




import unittest

from transformers import ElectraConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import (
				    MODEL_FOR_PRETRAINING_MAPPING,
				    ElectraForCausalLM,
				    ElectraForMaskedLM,
				    ElectraForMultipleChoice,
				    ElectraForPreTraining,
				    ElectraForQuestionAnswering,
				    ElectraForSequenceClassification,
				    ElectraForTokenClassification,
				    ElectraModel,
				)
				from transformers.models.electra.modeling_electra import ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST





class 		A__    :






	def __init__(     self    ,					lowerCamelCase    ,					lowerCamelCase=13    ,					lowerCamelCase=7    ,					lowerCamelCase=True    ,					lowerCamelCase=True    ,					lowerCamelCase=True    ,					lowerCamelCase=True    ,					lowerCamelCase=99    ,					lowerCamelCase=32    ,					lowerCamelCase=5    ,					lowerCamelCase=4    ,					lowerCamelCase=37    ,					lowerCamelCase="gelu"    ,					lowerCamelCase=0.1    ,					lowerCamelCase=0.1    ,					lowerCamelCase=512    ,					lowerCamelCase=16    ,					lowerCamelCase=2    ,					lowerCamelCase=0.0_2    ,					lowerCamelCase=3    ,					lowerCamelCase=4    ,					lowerCamelCase=None    ,					)				->       Any:




						"""simple docstring"""






						__magic_name__ :							Union[str, Any]		       =							parent
						__magic_name__ :							int		       =							batch_size
						__magic_name__ :							List[Any]		       =							seq_length
						__magic_name__ :							Dict		       =							is_training
						__magic_name__ :							Union[str, Any]		       =							use_input_mask
						__magic_name__ :							Any		       =							use_token_type_ids
						__magic_name__ :							str		       =							use_labels
						__magic_name__ :							Optional[int]		       =							vocab_size
						__magic_name__ :							Any		       =							hidden_size
						__magic_name__ :							Union[str, Any]		       =							num_hidden_layers
						__magic_name__ :							Union[str, Any]		       =							num_attention_heads
						__magic_name__ :							str		       =							intermediate_size
						__magic_name__ :							Optional[int]		       =							hidden_act
						__magic_name__ :							Dict		       =							hidden_dropout_prob
						__magic_name__ :							Tuple		       =							attention_probs_dropout_prob
						__magic_name__ :							Union[str, Any]		       =							max_position_embeddings
						__magic_name__ :							List[Any]		       =							type_vocab_size
						__magic_name__ :							Tuple		       =							type_sequence_label_size
						__magic_name__ :							List[Any]		       =							initializer_range
						__magic_name__ :							int		       =							num_labels
						__magic_name__ :							Optional[int]		       =							num_choices
						__magic_name__ :							Any		       =							scope






	def 			lowercase				(     self						)				->       str:




						"""simple docstring"""






						__magic_name__ :							str		       =							ids_tensor([self.batch_size, self.seq_length]    ,					self.vocab_size						)

						__magic_name__ :							List[Any]		       =							None
						if self.use_input_mask:
											__magic_name__ :							Union[str, Any]		       =							random_attention_mask([self.batch_size, self.seq_length]						)

						__magic_name__ :							Optional[Any]		       =							None
						if self.use_token_type_ids:
											__magic_name__ :							List[Any]		       =							ids_tensor([self.batch_size, self.seq_length]    ,					self.type_vocab_size						)

						__magic_name__ :							List[str]		       =							None
						__magic_name__ :							Optional[Any]		       =							None
						__magic_name__ :							Union[str, Any]		       =							None
						if self.use_labels:
											__magic_name__ :							Union[str, Any]		       =							ids_tensor([self.batch_size]    ,					self.type_sequence_label_size						)
											__magic_name__ :							str		       =							ids_tensor([self.batch_size, self.seq_length]    ,					self.num_labels						)
											__magic_name__ :							Union[str, Any]		       =							ids_tensor([self.batch_size]    ,					self.num_choices						)
											__magic_name__ :							List[Any]		       =							ids_tensor([self.batch_size, self.seq_length]    ,					1						)

