
"""simple docstring"""


import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
			from .tokenization_xglm import XGLMTokenizer
else:
			__A					=			None


__A					=			logging.get_logger(__name__)

__A					=			{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

__A					=			{
    'vocab_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model',
    },
    'tokenizer_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json',
    },
}

__A					=			{
    'facebook/xglm-564M': 2048,
}







class 						lowerCamelCase__   (   __magic_name__							):





			'''simple docstring'''
			lowerCamelCase       =						VOCAB_FILES_NAMES
			lowerCamelCase       =						PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase       =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase       =						['''input_ids''', '''attention_mask''']
			lowerCamelCase       =						XGLMTokenizer



			def __init__(				self	,	__UpperCAmelCase=None	,	__UpperCAmelCase=None	,	__UpperCAmelCase="<s>"	,	__UpperCAmelCase="</s>"	,	__UpperCAmelCase="</s>"	,	__UpperCAmelCase="<s>"	,	__UpperCAmelCase="<unk>"	,	__UpperCAmelCase="<pad>"	,	**__UpperCAmelCase	,	)					->	Tuple:
				# Compatibility with the original tokenizer
				_lowerCAmelCase								=7
				_lowerCAmelCase								=[f'''<madeupword{i}>''' for i in range(self.num_madeup_words    )]

				_lowerCAmelCase								=kwargs.get("""additional_special_tokens"""	,	[]    )
				kwargs["additional_special_tokens"] += [
				    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
				]

				super().__init__(
				    __UpperCAmelCase	,	tokenizer_file=__UpperCAmelCase	,	bos_token=__UpperCAmelCase	,	eos_token=__UpperCAmelCase	,	sep_token=__UpperCAmelCase	,	cls_token=__UpperCAmelCase	,	unk_token=__UpperCAmelCase	,	pad_token=__UpperCAmelCase	,	**__UpperCAmelCase	,	)

				_lowerCAmelCase								=vocab_file
				_lowerCAmelCase								=False if not self.vocab_file else True



			def    _lowerCAmelCase						(				self	,	__UpperCAmelCase	,	__UpperCAmelCase = None    )					->	List[int]:

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



			def    _lowerCAmelCase						(				self	,	__UpperCAmelCase	,	__UpperCAmelCase = None    )					->	List[int]:
				_lowerCAmelCase								=[self.sep_token_id]

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



			def    _lowerCAmelCase						(				self	,	__UpperCAmelCase	,	__UpperCAmelCase = None    )					->	Tuple[str]:
				if not self.can_save_slow_tokenizer:
					raise ValueError(
					    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
					    """tokenizer."""    )

				if not os.path.isdir(__UpperCAmelCase    ):
					logger.error(f'''Vocabulary path ({save_directory}) should be a directory.'''    )
					return
				_lowerCAmelCase								=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    ):
					copyfile(self.vocab_file	,	__UpperCAmelCase    )

				return (out_vocab_file,)





"""simple docstring"""


import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor







class 						lowerCamelCase__   (   unittest.TestCase							):





			'''simple docstring'''



			@property
			def    _lowerCAmelCase						(				self    )					->	List[str]:
				_lowerCAmelCase								=1
				_lowerCAmelCase								=3
				_lowerCAmelCase								=8
				_lowerCAmelCase								=8

				_lowerCAmelCase								=torch.rand((batch_size, num_channels, height, width)    )

				return sample



			def    _lowerCAmelCase						(				self	,	__UpperCAmelCase    )					->	Any:
				if isinstance(image[0]	,	PIL.Image.Image    ):
					return np.stack([np.array(__UpperCAmelCase    ) for i in image]	,	axis=0    )
				elif isinstance(__UpperCAmelCase	,	torch.Tensor    ):
					return image.cpu().numpy().transpose(0	,	2	,	3	,	1    )
				return image



			def    _lowerCAmelCase						(				self    )					->	Tuple:
				_lowerCAmelCase								=VaeImageProcessor(do_resize=__UpperCAmelCase	,	do_normalize=__UpperCAmelCase    )

				_lowerCAmelCase								=self.dummy_sample
				_lowerCAmelCase								=self.to_np(__UpperCAmelCase    )

				for output_type in ["pt", "np", "pil"]:
					_lowerCAmelCase								=image_processor.postprocess(
					    image_processor.preprocess(__UpperCAmelCase    )	,	output_type=__UpperCAmelCase	,	)
					_lowerCAmelCase								=self.to_np(__UpperCAmelCase    )
					_lowerCAmelCase								=(input_np * 2_55).round() if output_type == """pil""" else input_np
					assert (
					    np.abs(in_np - out_np    ).max() < 1e-6
					), f'''decoded output does not match input for output_type {output_type}'''



			def    _lowerCAmelCase						(				self    )					->	Union[str, Any]:
				_lowerCAmelCase								=VaeImageProcessor(do_resize=__UpperCAmelCase	,	do_normalize=__UpperCAmelCase    )
				_lowerCAmelCase								=self.dummy_sample.cpu().numpy().transpose(0	,	2	,	3	,	1    )

				for output_type in ["pt", "np", "pil"]:
					_lowerCAmelCase								=image_processor.postprocess(image_processor.preprocess(__UpperCAmelCase    )	,	output_type=__UpperCAmelCase    )

