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()


lowerCAmelCase__					=				'./tests/fixtures/elise_format0.mid'
@unittest.skip('The note-seq package currently throws an error on import'		)
class        a_				(		SCREAMING_SNAKE_CASE ,  unittest.TestCase		):






 '''simple docstring'''






 UpperCAmelCase_							=      SpectrogramDiffusionPipeline
 UpperCAmelCase_							=      PipelineTesterMixin.required_optional_params - {
     'callback',
     'latents',
     'callback_steps',
     'output_type',
     'num_images_per_prompt',
 }
 UpperCAmelCase_							=      False
 UpperCAmelCase_							=      False
 UpperCAmelCase_							=      TOKENS_TO_AUDIO_GENERATION_PARAMS
 UpperCAmelCase_							=      TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS




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







        '''simple docstring'''



        torch.manual_seed(0)
        lowerCAmelCase__  			=      SpectrogramNotesEncoder(
            max_length=2_048    ,					vocab_size=1_536    ,					d_model=768    ,					dropout_rate=0.1    ,					num_layers=1    ,					num_heads=1    ,					d_kv=4    ,					d_ff=2_048    ,					feed_forward_proj='gated-gelu'    ,					)

        lowerCAmelCase__  			=      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=2_048    ,					feed_forward_proj='gated-gelu'    ,					)

        lowerCAmelCase__  			=      TaFilmDecoder(
            input_dims=128    ,					targets_length=256    ,					max_decoder_noise_time=20_000.0    ,					d_model=768    ,					num_layers=1    ,					num_heads=1    ,					d_kv=4    ,					d_ff=2_048    ,					dropout_rate=0.1    ,					)

        lowerCAmelCase__  			=      DDPMScheduler()

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




 def 	__snake_case		(    self      :						List[Any]    ,					lowercase__      :						str    ,					lowercase__      :						Any=0):







        '''simple docstring'''



        if str(lowercase__).startswith('mps'):
               lowerCAmelCase__  			=      torch.manual_seed(lowercase__)
        else:
               lowerCAmelCase__  			=      torch.Generator(device=lowercase__).manual_seed(lowercase__)
        lowerCAmelCase__  			=      {
            'input_tokens': [
                [1_134, 90, 1_135, 1_133, 1_080, 112, 1_132, 1_080, 1_133, 1_079, 133, 1_132, 1_079, 1_133, 1] + [0] * 2_033
            ],
            'generator': generator,
            'num_inference_steps': 4,
            'output_type': 'mel',
        }
        return inputs




 def 	__snake_case		(    self      :						Dict):







        '''simple docstring'''



        lowerCAmelCase__  			=      'cpu'  # ensure determinism for the device-dependent torch.Generator
        lowerCAmelCase__  			=      self.get_dummy_components()
        lowerCAmelCase__  			=      SpectrogramDiffusionPipeline(**lowercase__)
        lowerCAmelCase__  			=      pipe.to(lowercase__)
        pipe.set_progress_bar_config(disable=lowercase__)

        lowerCAmelCase__  			=      self.get_dummy_inputs(lowercase__)
        lowerCAmelCase__  			=      pipe(**lowercase__)
        lowerCAmelCase__  			=      output.audios

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

        assert mel_slice.shape == (3, 3)
        lowerCAmelCase__  			=      np.array(
            [-11.512_925, -4.788_215, -0.46_172_905, -2.051_715, -10.539_147, -10.970_963, -9.091_634, 4.0, 4.0])
        assert np.abs(mel_slice.flatten() - expected_slice).max() < 1e-2




 @skip_mps
 def 	__snake_case		(    self      :						Any):







        '''simple docstring'''



        return super().test_save_load_local()




 @skip_mps
 def 	__snake_case		(    self      :						Any):







        '''simple docstring'''



        return super().test_dict_tuple_outputs_equivalent()




 @skip_mps
 def 	__snake_case		(    self      :						str):







        '''simple docstring'''



        return super().test_save_load_optional_components()




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







        '''simple docstring'''



        return super().test_attention_slicing_forward_pass()




 def 	__snake_case		(    self      :						Optional[Any]):







        '''simple docstring'''



        pass




 def 	__snake_case		(    self      :						Optional[Any]):







        '''simple docstring'''



        pass




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







        '''simple docstring'''



        return super().test_progress_bar()






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






 '''simple docstring'''






 def 	__snake_case		(    self      :						Optional[int]):







