



import json
import os
import re
import warnings
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

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


UpperCamelCase__	:     int    =			logging.get_logger(__name__)

UpperCamelCase__	:     List[Any]    =			{
    '''source_spm''': '''source.spm''',
    '''target_spm''': '''target.spm''',
    '''vocab''': '''vocab.json''',
    '''target_vocab_file''': '''target_vocab.json''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

UpperCamelCase__	:     List[str]    =			{
    '''source_spm''': {
        '''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/source.spm'''
    },
    '''target_spm''': {
        '''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/target.spm'''
    },
    '''vocab''': {
        '''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json'''
    },
    '''tokenizer_config_file''': {
        '''Helsinki-NLP/opus-mt-en-de''': (
            '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/tokenizer_config.json'''
        )
    },
}

UpperCamelCase__	:     Dict    =			{'''Helsinki-NLP/opus-mt-en-de''': 5_12}
UpperCamelCase__	:     List[Any]    =			{}






class 						_UpperCamelCase  (							lowerCamelCase__   ):


			'''simple docstring'''

			_A				:		Tuple         =		VOCAB_FILES_NAMES
			_A				:		Union[str, Any]         =		PRETRAINED_VOCAB_FILES_MAP
			_A				:		Optional[int]         =		PRETRAINED_INIT_CONFIGURATION
			_A				:		Union[str, Any]         =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_A				:		Optional[Any]         =		['''input_ids''', '''attention_mask''']
			_A				:		Tuple         =		re.compile('''>>.+<<'''   )  # type: re.Pattern

			def __init__(							self					:			Tuple							,					lowerCAmelCase__					:			List[Any]							,					lowerCAmelCase__					:			Any							,					lowerCAmelCase__					:			str							,					lowerCAmelCase__					:			Optional[Any]=None							,					lowerCAmelCase__					:			int=None							,					lowerCAmelCase__					:			str=None							,					lowerCAmelCase__					:			str="<unk>"							,					lowerCAmelCase__					:			List[Any]="</s>"							,					lowerCAmelCase__					:			List[Any]="<pad>"							,					lowerCAmelCase__					:			Dict=5_1_2							,					lowerCAmelCase__					:			Optional[Dict[str, Any]] = None							,					lowerCAmelCase__					:			Optional[Any]=False							,					**lowerCAmelCase__					:			List[Any]							,					):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Tuple   	=		{} if sp_model_kwargs is None else sp_model_kwargs

					super().__init__(
					    # bos_token=bos_token,  unused. Start decoding with config.decoder_start_token_id
					    source_lang=lowerCAmelCase__							,					target_lang=lowerCAmelCase__							,					unk_token=lowerCAmelCase__							,					eos_token=lowerCAmelCase__							,					pad_token=lowerCAmelCase__							,					model_max_length=lowerCAmelCase__							,					sp_model_kwargs=self.sp_model_kwargs							,					target_vocab_file=lowerCAmelCase__							,					separate_vocabs=lowerCAmelCase__							,					**lowerCAmelCase__							,					)
					assert Path(lowerCAmelCase__						).exists(), F"cannot find spm source {source_spm}"

					__SCREAMING_SNAKE_CASE	:		int   	=		separate_vocabs
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		load_json(lowerCAmelCase__						)
					if self.unk_token not in self.encoder:
							raise KeyError("""<unk> token must be in vocab"""						)
					assert self.pad_token in self.encoder

					if separate_vocabs:
							__SCREAMING_SNAKE_CASE	:		Dict   	=		load_json(lowerCAmelCase__						)
							__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		{v: k for k, v in self.target_encoder.items()}
							__SCREAMING_SNAKE_CASE	:		Dict   	=		[]
					else:
							__SCREAMING_SNAKE_CASE	:		List[str]   	=		{v: k for k, v in self.encoder.items()}
							__SCREAMING_SNAKE_CASE	:		list   	=		[k for k in self.encoder if k.startswith(""">>"""						) and k.endswith("""<<"""						)]

