



import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple

import numpy as np

from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation


__UpperCAmelCase			  =			logging.get_logger(__name__)

__UpperCAmelCase			  =			{'''vocab_file''': '''vocab.txt''', '''emoji_file''': '''emoji.json'''}

__UpperCAmelCase			  =			{
    '''vocab_file''': {
        '''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/vocab.txt''',
    },
    '''emoji_file''': {
        '''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/emoji.json''',
    },
}

__UpperCAmelCase			  =			{
    '''abeja/gpt-neox-japanese-2.7b''': 2_048,
}
def 						UpperCamelCase      (  snake_case__			:       int			,	snake_case__			:       int      )      ->	Dict:







   with open(snake_case_			,	'r'			,	encoding='utf-8'      ) as f:
      UpperCamelCase       :    List[Any]		      =      json.loads(f.read()      )

   UpperCamelCase       :    Optional[int]		      =      collections.OrderedDict()
   UpperCamelCase       :    Dict		      =      collections.OrderedDict()
   UpperCamelCase       :    List[Any]		      =      collections.OrderedDict()
   with open(snake_case_			,	'r'			,	encoding='utf-8'      ) as f:
      UpperCamelCase       :    Optional[Any]		      =      f.readlines()
   UpperCamelCase       :    List[str]		      =      [[t.rstrip('\n'      )] if (t == ''',''' or ''',''' not in t) else t.rstrip('\n'      ).split(','      ) for t in token]
   for idx, b in enumerate(snake_case_      ):
      UpperCamelCase       :    List[str]		      =      b
      UpperCamelCase       :    List[Any]		      =      idx
      for wd in b:
         UpperCamelCase       :    Optional[int]		      =      idx

   return vocab, raw_vocab, ids_to_tokens, emoji



class 							lowerCAmelCase_						(    SCREAMING_SNAKE_CASE__				):
       UpperCAmelCase__							:   List[str]  	=    VOCAB_FILES_NAMES
       UpperCAmelCase__							:   Tuple  	=    PRETRAINED_VOCAB_FILES_MAP
       UpperCAmelCase__							:   int  	=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCAmelCase__							:   Union[str, Any]  	=    ["input_ids", "attention_mask"]
       def __init__(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_="<|endoftext|>",     SCREAMING_SNAKE_CASE_="<|endoftext|>",     SCREAMING_SNAKE_CASE_="<|startoftext|>",     SCREAMING_SNAKE_CASE_="<|endoftext|>",     SCREAMING_SNAKE_CASE_=False,     **SCREAMING_SNAKE_CASE_,     )	->						Any:
          super().__init__(
              unk_token=__lowerCamelCase,     pad_token=__lowerCamelCase,     bos_token=__lowerCamelCase,     eos_token=__lowerCamelCase,     do_clean_text=__lowerCamelCase,     **__lowerCamelCase,     )
          if not os.path.isfile(__lowerCamelCase			):
             raise ValueError(
                 F"""Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained"""
                 ' model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`'			)
          if not os.path.isfile(__lowerCamelCase			):
             raise ValueError(
                 F"""Can't find a emoji file at path '{emoji_file}'. To load the emoji information from a Google"""
                 ' pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`'			)
          UpperCamelCase       :    Optional[Any]		      =      do_clean_text
          UpperCamelCase       :    Dict		      =      load_vocab_and_emoji(__lowerCamelCase,     __lowerCamelCase			)
          UpperCamelCase       :    Optional[int]		      =      SubWordJapaneseTokenizer(
              vocab=self.vocab,     ids_to_tokens=self.ids_to_tokens,     emoji=self.emoji			)
       @property
       def    snake_case_			(     self			)	->						List[Any]:
          return len(self.raw_vocab			)
       def    snake_case_			(     self			)	->						int:
          return dict(self.raw_vocab,     **self.added_tokens_encoder			)
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_			)	->						List[str]:
          return self.subword_tokenizer.tokenize(__lowerCamelCase,     clean=self.do_clean_text			)
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_			)	->						Union[str, Any]:
          return self.vocab.get(__lowerCamelCase,     self.vocab.get(self.unk_token			)			)
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_			)	->						Union[str, Any]:
          return self.subword_tokenizer.convert_id_to_token(__lowerCamelCase			)
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_			)	->						Optional[int]:
          UpperCamelCase       :    Optional[Any]		      =      ''''''.join(__lowerCamelCase			).strip()
          return out_string
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_			)	->						Dict:
          UpperCamelCase       :    Union[str, Any]		      =      []
          for is_user, text in conversation.iter_texts():
             input_ids.extend(self.encode(__lowerCamelCase,     add_special_tokens=__lowerCamelCase			) + [self.eos_token_id]			)

          if len(__lowerCamelCase			) > self.model_max_length:
             UpperCamelCase       :    Any		      =      input_ids[-self.model_max_length :]
          return input_ids