						__magic_name__ :							Tuple		       =							self.get_config()

						return (
						    config,
						    input_ids,
						    token_type_ids,
						    input_mask,
						    sequence_labels,
						    token_labels,
						    choice_labels,
						    fake_token_labels,
						)






	def 			lowercase				(     self						)				->       Optional[int]:




						"""simple docstring"""






						return ElectraConfig(
						    vocab_size=self.vocab_size    ,					hidden_size=self.hidden_size    ,					num_hidden_layers=self.num_hidden_layers    ,					num_attention_heads=self.num_attention_heads    ,					intermediate_size=self.intermediate_size    ,					hidden_act=self.hidden_act    ,					hidden_dropout_prob=self.hidden_dropout_prob    ,					attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,					max_position_embeddings=self.max_position_embeddings    ,					type_vocab_size=self.type_vocab_size    ,					is_decoder=lowerCamelCase    ,					initializer_range=self.initializer_range    ,					)






	def 			lowercase				(     self						)				->       Any:




						"""simple docstring"""






						(
						    (
						    __magic_name__
						)       ,			(
						    __magic_name__
						)       ,			(
						    __magic_name__
						)       ,			(
						    __magic_name__
						)       ,			(
						    __magic_name__
						)       ,			(
						    __magic_name__
						)       ,			(
						    __magic_name__
						)       ,			(
						    __magic_name__
						)       ,			
						) :							List[str]		       =							self.prepare_config_and_inputs()

						__magic_name__ :							List[str]		       =							True
						__magic_name__ :							Union[str, Any]		       =							floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
						__magic_name__ :							Dict		       =							ids_tensor([self.batch_size, self.seq_length]    ,					vocab_size=2						)

						return (
						    config,
						    input_ids,
						    token_type_ids,
						    input_mask,
						    sequence_labels,
						    token_labels,
						    choice_labels,
						    encoder_hidden_states,
						    encoder_attention_mask,
						)






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					)				->       Tuple:




						"""simple docstring"""






						__magic_name__ :							List[Any]		       =							ElectraModel(config=lowerCamelCase						)
						model.to(lowerCamelCase						)
						model.eval()
						__magic_name__ :							Dict		       =							model(lowerCamelCase    ,					attention_mask=lowerCamelCase    ,					token_type_ids=lowerCamelCase						)
						__magic_name__ :							Optional[Any]		       =							model(lowerCamelCase    ,					token_type_ids=lowerCamelCase						)
						__magic_name__ :							Optional[Any]		       =							model(lowerCamelCase						)
						self.parent.assertEqual(result.last_hidden_state.shape    ,					(self.batch_size, self.seq_length, self.hidden_size)						)






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					)				->       Optional[int]:




						"""simple docstring"""






						__magic_name__ :							Any		       =							True
						__magic_name__ :							Union[str, Any]		       =							ElectraModel(lowerCamelCase						)
						model.to(lowerCamelCase						)
						model.eval()
						__magic_name__ :							str		       =							model(
						    lowerCamelCase    ,					attention_mask=lowerCamelCase    ,					token_type_ids=lowerCamelCase    ,					encoder_hidden_states=lowerCamelCase    ,					encoder_attention_mask=lowerCamelCase    ,					)
						__magic_name__ :							str		       =							model(
						    lowerCamelCase    ,					attention_mask=lowerCamelCase    ,					token_type_ids=lowerCamelCase    ,					encoder_hidden_states=lowerCamelCase    ,					)
						__magic_name__ :							List[Any]		       =							model(lowerCamelCase    ,					attention_mask=lowerCamelCase    ,					token_type_ids=lowerCamelCase						)
						self.parent.assertEqual(result.last_hidden_state.shape    ,					(self.batch_size, self.seq_length, self.hidden_size)						)






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					)				->       List[str]:




						"""simple docstring"""






