




'''simple docstring'''

import os
from shutil import copyfile
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_reformer import ReformerTokenizer
else:
		a__         =     None


a__         =     logging.get_logger(__name__)


a__         =     '''▁'''

a__         =     {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

a__         =     {
    '''vocab_file''': {
        '''google/reformer-crime-and-punishment''': (
            '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'''
        )
    },
    '''tokenizer_file''': {
        '''google/reformer-crime-and-punishment''': (
            '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json'''
        )
    },
}

a__         =     {
    '''google/reformer-crime-and-punishment''': 524_288,
}





class __magic_name__(						__lowerCAmelCase      ):
						UpperCAmelCase_		:					Optional[int]    =  VOCAB_FILES_NAMES
						UpperCAmelCase_		:					Optional[int]    =  PRETRAINED_VOCAB_FILES_MAP
						UpperCAmelCase_		:					int    =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCAmelCase_		:					List[Any]    =  ["""input_ids""", """attention_mask"""]
						UpperCAmelCase_		:					Any    =  ReformerTokenizer






						def __init__(   self						:						Optional[int]					,   __UpperCamelCase						:						Tuple=None					,   __UpperCamelCase						:						Optional[Any]=None					,   __UpperCamelCase						:						str="</s>"					,   __UpperCamelCase						:						Optional[int]="<unk>"					,   __UpperCamelCase						:						Optional[int]=[]					,   **__UpperCamelCase						:						int					,   ):



									'''simple docstring'''






									super().__init__(
									    __UpperCamelCase					,   tokenizer_file=__UpperCamelCase					,   eos_token=__UpperCamelCase					,   unk_token=__UpperCamelCase					,   additional_special_tokens=__UpperCamelCase					,   **__UpperCamelCase					,   )

									snake_case__          =						vocab_file
									snake_case__          =						False if not self.vocab_file else True







						def       __lowerCAmelCase(   self						:						List[Any]					,   __UpperCamelCase						:						str					,   __UpperCamelCase						:						Optional[str] = None       ):



									'''simple docstring'''






									if not self.can_save_slow_tokenizer:
												raise ValueError(
												    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
												    """tokenizer."""       )

									if not os.path.isdir(__UpperCamelCase       ):
												logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""       )
												return
									snake_case__          =						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       ):
												copyfile(self.vocab_file					,   __UpperCamelCase       )

									return (out_vocab_file,)





'''simple docstring'''

import gc
import unittest

import numpy as np
import torch

from diffusers import DDPMScheduler, MidiProcessor, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, TaFilmDecoder
from diffusers.utils import require_torch_gpu, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_note_seq, require_onnxruntime

from ..pipeline_params import TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS, TOKENS_TO_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


a__         =     '''./tests/fixtures/elise_format0.mid'''





@unittest.skip("""The note-seq package currently throws an error on import"""      )
class __magic_name__(						__lowerCAmelCase    ,		unittest.TestCase      ):
						UpperCAmelCase_		:					Tuple    =  SpectrogramDiffusionPipeline
						UpperCAmelCase_		:					List[str]    =  PipelineTesterMixin.required_optional_params - {
						    """callback""",
						    """latents""",
						    """callback_steps""",
						    """output_type""",
						    """num_images_per_prompt""",
						}
						UpperCAmelCase_		:					Dict    =  False
						UpperCAmelCase_		:					Dict    =  False
						UpperCAmelCase_		:					List[str]    =  TOKENS_TO_AUDIO_GENERATION_PARAMS
						UpperCAmelCase_		:					Union[str, Any]    =  TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS






						def       __lowerCAmelCase(   self						:						Tuple       ):



									'''simple docstring'''






									torch.manual_seed(0       )
									snake_case__          =						SpectrogramNotesEncoder(
									    max_length=2_0_4_8					,   vocab_size=1_5_3_6					,   d_model=7_6_8					,   dropout_rate=0.1					,   num_layers=1					,   num_heads=1					,   d_kv=4					,   d_ff=2_0_4_8					,   feed_forward_proj="""gated-gelu"""					,   )

									snake_case__          =						SpectrogramContEncoder(
									    input_dims=1_2_8					,   targets_context_length=2_5_6					,   d_model=7_6_8					,   dropout_rate=0.1					,   num_layers=1					,   num_heads=1					,   d_kv=4					,   d_ff=2_0_4_8					,   feed_forward_proj="""gated-gelu"""					,   )

									snake_case__          =						TaFilmDecoder(
									    input_dims=1_2_8					,   targets_length=2_5_6					,   max_decoder_noise_time=2_00_00.0					,   d_model=7_6_8					,   num_layers=1					,   num_heads=1					,   d_kv=4					,   d_ff=2_0_4_8					,   dropout_rate=0.1					,   )

									snake_case__          =						DDPMScheduler()

									snake_case__          =						{
									    """notes_encoder""": notes_encoder.eval(),
									    """continuous_encoder""": continuous_encoder.eval(),
									    """decoder""": decoder.eval(),
									    """scheduler""": scheduler,
									    """melgan""": None,
									}
									return components






						def       __lowerCAmelCase(   self						:						Tuple					,   __UpperCamelCase						:						Any					,   __UpperCamelCase						:						str=0       ):