       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_ = None			)	->						Dict:
          UpperCamelCase       :    Any		      =      0
          if os.path.isdir(__lowerCamelCase			):
             UpperCamelCase       :    List[str]		      =      os.path.join(
                 __lowerCamelCase,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']			)
             UpperCamelCase       :    Optional[int]		      =      os.path.join(
                 __lowerCamelCase,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['emoji_file']			)
          else:
             UpperCamelCase       :    Optional[int]		      =      (
                 (filename_prefix + '''-''' if filename_prefix else '''''') + save_directory + VOCAB_FILES_NAMES['''vocab_file''']
             )
             UpperCamelCase       :    List[str]		      =      (
                 (filename_prefix + '''-''' if filename_prefix else '''''') + save_directory + VOCAB_FILES_NAMES['''emoji_file''']
             )
          with open(__lowerCamelCase,     'w',     encoding='utf-8'			) as writer:
             for token_index, token in self.ids_to_tokens.items():
                if index != token_index:
                   logger.warning(
                       F"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
                       ' Please check that the vocabulary is not corrupted!'			)
                   UpperCamelCase       :    List[Any]		      =      token_index
                writer.write(','.join(__lowerCamelCase			) + '\n'			)
                index += 1
          with open(__lowerCamelCase,     'w',     encoding='utf-8'			) as writer:
             json.dump(self.emoji,     __lowerCamelCase			)
          return vocab_file, emoji_file






class 							lowerCAmelCase_						(    SCREAMING_SNAKE_CASE__				):
       def __init__(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)	->						Optional[int]:
          UpperCamelCase       :    Optional[int]		      =      vocab  # same as swe
          UpperCamelCase       :    Tuple		      =      ids_to_tokens  # same as bpe
          UpperCamelCase       :    Union[str, Any]		      =      emoji
          UpperCamelCase       :    List[Any]		      =      np.max([len(__lowerCamelCase			) for w in self.vocab.keys()]			)
          UpperCamelCase       :    Tuple		      =      re.compile(R'(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)'			)
          UpperCamelCase       :    Optional[int]		      =      re.compile(R'[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*'			)
          UpperCamelCase       :    Tuple		      =      re.compile(R'[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}'			)
          UpperCamelCase       :    Dict		      =      re.compile(
              R'([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*'			)
          UpperCamelCase       :    Optional[Any]		      =      re.compile(
              R'(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*'			)
          UpperCamelCase       :    Dict		      =      re.compile(
              R'((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*'			)
          UpperCamelCase       :    List[str]		      =      '''─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿'''
          UpperCamelCase       :    Optional[int]		      =      '''▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟'''
          UpperCamelCase       :    List[Any]		      =      str.maketrans({k: '<BLOCK>' for k in keisen + blocks}			)
       def __len__(     self			)	->						List[str]:
          return len(self.ids_to_tokens			)
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_			)	->						str:
          UpperCamelCase       :    int		      =      self.content_repattera.sub('<URL>',     __lowerCamelCase			)
          UpperCamelCase       :    int		      =      self.content_repattera.sub('<EMAIL>',     __lowerCamelCase			)
          UpperCamelCase       :    List[str]		      =      self.content_repattera.sub('<TEL>',     __lowerCamelCase			)
          UpperCamelCase       :    Dict		      =      self.content_repattera.sub('<DATE>',     __lowerCamelCase			)
          UpperCamelCase       :    int		      =      self.content_repattera.sub('<DATE>',     __lowerCamelCase			)
          UpperCamelCase       :    Union[str, Any]		      =      self.content_repattera.sub('<PRICE>',     __lowerCamelCase			)
          UpperCamelCase       :    List[str]		      =      content.translate(self.content_transa			)
          while "<BLOCK><BLOCK>" in content:
             UpperCamelCase       :    Tuple		      =      content.replace('<BLOCK><BLOCK>',     '<BLOCK>'			)
          return content
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_=False			)	->						int:
          UpperCamelCase       :    Tuple		      =      text.replace(' ',     '<SP>'			)
          UpperCamelCase       :    Optional[Any]		      =      text.replace('　',     '<SP>'			)
          UpperCamelCase       :    Union[str, Any]		      =      text.replace('\r\n',     '<BR>'			)
          UpperCamelCase       :    int		      =      text.replace('\n',     '<BR>'			)
          UpperCamelCase       :    Optional[Any]		      =      text.replace('\r',     '<BR>'			)
          UpperCamelCase       :    Dict		      =      text.replace('\t',     '<TAB>'			)
          UpperCamelCase       :    List[str]		      =      text.replace('—',     'ー'			)
          UpperCamelCase       :    Dict		      =      text.replace('−',     'ー'			)
          for k, v in self.emoji["emoji"].items():
             if k in text:
                UpperCamelCase       :    str		      =      text.replace(__lowerCamelCase,     __lowerCamelCase			)
          if clean:
             UpperCamelCase       :    Any		      =      self.clean_text(__lowerCamelCase			)