						__magic_name__ :							Optional[int]		       =							ElectraForMaskedLM(config=lowerCamelCase						)
						model.to(lowerCamelCase						)
						model.eval()
						__magic_name__ :							str		       =							model(lowerCamelCase    ,					attention_mask=lowerCamelCase    ,					token_type_ids=lowerCamelCase    ,					labels=lowerCamelCase						)
						self.parent.assertEqual(result.logits.shape    ,					(self.batch_size, self.seq_length, self.vocab_size)						)






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					)				->       Optional[Any]:




						"""simple docstring"""






						__magic_name__ :							Optional[Any]		       =							ElectraForCausalLM(config=lowerCamelCase						)
						model.to(lowerCamelCase						)
						model.eval()
						__magic_name__ :							List[str]		       =							model(lowerCamelCase    ,					attention_mask=lowerCamelCase    ,					token_type_ids=lowerCamelCase    ,					labels=lowerCamelCase						)
						self.parent.assertEqual(result.logits.shape    ,					(self.batch_size, self.seq_length, self.vocab_size)						)






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					)				->       Union[str, Any]:




						"""simple docstring"""






						__magic_name__ :							Union[str, Any]		       =							self.num_labels
						__magic_name__ :							Tuple		       =							ElectraForTokenClassification(config=lowerCamelCase						)
						model.to(lowerCamelCase						)
						model.eval()
						__magic_name__ :							Optional[Any]		       =							model(lowerCamelCase    ,					attention_mask=lowerCamelCase    ,					token_type_ids=lowerCamelCase    ,					labels=lowerCamelCase						)
						self.parent.assertEqual(result.logits.shape    ,					(self.batch_size, self.seq_length, self.num_labels)						)






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					)				->       Union[str, Any]:




						"""simple docstring"""






						__magic_name__ :							Any		       =							self.num_labels
						__magic_name__ :							Optional[Any]		       =							ElectraForPreTraining(config=lowerCamelCase						)
						model.to(lowerCamelCase						)
						model.eval()
						__magic_name__ :							Union[str, Any]		       =							model(lowerCamelCase    ,					attention_mask=lowerCamelCase    ,					token_type_ids=lowerCamelCase    ,					labels=lowerCamelCase						)
						self.parent.assertEqual(result.logits.shape    ,					(self.batch_size, self.seq_length)						)






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					)				->       List[Any]:




						"""simple docstring"""






						__magic_name__ :							Any		       =							self.num_labels
						__magic_name__ :							Optional[int]		       =							ElectraForSequenceClassification(lowerCamelCase						)
						model.to(lowerCamelCase						)
						model.eval()
						__magic_name__ :							Optional[Any]		       =							model(lowerCamelCase    ,					attention_mask=lowerCamelCase    ,					token_type_ids=lowerCamelCase    ,					labels=lowerCamelCase						)
						self.parent.assertEqual(result.logits.shape    ,					(self.batch_size, self.num_labels)						)






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					)				->       Optional[Any]:




						"""simple docstring"""






						__magic_name__ :							Optional[Any]		       =							ElectraForQuestionAnswering(config=lowerCamelCase						)
						model.to(lowerCamelCase						)
						model.eval()
						__magic_name__ :							int		       =							model(
						    lowerCamelCase    ,					attention_mask=lowerCamelCase    ,					token_type_ids=lowerCamelCase    ,					start_positions=lowerCamelCase    ,					end_positions=lowerCamelCase    ,					)
						self.parent.assertEqual(result.start_logits.shape    ,					(self.batch_size, self.seq_length)						)
						self.parent.assertEqual(result.end_logits.shape    ,					(self.batch_size, self.seq_length)						)






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					)				->       Optional[int]:




						"""simple docstring"""






