






import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


a__       =     logging.get_logger(__name__)

a__       =     """▁"""

a__       =     {"""vocab_file""": """prophetnet.tokenizer"""}

a__       =     {
    """vocab_file""": {
        """microsoft/xprophetnet-large-wiki100-cased""": (
            """https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer"""
        ),
    }
}

a__       =     {
    """microsoft/xprophetnet-large-wiki100-cased""": {"""do_lower_case""": False},
}

a__       =     {
    """microsoft/xprophetnet-large-wiki100-cased""": 5_1_2,
}

def _UpperCAmelCase   (	a						:	Dict ):
							snake_case__	  =       collections.OrderedDict()
							with open(a  ,					"""r"""  ,					encoding="""utf-8""" ) as reader:
														snake_case__	  =       reader.readlines()
							for index, token in enumerate(a ):
														snake_case__	  =       token.rstrip("""\n""" )
														snake_case__	  =       index
							return vocab



class    _lowerCAmelCase			(		lowercase_      ):



				"""simple docstring"""



				_lowercase				:   List[Any]      =  VOCAB_FILES_NAMES
				_lowercase				:   List[Any]      =  PRETRAINED_VOCAB_FILES_MAP
				_lowercase				:   int      =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_lowercase				:   str      =  ['''input_ids''', '''attention_mask''']



				def __init__(      self    :    Union[str, Any]			,		UpperCamelCase__    :    List[Any]			,		UpperCamelCase__    :    Optional[Any]="[SEP]"			,		UpperCamelCase__    :    List[str]="[SEP]"			,		UpperCamelCase__    :    Optional[Any]="[SEP]"			,		UpperCamelCase__    :    int="[UNK]"			,		UpperCamelCase__    :    int="[PAD]"			,		UpperCamelCase__    :    Dict="[CLS]"			,		UpperCamelCase__    :    Tuple="[MASK]"			,		UpperCamelCase__    :    Optional[Dict[str, Any]] = None			,		**UpperCamelCase__    :    Any			,		):






											'''simple docstring'''







											snake_case__	  =       {} if sp_model_kwargs is None else sp_model_kwargs

											super().__init__(
											    bos_token=UpperCamelCase__			,		eos_token=UpperCamelCase__			,		sep_token=UpperCamelCase__			,		unk_token=UpperCamelCase__			,		pad_token=UpperCamelCase__			,		cls_token=UpperCamelCase__			,		mask_token=UpperCamelCase__			,		sp_model_kwargs=self.sp_model_kwargs			,		**UpperCamelCase__			,		)

											try:
																		import sentencepiece as spm
											except ImportError:
																		logger.warning(
																		    """You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"""
																		    """ pip install sentencepiece""")
																		raise

											snake_case__	  =       spm.SentencePieceProcessor(**self.sp_model_kwargs)
											self.sp_model.Load(str(UpperCamelCase__))
											snake_case__	  =       vocab_file

											# Original fairseq vocab and spm vocab must be "aligned":
											# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
											# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
											# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
											# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

											# put special tokens and [unused] tokens into the vocab
											snake_case__	  =       {"""[PAD]""": 0, """[CLS]""": 1, """[SEP]""": 2, """[UNK]""": 3, """[MASK]""": 4}

											for i in range(1_0):
																		snake_case__	  =       F'''[unused{i}]'''
																		snake_case__	  =       5 + i

											# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
											snake_case__	  =       1_2
											snake_case__	  =       {v: k for k, v in self.fairseq_tokens_to_ids.items()}
											for k in self.fairseq_tokens_to_ids.keys():
																		self.unique_no_split_tokens.append(UpperCamelCase__)



				def __getstate__(      self    :    Dict):






											'''simple docstring'''







											snake_case__	  =       self.__dict__.copy()
											snake_case__	  =       None
											return state



				def __setstate__(      self    :    Dict			,		UpperCamelCase__    :    Optional[Any]):