					__SCREAMING_SNAKE_CASE	:		int   	=		source_lang
					__SCREAMING_SNAKE_CASE	:		int   	=		target_lang
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		[source_spm, target_spm]

					# load SentencePiece model for pre-processing
					__SCREAMING_SNAKE_CASE	:		int   	=		load_spm(lowerCAmelCase__							,					self.sp_model_kwargs						)
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		load_spm(lowerCAmelCase__							,					self.sp_model_kwargs						)
					__SCREAMING_SNAKE_CASE	:		int   	=		self.spm_source
					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		self.encoder

					# Multilingual target side: default to using first supported language code.

					self._setup_normalizer()

			def UpperCamelCase__ (							self					:			Any						):





					"""simple docstring"""






					try:
							from sacremoses import MosesPunctNormalizer

							__SCREAMING_SNAKE_CASE	:		int   	=		MosesPunctNormalizer(self.source_lang						).normalize
					except (ImportError, FileNotFoundError):
							warnings.warn("""Recommended: pip install sacremoses."""						)
							__SCREAMING_SNAKE_CASE	:		List[str]   	=		lambda lowerCAmelCase__						: x

			def UpperCamelCase__ (							self					:			Dict							,					lowerCAmelCase__					:			str						):





					"""simple docstring"""






					return self.punc_normalizer(lowerCAmelCase__						) if x else ""

			def UpperCamelCase__ (							self					:			List[str]							,					lowerCAmelCase__					:			List[Any]						):





					"""simple docstring"""






					return self.current_encoder.get(lowerCAmelCase__							,					self.current_encoder[self.unk_token]						)

			def UpperCamelCase__ (							self					:			str							,					lowerCAmelCase__					:			str						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Dict   	=		self.language_code_re.match(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		list   	=		[match.group(0						)] if match else []
					return code, self.language_code_re.sub(""""""							,					lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			Dict							,					lowerCAmelCase__					:			str						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		self.remove_language_code(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		self.current_spm.encode(lowerCAmelCase__							,					out_type=lowerCAmelCase__						)
					return code + pieces

			def UpperCamelCase__ (							self					:			Optional[Any]							,					lowerCAmelCase__					:			int						):





					"""simple docstring"""






					return self.decoder.get(lowerCAmelCase__							,					self.unk_token						)

			def UpperCamelCase__ (							self					:			List[str]							,					lowerCAmelCase__					:			Optional[int]							,					**lowerCAmelCase__					:			Any						):





					"""simple docstring"""






					return super().batch_decode(lowerCAmelCase__							,					**lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			Union[str, Any]							,					lowerCAmelCase__					:			Union[str, Any]							,					**lowerCAmelCase__					:			str						):





					"""simple docstring"""






					return super().decode(lowerCAmelCase__							,					**lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			int							,					lowerCAmelCase__					:			List[str]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		self.spm_source if self._decode_use_source_tokenizer else self.spm_target
					__SCREAMING_SNAKE_CASE	:		Tuple   	=		[]
					__SCREAMING_SNAKE_CASE	:		int   	=		""""""
					for token in tokens:
							# make sure that special tokens are not decoded using sentencepiece model
							if token in self.all_special_tokens:
									out_string += sp_model.decode_pieces(lowerCAmelCase__						) + token + " "
									__SCREAMING_SNAKE_CASE	:		Any   	=		[]
							else:
									current_sub_tokens.append(lowerCAmelCase__						)
					out_string += sp_model.decode_pieces(lowerCAmelCase__						)
					return out_string.strip()

			def UpperCamelCase__ (							self					:			str							,					lowerCAmelCase__					:			Tuple							,					lowerCAmelCase__					:			Optional[Any]=None						):





					"""simple docstring"""






					if token_ids_a is None:
							return token_ids_a + [self.eos_token_id]
					# We don't expect to process pairs, but leave the pair logic for API consistency
					return token_ids_a + token_ids_a + [self.eos_token_id]

			def UpperCamelCase__ (							self					:			Optional[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Tuple   	=		self.spm_source
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		self.encoder

			def UpperCamelCase__ (							self					:			str						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		self.spm_target
					if self.separate_vocabs:
							__SCREAMING_SNAKE_CASE	:		List[Any]   	=		self.target_encoder