        '''simple docstring'''



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




 def 	__snake_case		(    self      :						List[str]):







        '''simple docstring'''



        lowerCAmelCase__  			=      torch_device

        lowerCAmelCase__  			=      SpectrogramDiffusionPipeline.from_pretrained('google/music-spectrogram-diffusion')
        lowerCAmelCase__  			=      pipe.melgan
        lowerCAmelCase__  			=      None

        lowerCAmelCase__  			=      pipe.to(lowercase__)
        pipe.set_progress_bar_config(disable=lowercase__)

        def callback(lowercase__      :						Optional[Any]    ,					lowercase__      :						Union[str, Any]):
               # decode mel to audio
               lowerCAmelCase__  			=      melgan(input_features=mel_output.astype(np.floataa))[0]
               assert len(audio[0]) == 81_920 * (step + 1)
               # simulate that audio is played
               return audio

        lowerCAmelCase__  			=      MidiProcessor()
        lowerCAmelCase__  			=      processor(lowercase__)

        lowerCAmelCase__  			=      input_tokens[:3]
        lowerCAmelCase__  			=      torch.manual_seed(0)
        pipe(lowercase__    ,					num_inference_steps=5    ,					generator=lowercase__    ,					callback=lowercase__    ,					output_type='mel')




 def 	__snake_case		(    self      :						List[Any]):







        '''simple docstring'''



        lowerCAmelCase__  			=      torch_device

        lowerCAmelCase__  			=      SpectrogramDiffusionPipeline.from_pretrained('google/music-spectrogram-diffusion')
        lowerCAmelCase__  			=      pipe.to(lowercase__)
        pipe.set_progress_bar_config(disable=lowercase__)
        lowerCAmelCase__  			=      MidiProcessor()

        lowerCAmelCase__  			=      processor(lowercase__)
        # just run two denoising loops
        lowerCAmelCase__  			=      input_tokens[:2]

        lowerCAmelCase__  			=      torch.manual_seed(0)
        lowerCAmelCase__  			=      pipe(lowercase__    ,					num_inference_steps=2    ,					generator=lowercase__)

        lowerCAmelCase__  			=      output.audios[0]

        assert abs(np.abs(lowercase__).sum() - 3_612.841) < 1e-1




 def 	__snake_case		(    self      :						str):







        '''simple docstring'''



        lowerCAmelCase__  			=      torch_device

        lowerCAmelCase__  			=      SpectrogramDiffusionPipeline.from_pretrained('google/music-spectrogram-diffusion')
        lowerCAmelCase__  			=      pipe.to(lowercase__)
        pipe.set_progress_bar_config(disable=lowercase__)

        lowerCAmelCase__  			=      MidiProcessor()

        lowerCAmelCase__  			=      processor(lowercase__)

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

        lowerCAmelCase__  			=      torch.manual_seed(0)
        lowerCAmelCase__  			=      pipe(lowercase__    ,					num_inference_steps=100    ,					generator=lowercase__)

        lowerCAmelCase__  			=      output.audios[0]
        assert abs(np.abs(lowercase__).sum() - 9_389.1_111) < 5e-2

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

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


lowerCAmelCase__					=				logging.get_logger(__name__)

lowerCAmelCase__					=				{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

lowerCAmelCase__					=				{
    'vocab_file': {
        'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/vocab.json',
    },
    'merges_file': {'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/merges.txt'},
}

lowerCAmelCase__					=				{
    'microsoft/biogpt': 1024,
}






def    __lowerCamelCase	(		lowerCAmelCase__			):
       lowerCAmelCase__  			=      set()
       lowerCAmelCase__  			=      word[0]
       for char in word[1:]:
              pairs.add((prev_char, char)			)
              lowerCAmelCase__  			=      char
       return pairs



class        a_				(		SCREAMING_SNAKE_CASE		):






 '''simple docstring'''






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




 def __init__(    self      :						List[str]    ,					lowercase__      :						Any    ,					lowercase__      :						int    ,					lowercase__      :						Union[str, Any]="<unk>"    ,					lowercase__      :						Optional[Any]="<s>"    ,					lowercase__      :						Dict="</s>"    ,					lowercase__      :						Tuple="</s>"    ,					lowercase__      :						Optional[Any]="<pad>"    ,					**lowercase__      :						Optional[int]    ,					):







