy_format=False
																										__UpperCAmelCase   :						List[Any]      							=					tempfile.mkdtemp()

																										__UpperCAmelCase   :						Any      							=					tokenizer_r.save_pretrained(lowerCAmelCase__ ,       legacy_format=lowerCAmelCase__  )
																										__UpperCAmelCase   :						Dict      							=					tokenizer_p.save_pretrained(lowerCAmelCase__  )

																										# Checks it saved the tokenizer.json file
																										self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files  )  )

																										# Checks everything loads correctly in the same way
																										__UpperCAmelCase   :						Union[str, Any]      							=					tokenizer_r.from_pretrained(lowerCAmelCase__  )
																										__UpperCAmelCase   :						Union[str, Any]      							=					tokenizer_p.from_pretrained(lowerCAmelCase__  )

																										# Check special tokens are set accordingly on Rust and Python
																										for key in tokenizer_pp.special_tokens_map:
																																	self.assertTrue(hasattr(lowerCAmelCase__ ,       lowerCAmelCase__  )  )

																										shutil.rmtree(lowerCAmelCase__  )






					@require_torch
					def lowerCamelCase__							(       self    : List[Any]  ):
												'''simple docstring'''






												if not self.test_seqaseq:
																			return

												__UpperCAmelCase   :						str      							=					self.get_tokenizers()
												for tokenizer in tokenizers:
																			with self.subTest(f'''{tokenizer.__class__.__name__}'''  ):
																										# Longer text that will definitely require truncation.
																										__UpperCAmelCase   :						Dict      							=					[
																										    """ UN Chief Says There Is No Military Solution in Syria""",
																										    """ Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for"""
																										    """ Syria is that \'there is no military solution\' to the nearly five-year conflict and more weapons"""
																										    """ will only worsen the violence and misery for millions of people.""",
																										]
																										__UpperCAmelCase   :						Any      							=					[
																										    """Şeful ONU declară că nu există o soluţie militară în Siria""",
																										    """Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al"""
																										    """ Rusiei pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi"""
																										    """ că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
																										]
																										try:
																																	__UpperCAmelCase   :						Tuple      							=					tokenizer.prepare_seqaseq_batch(
																																	    src_texts=lowerCAmelCase__ ,       tgt_texts=lowerCAmelCase__ ,       max_length=3 ,       max_target_length=10 ,       return_tensors="""pt""" ,       src_lang="""eng_Latn""" ,       tgt_lang="""ron_Latn""" ,       )
																										except NotImplementedError:
																																	return
																										self.assertEqual(batch.input_ids.shape[1] ,       3  )
																										self.assertEqual(batch.labels.shape[1] ,       10  )
																										# max_target_length will default to max_length if not specified
																										__UpperCAmelCase   :						int      							=					tokenizer.prepare_seqaseq_batch(
																										    lowerCAmelCase__ ,       tgt_texts=lowerCAmelCase__ ,       max_length=3 ,       return_tensors="""pt"""  )
																										self.assertEqual(batch.input_ids.shape[1] ,       3  )
																										self.assertEqual(batch.labels.shape[1] ,       3  )

																										__UpperCAmelCase   :						Any      							=					tokenizer.prepare_seqaseq_batch(
																										    src_texts=lowerCAmelCase__ ,       max_length=3 ,       max_target_length=10 ,       return_tensors="""pt"""  )
																										self.assertEqual(batch_encoder_only.input_ids.shape[1] ,       3  )
																										self.assertEqual(batch_encoder_only.attention_mask.shape[1] ,       3  )
																										self.assertNotIn("""decoder_input_ids""" ,       lowerCAmelCase__  )






					@unittest.skip("""Unfortunately way too slow to build a BPE with SentencePiece."""  )
					def lowerCamelCase__							(       self    : str  ):
												'''simple docstring'''






												pass






					def lowerCamelCase__							(       self    : str  ):
												'''simple docstring'''






												for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																			with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''  ):
																										__UpperCAmelCase   :						List[str]      							=					[AddedToken("""<special>""" ,       lstrip=lowerCAmelCase__  )]

																										__UpperCAmelCase   :						int      							=					self.rust_tokenizer_class.from_pretrained(
																										    lowerCAmelCase__ ,       additional_special_tokens=lowerCAmelCase__ ,       **lowerCAmelCase__  )
																										__UpperCAmelCase   :						Union[str, Any]      							=					tokenizer_r.encode("""Hey this is a <special> token"""  )

																										__UpperCAmelCase   :						str      							=					tokenizer_r.encode("""<special>""" ,       add_special_tokens=lowerCAmelCase__  )[0]