			@property
			def UpperCamelCase__ (							self					:			int						):





					"""simple docstring"""






					return len(self.encoder						)

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





					"""simple docstring"""






					if not os.path.isdir(lowerCAmelCase__						):
							logger.error(F"Vocabulary path ({save_directory}) should be a directory"						)
							return
					__SCREAMING_SNAKE_CASE	:		int   	=		[]

					if self.separate_vocabs:
							__SCREAMING_SNAKE_CASE	:		Tuple   	=		os.path.join(
							    lowerCAmelCase__							,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab"""]							,					)
							__SCREAMING_SNAKE_CASE	:		int   	=		os.path.join(
							    lowerCAmelCase__							,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""target_vocab_file"""]							,					)
							save_json(self.encoder							,					lowerCAmelCase__						)
							save_json(self.target_encoder							,					lowerCAmelCase__						)
							saved_files.append(lowerCAmelCase__						)
							saved_files.append(lowerCAmelCase__						)
					else:
							__SCREAMING_SNAKE_CASE	:		List[str]   	=		os.path.join(
							    lowerCAmelCase__							,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab"""]						)
							save_json(self.encoder							,					lowerCAmelCase__						)
							saved_files.append(lowerCAmelCase__						)

					for spm_save_filename, spm_orig_path, spm_model in zip(
					    [VOCAB_FILES_NAMES["""source_spm"""], VOCAB_FILES_NAMES["""target_spm"""]]							,					self.spm_files							,					[self.spm_source, self.spm_target]							,					):
							__SCREAMING_SNAKE_CASE	:		Tuple   	=		os.path.join(
							    lowerCAmelCase__							,					(filename_prefix + """-""" if filename_prefix else """""") + spm_save_filename						)
							if os.path.abspath(lowerCAmelCase__						) != os.path.abspath(lowerCAmelCase__						) and os.path.isfile(lowerCAmelCase__						):
									copyfile(lowerCAmelCase__							,					lowerCAmelCase__						)
									saved_files.append(lowerCAmelCase__						)
							elif not os.path.isfile(lowerCAmelCase__						):
									with open(lowerCAmelCase__							,					"""wb"""						) as fi:
											__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		spm_model.serialized_model_proto()
											fi.write(lowerCAmelCase__						)
									saved_files.append(lowerCAmelCase__						)

					return tuple(lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			Tuple						):





					"""simple docstring"""






					return self.get_src_vocab()

			def UpperCamelCase__ (							self					:			Union[str, Any]						):





					"""simple docstring"""






					return dict(self.encoder							,					**self.added_tokens_encoder						)

			def UpperCamelCase__ (							self					:			str						):





					"""simple docstring"""






					return dict(self.target_encoder							,					**self.added_tokens_decoder						)

			def __getstate__(							self					:			List[str]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		str   	=		self.__dict__.copy()
					state.update(
					    {k: None for k in ["""spm_source""", """spm_target""", """current_spm""", """punc_normalizer""", """target_vocab_file"""]}						)
					return state

			def __setstate__(							self					:			Any							,					lowerCAmelCase__					:			Dict						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		str   	=		d

					# for backward compatibility
					if not hasattr(self							,					"""sp_model_kwargs"""						):
							__SCREAMING_SNAKE_CASE	:		List[str]   	=		{}

					__SCREAMING_SNAKE_CASE	:		Tuple   	=		(load_spm(lowerCAmelCase__							,					self.sp_model_kwargs						) for f in self.spm_files)
					__SCREAMING_SNAKE_CASE	:		Tuple   	=		self.spm_source
					self._setup_normalizer()

			def UpperCamelCase__ (							self					:			Tuple							,					*lowerCAmelCase__					:			Optional[int]							,					**lowerCAmelCase__					:			List[str]						):





