


import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
			from transformers.pipelines.conversational import Conversation


snake_case__  				=			logging.get_logger(__name__)

snake_case__  				=			{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

snake_case__  				=			{
    'vocab_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt'
        ),
    },
}

snake_case__  				=			{
    'microsoft/deberta-base': 512,
    'microsoft/deberta-large': 512,
    'microsoft/deberta-xlarge': 512,
    'microsoft/deberta-base-mnli': 512,
    'microsoft/deberta-large-mnli': 512,
    'microsoft/deberta-xlarge-mnli': 512,
}

snake_case__  				=			{
    'microsoft/deberta-base': {'do_lower_case': False},
    'microsoft/deberta-large': {'do_lower_case': False},
}



class      snake_case__	(   _A	):
				__lowerCAmelCase		=   VOCAB_FILES_NAMES
				__lowerCAmelCase		=   PRETRAINED_VOCAB_FILES_MAP
				__lowerCAmelCase		=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__lowerCAmelCase		=   ['''input_ids''', '''attention_mask''', '''token_type_ids''']
				__lowerCAmelCase		=   DebertaTokenizer
				def __init__(	self  ,   _UpperCAmelCase=None  ,   _UpperCAmelCase=None  ,   _UpperCAmelCase=None  ,   _UpperCAmelCase="replace"  ,   _UpperCAmelCase="[CLS]"  ,   _UpperCAmelCase="[SEP]"  ,   _UpperCAmelCase="[SEP]"  ,   _UpperCAmelCase="[CLS]"  ,   _UpperCAmelCase="[UNK]"  ,   _UpperCAmelCase="[PAD]"  ,   _UpperCAmelCase="[MASK]"  ,   _UpperCAmelCase=False  ,   **_UpperCAmelCase  ,   ):




										"""simple docstring"""




										super().__init__(
										    _UpperCAmelCase  ,   _UpperCAmelCase  ,   tokenizer_file=_UpperCAmelCase  ,   errors=_UpperCAmelCase  ,   bos_token=_UpperCAmelCase  ,   eos_token=_UpperCAmelCase  ,   unk_token=_UpperCAmelCase  ,   sep_token=_UpperCAmelCase  ,   cls_token=_UpperCAmelCase  ,   pad_token=_UpperCAmelCase  ,   mask_token=_UpperCAmelCase  ,   add_prefix_space=_UpperCAmelCase  ,   **_UpperCAmelCase  ,   )
										__magic_name__					:				Optional[int]				=   kwargs.pop('add_bos_token'  ,   _UpperCAmelCase   )

										__magic_name__					:				Optional[Any]				=   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()   )
										if pre_tok_state.get('add_prefix_space'  ,   _UpperCAmelCase   ) != add_prefix_space:
																__magic_name__					:				Optional[Any]				=   getattr(_UpperCAmelCase  ,   pre_tok_state.pop('type'   )   )
																__magic_name__					:				List[Any]				=   add_prefix_space
																__magic_name__					:				Union[str, Any]				=   pre_tok_class(**_UpperCAmelCase   )

										__magic_name__					:				Tuple				=   add_prefix_space
				@property
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										if self._mask_token is None:
																if self.verbose:
																						logger.error('Using mask_token, but it is not set yet.'   )
																return None
										return str(self._mask_token   )
				@mask_token.setter
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase   ):




										"""simple docstring"""




										__magic_name__					:				Union[str, Any]				=   AddedToken(_UpperCAmelCase  ,   lstrip=_UpperCAmelCase  ,   rstrip=_UpperCAmelCase   ) if isinstance(_UpperCAmelCase  ,   _UpperCAmelCase   ) else value
										__magic_name__					:				Optional[Any]				=   value
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase  ,   _UpperCAmelCase = None   ):




										"""simple docstring"""




										if token_ids_a is None:
																return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
										__magic_name__					:				Dict				=   [self.cls_token_id]
										__magic_name__					:				List[Any]				=   [self.sep_token_id]
										return cls + token_ids_a + sep + token_ids_a + sep
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase  ,   _UpperCAmelCase = None   ):




										"""simple docstring"""




										__magic_name__					:				str				=   [self.sep_token_id]
										__magic_name__					:				Tuple				=   [self.cls_token_id]

										if token_ids_a is None:
																return len(cls + token_ids_a + sep   ) * [0]
										return len(cls + token_ids_a + sep   ) * [0] + len(token_ids_a + sep   ) * [1]
				def 			UpperCamelCase_    (	self  ,   *_UpperCAmelCase  ,   **_UpperCAmelCase   ):




										"""simple docstring"""