									'''simple docstring'''






									if str(__UpperCamelCase       ).startswith("""mps"""       ):
												snake_case__          =						torch.manual_seed(__UpperCamelCase       )
									else:
												snake_case__          =						torch.Generator(device=__UpperCamelCase       ).manual_seed(__UpperCamelCase       )
									snake_case__          =						{
									    """input_tokens""": [
									        [1_1_3_4, 9_0, 1_1_3_5, 1_1_3_3, 1_0_8_0, 1_1_2, 1_1_3_2, 1_0_8_0, 1_1_3_3, 1_0_7_9, 1_3_3, 1_1_3_2, 1_0_7_9, 1_1_3_3, 1] + [0] * 2_0_3_3
									    ],
									    """generator""": generator,
									    """num_inference_steps""": 4,
									    """output_type""": """mel""",
									}
									return inputs






						def       __lowerCAmelCase(   self						:						Optional[Any]       ):



									'''simple docstring'''






									snake_case__          =						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
									snake_case__          =						self.get_dummy_components()
									snake_case__          =						SpectrogramDiffusionPipeline(**__UpperCamelCase       )
									snake_case__          =						pipe.to(__UpperCamelCase       )
									pipe.set_progress_bar_config(disable=__UpperCamelCase       )

									snake_case__          =						self.get_dummy_inputs(__UpperCamelCase       )
									snake_case__          =						pipe(**__UpperCamelCase       )
									snake_case__          =						output.audios

									snake_case__          =						mel[0, -3:, -3:]

									assert mel_slice.shape == (3, 3)
									snake_case__          =						np.array(
									    [-11.51_29_25, -4.78_82_15, -0.46_17_29_05, -2.05_17_15, -10.53_91_47, -10.97_09_63, -9.09_16_34, 4.0, 4.0]       )
									assert np.abs(mel_slice.flatten() - expected_slice       ).max() < 1E-2






						@skip_mps
						def       __lowerCAmelCase(   self						:						Tuple       ):



									'''simple docstring'''






									return super().test_save_load_local()






						@skip_mps
						def       __lowerCAmelCase(   self						:						List[Any]       ):



									'''simple docstring'''






									return super().test_dict_tuple_outputs_equivalent()






						@skip_mps
						def       __lowerCAmelCase(   self						:						Optional[int]       ):



									'''simple docstring'''






									return super().test_save_load_optional_components()






						@skip_mps
						def       __lowerCAmelCase(   self						:						Union[str, Any]       ):



									'''simple docstring'''






									return super().test_attention_slicing_forward_pass()






						def       __lowerCAmelCase(   self						:						Dict       ):



									'''simple docstring'''






									pass






						def       __lowerCAmelCase(   self						:						int       ):



									'''simple docstring'''






									pass







						@skip_mps
						def       __lowerCAmelCase(   self						:						Dict       ):



									'''simple docstring'''






									return super().test_progress_bar()







@slow
@require_torch_gpu
@require_onnxruntime
@require_note_seq
class __magic_name__(						unittest.TestCase      ):






						def       __lowerCAmelCase(   self						:						List[Any]       ):



									'''simple docstring'''






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






						def       __lowerCAmelCase(   self						:						int       ):



									'''simple docstring'''






									snake_case__          =						torch_device

									snake_case__          =						SpectrogramDiffusionPipeline.from_pretrained("""google/music-spectrogram-diffusion"""       )
									snake_case__          =						pipe.melgan
									snake_case__          =						None

									snake_case__          =						pipe.to(__UpperCamelCase       )
									pipe.set_progress_bar_config(disable=__UpperCamelCase       )

									def callback(__UpperCamelCase						:						List[str]					,   __UpperCamelCase						:						List[str]       ):
												# decode mel to audio
												snake_case__          =						melgan(input_features=mel_output.astype(np.floataa       )       )[0]
												assert len(audio[0]       ) == 8_1_9_2_0 * (step + 1)
												# simulate that audio is played
												return audio

									snake_case__          =						MidiProcessor()
									snake_case__          =						processor(__UpperCamelCase       )

									snake_case__          =						input_tokens[:3]
									snake_case__          =						torch.manual_seed(0       )
									pipe(__UpperCamelCase					,   num_inference_steps=5					,   generator=__UpperCamelCase					,   callback=__UpperCamelCase					,   output_type="""mel"""       )






						def       __lowerCAmelCase(   self						:						List[Any]       ):



									'''simple docstring'''






									snake_case__          =						torch_device

									snake_case__          =						SpectrogramDiffusionPipeline.from_pretrained("""google/music-spectrogram-diffusion"""       )
									snake_case__          =						pipe.to(__UpperCamelCase       )
									pipe.set_progress_bar_config(disable=__UpperCamelCase       )
									snake_case__          =						MidiProcessor()

									snake_case__          =						processor(__UpperCamelCase       )
									# just run two denoising loops
									snake_case__          =						input_tokens[:2]

									snake_case__          =						torch.manual_seed(0       )
									snake_case__          =						pipe(__UpperCamelCase					,   num_inference_steps=2					,   generator=__UpperCamelCase       )

									snake_case__          =						output.audios[0]

									assert abs(np.abs(__UpperCamelCase       ).sum() - 36_12.8_41       ) < 1E-1







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



									'''simple docstring'''






									snake_case__          =						torch_device

									snake_case__          =						SpectrogramDiffusionPipeline.from_pretrained("""google/music-spectrogram-diffusion"""       )
									snake_case__          =						pipe.to(__UpperCamelCase       )
									pipe.set_progress_bar_config(disable=__UpperCamelCase       )

									snake_case__          =						MidiProcessor()

									snake_case__          =						processor(__UpperCamelCase       )

									# just run 4 denoising loops
									snake_case__          =						input_tokens[:4]

									snake_case__          =						torch.manual_seed(0       )
									snake_case__          =						pipe(__UpperCamelCase					,   num_inference_steps=1_0_0					,   generator=__UpperCamelCase       )

									snake_case__          =						output.audios[0]
									assert abs(np.abs(__UpperCamelCase       ).sum() - 93_89.11_11       ) < 5E-2