          def check_simbol(SCREAMING_SNAKE_CASE_			):
             UpperCamelCase       :    Union[str, Any]		      =      x.encode()
             if len(__lowerCamelCase			) == 1 and len(__lowerCamelCase			) == 2:
                UpperCamelCase       :    Tuple		      =      (int(e[0]			) << 8) + int(e[1]			)
                if (
                    (c >= 0XC2A1 and c <= 0XC2BF)
                    or (c >= 0XC780 and c <= 0XC783)
                    or (c >= 0XCAB9 and c <= 0XCBBF)
                    or (c >= 0XCC80 and c <= 0XCDA2)
                ):
                   return True
             return False

          def checkuae(SCREAMING_SNAKE_CASE_			):
             UpperCamelCase       :    List[Any]		      =      x.encode()
             if len(__lowerCamelCase			) == 1 and len(__lowerCamelCase			) == 3:
                UpperCamelCase       :    Dict		      =      (int(e[0]			) << 16) + (int(e[1]			) << 8) + int(e[2]			)
                if c >= 0XE28080 and c <= 0XE2B07F:
                   return True
             return False

          UpperCamelCase       :    str		      =      0
          UpperCamelCase       :    Any		      =      []
          while pos < len(__lowerCamelCase			):
             UpperCamelCase       :    List[str]		      =      min(len(__lowerCamelCase			),     pos + self.maxlen + 1			) if text[pos] == '''<''' else pos + 3
             UpperCamelCase       :    int		      =      []  # (token_id, token, pos)
             for e in range(__lowerCamelCase,     __lowerCamelCase,     -1			):
                UpperCamelCase       :    Tuple		      =      text[pos:e]
                if wd in self.vocab:
                   if wd[0] == "<" and len(__lowerCamelCase			) > 2:
                      UpperCamelCase       :    Dict		      =      [(self.vocab[wd], wd, e)]
                      break
                   else:
                      candidates.append((self.vocab[wd], wd, e)			)
             if len(__lowerCamelCase			) > 0:
                # the smallest token_id is adopted
                UpperCamelCase       :    Optional[int]		      =      sorted(__lowerCamelCase,     key=lambda SCREAMING_SNAKE_CASE_			: x[0]			)[0]
                result.append(__lowerCamelCase			)
                UpperCamelCase       :    Optional[Any]		      =      e
             else:
                UpperCamelCase       :    Optional[int]		      =      pos + 1
                UpperCamelCase       :    Any		      =      text[pos:end]
                if check_simbol(__lowerCamelCase			):
                   result.append('<KIGOU>'			)
                elif checkuae(__lowerCamelCase			):
                   result.append('<U2000U2BFF>'			)
                else:
                   for i in wd.encode('utf-8'			):
                      result.append('<|byte%d|>' % i			)
                UpperCamelCase       :    Dict		      =      end
          return result






       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_="\n"			)	->						Optional[Any]:
          UpperCamelCase       :    List[Any]		      =      []
          UpperCamelCase       :    Optional[Any]		      =      []
          UpperCamelCase       :    Dict		      =      self.ids_to_tokens[index][0]
          if word[:6] == "<|byte" and word[-2:] == "|>":
             byte_tokens.append(int(word[6:-2]			)			)
          else:
             if len(__lowerCamelCase			) > 0:
                words.append(bytearray(__lowerCamelCase			).decode('utf-8',     errors='replace'			)			)
                UpperCamelCase       :    int		      =      []
             if word[:7] == "<|emoji" and word[-2:] == "|>":
                words.append(self.emoji['emoji_inv'][word]			)
             elif word == "<SP>":
                words.append(' '			)
             elif word == "<BR>":
                words.append(__lowerCamelCase			)
             elif word == "<TAB>":
                words.append('\t'			)
             elif word == "<BLOCK>":
                words.append('▀'			)
             elif word == "<KIGOU>":
                words.append('ǀ'			)
             elif word == "<U2000U2BFF>":
                words.append('‖'			)
             else:
                words.append(__lowerCamelCase			)
          if len(__lowerCamelCase			) > 0:
             words.append(bytearray(__lowerCamelCase			).decode('utf-8',     errors='replace'			)			)
          UpperCamelCase       :    Dict		      =      ''''''.join(__lowerCamelCase			)
          return text










