
import gc
import unittest

import numpy as np
import torch

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS, UNCONDITIONAL_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





class 			_lowerCAmelCase      (			__a      ,   unittest.TestCase ):
				_lowercase    	=DanceDiffusionPipeline
				_lowercase    	=UNCONDITIONAL_AUDIO_GENERATION_PARAMS
				_lowercase    	=PipelineTesterMixin.required_optional_params - {
				    '''callback''',
				    '''latents''',
				    '''callback_steps''',
				    '''output_type''',
				    '''num_images_per_prompt''',
				}
				_lowercase    	=UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS
				_lowercase    	=False
				_lowercase    	=False




				def   __a		(      self  )       ->   Tuple:
							torch.manual_seed(0  )
							lowerCAmelCase_      			=			UNetaDModel(
							    block_out_channels=(32, 32, 64)       ,       extra_in_channels=16       ,       sample_size=512       ,       sample_rate=16_000       ,       in_channels=2       ,       out_channels=2       ,       flip_sin_to_cos=_UpperCamelCase       ,       use_timestep_embedding=_UpperCamelCase       ,       time_embedding_type="fourier"       ,       mid_block_type="UNetMidBlock1D"       ,       down_block_types=("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")       ,       up_block_types=("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")       ,       )
							lowerCAmelCase_      			=			IPNDMScheduler()

							lowerCAmelCase_      			=			{
							    "unet": unet,
							    "scheduler": scheduler,
							}
							return components




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase=0  )       ->   List[Any]:
							if str(_UpperCamelCase  ).startswith("mps"  ):
										lowerCAmelCase_      			=			torch.manual_seed(_UpperCamelCase  )
							else:
										lowerCAmelCase_      			=			torch.Generator(device=_UpperCamelCase  ).manual_seed(_UpperCamelCase  )
							lowerCAmelCase_      			=			{
							    "batch_size": 1,
							    "generator": generator,
							    "num_inference_steps": 4,
							}
							return inputs




				def   __a		(      self  )       ->   Optional[int]:
							lowerCAmelCase_      			=			"cpu"  # ensure determinism for the device-dependent torch.Generator
							lowerCAmelCase_      			=			self.get_dummy_components()
							lowerCAmelCase_      			=			DanceDiffusionPipeline(**_UpperCamelCase  )
							lowerCAmelCase_      			=			pipe.to(_UpperCamelCase  )
							pipe.set_progress_bar_config(disable=_UpperCamelCase  )

							lowerCAmelCase_      			=			self.get_dummy_inputs(_UpperCamelCase  )
							lowerCAmelCase_      			=			pipe(**_UpperCamelCase  )
							lowerCAmelCase_      			=			output.audios

							lowerCAmelCase_      			=			audio[0, -3:, -3:]

							assert audio.shape == (1, 2, components["unet"].sample_size)
							lowerCAmelCase_      			=			np.array([-0.7265, 1.0000, -0.8388, 0.1175, 0.9498, -1.0000]  )
							assert np.abs(audio_slice.flatten() - expected_slice  ).max() < 1e-2




				@skip_mps
				def   __a		(      self  )       ->   Optional[Any]:
							return super().test_save_load_local()




				@skip_mps
				def   __a		(      self  )       ->   Optional[int]:
							return super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3  )




				@skip_mps
				def   __a		(      self  )       ->   List[str]:
							return super().test_save_load_optional_components()




				@skip_mps
				def   __a		(      self  )       ->   List[Any]:
							return super().test_attention_slicing_forward_pass()




				def   __a		(      self  )       ->   List[str]:
							super().test_inference_batch_single_identical(expected_max_diff=3e-3  )