																										self.assertTrue(special_token_id in r_output  )

																										if self.test_slow_tokenizer:
																																	__UpperCAmelCase   :						List[Any]      							=					self.rust_tokenizer_class.from_pretrained(
																																	    lowerCAmelCase__ ,       additional_special_tokens=lowerCAmelCase__ ,       **lowerCAmelCase__ ,       )
																																	__UpperCAmelCase   :						Dict      							=					self.tokenizer_class.from_pretrained(
																																	    lowerCAmelCase__ ,       additional_special_tokens=lowerCAmelCase__ ,       **lowerCAmelCase__  )

																																	__UpperCAmelCase   :						List[Any]      							=					tokenizer_p.encode("""Hey this is a <special> token"""  )

																																	__UpperCAmelCase   :						Union[str, Any]      							=					tokenizer_cr.encode("""Hey this is a <special> token"""  )

																																	self.assertEqual(lowerCAmelCase__ ,       lowerCAmelCase__  )
																																	self.assertEqual(lowerCAmelCase__ ,       lowerCAmelCase__  )
																																	self.assertTrue(special_token_id in p_output  )
																																	self.assertTrue(special_token_id in cr_output  )






@require_torch
@require_sentencepiece
@require_tokenizers
class      lowerCamelCase__   (     unittest.TestCase				):







					"""simple docstring"""


					__a	       =							"""facebook/nllb-200-distilled-600M"""
					__a	       =							[
					    """ UN Chief Says There Is No Military Solution in Syria""",
					    """ Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""",
					]
					__a	       =							[
					    """Şeful ONU declară că nu există o soluţie militară în Siria""",
					    """Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"""
					    """ pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"""
					    """ face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
					]
					__a	       =							[
					    25_6047,
					    1_6297,
					    13_4408,
					    8165,
					    24_8066,
					    1_4734,
					    950,
					    1135,
					    10_5721,
					    3573,
					    83,
					    2_7352,
					    108,
					    4_9486,
					    2,
					]






					@classmethod
					def lowerCamelCase__							(       cls    : Optional[Any]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						List[Any]      							=					NllbTokenizer.from_pretrained(
												    cls.checkpoint_name ,       src_lang="""eng_Latn""" ,       tgt_lang="""ron_Latn"""  )
												__UpperCAmelCase   :						List[str]      							=					1
												return cls






					def lowerCamelCase__							(       self    : Any  ):
												'''simple docstring'''






												self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ace_Arab"""] ,       256_001  )
												self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ace_Latn"""] ,       256_002  )
												self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""fra_Latn"""] ,       256_057  )






					def lowerCamelCase__							(       self    : List[Any]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Optional[Any]      							=					self.tokenizer.batch_encode_plus(self.src_text  ).input_ids[0]
												self.assertListEqual(self.expected_src_tokens ,       lowerCAmelCase__  )






					def lowerCamelCase__							(       self    : Dict  ):
												'''simple docstring'''






												self.assertIn(lowerCAmelCase__ ,       self.tokenizer.all_special_ids  )
												# fmt: off
												__UpperCAmelCase   :						int      							=					[RO_CODE, 4_254, 98_068, 112_923, 39_072, 3_909, 713, 102_767, 26, 17_314, 35_642, 14_683, 33_118, 2_022, 66_987, 2, 256_047]
												# fmt: on

												__UpperCAmelCase   :						Tuple      							=					self.tokenizer.decode(lowerCAmelCase__ ,       skip_special_tokens=lowerCAmelCase__  )
												__UpperCAmelCase   :						Tuple      							=					self.tokenizer.decode(generated_ids[1:] ,       skip_special_tokens=lowerCAmelCase__  )
												self.assertEqual(lowerCAmelCase__ ,       lowerCAmelCase__  )
												self.assertNotIn(self.tokenizer.eos_token ,       lowerCAmelCase__  )






					def lowerCamelCase__							(       self    : List[Any]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Dict      							=					["""this is gunna be a long sentence """ * 20]
												assert isinstance(src_text[0] ,       lowerCAmelCase__  )
												__UpperCAmelCase   :						str      							=					10
												__UpperCAmelCase   :						Tuple      							=					self.tokenizer(lowerCAmelCase__ ,       max_length=lowerCAmelCase__ ,       truncation=lowerCAmelCase__  ).input_ids[0]
												self.assertEqual(ids[-1] ,       2  )
												self.assertEqual(ids[0] ,       lowerCAmelCase__  )
												self.assertEqual(len(lowerCAmelCase__  ) ,       lowerCAmelCase__  )