"""simple docstring"""



import gc
import unittest

import numpy as np
import torch

from diffusers import (
    AudioDiffusionPipeline,
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    DiffusionPipeline,
    Mel,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()






class 						__snake_case   (						unittest.TestCase	):





 """simple docstring"""


 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()

 @property
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     torch.manual_seed(0    )
     __A       :		List[Any]							=	UNetaDModel(
         sample_size=(32, 64)   ,		in_channels=1   ,		out_channels=1   ,		layers_per_block=2   ,		block_out_channels=(128, 128)   ,		down_block_types=('''AttnDownBlock2D''', '''DownBlock2D''')   ,		up_block_types=('''UpBlock2D''', '''AttnUpBlock2D''')   ,		)
     return model

 @property
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     torch.manual_seed(0    )
     __A       :		str							=	UNetaDConditionModel(
         sample_size=(64, 32)   ,		in_channels=1   ,		out_channels=1   ,		layers_per_block=2   ,		block_out_channels=(128, 128)   ,		down_block_types=('''CrossAttnDownBlock2D''', '''DownBlock2D''')   ,		up_block_types=('''UpBlock2D''', '''CrossAttnUpBlock2D''')   ,		cross_attention_dim=10   ,		)
     return model

 @property
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     torch.manual_seed(0    )
     __A       :		int							=	AutoencoderKL(
         sample_size=(128, 64)   ,		in_channels=1   ,		out_channels=1   ,		latent_channels=1   ,		layers_per_block=2   ,		block_out_channels=(128, 128)   ,		down_block_types=('''DownEncoderBlock2D''', '''DownEncoderBlock2D''')   ,		up_block_types=('''UpDecoderBlock2D''', '''UpDecoderBlock2D''')   ,		)
     __A       :		int							=	UNetaDModel(
         sample_size=(64, 32)   ,		in_channels=1   ,		out_channels=1   ,		layers_per_block=2   ,		block_out_channels=(128, 128)   ,		down_block_types=('''AttnDownBlock2D''', '''DownBlock2D''')   ,		up_block_types=('''UpBlock2D''', '''AttnUpBlock2D''')   ,		)
     return vqvae, unet







 @slow
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		str							=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
     __A       :		Union[str, Any]							=	Mel(
         x_res=self.dummy_unet.config.sample_size[1]   ,		y_res=self.dummy_unet.config.sample_size[0]   ,		)

     __A       :		List[Any]							=	DDPMScheduler()
     __A       :		Optional[Any]							=	AudioDiffusionPipeline(vqvae=__lowerCamelCase   ,		unet=self.dummy_unet   ,		mel=__lowerCamelCase   ,		scheduler=__lowerCamelCase    )
     __A       :		Tuple							=	pipe.to(__lowerCamelCase    )
     pipe.set_progress_bar_config(disable=__lowerCamelCase    )

     __A       :		Tuple							=	torch.Generator(device=__lowerCamelCase    ).manual_seed(42    )
     __A       :		List[str]							=	pipe(generator=__lowerCamelCase   ,		steps=4    )
     __A       :		Union[str, Any]							=	output.audios[0]
     __A       :		str							=	output.images[0]

     __A       :		List[Any]							=	torch.Generator(device=__lowerCamelCase    ).manual_seed(42    )
     __A       :		Any							=	pipe(generator=__lowerCamelCase   ,		steps=4   ,		return_dict=__lowerCamelCase    )
     __A       :		int							=	output[0][0]

     assert audio.shape == (1, (self.dummy_unet.config.sample_size[1] - 1) * mel.hop_length)
     assert (
         image.height == self.dummy_unet.config.sample_size[0]
         and image.width == self.dummy_unet.config.sample_size[1]
     )
     __A       :		Tuple							=	np.frombuffer(image.tobytes()   ,		dtype='''uint8'''    )[:10]
     __A       :		str							=	np.frombuffer(image_from_tuple.tobytes()   ,		dtype='''uint8'''    )[:10]
     __A       :		Any							=	np.array([69, 255, 255, 255, 0, 0, 77, 181, 12, 127]    )

     assert np.abs(image_slice.flatten() - expected_slice    ).max() == 0
     assert np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() == 0

