





import json
import os
import unittest

from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES, FSMTTokenizer
from transformers.testing_utils import slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


# using a different tiny model than the one used for default params defined in init to ensure proper testing
_lowercase		:     Optional[int] 					="""stas/tiny-wmt19-en-ru"""






class 	UpperCamelCase_     (     snake_case__     ,    unittest.TestCase       ):
					_a			:	Any							=				FSMTTokenizer
					_a			:	Dict							=				False



					def      __a    (		self    :    Optional[Any]	):
							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							lowerCamelCase_  :    List[str]   	=			[
							    'l',
							    'o',
							    'w',
							    'e',
							    'r',
							    's',
							    't',
							    'i',
							    'd',
							    'n',
							    'w</w>',
							    'r</w>',
							    't</w>',
							    'lo',
							    'low',
							    'er</w>',
							    'low</w>',
							    'lowest</w>',
							    'newer</w>',
							    'wider</w>',
							    '<unk>',
							]
							lowerCamelCase_  :    Optional[int]   	=			dict(zip(lowerCamelCase					,			range(len(lowerCamelCase	)	)	)	)
							lowerCamelCase_  :    List[str]   	=			['l o 123', 'lo w 1456', 'e r</w> 1789', '']

							lowerCamelCase_  :    Tuple   	=			['en', 'ru']
							lowerCamelCase_  :    str   	=			{
							    'langs': self.langs,
							    'src_vocab_size': 10,
							    'tgt_vocab_size': 20,
							}

							lowerCamelCase_  :    List[Any]   	=			os.path.join(self.tmpdirname					,			VOCAB_FILES_NAMES['src_vocab_file']	)
							lowerCamelCase_  :    int   	=			os.path.join(self.tmpdirname					,			VOCAB_FILES_NAMES['tgt_vocab_file']	)
							lowerCamelCase_  :    int   	=			os.path.join(self.tmpdirname					,			'tokenizer_config.json'	)
							lowerCamelCase_  :    int   	=			os.path.join(self.tmpdirname					,			VOCAB_FILES_NAMES['merges_file']	)
							with open(self.src_vocab_file					,			'w'	) as fp:
									fp.write(json.dumps(lowerCamelCase	)	)
							with open(self.tgt_vocab_file					,			'w'	) as fp:
									fp.write(json.dumps(lowerCamelCase	)	)
							with open(self.merges_file					,			'w'	) as fp:
									fp.write('\n'.join(lowerCamelCase	)	)
							with open(lowerCamelCase					,			'w'	) as fp:
									fp.write(json.dumps(lowerCamelCase	)	)



					@cached_property
					def      __a    (		self    :    Tuple	):
							return FSMTTokenizer.from_pretrained('facebook/wmt19-ru-en'	)



					@cached_property
					def      __a    (		self    :    List[Any]	):
							return FSMTTokenizer.from_pretrained('facebook/wmt19-en-ru'	)



					def      __a    (		self    :    Tuple	):
							lowerCamelCase_  :    List[str]   	=			FSMTTokenizer.from_pretrained(lowerCamelCase	)
							self.assertListEqual([tokenizer.src_lang, tokenizer.tgt_lang]					,			['en', 'ru']	)
							self.assertEqual(tokenizer.src_vocab_size					,			21	)
							self.assertEqual(tokenizer.tgt_vocab_size					,			21	)



					def      __a    (		self    :    List[str]	):
							lowerCamelCase_  :    Optional[Any]   	=			FSMTTokenizer(self.langs					,			self.src_vocab_file					,			self.tgt_vocab_file					,			self.merges_file	)

							lowerCamelCase_  :    Tuple   	=			'lower'
							lowerCamelCase_  :    List[str]   	=			['low', 'er</w>']
							lowerCamelCase_  :    Any   	=			tokenizer.tokenize(lowerCamelCase	)
							self.assertListEqual(lowerCamelCase					,			lowerCamelCase	)

							lowerCamelCase_  :    List[str]   	=			tokens + ['<unk>']
							lowerCamelCase_  :    List[Any]   	=			[14, 15, 20]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase	)					,			lowerCamelCase	)



