'''simple docstring'''





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  :List[str]  				=    logging.get_logger(__name__)

_lowerCAmelCase  :List[str]  				=    """▁"""

_lowerCAmelCase  :Tuple  				=    {
    """vocab_file""": """vocab.json""",
    """spm_file""": """sentencepiece.bpe.model""",
}

_lowerCAmelCase  :str  				=    {
    """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  :Tuple  				=    {
    """facebook/s2t-small-librispeech-asr""": 1_024,
}

_lowerCAmelCase  :Any  				=    ["""pt""", """fr""", """ru""", """nl""", """ro""", """it""", """es""", """de"""]

_lowerCAmelCase  :Tuple  				=    {"""mustc""": MUSTC_LANGS}






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




				'''simple docstring'''







				snake_case__							:   List[str]							=						VOCAB_FILES_NAMES
				snake_case__							:   Optional[Any]							=						PRETRAINED_VOCAB_FILES_MAP
				snake_case__							:   List[Any]							=						MAX_MODEL_INPUT_SIZES
				snake_case__							:   Optional[int]							=						["input_ids", "attention_mask"]

				snake_case__							:   List[int]							=						[]






				def __init__(       self    ,							lowercase__    ,							lowercase__    ,							lowercase__="<s>"    ,							lowercase__="</s>"    ,							lowercase__="<pad>"    ,							lowercase__="<unk>"    ,							lowercase__=False    ,							lowercase__=False    ,							lowercase__=None    ,							lowercase__=None    ,							lowercase__ = None    ,							**lowercase__    ,							)							-> None:
									SCREAMING_SNAKE_CASE					:			int    	=       {} if sp_model_kwargs is None else sp_model_kwargs

									super().__init__(
									    bos_token=lowercase__    ,							eos_token=lowercase__    ,							unk_token=lowercase__    ,							pad_token=lowercase__    ,							do_upper_case=lowercase__    ,							do_lower_case=lowercase__    ,							tgt_lang=lowercase__    ,							lang_codes=lowercase__    ,							sp_model_kwargs=self.sp_model_kwargs    ,							**lowercase__    ,							)
									SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       do_upper_case
									SCREAMING_SNAKE_CASE					:			Tuple    	=       do_lower_case

									SCREAMING_SNAKE_CASE					:			Any    	=       load_json(lowercase__ )
									SCREAMING_SNAKE_CASE					:			Tuple    	=       {v: k for k, v in self.encoder.items()}
									SCREAMING_SNAKE_CASE					:			Tuple    	=       spm_file
									SCREAMING_SNAKE_CASE					:			Optional[int]    	=       load_spm(lowercase__    ,							self.sp_model_kwargs )

									if lang_codes is not None:
														SCREAMING_SNAKE_CASE					:			List[Any]    	=       lang_codes
														SCREAMING_SNAKE_CASE					:			Optional[int]    	=       LANGUAGES[lang_codes]
														SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       [F"""<lang:{lang}>""" for lang in self.langs]
														SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       {lang: self.sp_model.PieceToId(F"""<lang:{lang}>""" ) for lang in self.langs}

														SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.lang_tokens
														SCREAMING_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:
														SCREAMING_SNAKE_CASE					:			Dict    	=       {}






				@property
				def       _UpperCamelCase    (       self )							-> int:
									return len(self.encoder )






				@property
				def       _UpperCamelCase    (       self )							-> str:
									return self._tgt_lang






				@tgt_lang.setter
				def       _UpperCamelCase    (       self    ,							lowercase__ )							-> None:
									SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       new_tgt_lang
									self.set_tgt_lang_special_tokens(lowercase__ )






				def       _UpperCamelCase    (       self    ,							lowercase__ )							-> None:
									SCREAMING_SNAKE_CASE					:			int    	=       self.lang_code_to_id[tgt_lang]
									SCREAMING_SNAKE_CASE					:			Tuple    	=       [lang_code_id]






				def       _UpperCamelCase    (       self    ,							lowercase__ )							-> List[str]:
									return self.sp_model.encode(lowercase__    ,							out_type=lowercase__ )






				def       _UpperCamelCase    (       self    ,							lowercase__ )							-> int:
									return self.encoder.get(lowercase__    ,							self.encoder[self.unk_token] )






				def       _UpperCamelCase    (       self    ,							lowercase__ )							-> str:
									return self.decoder.get(lowercase__    ,							self.unk_token )






				def       _UpperCamelCase    (       self    ,							lowercase__ )							-> str:
									SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       []
									SCREAMING_SNAKE_CASE					:			List[str]    	=       ''
									for token in tokens:
														# make sure that special tokens are not decoded using sentencepiece model
														if token in self.all_special_tokens:
																			SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.sp_model.decode(lowercase__ )
																			out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
																			SCREAMING_SNAKE_CASE					:			Any    	=       []
														else:
																			current_sub_tokens.append(lowercase__ )
									SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.sp_model.decode(lowercase__ )
									out_string += decoded.upper() if self.do_upper_case else decoded
									return out_string.strip()






				def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__=None )							-> List[int]:
									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       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ = None    ,							lowercase__ = False )							-> List[int]:

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

									SCREAMING_SNAKE_CASE					:			Dict    	=       [1] * len(self.prefix_tokens )
									SCREAMING_SNAKE_CASE					:			str    	=       [1]
									if token_ids_a is None:
														return prefix_ones + ([0] * len(lowercase__ )) + suffix_ones
									return prefix_ones + ([0] * len(lowercase__ )) + ([0] * len(lowercase__ )) + suffix_ones






				def       _UpperCamelCase    (       self )							-> Dict:
									SCREAMING_SNAKE_CASE					:			Any    	=       self.encoder.copy()
									vocab.update(self.added_tokens_encoder )
									return vocab






				def __getstate__(       self )							-> Dict:
									SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.__dict__.copy()
									SCREAMING_SNAKE_CASE					:			Tuple    	=       None
									return state






				def __setstate__(       self    ,							lowercase__ )							-> None:
									SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       d

									# for backward compatibility
									if not hasattr(self    ,							'sp_model_kwargs' ):
														SCREAMING_SNAKE_CASE					:			Dict    	=       {}

									SCREAMING_SNAKE_CASE					:			List[str]    	=       load_spm(self.spm_file    ,							self.sp_model_kwargs )






				def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ = None )							-> Tuple[str]:
									SCREAMING_SNAKE_CASE					:			Optional[int]    	=       Path(lowercase__ )
									assert save_dir.is_dir(), F"""{save_directory} should be a directory"""
									SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       save_dir / (
									    (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['vocab_file']
									)
									SCREAMING_SNAKE_CASE					:			List[Any]    	=       save_dir / (
									    (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['spm_file']
									)

									save_json(self.encoder    ,							lowercase__ )

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

									return (str(lowercase__ ), str(lowercase__ ))






def     __lowerCAmelCase						(       a_       ,      a_		)      ->	sentencepiece.SentencePieceProcessor:





					'''simple docstring'''





					SCREAMING_SNAKE_CASE					:			Any    	=       sentencepiece.SentencePieceProcessor(**a_		)
					spm.Load(str(a_		)		)
					return spm


def     __lowerCAmelCase						(       a_		)      ->	Union[Dict, List]:





					'''simple docstring'''





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


def     __lowerCAmelCase						(       a_       ,      a_		)      ->	None:





					'''simple docstring'''





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




'''simple docstring'''





import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


_lowerCAmelCase  :List[Any]  				=    False






class 				UpperCAmelCase (       unittest.TestCase ):




    '''simple docstring'''







    pass






@nightly
@require_torch_gpu
class 				UpperCAmelCase (       unittest.TestCase ):




    '''simple docstring'''







    def       _UpperCamelCase    (       self )							-> Any:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()






    def       _UpperCamelCase    (       self )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			Dict    	=       VersatileDiffusionTextToImagePipeline.from_pretrained('shi-labs/versatile-diffusion' )
         # remove text_unet
         pipe.remove_unused_weights()
         pipe.to(lowercase__ )
         pipe.set_progress_bar_config(disable=lowercase__ )

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       'A painting of a squirrel eating a burger '
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       torch.manual_seed(0 )
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       pipe(
             prompt=lowercase__    ,							generator=lowercase__    ,							guidance_scale=7.5    ,							num_inference_steps=2    ,							output_type='numpy' ).images

         with tempfile.TemporaryDirectory() as tmpdirname:
              pipe.save_pretrained(lowercase__ )
              SCREAMING_SNAKE_CASE					:			int    	=       VersatileDiffusionTextToImagePipeline.from_pretrained(lowercase__ )
         pipe.to(lowercase__ )
         pipe.set_progress_bar_config(disable=lowercase__ )

         SCREAMING_SNAKE_CASE					:			List[str]    	=       generator.manual_seed(0 )
         SCREAMING_SNAKE_CASE					:			List[str]    	=       pipe(
             prompt=lowercase__    ,							generator=lowercase__    ,							guidance_scale=7.5    ,							num_inference_steps=2    ,							output_type='numpy' ).images

         assert np.abs(image - new_image ).sum() < 1E-5, "Models don't have the same forward pass"






    def       _UpperCamelCase    (       self )							-> Any:
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       VersatileDiffusionTextToImagePipeline.from_pretrained(
             'shi-labs/versatile-diffusion'    ,							torch_dtype=torch.floataa )
         pipe.to(lowercase__ )
         pipe.set_progress_bar_config(disable=lowercase__ )

         SCREAMING_SNAKE_CASE					:			int    	=       'A painting of a squirrel eating a burger '
         SCREAMING_SNAKE_CASE					:			Tuple    	=       torch.manual_seed(0 )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       pipe(
             prompt=lowercase__    ,							generator=lowercase__    ,							guidance_scale=7.5    ,							num_inference_steps=50    ,							output_type='numpy' ).images

         SCREAMING_SNAKE_CASE					:			Dict    	=       image[0, 253:256, 253:256, -1]

         assert image.shape == (1, 512, 512, 3)
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8] )

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