											'''simple docstring'''







											snake_case__	  =       d
											try:
																		import sentencepiece as spm
											except ImportError:
																		logger.warning(
																		    """You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"""
																		    """ pip install sentencepiece""")
																		raise

											# for backward compatibility
											if not hasattr(self			,		"""sp_model_kwargs"""):
																		snake_case__	  =       {}

											snake_case__	  =       spm.SentencePieceProcessor(**self.sp_model_kwargs)
											self.sp_model.Load(self.vocab_file)



				def 					__magic_name__							(      self    :    List[Any]			,		UpperCamelCase__    :    List[int]			,		UpperCamelCase__    :    Optional[List[int]] = None			,		UpperCamelCase__    :    bool = False):






											'''simple docstring'''







											if already_has_special_tokens:
																		return super().get_special_tokens_mask(
																		    token_ids_a=UpperCamelCase__			,		token_ids_a=UpperCamelCase__			,		already_has_special_tokens=UpperCamelCase__)

											if token_ids_a is None:
																		return ([0] * len(UpperCamelCase__)) + [1]
											return ([0] * len(UpperCamelCase__)) + [1] + ([0] * len(UpperCamelCase__)) + [1]



				def 					__magic_name__							(      self    :    Tuple			,		UpperCamelCase__    :    List[int]			,		UpperCamelCase__    :    Optional[List[int]] = None):






											'''simple docstring'''







											snake_case__	  =       [self.sep_token_id]

											if token_ids_a is None:
																		return len(token_ids_a + sep) * [0]
											return len(token_ids_a + sep + sep + token_ids_a + sep) * [0]



				@property
				def 					__magic_name__							(      self    :    List[str]):






											'''simple docstring'''







											return len(self.sp_model) + self.fairseq_offset



				def 					__magic_name__							(      self    :    Optional[int]):






											'''simple docstring'''







											snake_case__	  =       {self.convert_ids_to_tokens(UpperCamelCase__): i for i in range(self.vocab_size)}
											vocab.update(self.added_tokens_encoder)
											return vocab



				def 					__magic_name__							(      self    :    List[str]			,		UpperCamelCase__    :    str):






											'''simple docstring'''







											return self.sp_model.encode(UpperCamelCase__			,		out_type=UpperCamelCase__)



				def 					__magic_name__							(      self    :    List[Any]			,		UpperCamelCase__    :    Tuple):






											'''simple docstring'''







											if token in self.fairseq_tokens_to_ids:
																		return self.fairseq_tokens_to_ids[token]
											snake_case__	  =       self.sp_model.PieceToId(UpperCamelCase__)

											# Need to return unknown token if the SP model returned 0
											return spm_id + self.fairseq_offset if spm_id else self.unk_token_id



				def 					__magic_name__							(      self    :    int			,		UpperCamelCase__    :    Tuple):






											'''simple docstring'''







											if index in self.fairseq_ids_to_tokens:
																		return self.fairseq_ids_to_tokens[index]
											return self.sp_model.IdToPiece(index - self.fairseq_offset)



				def 					__magic_name__							(      self    :    Optional[Any]			,		UpperCamelCase__    :    List[Any]):






											'''simple docstring'''







											snake_case__	  =       """""".join(UpperCamelCase__).replace(UpperCamelCase__			,		""" """).strip()
											return out_string



				def 					__magic_name__							(      self    :    str			,		UpperCamelCase__    :    str			,		UpperCamelCase__    :    Optional[str] = None):