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




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




				def   __a		(      self  )       ->   Optional[Any]:
							lowerCAmelCase_      			=			torch_device

							lowerCAmelCase_      			=			DanceDiffusionPipeline.from_pretrained("harmonai/maestro-150k"  )
							lowerCAmelCase_      			=			pipe.to(_UpperCamelCase  )
							pipe.set_progress_bar_config(disable=_UpperCamelCase  )

							lowerCAmelCase_      			=			torch.manual_seed(0  )
							lowerCAmelCase_      			=			pipe(generator=_UpperCamelCase       ,       num_inference_steps=100       ,       audio_length_in_s=4.096  )
							lowerCAmelCase_      			=			output.audios

							lowerCAmelCase_      			=			audio[0, -3:, -3:]

							assert audio.shape == (1, 2, pipe.unet.sample_size)
							lowerCAmelCase_      			=			np.array([-0.0192, -0.0231, -0.0318, -0.0059, 0.0002, -0.0020]  )

							assert np.abs(audio_slice.flatten() - expected_slice  ).max() < 1e-2




				def   __a		(      self  )       ->   Dict:
							lowerCAmelCase_      			=			torch_device

							lowerCAmelCase_      			=			DanceDiffusionPipeline.from_pretrained("harmonai/maestro-150k"       ,       torch_dtype=torch.floataa  )
							lowerCAmelCase_      			=			pipe.to(_UpperCamelCase  )
							pipe.set_progress_bar_config(disable=_UpperCamelCase  )

							lowerCAmelCase_      			=			torch.manual_seed(0  )
							lowerCAmelCase_      			=			pipe(generator=_UpperCamelCase       ,       num_inference_steps=100       ,       audio_length_in_s=4.096  )
							lowerCAmelCase_      			=			output.audios

							lowerCAmelCase_      			=			audio[0, -3:, -3:]

							assert audio.shape == (1, 2, pipe.unet.sample_size)
							lowerCAmelCase_      			=			np.array([-0.0367, -0.0488, -0.0771, -0.0525, -0.0444, -0.0341]  )

							assert np.abs(audio_slice.flatten() - expected_slice  ).max() < 1e-2





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

import sentencepiece as spm

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


_A     =  "▁"

_A     =  {"vocab_file": "spiece.model"}

_A     =  {
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"}
}

_A     =  {
    "google/pegasus-xsum": 5_12,
}


_A     =  logging.get_logger(__name__)





class 			_lowerCAmelCase      (			__a ):
				_lowercase    	=VOCAB_FILES_NAMES

				_lowercase    	=VOCAB_FILES_NAMES
				_lowercase    	=PRETRAINED_VOCAB_FILES_MAP
				_lowercase    	=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_lowercase    	=['''input_ids''', '''attention_mask''']




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase="<pad>"       ,       _UpperCamelCase="</s>"       ,       _UpperCamelCase="<unk>"       ,       _UpperCamelCase="<mask_2>"       ,       _UpperCamelCase="<mask_1>"       ,       _UpperCamelCase=None       ,       _UpperCamelCase=103       ,       _UpperCamelCase = None       ,       **_UpperCamelCase       ,       )       ->   None:
							lowerCAmelCase_      			=			offset
							if additional_special_tokens is not None:
										if not isinstance(_UpperCamelCase       ,       _UpperCamelCase  ):
													raise TypeError(
													    f"""additional_special_tokens should be of type {type(_UpperCamelCase  )}, but is"""
													    f""" {type(_UpperCamelCase  )}"""  )

										lowerCAmelCase_      			=			(
										    ([mask_token_sent] + additional_special_tokens)
										    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
										    else additional_special_tokens
										)
										# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
										additional_special_tokens_extended += [
										    f"""<unk_{i}>""" for i in range(len(_UpperCamelCase  )       ,       self.offset - 1  )
										]

										if len(set(_UpperCamelCase  )  ) != len(_UpperCamelCase  ):
													raise ValueError(
													    "Please make sure that the provided additional_special_tokens do not contain an incorrectly"
													    f""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."""  )
										lowerCAmelCase_      			=			additional_special_tokens_extended
							else:
										lowerCAmelCase_      			=			[mask_token_sent] if mask_token_sent is not None else []
										additional_special_tokens += [f"""<unk_{i}>""" for i in range(2       ,       self.offset  )]