        '''simple docstring'''



        super().__init__(
            bos_token=lowercase__    ,					eos_token=lowercase__    ,					sep_token=lowercase__    ,					unk_token=lowercase__    ,					pad_token=lowercase__    ,					**lowercase__    ,					)

        try:
               import sacremoses
        except ImportError:
               raise ImportError(
                   'You need to install sacremoses to use BioGptTokenizer. '
                   'See https://pypi.org/project/sacremoses/ for installation.')

        lowerCAmelCase__  			=      'en'
        lowerCAmelCase__  			=      sacremoses
        # cache of sm.MosesTokenizer instance
        lowerCAmelCase__  			=      {}
        lowerCAmelCase__  			=      {}
        with open(lowercase__    ,					encoding='utf-8') as vocab_handle:
               lowerCAmelCase__  			=      json.load(lowercase__)
        lowerCAmelCase__  			=      {v: k for k, v in self.encoder.items()}
        with open(lowercase__    ,					encoding='utf-8') as merges_handle:
               lowerCAmelCase__  			=      merges_handle.read().split('\n')[:-1]
        lowerCAmelCase__  			=      [tuple(merge.split()[:2]) for merge in merges]
        lowerCAmelCase__  			=      dict(zip(lowercase__    ,					range(len(lowercase__))))
        lowerCAmelCase__  			=      {}




 @property
 def 	__snake_case		(    self      :						Optional[Any]):







        '''simple docstring'''



        return len(self.encoder)




 def 	__snake_case		(    self      :						List[str]):







        '''simple docstring'''



        return dict(self.encoder    ,					**self.added_tokens_encoder)




 def 	__snake_case		(    self      :						Optional[int]    ,					lowercase__      :						List[Any]    ,					lowercase__      :						int):







        '''simple docstring'''



        if lang not in self.cache_moses_tokenizer:
               lowerCAmelCase__  			=      self.sm.MosesTokenizer(lang=lowercase__)
               lowerCAmelCase__  			=      moses_tokenizer
        return self.cache_moses_tokenizer[lang].tokenize(
            lowercase__    ,					aggressive_dash_splits=lowercase__    ,					return_str=lowercase__    ,					escape=lowercase__)




 def 	__snake_case		(    self      :						Optional[int]    ,					lowercase__      :						Tuple    ,					lowercase__      :						Any):







        '''simple docstring'''



        if lang not in self.cache_moses_detokenizer:
               lowerCAmelCase__  			=      self.sm.MosesDetokenizer(lang=lowercase__)
               lowerCAmelCase__  			=      moses_detokenizer
        return self.cache_moses_detokenizer[lang].detokenize(lowercase__)




 def 	__snake_case		(    self      :						Dict    ,					lowercase__      :						Union[str, Any]):







        '''simple docstring'''



        lowerCAmelCase__  			=      tuple(token[:-1]) + (token[-1] + '</w>',)
        if token in self.cache:
               return self.cache[token]
        lowerCAmelCase__  			=      get_pairs(lowercase__)

        if not pairs:
               return token + "</w>"

        while True:
               lowerCAmelCase__  			=      min(lowercase__    ,					key=lambda lowercase__: self.bpe_ranks.get(lowercase__    ,					float('inf')))
               if bigram not in self.bpe_ranks:
                      break
               lowerCAmelCase__			,      lowerCAmelCase__  			=      bigram
               lowerCAmelCase__  			=      []
               lowerCAmelCase__  			=      0
               while i < len(lowercase__):
                      try:
                             lowerCAmelCase__  			=      word.index(lowercase__    ,					lowercase__)
                      except ValueError:
                             new_word.extend(word[i:])
                             break
                      else:
                             new_word.extend(word[i:j])
                             lowerCAmelCase__  			=      j

                      if word[i] == first and i < len(lowercase__) - 1 and word[i + 1] == second:
                             new_word.append(first + second)
                             i += 2
                      else:
                             new_word.append(word[i])
                             i += 1
               lowerCAmelCase__  			=      tuple(lowercase__)
               lowerCAmelCase__  			=      new_word
               if len(lowercase__) == 1:
                      break
               else:
                      lowerCAmelCase__  			=      get_pairs(lowercase__)
        lowerCAmelCase__  			=      ' '.join(lowercase__)
        if word == "\n  </w>":
               lowerCAmelCase__  			=      '\n</w>'
        lowerCAmelCase__  			=      word
        return word




 def 	__snake_case		(    self      :						Optional[Any]    ,					lowercase__      :						Any    ,					lowercase__      :						Dict=False):