											'''simple docstring'''







											if not os.path.isdir(UpperCamelCase__):
																		logger.error(F'''Vocabulary path ({save_directory}) should be a directory''')
																		return
											snake_case__	  =       os.path.join(
											    UpperCamelCase__			,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])

											if os.path.abspath(self.vocab_file) != os.path.abspath(UpperCamelCase__) and os.path.isfile(self.vocab_file):
																		copyfile(self.vocab_file			,		UpperCamelCase__)
											elif not os.path.isfile(self.vocab_file):
																		with open(UpperCamelCase__			,		"""wb""") as fi:
																									snake_case__	  =       self.sp_model.serialized_model_proto()
																									fi.write(UpperCamelCase__)

											return (out_vocab_file,)



				def 					__magic_name__							(      self    :    List[str]			,		UpperCamelCase__    :    List[int]			,		UpperCamelCase__    :    Optional[List[int]] = None):






											'''simple docstring'''







											if token_ids_a is None:
																		return token_ids_a + [self.sep_token_id]
											snake_case__	  =       [self.sep_token_id]
											return token_ids_a + sep + token_ids_a + sep








import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()


class    _lowerCAmelCase			(		lowercase_     ,      lowercase_     ,      unittest.TestCase      ):



				"""simple docstring"""



				_lowercase				:   Optional[int]      =  VQModel
				_lowercase				:   str      =  '''sample'''



				@property
				def 					__magic_name__							(      self    :    Union[str, Any]			,		UpperCamelCase__    :    Tuple=(3_2, 3_2)):






											'''simple docstring'''







											snake_case__	  =       4
											snake_case__	  =       3

											snake_case__	  =       floats_tensor((batch_size, num_channels) + sizes).to(UpperCamelCase__)

											return {"sample": image}



				@property
				def 					__magic_name__							(      self    :    str):






											'''simple docstring'''







											return (3, 3_2, 3_2)



				@property
				def 					__magic_name__							(      self    :    List[str]):






											'''simple docstring'''







											return (3, 3_2, 3_2)



				def 					__magic_name__							(      self    :    int):






											'''simple docstring'''







											snake_case__	  =       {
											    """block_out_channels""": [3_2, 6_4],
											    """in_channels""": 3,
											    """out_channels""": 3,
											    """down_block_types""": ["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],
											    """up_block_types""": ["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],
											    """latent_channels""": 3,
											}
											snake_case__	  =       self.dummy_input
											return init_dict, inputs_dict



				def 					__magic_name__							(      self    :    Optional[int]):






											'''simple docstring'''







											pass



				def 					__magic_name__							(      self    :    Tuple):






											'''simple docstring'''







											pass



				def 					__magic_name__							(      self    :    str):






											'''simple docstring'''







											snake_case__  , snake_case__	  =       VQModel.from_pretrained("""fusing/vqgan-dummy"""			,		output_loading_info=UpperCamelCase__)
											self.assertIsNotNone(UpperCamelCase__)
											self.assertEqual(len(loading_info["""missing_keys"""])			,		0)

											model.to(UpperCamelCase__)
											snake_case__	  =       model(**self.dummy_input)

											assert image is not None, "Make sure output is not None"



				def 					__magic_name__							(      self    :    Dict):






											'''simple docstring'''







											snake_case__	  =       VQModel.from_pretrained("""fusing/vqgan-dummy""")
											model.to(UpperCamelCase__).eval()

											torch.manual_seed(0)
											if torch.cuda.is_available():
																		torch.cuda.manual_seed_all(0)

											snake_case__	  =       torch.randn(1			,		model.config.in_channels			,		model.config.sample_size			,		model.config.sample_size)
											snake_case__	  =       image.to(UpperCamelCase__)
											with torch.no_grad():
																		snake_case__	  =       model(UpperCamelCase__).sample

											snake_case__	  =       output[0, -1, -3:, -3:].flatten().cpu()
											# fmt: off
											snake_case__	  =       torch.tensor([-0.01_53, -0.40_44, -0.18_80, -0.51_61, -0.24_18, -0.40_72, -0.16_12, -0.06_33, -0.01_43])
											# fmt: on
											self.assertTrue(torch.allclose(UpperCamelCase__			,		UpperCamelCase__			,		atol=1E-3))