										__magic_name__					:				Any				=   kwargs.get('is_split_into_words'  ,   _UpperCAmelCase   )
										assert self.add_prefix_space or not is_split_into_words, (
										    F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
										    "to use it with pretokenized inputs."
										)

										return super()._batch_encode_plus(*_UpperCAmelCase  ,   **_UpperCAmelCase   )
				def 			UpperCamelCase_    (	self  ,   *_UpperCAmelCase  ,   **_UpperCAmelCase   ):




										"""simple docstring"""




										__magic_name__					:				int				=   kwargs.get('is_split_into_words'  ,   _UpperCAmelCase   )

										assert self.add_prefix_space or not is_split_into_words, (
										    F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
										    "to use it with pretokenized inputs."
										)

										return super()._encode_plus(*_UpperCAmelCase  ,   **_UpperCAmelCase   )
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase  ,   _UpperCAmelCase = None   ):




										"""simple docstring"""




										__magic_name__					:				List[str]				=   self._tokenizer.model.save(_UpperCAmelCase  ,   name=_UpperCAmelCase   )
										return tuple(_UpperCAmelCase   )





				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase   ):




										"""simple docstring"""




										__magic_name__					:				Dict				=   []
										for is_user, text in conversation.iter_texts():
																input_ids.extend(self.encode(_UpperCAmelCase  ,   add_special_tokens=_UpperCAmelCase   ) + [self.eos_token_id]   )

										if len(_UpperCAmelCase   ) > self.model_max_length:
																__magic_name__					:				Optional[int]				=   input_ids[-self.model_max_length :]
										return input_ids






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()


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



@unittest.skip('''The note-seq package currently throws an error on import'''	)
class      snake_case__	(   _A    ,							unittest.TestCase	):
				__lowerCAmelCase		=   SpectrogramDiffusionPipeline
				__lowerCAmelCase		=   PipelineTesterMixin.required_optional_params - {
				    '''callback''',
				    '''latents''',
				    '''callback_steps''',
				    '''output_type''',
				    '''num_images_per_prompt''',
				}
				__lowerCAmelCase		=   False
				__lowerCAmelCase		=   False
				__lowerCAmelCase		=   TOKENS_TO_AUDIO_GENERATION_PARAMS
				__lowerCAmelCase		=   TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										torch.manual_seed(0   )
										__magic_name__					:				Tuple				=   SpectrogramNotesEncoder(
										    max_length=2048  ,   vocab_size=1536  ,   d_model=768  ,   dropout_rate=0.1  ,   num_layers=1  ,   num_heads=1  ,   d_kv=4  ,   d_ff=2048  ,   feed_forward_proj='gated-gelu'  ,   )

										__magic_name__					:				Union[str, Any]				=   SpectrogramContEncoder(
										    input_dims=128  ,   targets_context_length=256  ,   d_model=768  ,   dropout_rate=0.1  ,   num_layers=1  ,   num_heads=1  ,   d_kv=4  ,   d_ff=2048  ,   feed_forward_proj='gated-gelu'  ,   )

										__magic_name__					:				Optional[Any]				=   TaFilmDecoder(
										    input_dims=128  ,   targets_length=256  ,   max_decoder_noise_time=20000.0  ,   d_model=768  ,   num_layers=1  ,   num_heads=1  ,   d_kv=4  ,   d_ff=2048  ,   dropout_rate=0.1  ,   )

										__magic_name__					:				Any				=   DDPMScheduler()

										__magic_name__					:				Tuple				=   {
										    'notes_encoder': notes_encoder.eval(),
										    'continuous_encoder': continuous_encoder.eval(),
										    'decoder': decoder.eval(),
										    'scheduler': scheduler,
										    'melgan': None,
										}
										return components
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase  ,   _UpperCAmelCase=0   ):




										"""simple docstring"""




										if str(_UpperCAmelCase   ).startswith('mps'   ):
																__magic_name__					:				str				=   torch.manual_seed(_UpperCAmelCase   )
										else:
																__magic_name__					:				Optional[int]				=   torch.Generator(device=_UpperCAmelCase   ).manual_seed(_UpperCAmelCase   )
										__magic_name__					:				List[str]				=   {
										    'input_tokens': [
										        [1134, 90, 1135, 1133, 1080, 112, 1132, 1080, 1133, 1079, 133, 1132, 1079, 1133, 1] + [0] * 2033
										    ],
										    'generator': generator,
										    'num_inference_steps': 4,
										    'output_type': 'mel',
										}
										return inputs
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				Dict				=   'cpu'  # ensure determinism for the device-dependent torch.Generator
										__magic_name__					:				Optional[Any]				=   self.get_dummy_components()
										__magic_name__					:				Dict				=   SpectrogramDiffusionPipeline(**_UpperCAmelCase   )
										__magic_name__					:				Union[str, Any]				=   pipe.to(_UpperCAmelCase   )
										pipe.set_progress_bar_config(disable=_UpperCAmelCase   )