					@slow
					def      __a    (		self    :    Optional[Any]	):
							lowerCamelCase_  :    Optional[Any]   	=			self.tokenizer_ru_en

							lowerCamelCase_  :    List[str]   	=			tokenizer.encode('sequence builders'					,			add_special_tokens=lowerCamelCase	)
							lowerCamelCase_  :    Optional[int]   	=			tokenizer.encode('multi-sequence build'					,			add_special_tokens=lowerCamelCase	)

							lowerCamelCase_  :    Optional[Any]   	=			tokenizer.build_inputs_with_special_tokens(lowerCamelCase	)
							lowerCamelCase_  :    List[str]   	=			tokenizer.build_inputs_with_special_tokens(lowerCamelCase					,			lowerCamelCase	)

							assert encoded_sentence == text + [2]
							assert encoded_pair == text + [2] + text_a + [2]



					@slow
					def      __a    (		self    :    List[str]	):
							lowerCamelCase_  :    List[Any]   	=			self.tokenizer_en_ru
							lowerCamelCase_  :    int   	=			self.tokenizer_ru_en

							lowerCamelCase_  :    List[Any]   	=			[
							    [
							        'Here\'s a little song I wrote. Don\'t worry, be happy.',
							        [24_70, 39, 11, 23_49, 72_22, 70, 59_79, 7, 84_50, 10_50, 1_31_60, 5, 26, 64_45, 7, 2],
							    ],
							    ['This is it. No more. I\'m done!', [1_32, 21, 37, 7, 14_34, 86, 7, 70, 64_76, 13_05, 4_27, 2]],
							]

							# if data needs to be recreated or added, run:
							# import torch
							# model = torch.hub.load("pytorch/fairseq", "transformer.wmt19.en-ru", checkpoint_file="model4.pt", tokenizer="moses", bpe="fastbpe")
							# for src_text, _ in targets: print(f"""[\n"{src_text}",\n {model.encode(src_text).tolist()}\n],""")

							for src_text, tgt_input_ids in targets:
									lowerCamelCase_  :    Any   	=			tokenizer_enc.encode(lowerCamelCase					,			return_tensors=lowerCamelCase	)
									self.assertListEqual(lowerCamelCase					,			lowerCamelCase	)

									# and decode backward, using the reversed languages model
									lowerCamelCase_  :    Optional[Any]   	=			tokenizer_dec.decode(lowerCamelCase					,			skip_special_tokens=lowerCamelCase	)
									self.assertEqual(lowerCamelCase					,			lowerCamelCase	)



					@slow
					def      __a    (		self    :    Optional[Any]	):
							lowerCamelCase_  :    Any   	=			FSMTTokenizer.from_pretrained('facebook/wmt19-ru-en'					,			do_lower_case=lowerCamelCase	)
							lowerCamelCase_  :    Dict   	=			tokenizer.tokenize('USA is United States of America'	)
							lowerCamelCase_  :    Optional[int]   	=			['us', 'a</w>', 'is</w>', 'un', 'i', 'ted</w>', 'st', 'ates</w>', 'of</w>', 'am', 'er', 'ica</w>']
							self.assertListEqual(lowerCamelCase					,			lowerCamelCase	)



					@unittest.skip('FSMTConfig.__init__  requires non-optional args'	)
					def      __a    (		self    :    str	):
							pass





					@unittest.skip('FSMTConfig.__init__  requires non-optional args'	)
					def      __a    (		self    :    List[Any]	):
							pass







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()


_lowercase		:     Optional[Any] 					="""./tests/fixtures/elise_format0.mid"""