					_lowerCAmelCase								=self.to_np(__UpperCAmelCase    )
					_lowerCAmelCase								=(input_np * 2_55).round() if output_type == """pil""" else input_np
					assert (
					    np.abs(in_np - out_np    ).max() < 1e-6
					), f'''decoded output does not match input for output_type {output_type}'''



			def    _lowerCAmelCase						(				self    )					->	List[str]:
				_lowerCAmelCase								=VaeImageProcessor(do_resize=__UpperCAmelCase	,	do_normalize=__UpperCAmelCase    )

				_lowerCAmelCase								=self.dummy_sample.cpu().numpy().transpose(0	,	2	,	3	,	1    )
				_lowerCAmelCase								=image_processor.numpy_to_pil(__UpperCAmelCase    )

				for output_type in ["pt", "np", "pil"]:
					_lowerCAmelCase								=image_processor.postprocess(image_processor.preprocess(__UpperCAmelCase    )	,	output_type=__UpperCAmelCase    )
					for i, o in zip(__UpperCAmelCase	,	__UpperCAmelCase    ):
						_lowerCAmelCase								=np.array(__UpperCAmelCase    )
						_lowerCAmelCase								=self.to_np(__UpperCAmelCase    ) if output_type == """pil""" else (self.to_np(__UpperCAmelCase    ) * 2_55).round()
						assert (
						    np.abs(in_np - out_np    ).max() < 1e-6
						), f'''decoded output does not match input for output_type {output_type}'''



			def    _lowerCAmelCase						(				self    )					->	Tuple:
				_lowerCAmelCase								=VaeImageProcessor(do_resize=__UpperCAmelCase	,	do_normalize=__UpperCAmelCase    )

				_lowerCAmelCase								=self.dummy_sample
				_lowerCAmelCase								=input_pt_ad.squeeze(0    )

				_lowerCAmelCase								=image_processor.postprocess(
				    image_processor.preprocess(__UpperCAmelCase    )	,	output_type="""np"""	,	)
				_lowerCAmelCase								=image_processor.postprocess(
				    image_processor.preprocess(__UpperCAmelCase    )	,	output_type="""np"""	,	)

				_lowerCAmelCase								=self.to_np(self.dummy_sample    )
				_lowerCAmelCase								=input_np_ad.squeeze(0    )

				_lowerCAmelCase								=image_processor.postprocess(
				    image_processor.preprocess(__UpperCAmelCase    )	,	output_type="""np"""	,	)
				_lowerCAmelCase								=image_processor.postprocess(
				    image_processor.preprocess(__UpperCAmelCase    )	,	output_type="""np"""	,	)

				assert np.abs(out_pt_ad - out_pt_ad    ).max() < 1e-6
				assert np.abs(out_np_ad - out_np_ad    ).max() < 1e-6



			def    _lowerCAmelCase						(				self    )					->	Optional[Any]:
				_lowerCAmelCase								=VaeImageProcessor(do_resize=__UpperCAmelCase	,	do_normalize=__UpperCAmelCase    )

				_lowerCAmelCase								=self.dummy_sample
				_lowerCAmelCase								=list(__UpperCAmelCase    )

				_lowerCAmelCase								=image_processor.postprocess(
				    image_processor.preprocess(__UpperCAmelCase    )	,	output_type="""np"""	,	)

				_lowerCAmelCase								=image_processor.postprocess(
				    image_processor.preprocess(__UpperCAmelCase    )	,	output_type="""np"""	,	)

				_lowerCAmelCase								=self.to_np(self.dummy_sample    )
				list(__UpperCAmelCase    )

				_lowerCAmelCase								=image_processor.postprocess(
				    image_processor.preprocess(__UpperCAmelCase    )	,	output_type="""np"""	,	)

				_lowerCAmelCase								=image_processor.postprocess(
				    image_processor.preprocess(__UpperCAmelCase    )	,	output_type="""np"""	,	)

				assert np.abs(out_pt_ad - out_pt_list    ).max() < 1e-6
				assert np.abs(out_np_ad - out_np_list    ).max() < 1e-6



