





import json
import os
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


lowerCAmelCase_        =							logging.get_logger(__name__)

lowerCAmelCase_        =							'''▁'''

lowerCAmelCase_        =							{
    '''vocab_file''': '''vocab.json''',
    '''spm_file''': '''sentencepiece.bpe.model''',
}

lowerCAmelCase_        =							{
    '''vocab_file''': {
        '''facebook/s2t-small-librispeech-asr''': (
            '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json'''
        ),
    },
    '''spm_file''': {
        '''facebook/s2t-small-librispeech-asr''': (
            '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model'''
        )
    },
}

lowerCAmelCase_        =							{
    '''facebook/s2t-small-librispeech-asr''': 1_0_2_4,
}

lowerCAmelCase_        =							['''pt''', '''fr''', '''ru''', '''nl''', '''ro''', '''it''', '''es''', '''de''']

lowerCAmelCase_        =							{'''mustc''': MUSTC_LANGS}



class    __lowerCAmelCase			(     _a	):
			lowerCamelCase_	:	Optional[Any]			     = VOCAB_FILES_NAMES
			lowerCamelCase_	:	List[Any]			     = PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase_	:	int			     = MAX_MODEL_INPUT_SIZES
			lowerCamelCase_	:	List[str]			     = ["input_ids", "attention_mask"]

			lowerCamelCase_	:	List[int]			     = []






			def __init__(self   ,				__magic_name__   ,				__magic_name__   ,				__magic_name__="<s>"   ,				__magic_name__="</s>"   ,				__magic_name__="<pad>"   ,				__magic_name__="<unk>"   ,				__magic_name__=False   ,				__magic_name__=False   ,				__magic_name__=None   ,				__magic_name__=None   ,				__magic_name__ = None   ,				**__magic_name__   ,				)	->  Any:


							'''simple docstring'''



							snake_case_		:       int           =    {} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    bos_token=__UpperCAmelCase   ,				eos_token=__UpperCAmelCase   ,				unk_token=__UpperCAmelCase   ,				pad_token=__UpperCAmelCase   ,				do_upper_case=__UpperCAmelCase   ,				do_lower_case=__UpperCAmelCase   ,				tgt_lang=__UpperCAmelCase   ,				lang_codes=__UpperCAmelCase   ,				sp_model_kwargs=self.sp_model_kwargs   ,				**__UpperCAmelCase   ,				)
							snake_case_		:       Optional[int]           =    do_upper_case
							snake_case_		:       Any           =    do_lower_case

							snake_case_		:       Tuple           =    load_json(__UpperCAmelCase       )
							snake_case_		:       Tuple           =    {v: k for k, v in self.encoder.items()}
							snake_case_		:       Any           =    spm_file
							snake_case_		:       List[str]           =    load_spm(__UpperCAmelCase   ,				self.sp_model_kwargs       )

							if lang_codes is not None:
											snake_case_		:       Any           =    lang_codes
											snake_case_		:       Union[str, Any]           =    LANGUAGES[lang_codes]
											snake_case_		:       Union[str, Any]           =    [F'''<lang:{lang}>''' for lang in self.langs]
											snake_case_		:       List[Any]           =    {lang: self.sp_model.PieceToId(F'''<lang:{lang}>'''       ) for lang in self.langs}

											snake_case_		:       Optional[int]           =    self.lang_tokens
											snake_case_		:       Tuple           =    tgt_lang if tgt_lang is not None else self.langs[0]

											self.set_tgt_lang_special_tokens(self._tgt_lang       )
							else:
											snake_case_		:       Tuple           =    {}






			@property
			def      lowerCamelCase    (self       )	->  Optional[int]:


							'''simple docstring'''



							return len(self.encoder       )






			@property
			def      lowerCamelCase    (self       )	->  List[Any]:


							'''simple docstring'''



							return self._tgt_lang






			@tgt_lang.setter
			def      lowerCamelCase    (self   ,				__magic_name__       )	->  int:


							'''simple docstring'''



							snake_case_		:       str           =    new_tgt_lang
							self.set_tgt_lang_special_tokens(__UpperCAmelCase       )






			def      lowerCamelCase    (self   ,				__magic_name__       )	->  Optional[int]:


							'''simple docstring'''



							snake_case_		:       List[str]           =    self.lang_code_to_id[tgt_lang]
							snake_case_		:       Dict           =    [lang_code_id]






			def      lowerCamelCase    (self   ,				__magic_name__       )	->  int:


							'''simple docstring'''



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






			def      lowerCamelCase    (self   ,				__magic_name__       )	->  Optional[Any]:


							'''simple docstring'''



							return self.encoder.get(__UpperCAmelCase   ,				self.encoder[self.unk_token]       )






			def      lowerCamelCase    (self   ,				__magic_name__       )	->  Tuple:


							'''simple docstring'''



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






			def      lowerCamelCase    (self   ,				__magic_name__       )	->  Union[str, Any]:


							'''simple docstring'''



							snake_case_		:       Dict           =    []
							snake_case_		:       Tuple           =    ""
							for token in tokens:
											# make sure that special tokens are not decoded using sentencepiece model
											if token in self.all_special_tokens:
															snake_case_		:       Optional[int]           =    self.sp_model.decode(__UpperCAmelCase       )
															out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
															snake_case_		:       Optional[int]           =    []
											else:
															current_sub_tokens.append(__UpperCAmelCase       )
							snake_case_		:       Union[str, Any]           =    self.sp_model.decode(__UpperCAmelCase       )
							out_string += decoded.upper() if self.do_upper_case else decoded
							return out_string.strip()






			def      lowerCamelCase    (self   ,				__magic_name__   ,				__magic_name__=None       )	->  Tuple:


							'''simple docstring'''



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






			def      lowerCamelCase    (self   ,				__magic_name__   ,				__magic_name__ = None   ,				__magic_name__ = False       )	->  Dict:


							'''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       )

							snake_case_		:       int           =    [1] * len(self.prefix_tokens       )
							snake_case_		:       str           =    [1]
							if token_ids_a is None:
											return prefix_ones + ([0] * len(__UpperCAmelCase       )) + suffix_ones
							return prefix_ones + ([0] * len(__UpperCAmelCase       )) + ([0] * len(__UpperCAmelCase       )) + suffix_ones






			def      lowerCamelCase    (self       )	->  Union[str, Any]:


							'''simple docstring'''



							snake_case_		:       Optional[int]           =    self.encoder.copy()
							vocab.update(self.added_tokens_encoder       )
							return vocab






			def __getstate__(self       )	->  Optional[Any]:


							'''simple docstring'''



							snake_case_		:       Any           =    self.__dict__.copy()
							snake_case_		:       List[str]           =    None
							return state






			def __setstate__(self   ,				__magic_name__       )	->  Optional[Any]:


							'''simple docstring'''



							snake_case_		:       Optional[int]           =    d

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

							snake_case_		:       Union[str, Any]           =    load_spm(self.spm_file   ,				self.sp_model_kwargs       )






			def      lowerCamelCase    (self   ,				__magic_name__   ,				__magic_name__ = None       )	->  Dict:


							'''simple docstring'''



							snake_case_		:       int           =    Path(__UpperCAmelCase       )
							assert save_dir.is_dir(), F'''{save_directory} should be a directory'''
							snake_case_		:       List[str]           =    save_dir / (
							    (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["vocab_file"]
							)
							snake_case_		:       Union[str, Any]           =    save_dir / (
							    (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["spm_file"]
							)

							save_json(self.encoder   ,				__UpperCAmelCase       )

							if os.path.abspath(self.spm_file       ) != os.path.abspath(__UpperCAmelCase       ) and os.path.isfile(self.spm_file       ):
											copyfile(self.spm_file   ,				__UpperCAmelCase       )
							elif not os.path.isfile(self.spm_file       ):
											with open(__UpperCAmelCase   ,				'''wb'''       ) as fi:
															snake_case_		:       Any           =    self.sp_model.serialized_model_proto()
															fi.write(__UpperCAmelCase       )

							return (str(__UpperCAmelCase       ), str(__UpperCAmelCase       ))






def 	lowerCamelCase_		(							_UpperCamelCase					,							_UpperCamelCase					)						->		sentencepiece.SentencePieceProcessor:






				"""simple docstring"""


				snake_case_		:       str           =    sentencepiece.SentencePieceProcessor(**__UpperCamelCase					)
				spm.Load(str(__UpperCamelCase					)					)
				return spm






def 	lowerCamelCase_		(							_UpperCamelCase					)						->		Union[Dict, List]:






				"""simple docstring"""


				with open(__UpperCamelCase					,							'''r'''					) as f:
								return json.load(__UpperCamelCase					)






def 	lowerCamelCase_		(							_UpperCamelCase					,							_UpperCamelCase					)						->		None:






				"""simple docstring"""


				with open(__UpperCamelCase					,							'''w'''					) as f:
								json.dump(__UpperCamelCase					,							__UpperCamelCase					,							indent=2					)





import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class 				_a						(       unittest.TestCase			):





							'''simple docstring'''

							@property
							def 		_A	(							self				):







											"""simple docstring"""



											torch.manual_seed(0				)
											a__	:							str      						= UNetaDModel(
											    block_out_channels=(32, 64) ,					layers_per_block=2 ,					sample_size=32 ,					in_channels=3 ,					out_channels=3 ,					down_block_types=("DownBlock2D", "AttnDownBlock2D") ,					up_block_types=("AttnUpBlock2D", "UpBlock2D") ,					)
											return model




							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Dict      						= self.dummy_uncond_unet
											a__	:							Optional[Any]      						= PNDMScheduler()

											a__	:							Union[str, Any]      						= PNDMPipeline(unet=__UpperCAmelCase ,					scheduler=__UpperCAmelCase				)
											pndm.to(__UpperCAmelCase				)
											pndm.set_progress_bar_config(disable=__UpperCAmelCase				)

											a__	:							Tuple      						= torch.manual_seed(0				)
											a__	:							Tuple      						= pndm(generator=__UpperCAmelCase ,					num_inference_steps=20 ,					output_type="numpy"				).images

											a__	:							Optional[int]      						= torch.manual_seed(0				)
											a__	:							Tuple      						= pndm(generator=__UpperCAmelCase ,					num_inference_steps=20 ,					output_type="numpy" ,					return_dict=__UpperCAmelCase				)[0]

											a__	:							str      						= image[0, -3:, -3:, -1]
											a__	:							int      						= image_from_tuple[0, -3:, -3:, -1]

											assert image.shape == (1, 32, 32, 3)
											a__	:							int      						= np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]				)

											assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2
											assert np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1E-2





@slow
@require_torch
class 				_a						(       unittest.TestCase			):





							'''simple docstring'''

							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Dict      						= "google/ddpm-cifar10-32"

											a__	:							List[Any]      						= UNetaDModel.from_pretrained(__UpperCAmelCase				)
											a__	:							Optional[Any]      						= PNDMScheduler()

											a__	:							int      						= PNDMPipeline(unet=__UpperCAmelCase ,					scheduler=__UpperCAmelCase				)
											pndm.to(__UpperCAmelCase				)
											pndm.set_progress_bar_config(disable=__UpperCAmelCase				)
											a__	:							Union[str, Any]      						= torch.manual_seed(0				)
											a__	:							Union[str, Any]      						= pndm(generator=__UpperCAmelCase ,					output_type="numpy"				).images

											a__	:							List[Any]      						= image[0, -3:, -3:, -1]

											assert image.shape == (1, 32, 32, 3)
											a__	:							Dict      						= np.array([0.1_5_6_4, 0.1_4_6_4_5, 0.1_4_0_6, 0.1_4_7_1_5, 0.1_2_4_2_5, 0.1_4_0_4_5, 0.1_3_1_1_5, 0.1_2_1_7_5, 0.1_2_5]				)

											assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2