							lowerCAmelCase_      			=			{} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    eos_token=_UpperCamelCase       ,       unk_token=_UpperCamelCase       ,       mask_token=_UpperCamelCase       ,       pad_token=_UpperCamelCase       ,       mask_token_sent=_UpperCamelCase       ,       offset=_UpperCamelCase       ,       additional_special_tokens=_UpperCamelCase       ,       sp_model_kwargs=self.sp_model_kwargs       ,       **_UpperCamelCase       ,       )
							lowerCAmelCase_      			=			mask_token_sent
							lowerCAmelCase_      			=			vocab_file
							lowerCAmelCase_      			=			spm.SentencePieceProcessor(**self.sp_model_kwargs  )
							self.sp_model.Load(_UpperCamelCase  )

							# add special tokens to encoder dict
							lowerCAmelCase_      			=			{
							    0: self.pad_token,
							    1: self.eos_token,
							}

							if self.mask_token_sent is not None:
										self.encoder.update(
										    {
										        2: self.mask_token_sent,
										        3: self.mask_token,
										    }  )

							if self.offset > 0:
										# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
										# mask_token_sent is already added to list -> so start at 1
										self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1       ,       self.offset - 1  )}  )

							lowerCAmelCase_      			=			{v: k for k, v in self.encoder.items()}




				@property
				def   __a		(      self  )       ->   int:
							return len(self.sp_model  ) + self.offset




				def   __a		(      self  )       ->   Dict[str, int]:
							lowerCAmelCase_      			=			{self.convert_ids_to_tokens(_UpperCamelCase  ): i for i in range(self.vocab_size  )}
							vocab.update(self.added_tokens_encoder  )
							return vocab




				def __getstate__(      self  )       ->   List[Any]:
							lowerCAmelCase_      			=			self.__dict__.copy()
							lowerCAmelCase_      			=			None
							return state




				def __setstate__(      self       ,       _UpperCamelCase  )       ->   Optional[int]:
							lowerCAmelCase_      			=			d

							# for backward compatibility
							if not hasattr(self       ,       "sp_model_kwargs"  ):
										lowerCAmelCase_      			=			{}

							lowerCAmelCase_      			=			spm.SentencePieceProcessor(**self.sp_model_kwargs  )
							self.sp_model.Load(self.vocab_file  )




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




				def   __a		(      self       ,       _UpperCamelCase  )       ->   int:
							if token in self.decoder:
										return self.decoder[token]
							elif token in self.added_tokens_decoder:
										return self.added_tokens_decoder[token]
							lowerCAmelCase_      			=			self.sp_model.piece_to_id(_UpperCamelCase  )
							return sp_id + self.offset




				def   __a		(      self       ,       _UpperCamelCase  )       ->   str:
							if index in self.encoder:
										return self.encoder[index]
							elif index in self.added_tokens_encoder:
										return self.added_tokens_encoder[index]
							else:
										lowerCAmelCase_      			=			self.sp_model.IdToPiece(index - self.offset  )
							return token




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




				def   __a		(      self       ,       _UpperCamelCase=False  )       ->   Optional[int]:
							return 1




				def   __a		(      self       ,       _UpperCamelCase  )       ->   int:
							lowerCAmelCase_      			=			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   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase = None       ,       _UpperCamelCase = False  )       ->   List[int]:
							if already_has_special_tokens:
										return self._special_token_mask(_UpperCamelCase  )
							elif token_ids_a is None:
										return self._special_token_mask(_UpperCamelCase  ) + [1]
							else:
										return self._special_token_mask(token_ids_a + token_ids_a  ) + [1]




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase=None  )       ->   List[int]:
							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   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase = None  )       ->   Tuple[str]:
							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  ) and os.path.isfile(self.vocab_file  ):
										copyfile(self.vocab_file       ,       _UpperCamelCase  )
							elif not os.path.isfile(self.vocab_file  ):
										with open(_UpperCamelCase       ,       "wb"  ) as fi:
													lowerCAmelCase_      			=			self.sp_model.serialized_model_proto()
													fi.write(_UpperCamelCase  )

							return (out_vocab_file,)