@unittest.skip('The note-seq package currently throws an error on import'       )
class 	UpperCamelCase_     (     snake_case__     ,    unittest.TestCase       ):
					_a			:	int							=				SpectrogramDiffusionPipeline
					_a			:	Dict							=				PipelineTesterMixin.required_optional_params - {
					    'callback',
					    'latents',
					    'callback_steps',
					    'output_type',
					    'num_images_per_prompt',
					}
					_a			:	str							=				False
					_a			:	Union[str, Any]							=				False
					_a			:	Optional[int]							=				TOKENS_TO_AUDIO_GENERATION_PARAMS
					_a			:	Optional[Any]							=				TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS



					def      __a    (		self    :    Dict	):
							torch.manual_seed(0	)
							lowerCamelCase_  :    List[str]   	=			SpectrogramNotesEncoder(
							    max_length=20_48					,			vocab_size=15_36					,			d_model=7_68					,			dropout_rate=0.1					,			num_layers=1					,			num_heads=1					,			d_kv=4					,			d_ff=20_48					,			feed_forward_proj='gated-gelu'					,			)

							lowerCamelCase_  :    Union[str, Any]   	=			SpectrogramContEncoder(
							    input_dims=1_28					,			targets_context_length=2_56					,			d_model=7_68					,			dropout_rate=0.1					,			num_layers=1					,			num_heads=1					,			d_kv=4					,			d_ff=20_48					,			feed_forward_proj='gated-gelu'					,			)

							lowerCamelCase_  :    Union[str, Any]   	=			TaFilmDecoder(
							    input_dims=1_28					,			targets_length=2_56					,			max_decoder_noise_time=20_000.0					,			d_model=7_68					,			num_layers=1					,			num_heads=1					,			d_kv=4					,			d_ff=20_48					,			dropout_rate=0.1					,			)

							lowerCamelCase_  :    Any   	=			DDPMScheduler()

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



					def      __a    (		self    :    Any					,			lowerCamelCase    :    Any					,			lowerCamelCase    :    int=0	):
							if str(lowerCamelCase	).startswith('mps'	):
									lowerCamelCase_  :    Optional[int]   	=			torch.manual_seed(lowerCamelCase	)
							else:
									lowerCamelCase_  :    Dict   	=			torch.Generator(device=lowerCamelCase	).manual_seed(lowerCamelCase	)
							lowerCamelCase_  :    str   	=			{
							    'input_tokens': [
							        [11_34, 90, 11_35, 11_33, 10_80, 1_12, 11_32, 10_80, 11_33, 10_79, 1_33, 11_32, 10_79, 11_33, 1] + [0] * 20_33
							    ],
							    'generator': generator,
							    'num_inference_steps': 4,
							    'output_type': 'mel',
							}
							return inputs



					def      __a    (		self    :    Dict	):
							lowerCamelCase_  :    str   	=			'cpu'  # ensure determinism for the device-dependent torch.Generator
							lowerCamelCase_  :    Dict   	=			self.get_dummy_components()
							lowerCamelCase_  :    Tuple   	=			SpectrogramDiffusionPipeline(**lowerCamelCase	)
							lowerCamelCase_  :    List[str]   	=			pipe.to(lowerCamelCase	)
							pipe.set_progress_bar_config(disable=lowerCamelCase	)

							lowerCamelCase_  :    Optional[Any]   	=			self.get_dummy_inputs(lowerCamelCase	)
							lowerCamelCase_  :    str   	=			pipe(**lowerCamelCase	)
							lowerCamelCase_  :    Tuple   	=			output.audios

							lowerCamelCase_  :    Tuple   	=			mel[0, -3:, -3:]

							assert mel_slice.shape == (3, 3)
							lowerCamelCase_  :    List[str]   	=			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      __a    (		self    :    Any	):
							return super().test_save_load_local()



					@skip_mps
					def      __a    (		self    :    Tuple	):
							return super().test_dict_tuple_outputs_equivalent()