        '''simple docstring'''



        if bypass_tokenizer:
               lowerCAmelCase__  			=      text.split()
        else:
               lowerCAmelCase__  			=      self.moses_tokenize(lowercase__    ,					self.lang)

        lowerCAmelCase__  			=      []
        for token in text:
               if token:
                      split_tokens.extend(list(self.bpe(lowercase__).split(' ')))

        return split_tokens




 def 	__snake_case		(    self      :						List[str]    ,					lowercase__      :						List[Any]):







        '''simple docstring'''



        return self.encoder.get(lowercase__    ,					self.encoder.get(self.unk_token))




 def 	__snake_case		(    self      :						Optional[int]    ,					lowercase__      :						Optional[Any]):







        '''simple docstring'''



        return self.decoder.get(lowercase__    ,					self.unk_token)




 def 	__snake_case		(    self      :						Any    ,					lowercase__      :						int):







        '''simple docstring'''



        lowerCAmelCase__  			=      [t.replace(' '    ,					'').replace('</w>'    ,					' ') for t in tokens]
        lowerCAmelCase__  			=      ''.join(lowercase__).split()
        # detokenize
        lowerCAmelCase__  			=      self.moses_detokenize(lowercase__    ,					self.lang)
        return text




 def 	__snake_case		(    self      :						Optional[int]    ,					lowercase__      :						List[int]    ,					lowercase__      :						Optional[List[int]] = None):







        '''simple docstring'''



        if token_ids_a is None:
               return [self.sep_token_id] + token_ids_a
        lowerCAmelCase__  			=      [self.sep_token_id]
        return sep + token_ids_a + sep + token_ids_a




 def 	__snake_case		(    self      :						Union[str, Any]    ,					lowercase__      :						List[int]    ,					lowercase__      :						Optional[List[int]] = None    ,					lowercase__      :						bool = False):







        '''simple docstring'''



        if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=lowercase__    ,					token_ids_a=lowercase__    ,					already_has_special_tokens=lowercase__)
        # no bos used in fairseq
        if token_ids_a is not None:
               return [1] + ([0] * len(lowercase__)) + [1] + ([0] * len(lowercase__))
        return [1] + ([0] * len(lowercase__))




 def 	__snake_case		(    self      :						Union[str, Any]    ,					lowercase__      :						List[int]    ,					lowercase__      :						Optional[List[int]] = None):







        '''simple docstring'''



        lowerCAmelCase__  			=      [self.sep_token_id]

        # no bos used in fairseq
        if token_ids_a is None:
               return len(token_ids_a + sep) * [0]
        return len(token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1]




 def 	__snake_case		(    self      :						Tuple    ,					lowercase__      :						str    ,					lowercase__      :						Optional[str] = None):







        '''simple docstring'''



        if not os.path.isdir(lowercase__):
               logger.error(F"""Vocabulary path ({save_directory}) should be a directory""")
               return
        lowerCAmelCase__  			=      os.path.join(
            lowercase__    ,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])
        lowerCAmelCase__  			=      os.path.join(
            lowercase__    ,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'])

        with open(lowercase__    ,					'w'    ,					encoding='utf-8') as f:
               f.write(json.dumps(self.encoder    ,					indent=2    ,					sort_keys=lowercase__    ,					ensure_ascii=lowercase__) + '\n')

        lowerCAmelCase__  			=      0
        with open(lowercase__    ,					'w'    ,					encoding='utf-8') as writer:
               for bpe_tokens, token_index in sorted(self.bpe_ranks.items()    ,					key=lambda lowercase__: kv[1]):
                      if index != token_index:
                             logger.warning(
                                 F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
                                 ' Please check that the tokenizer is not corrupted!')
                             lowerCAmelCase__  			=      token_index
                      writer.write(' '.join(lowercase__) + '\n')
                      index += 1

        return vocab_file, merge_file




 def __getstate__(    self      :						List[str]):







        '''simple docstring'''



        lowerCAmelCase__  			=      self.__dict__.copy()
        lowerCAmelCase__  			=      None
        return state




 def __setstate__(    self      :						List[str]    ,					lowercase__      :						Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__  			=      d

        try:
               import sacremoses
        except ImportError:
               raise ImportError(
                   'You need to install sacremoses to use XLMTokenizer. '
                   'See https://pypi.org/project/sacremoses/ for installation.')

        lowerCAmelCase__  			=      sacremoses