					def lowerCamelCase__							(       self    : Optional[Any]  ):
												'''simple docstring'''






												self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""]  ) ,       [256_203, 3]  )






					def lowerCamelCase__							(       self    : Optional[Any]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						int      							=					tempfile.mkdtemp()
												__UpperCAmelCase   :						Optional[int]      							=					self.tokenizer.fairseq_tokens_to_ids
												self.tokenizer.save_pretrained(lowerCAmelCase__  )
												__UpperCAmelCase   :						Union[str, Any]      							=					NllbTokenizer.from_pretrained(lowerCAmelCase__  )
												self.assertDictEqual(new_tok.fairseq_tokens_to_ids ,       lowerCAmelCase__  )






					@require_torch
					def lowerCamelCase__							(       self    : List[str]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Tuple      							=					self.tokenizer(
												    self.src_text ,       text_target=self.tgt_text ,       padding=lowerCAmelCase__ ,       truncation=lowerCAmelCase__ ,       max_length=len(self.expected_src_tokens  ) ,       return_tensors="""pt""" ,       )
												__UpperCAmelCase   :						List[Any]      							=					shift_tokens_right(
												    batch["""labels"""] ,       self.tokenizer.pad_token_id ,       self.tokenizer.lang_code_to_id["""ron_Latn"""]  )

												self.assertIsInstance(lowerCAmelCase__ ,       lowerCAmelCase__  )

												self.assertEqual((2, 15) ,       batch.input_ids.shape  )
												self.assertEqual((2, 15) ,       batch.attention_mask.shape  )
												__UpperCAmelCase   :						Any      							=					batch.input_ids.tolist()[0]
												self.assertListEqual(self.expected_src_tokens ,       lowerCAmelCase__  )
												self.assertEqual(lowerCAmelCase__ ,       batch.decoder_input_ids[0, 0]  )  # EOS
												# Test that special tokens are reset
												self.assertEqual(self.tokenizer.prefix_tokens ,       [EN_CODE]  )
												self.assertEqual(self.tokenizer.suffix_tokens ,       [self.tokenizer.eos_token_id]  )






					def lowerCamelCase__							(       self    : Tuple  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Optional[Any]      							=					self.tokenizer(self.src_text ,       padding=lowerCAmelCase__ ,       truncation=lowerCAmelCase__ ,       max_length=3 ,       return_tensors="""pt"""  )
												__UpperCAmelCase   :						int      							=					self.tokenizer(
												    text_target=self.tgt_text ,       padding=lowerCAmelCase__ ,       truncation=lowerCAmelCase__ ,       max_length=10 ,       return_tensors="""pt"""  )
												__UpperCAmelCase   :						Union[str, Any]      							=					targets["""input_ids"""]
												__UpperCAmelCase   :						List[str]      							=					shift_tokens_right(
												    lowerCAmelCase__ ,       self.tokenizer.pad_token_id ,       decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang] ,       )

												self.assertEqual(batch.input_ids.shape[1] ,       3  )
												self.assertEqual(batch.decoder_input_ids.shape[1] ,       10  )






					@require_torch
					def lowerCamelCase__							(       self    : Optional[int]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						List[Any]      							=					self.tokenizer._build_translation_inputs(
												    """A test""" ,       return_tensors="""pt""" ,       src_lang="""eng_Latn""" ,       tgt_lang="""fra_Latn"""  )

												self.assertEqual(
												    nested_simplify(lowerCAmelCase__  ) ,       {
												        # A, test, EOS, en_XX
												        """input_ids""": [[256_047, 70, 7_356, 2]],
												        """attention_mask""": [[1, 1, 1, 1]],
												        # ar_AR
												        """forced_bos_token_id""": 256_057,
												    } ,       )






					@require_torch
					def lowerCamelCase__							(       self    : Optional[Any]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						List[str]      							=					True
												__UpperCAmelCase   :						Optional[int]      							=					self.tokenizer(
												    """UN Chief says there is no military solution in Syria""" ,       src_lang="""eng_Latn""" ,       tgt_lang="""fra_Latn"""  )
												self.assertEqual(
												    inputs.input_ids ,       [16_297, 134_408, 25_653, 6_370, 248, 254, 103_929, 94_995, 108, 49_486, 2, 256_047]  )

												__UpperCAmelCase   :						List[str]      							=					False
												__UpperCAmelCase   :						int      							=					self.tokenizer(
												    """UN Chief says there is no military solution in Syria""" ,       src_lang="""eng_Latn""" ,       tgt_lang="""fra_Latn"""  )
												self.assertEqual(
												    inputs.input_ids ,       [256_047, 16_297, 134_408, 25_653, 6_370, 248, 254, 103_929, 94_995, 108, 49_486, 2]  )