     __A       :		int							=	Mel(
         x_res=self.dummy_vqvae_and_unet[0].config.sample_size[1]   ,		y_res=self.dummy_vqvae_and_unet[0].config.sample_size[0]   ,		)

     __A       :		Any							=	DDIMScheduler()
     __A       :		Optional[Any]							=	self.dummy_vqvae_and_unet
     __A       :		int							=	AudioDiffusionPipeline(
         vqvae=self.dummy_vqvae_and_unet[0]   ,		unet=dummy_vqvae_and_unet[1]   ,		mel=__lowerCamelCase   ,		scheduler=__lowerCamelCase    )
     __A       :		Any							=	pipe.to(__lowerCamelCase    )
     pipe.set_progress_bar_config(disable=__lowerCamelCase    )

     np.random.seed(0    )
     __A       :		str							=	np.random.uniform(-1   ,		1   ,		((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,)    )
     __A       :		Union[str, Any]							=	torch.Generator(device=__lowerCamelCase    ).manual_seed(42    )
     __A       :		int							=	pipe(raw_audio=__lowerCamelCase   ,		generator=__lowerCamelCase   ,		start_step=5   ,		steps=10    )
     __A       :		Any							=	output.images[0]

     assert (
         image.height == self.dummy_vqvae_and_unet[0].config.sample_size[0]
         and image.width == self.dummy_vqvae_and_unet[0].config.sample_size[1]
     )
     __A       :		str							=	np.frombuffer(image.tobytes()   ,		dtype='''uint8'''    )[:10]
     __A       :		Optional[Any]							=	np.array([120, 117, 110, 109, 138, 167, 138, 148, 132, 121]    )

     assert np.abs(image_slice.flatten() - expected_slice    ).max() == 0

     __A       :		Union[str, Any]							=	self.dummy_unet_condition
     __A       :		List[str]							=	AudioDiffusionPipeline(
         vqvae=self.dummy_vqvae_and_unet[0]   ,		unet=__lowerCamelCase   ,		mel=__lowerCamelCase   ,		scheduler=__lowerCamelCase    )
     __A       :		Dict							=	pipe.to(__lowerCamelCase    )
     pipe.set_progress_bar_config(disable=__lowerCamelCase    )

     np.random.seed(0    )
     __A       :		int							=	torch.rand((1, 1, 10)    )
     __A       :		Optional[Any]							=	pipe(generator=__lowerCamelCase   ,		encoding=__lowerCamelCase    )
     __A       :		str							=	output.images[0]
     __A       :		Optional[Any]							=	np.frombuffer(image.tobytes()   ,		dtype='''uint8'''    )[:10]
     __A       :		Tuple							=	np.array([107, 103, 120, 127, 142, 122, 113, 122, 97, 111]    )

     assert np.abs(image_slice.flatten() - expected_slice    ).max() == 0






@slow
@require_torch_gpu
class 						__snake_case   (						unittest.TestCase	):





 """simple docstring"""


 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()







 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		int							=	torch_device

     __A       :		List[Any]							=	DiffusionPipeline.from_pretrained('''teticio/audio-diffusion-ddim-256'''    )
     __A       :		List[Any]							=	pipe.to(__lowerCamelCase    )
     pipe.set_progress_bar_config(disable=__lowerCamelCase    )

     __A       :		Dict							=	torch.Generator(device=__lowerCamelCase    ).manual_seed(42    )
     __A       :		str							=	pipe(generator=__lowerCamelCase    )
     __A       :		Dict							=	output.audios[0]
     __A       :		List[Any]							=	output.images[0]

     assert audio.shape == (1, (pipe.unet.config.sample_size[1] - 1) * pipe.mel.hop_length)
     assert image.height == pipe.unet.config.sample_size[0] and image.width == pipe.unet.config.sample_size[1]
     __A       :		Optional[int]							=	np.frombuffer(image.tobytes()   ,		dtype='''uint8'''    )[:10]
     __A       :		Optional[Any]							=	np.array([151, 167, 154, 144, 122, 134, 121, 105, 70, 26]    )

     assert np.abs(image_slice.flatten() - expected_slice    ).max() == 0