										__magic_name__					:				List[Any]				=   self.get_dummy_inputs(_UpperCAmelCase   )
										__magic_name__					:				Dict				=   pipe(**_UpperCAmelCase   )
										__magic_name__					:				Optional[Any]				=   output.audios

										__magic_name__					:				Dict				=   mel[0, -3:, -3:]

										assert mel_slice.shape == (3, 3)
										__magic_name__					:				Union[str, Any]				=   np.array(
										    [-11.512925, -4.788215, -0.46172905, -2.051715, -10.539147, -10.970963, -9.091634, 4.0, 4.0]   )
										assert np.abs(mel_slice.flatten() - expected_slice   ).max() < 1E-2
				@skip_mps
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										return super().test_save_load_local()
				@skip_mps
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										return super().test_dict_tuple_outputs_equivalent()
				@skip_mps
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										return super().test_save_load_optional_components()
				@skip_mps
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										return super().test_attention_slicing_forward_pass()
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										pass
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										pass





				@skip_mps
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										return super().test_progress_bar()






@slow
@require_torch_gpu
@require_onnxruntime
@require_note_seq
class      snake_case__	(   unittest.TestCase	):
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				Tuple				=   torch_device

										__magic_name__					:				Optional[int]				=   SpectrogramDiffusionPipeline.from_pretrained('google/music-spectrogram-diffusion'   )
										__magic_name__					:				Dict				=   pipe.melgan
										__magic_name__					:				int				=   None

										__magic_name__					:				List[str]				=   pipe.to(_UpperCAmelCase   )
										pipe.set_progress_bar_config(disable=_UpperCAmelCase   )

										def callback(_UpperCAmelCase  ,   _UpperCAmelCase   ):
																# decode mel to audio
																__magic_name__					:				Any				=   melgan(input_features=mel_output.astype(np.floataa   )   )[0]
																assert len(audio[0]   ) == 8_1920 * (step + 1)
																# simulate that audio is played
																return audio

										__magic_name__					:				Optional[Any]				=   MidiProcessor()
										__magic_name__					:				str				=   processor(_UpperCAmelCase   )

										__magic_name__					:				Optional[int]				=   input_tokens[:3]
										__magic_name__					:				Optional[Any]				=   torch.manual_seed(0   )
										pipe(_UpperCAmelCase  ,   num_inference_steps=5  ,   generator=_UpperCAmelCase  ,   callback=_UpperCAmelCase  ,   output_type='mel'   )
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				Tuple				=   torch_device

										__magic_name__					:				str				=   SpectrogramDiffusionPipeline.from_pretrained('google/music-spectrogram-diffusion'   )
										__magic_name__					:				List[str]				=   pipe.to(_UpperCAmelCase   )
										pipe.set_progress_bar_config(disable=_UpperCAmelCase   )
										__magic_name__					:				Union[str, Any]				=   MidiProcessor()

										__magic_name__					:				List[Any]				=   processor(_UpperCAmelCase   )
										# just run two denoising loops
										__magic_name__					:				List[Any]				=   input_tokens[:2]

										__magic_name__					:				int				=   torch.manual_seed(0   )
										__magic_name__					:				int				=   pipe(_UpperCAmelCase  ,   num_inference_steps=2  ,   generator=_UpperCAmelCase   )

										__magic_name__					:				List[str]				=   output.audios[0]

										assert abs(np.abs(_UpperCAmelCase   ).sum() - 3612.841   ) < 1E-1





				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				Any				=   torch_device

										__magic_name__					:				List[Any]				=   SpectrogramDiffusionPipeline.from_pretrained('google/music-spectrogram-diffusion'   )
										__magic_name__					:				Any				=   pipe.to(_UpperCAmelCase   )
										pipe.set_progress_bar_config(disable=_UpperCAmelCase   )

										__magic_name__					:				List[str]				=   MidiProcessor()

										__magic_name__					:				Union[str, Any]				=   processor(_UpperCAmelCase   )

										# just run 4 denoising loops
										__magic_name__					:				List[Any]				=   input_tokens[:4]

										__magic_name__					:				Dict				=   torch.manual_seed(0   )
										__magic_name__					:				Union[str, Any]				=   pipe(_UpperCAmelCase  ,   num_inference_steps=100  ,   generator=_UpperCAmelCase   )

										__magic_name__					:				Dict				=   output.audios[0]
										assert abs(np.abs(_UpperCAmelCase   ).sum() - 9389.1111   ) < 5E-2