						__magic_name__ :							int		       =							self.num_choices
						__magic_name__ :							Union[str, Any]		       =							ElectraForMultipleChoice(config=lowerCamelCase						)
						model.to(lowerCamelCase						)
						model.eval()
						__magic_name__ :							Optional[int]		       =							input_ids.unsqueeze(1						).expand(-1    ,					self.num_choices    ,					-1						).contiguous()
						__magic_name__ :							int		       =							token_type_ids.unsqueeze(1						).expand(-1    ,					self.num_choices    ,					-1						).contiguous()
						__magic_name__ :							Any		       =							input_mask.unsqueeze(1						).expand(-1    ,					self.num_choices    ,					-1						).contiguous()
						__magic_name__ :							Tuple		       =							model(
						    lowerCamelCase    ,					attention_mask=lowerCamelCase    ,					token_type_ids=lowerCamelCase    ,					labels=lowerCamelCase    ,					)
						self.parent.assertEqual(result.logits.shape    ,					(self.batch_size, self.num_choices)						)






	def 			lowercase				(     self						)				->       List[Any]:




						"""simple docstring"""






						__magic_name__ :							Optional[Any]		       =							self.prepare_config_and_inputs()
						(
						    (
						    __magic_name__
						)       ,			(
						    __magic_name__
						)       ,			(
						    __magic_name__
						)       ,			(
						    __magic_name__
						)       ,			(
						    __magic_name__
						)       ,			(
						    __magic_name__
						)       ,			(
						    __magic_name__
						)       ,			(
						    __magic_name__
						)       ,			
						) :							Optional[Any]		       =							config_and_inputs
						__magic_name__ :							Optional[int]		       =							{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
						return config, inputs_dict





@require_torch
class 		A__    (       __SCREAMING_SNAKE_CASE		,					__SCREAMING_SNAKE_CASE		,					unittest.TestCase			):
	lowerCamelCase__		:			Dict       =(
	    (
	        ElectraModel,
	        ElectraForPreTraining,
	        ElectraForMaskedLM,
	        ElectraForCausalLM,
	        ElectraForMultipleChoice,
	        ElectraForTokenClassification,
	        ElectraForSequenceClassification,
	        ElectraForQuestionAnswering,
	    )
	    if is_torch_available()
	    else ()
	)
	lowerCamelCase__		:			str       =(
	    {
	        "feature-extraction": ElectraModel,
	        "fill-mask": ElectraForMaskedLM,
	        "question-answering": ElectraForQuestionAnswering,
	        "text-classification": ElectraForSequenceClassification,
	        "text-generation": ElectraForCausalLM,
	        "token-classification": ElectraForTokenClassification,
	        "zero-shot": ElectraForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	lowerCamelCase__		:			str       =True






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase=False						)				->       Tuple:




						"""simple docstring"""






						__magic_name__ :							str		       =							super()._prepare_for_class(lowerCamelCase    ,					lowerCamelCase    ,					return_labels=lowerCamelCase						)

						if return_labels:
											if model_class in get_values(lowerCamelCase						):
																__magic_name__ :							Optional[int]		       =							torch.zeros(
																    (self.model_tester.batch_size, self.model_tester.seq_length)    ,					dtype=torch.long    ,					device=lowerCamelCase						)
						return inputs_dict






	def 			lowercase				(     self						)				->       Optional[Any]:




						"""simple docstring"""






						__magic_name__ :							List[str]		       =							ElectraModelTester(self						)
						__magic_name__ :							int		       =							ConfigTester(self    ,					config_class=lowerCamelCase    ,					hidden_size=37						)






	def 			lowercase				(     self						)				->       str:




						"""simple docstring"""






						self.config_tester.run_common_tests()






	def 			lowercase				(     self						)				->       List[Any]:




						"""simple docstring"""






						__magic_name__ :							Dict		       =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_electra_model(*lowerCamelCase						)






	def 			lowercase				(     self						)				->       Tuple:




						"""simple docstring"""






						__magic_name__ :							Optional[int]		       =							self.model_tester.prepare_config_and_inputs_for_decoder()
						self.model_tester.create_and_check_electra_model_as_decoder(*lowerCamelCase						)






	def 			lowercase				(     self						)				->       Any:




						"""simple docstring"""