"""simple docstring"""




import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel


def     lowercase			(SCREAMING_SNAKE_CASE_		:				int ,			SCREAMING_SNAKE_CASE_		:				Any=False  )   ->						str:
						SCREAMING_SNAKE_CASE													=					OmegaConf.load(SCREAMING_SNAKE_CASE_  )
						if display:
												print(yaml.dump(OmegaConf.to_container(SCREAMING_SNAKE_CASE_  )  )  )
						return config


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Tuple ,			SCREAMING_SNAKE_CASE_		:				Tuple=None ,			SCREAMING_SNAKE_CASE_		:				Tuple=None  )   ->						Any:
						if conf_path is None:
												SCREAMING_SNAKE_CASE													=					'./model_checkpoints/vqgan_only.yaml'
						SCREAMING_SNAKE_CASE													=					load_config(SCREAMING_SNAKE_CASE_ ,			display=SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					VQModel(**config.model.params  )
						if ckpt_path is None:
												SCREAMING_SNAKE_CASE													=					'./model_checkpoints/vqgan_only.pt'
						SCREAMING_SNAKE_CASE													=					torch.load(SCREAMING_SNAKE_CASE_ ,			map_location=SCREAMING_SNAKE_CASE_  )
						if ".ckpt" in ckpt_path:
												SCREAMING_SNAKE_CASE													=					sd['state_dict']
						model.load_state_dict(SCREAMING_SNAKE_CASE_ ,			strict=SCREAMING_SNAKE_CASE_  )
						model.to(SCREAMING_SNAKE_CASE_  )
						del sd
						return model


def     lowercase			(SCREAMING_SNAKE_CASE_		:				List[str] ,			SCREAMING_SNAKE_CASE_		:				int  )   ->						List[Any]:
						SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					model.encode(SCREAMING_SNAKE_CASE_  )
						print(F'VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'  )
						SCREAMING_SNAKE_CASE													=					model.decode(SCREAMING_SNAKE_CASE_  )
						return xrec


def     lowercase			(SCREAMING_SNAKE_CASE_		:				int ,			SCREAMING_SNAKE_CASE_		:				Any=False  )   ->						Dict:
						SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					string.rsplit('.' ,			1  )
						if reload:
												SCREAMING_SNAKE_CASE													=					importlib.import_module(SCREAMING_SNAKE_CASE_  )
												importlib.reload(SCREAMING_SNAKE_CASE_  )
						return getattr(importlib.import_module(SCREAMING_SNAKE_CASE_ ,			package=SCREAMING_SNAKE_CASE_  ) ,			cls  )


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Any  )   ->						Dict:
						if "target" not in config:
												raise KeyError('Expected key `target` to instantiate.'  )
						return get_obj_from_str(config['target']  )(**config.get('params' ,			{}  )  )


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Tuple ,			SCREAMING_SNAKE_CASE_		:				Any ,			SCREAMING_SNAKE_CASE_		:				List[Any]=True ,			SCREAMING_SNAKE_CASE_		:				int=True  )   ->						Any:
						SCREAMING_SNAKE_CASE													=					instantiate_from_config(SCREAMING_SNAKE_CASE_  )
						if sd is not None:
												model.load_state_dict(SCREAMING_SNAKE_CASE_  )
						if gpu:
												model.cuda()
						if eval_mode:
												model.eval()
						return {"model": model}


def     lowercase			(SCREAMING_SNAKE_CASE_		:				str ,			SCREAMING_SNAKE_CASE_		:				List[Any] ,			SCREAMING_SNAKE_CASE_		:				Optional[Any] ,			SCREAMING_SNAKE_CASE_		:				List[str]  )   ->						Any:
						# load the specified checkpoint
						if ckpt:
												SCREAMING_SNAKE_CASE													=					torch.load(SCREAMING_SNAKE_CASE_ ,			map_location='cpu'  )
												SCREAMING_SNAKE_CASE													=					pl_sd['global_step']
												print(F'loaded model from global step {global_step}.'  )
						else:
												SCREAMING_SNAKE_CASE													=					{'state_dict': None}
												SCREAMING_SNAKE_CASE													=					None
						SCREAMING_SNAKE_CASE													=					load_model_from_config(config.model ,			pl_sd['state_dict'] ,			gpu=SCREAMING_SNAKE_CASE_ ,			eval_mode=SCREAMING_SNAKE_CASE_  )['model']
						return model, global_step