					"""simple docstring"""






					return 1

			def UpperCamelCase__ (							self					:			Union[str, Any]							,					lowerCAmelCase__					:			Any						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Tuple   	=		set(self.all_special_ids						)  # call it once instead of inside list comp
					all_special_ids.remove(self.unk_token_id						)  # <unk> is only sometimes special
					return [1 if x in all_special_ids else 0 for x in seq]



			def UpperCamelCase__ (							self					:			List[str]							,					lowerCAmelCase__					:			List							,					lowerCAmelCase__					:			Optional[List] = None							,					lowerCAmelCase__					:			bool = False						):





					"""simple docstring"""






					if already_has_special_tokens:
							return self._special_token_mask(lowerCAmelCase__						)
					elif token_ids_a is None:
							return self._special_token_mask(lowerCAmelCase__						) + [1]
					else:
							return self._special_token_mask(token_ids_a + token_ids_a						) + [1]


def     lowerCAmelCase_       (     _lowerCamelCase:       str	,				_lowerCamelCase:       Dict[str, Any] ):
		__SCREAMING_SNAKE_CASE	:		str   	=		sentencepiece.SentencePieceProcessor(**_lowerCamelCase )
		spm.Load(_lowerCamelCase )
		return spm


def     lowerCAmelCase_       (     _lowerCamelCase:       Any	,				_lowerCamelCase:       str ):
		with open(_lowerCamelCase	,				"""w""" ) as f:
				json.dump(_lowerCamelCase	,				_lowerCamelCase	,				indent=2 )


def     lowerCAmelCase_       (     _lowerCamelCase:       str ):
		with open(_lowerCamelCase	,				"""r""" ) as f:
				return json.load(_lowerCamelCase )




'''simple docstring'''




import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest






class 						_UpperCamelCase  (							lowerCamelCase__   ):


			'''simple docstring'''

			_A				:		int         =		(VQDiffusionScheduler,)

			def UpperCamelCase__ (							self					:			List[str]							,					**lowerCAmelCase__					:			Dict						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		int   	=		{
					    """num_vec_classes""": 4_0_9_7,
					    """num_train_timesteps""": 1_0_0,
					}

					config.update(**lowerCAmelCase__						)
					return config

			def UpperCamelCase__ (							self					:			Dict							,					lowerCAmelCase__					:			int						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Dict   	=		4
					__SCREAMING_SNAKE_CASE	:		Tuple   	=		8
					__SCREAMING_SNAKE_CASE	:		int   	=		8

					__SCREAMING_SNAKE_CASE	:		Dict   	=		torch.randint(0							,					lowerCAmelCase__							,					(batch_size, height * width)						)

					return sample

			@property
			def UpperCamelCase__ (							self					:			str						):





					"""simple docstring"""






					assert False

			def UpperCamelCase__ (							self					:			Dict							,					lowerCAmelCase__					:			str						):





					"""simple docstring"""






					def model(lowerCAmelCase__					:			Optional[Any]							,					lowerCAmelCase__					:			Any							,					*lowerCAmelCase__					:			Optional[int]						):
							__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		sample.shape
							__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)						)
							__SCREAMING_SNAKE_CASE	:		str   	=		F.log_softmax(logits.double()							,					dim=1						).float()
							return return_value

					return model

			def UpperCamelCase__ (							self					:			Optional[int]						):





					"""simple docstring"""






					for timesteps in [2, 5, 1_0_0, 1_0_0_0]:
							self.check_over_configs(num_train_timesteps=lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			int						):





					"""simple docstring"""






					for num_vec_classes in [5, 1_0_0, 1_0_0_0, 4_0_0_0]:
							self.check_over_configs(num_vec_classes=lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			Any						):





					"""simple docstring"""






					for t in [0, 5_0, 9_9]:
							self.check_over_forward(time_step=lowerCAmelCase__						)



			def UpperCamelCase__ (							self					:			List[Any]						):





					"""simple docstring"""






					pass