						__magic_name__ :							Tuple		       =							self.model_tester.prepare_config_and_inputs()
						for type in ["absolute", "relative_key", "relative_key_query"]:
											__magic_name__ :							Tuple		       =							type
											self.model_tester.create_and_check_electra_model(*lowerCamelCase						)






	def 			lowercase				(     self						)				->       int:




						"""simple docstring"""






						__magic_name__ :							List[Any]		       =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_electra_for_masked_lm(*lowerCamelCase						)






	def 			lowercase				(     self						)				->       List[Any]:




						"""simple docstring"""






						__magic_name__ :							Dict		       =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_electra_for_token_classification(*lowerCamelCase						)






	def 			lowercase				(     self						)				->       Optional[Any]:




						"""simple docstring"""






						__magic_name__ :							Optional[Any]		       =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_electra_for_pretraining(*lowerCamelCase						)






	def 			lowercase				(     self						)				->       int:




						"""simple docstring"""






						__magic_name__ :							List[Any]		       =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_electra_for_sequence_classification(*lowerCamelCase						)






	def 			lowercase				(     self						)				->       List[str]:




						"""simple docstring"""






						__magic_name__ :							Union[str, Any]		       =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_electra_for_question_answering(*lowerCamelCase						)






	def 			lowercase				(     self						)				->       List[str]:




						"""simple docstring"""






						__magic_name__ :							Optional[Any]		       =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_electra_for_multiple_choice(*lowerCamelCase						)






	@slow
	def 			lowercase				(     self						)				->       List[str]:




						"""simple docstring"""






						for model_name in ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											__magic_name__ :							str		       =							ElectraModel.from_pretrained(lowerCamelCase						)
											self.assertIsNotNone(lowerCamelCase						)






	def 			lowercase				(     self						)				->       Tuple:




						"""simple docstring"""






						__magic_name__ :							List[Any]		       =							self.model_tester.prepare_config_and_inputs_for_decoder()
						self.model_tester.create_and_check_electra_for_causal_lm(*lowerCamelCase						)





@require_torch
class 		A__    (       unittest.TestCase			):






	@slow
	def 			lowercase				(     self						)				->       Optional[Any]:




						"""simple docstring"""






						__magic_name__ :							str		       =							ElectraModel.from_pretrained('''google/electra-small-discriminator'''						)
						__magic_name__ :							List[Any]		       =							torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]						)
						__magic_name__ :							Optional[int]		       =							torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]						)
						__magic_name__ :							Any		       =							model(lowerCamelCase    ,					attention_mask=lowerCamelCase						)[0]
						__magic_name__ :							List[str]		       =							torch.Size((1, 11, 256)						)
						self.assertEqual(output.shape    ,					lowerCamelCase						)
						__magic_name__ :							List[Any]		       =							torch.tensor(
						    [[[0.4_4_7_1, 0.6_8_2_1, -0.3_2_6_5], [0.4_6_2_7, 0.5_2_5_5, -0.3_6_6_8], [0.4_5_3_2, 0.3_3_1_3, -0.4_3_4_4]]]						)

						self.assertTrue(torch.allclose(output[:, 1:4, 1:4]    ,					lowerCamelCase    ,					atol=1e-4						)						)









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





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






	def 			lowercase				(     self						)				->       Any:




						"""simple docstring"""






						torch.manual_seed(0						)
						__magic_name__ :							Optional[Any]		       =							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__ :							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__ :							List[Any]		       =							TaFilmDecoder(
						    input_dims=128    ,					targets_length=256    ,					max_decoder_noise_time=2_0_0_0_0.0    ,					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__ :							Dict		       =							{
						    '''notes_encoder''': notes_encoder.eval(),
						    '''continuous_encoder''': continuous_encoder.eval(),
						    '''decoder''': decoder.eval(),
						    '''scheduler''': scheduler,
						    '''melgan''': None,
						}
						return components






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase=0						)				->       Dict:




						"""simple docstring"""






						if str(lowerCamelCase						).startswith('''mps'''						):
											__magic_name__ :							List[str]		       =							torch.manual_seed(lowerCamelCase						)
						else:
											__magic_name__ :							Any		       =							torch.Generator(device=lowerCamelCase						).manual_seed(lowerCamelCase						)
						__magic_name__ :							Any		       =							{
						    '''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 			lowercase				(     self						)				->       Optional[int]:




						"""simple docstring"""






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

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

						__magic_name__ :							Union[str, Any]		       =							mel[0, -3:, -3:]

						assert mel_slice.shape == (3, 3)
						__magic_name__ :							Dict		       =							np.array(
						    [-1_1.5_1_2_9_2_5, -4.7_8_8_2_1_5, -0.4_6_1_7_2_9_0_5, -2.0_5_1_7_1_5, -1_0.5_3_9_1_4_7, -1_0.9_7_0_9_6_3, -9.0_9_1_6_3_4, 4.0, 4.0]						)
						assert np.abs(mel_slice.flatten() - expected_slice						).max() < 1e-2






	@skip_mps
	def 			lowercase				(     self						)				->       List[Any]:




						"""simple docstring"""






						return super().test_save_load_local()






	@skip_mps
	def 			lowercase				(     self						)				->       str:




						"""simple docstring"""






						return super().test_dict_tuple_outputs_equivalent()






	@skip_mps
	def 			lowercase				(     self						)				->       List[Any]:




						"""simple docstring"""






						return super().test_save_load_optional_components()






	@skip_mps
	def 			lowercase				(     self						)				->       Union[str, Any]:




						"""simple docstring"""






						return super().test_attention_slicing_forward_pass()






	def 			lowercase				(     self						)				->       Tuple:




						"""simple docstring"""






						pass






	def 			lowercase				(     self						)				->       int:




						"""simple docstring"""






						pass






	@skip_mps
	def 			lowercase				(     self						)				->       Optional[Any]:




						"""simple docstring"""






						return super().test_progress_bar()





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






	def 			lowercase				(     self						)				->       List[Any]:




						"""simple docstring"""






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






	def 			lowercase				(     self						)				->       Optional[Any]:




						"""simple docstring"""






						__magic_name__ :							List[Any]		       =							torch_device

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

						__magic_name__ :							Tuple		       =							pipe.to(lowerCamelCase						)
						pipe.set_progress_bar_config(disable=lowerCamelCase						)

						def callback(lowerCamelCase    ,					lowerCamelCase						):
											# decode mel to audio
											__magic_name__ :							Tuple		       =							melgan(input_features=mel_output.astype(np.floataa						)						)[0]
											assert len(audio[0]						) == 81920 * (step + 1)
											# simulate that audio is played
											return audio

						__magic_name__ :							int		       =							MidiProcessor()
						__magic_name__ :							List[str]		       =							processor(lowerCamelCase						)

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






	def 			lowercase				(     self						)				->       int:




						"""simple docstring"""






						__magic_name__ :							Optional[int]		       =							torch_device

						__magic_name__ :							int		       =							SpectrogramDiffusionPipeline.from_pretrained('''google/music-spectrogram-diffusion'''						)
						__magic_name__ :							str		       =							pipe.to(lowerCamelCase						)
						pipe.set_progress_bar_config(disable=lowerCamelCase						)
						__magic_name__ :							Any		       =							MidiProcessor()

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

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

						__magic_name__ :							Union[str, Any]		       =							output.audios[0]

						assert abs(np.abs(lowerCamelCase						).sum() - 3_6_1_2.8_4_1						) < 1e-1






	def 			lowercase				(     self						)				->       List[str]:




						"""simple docstring"""






						__magic_name__ :							Optional[int]		       =							torch_device

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

						__magic_name__ :							Optional[int]		       =							MidiProcessor()

						__magic_name__ :							str		       =							processor(lowerCamelCase						)

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

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

						__magic_name__ :							Optional[int]		       =							output.audios[0]
						assert abs(np.abs(lowerCamelCase						).sum() - 9_3_8_9.1_1_1_1						) < 5e-2