					@skip_mps
					def      __a    (		self    :    Dict	):
							return super().test_save_load_optional_components()



					@skip_mps
					def      __a    (		self    :    str	):
							return super().test_attention_slicing_forward_pass()



					def      __a    (		self    :    Any	):
							pass



					def      __a    (		self    :    Optional[int]	):
							pass





					@skip_mps
					def      __a    (		self    :    Optional[Any]	):
							return super().test_progress_bar()






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



					def      __a    (		self    :    Optional[int]	):
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()



					def      __a    (		self    :    str	):
							# TODO - test that pipeline can decode tokens in a callback
							# so that music can be played live
							lowerCamelCase_  :    Optional[Any]   	=			torch_device

							lowerCamelCase_  :    Union[str, Any]   	=			SpectrogramDiffusionPipeline.from_pretrained('google/music-spectrogram-diffusion'	)
							lowerCamelCase_  :    str   	=			pipe.melgan
							lowerCamelCase_  :    Optional[int]   	=			None

							lowerCamelCase_  :    str   	=			pipe.to(lowerCamelCase	)
							pipe.set_progress_bar_config(disable=lowerCamelCase	)

							def callback(lowerCamelCase    :    int					,			lowerCamelCase    :    int	):
									# decode mel to audio
									lowerCamelCase_  :    Union[str, Any]   	=			melgan(input_features=mel_output.astype(np.floataa	)	)[0]
									assert len(audio[0]	) == 8_19_20 * (step + 1)
									# simulate that audio is played
									return audio

							lowerCamelCase_  :    Any   	=			MidiProcessor()
							lowerCamelCase_  :    str   	=			processor(lowerCamelCase	)

							lowerCamelCase_  :    str   	=			input_tokens[:3]
							lowerCamelCase_  :    Dict   	=			torch.manual_seed(0	)
							pipe(lowerCamelCase					,			num_inference_steps=5					,			generator=lowerCamelCase					,			callback=lowerCamelCase					,			output_type='mel'	)



					def      __a    (		self    :    str	):
							lowerCamelCase_  :    List[str]   	=			torch_device

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

							lowerCamelCase_  :    Dict   	=			processor(lowerCamelCase	)
							# just run two denoising loops
							lowerCamelCase_  :    Optional[Any]   	=			input_tokens[:2]

							lowerCamelCase_  :    List[Any]   	=			torch.manual_seed(0	)
							lowerCamelCase_  :    str   	=			pipe(lowerCamelCase					,			num_inference_steps=2					,			generator=lowerCamelCase	)

							lowerCamelCase_  :    Dict   	=			output.audios[0]

							assert abs(np.abs(lowerCamelCase	).sum() - 3_612.841	) < 1E-1





					def      __a    (		self    :    List[Any]	):
							lowerCamelCase_  :    int   	=			torch_device

							lowerCamelCase_  :    str   	=			SpectrogramDiffusionPipeline.from_pretrained('google/music-spectrogram-diffusion'	)
							lowerCamelCase_  :    Optional[int]   	=			pipe.to(lowerCamelCase	)
							pipe.set_progress_bar_config(disable=lowerCamelCase	)

							lowerCamelCase_  :    Tuple   	=			MidiProcessor()

							lowerCamelCase_  :    int   	=			processor(lowerCamelCase	)

							# just run 4 denoising loops
							lowerCamelCase_  :    Optional[Any]   	=			input_tokens[:4]

							lowerCamelCase_  :    List[Any]   	=			torch.manual_seed(0	)
							lowerCamelCase_  :    int   	=			pipe(lowerCamelCase					,			num_inference_steps=1_00					,			generator=lowerCamelCase	)

							lowerCamelCase_  :    List[Any]   	=			output.audios[0]
							assert abs(np.abs(lowerCamelCase	).sum() - 9_389.1_111	) < 5E-2
