
'''simple docstring'''
import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_     =  logging.get_logger(__name__)

a_     =  {
    "facebook/sam-vit-huge": "https://huggingface.co/facebook/sam-vit-huge/resolve/main/config.json",
    "facebook/sam-vit-large": "https://huggingface.co/facebook/sam-vit-large/resolve/main/config.json",
    "facebook/sam-vit-base": "https://huggingface.co/facebook/sam-vit-base/resolve/main/config.json",
}






class 				UpperCAmelCase_			(		snake_case__   ):





 def __init__(      self	,    lowercase_=2_56	,    lowercase_=10_24	,    lowercase_=16	,    lowercase_=16	,    lowercase_=4	,    lowercase_="gelu"	,    lowercase_=1E-6	,    **lowercase_	,    ):
     super().__init__(**lowercase_)
     snake_case_	:      int            =  hidden_size
     snake_case_	:      Optional[int]            =  image_size
     snake_case_	:      List[Any]            =  patch_size
     snake_case_	:      List[str]            =  image_size // patch_size
     snake_case_	:      Optional[int]            =  mask_input_channels
     snake_case_	:      str            =  num_point_embeddings
     snake_case_	:      Tuple            =  hidden_act
     snake_case_	:      List[Any]            =  layer_norm_eps






class 				UpperCAmelCase_			(		snake_case__   ):





 def __init__(      self	,    lowercase_=2_56	,    lowercase_="relu"	,    lowercase_=20_48	,    lowercase_=2	,    lowercase_=8	,    lowercase_=2	,    lowercase_=3	,    lowercase_=3	,    lowercase_=2_56	,    lowercase_=1E-6	,    **lowercase_	,    ):
     super().__init__(**lowercase_)
     snake_case_	:      Optional[Any]            =  hidden_size
     snake_case_	:      Dict            =  hidden_act
     snake_case_	:      int            =  mlp_dim
     snake_case_	:      List[str]            =  num_hidden_layers
     snake_case_	:      Any            =  num_attention_heads
     snake_case_	:      str            =  attention_downsample_rate
     snake_case_	:      int            =  num_multimask_outputs
     snake_case_	:      List[str]            =  iou_head_depth
     snake_case_	:      Union[str, Any]            =  iou_head_hidden_dim
     snake_case_	:      List[str]            =  layer_norm_eps






class 				UpperCAmelCase_			(		snake_case__   ):





 def __init__(      self	,    lowercase_=7_68	,    lowercase_=2_56	,    lowercase_=12	,    lowercase_=12	,    lowercase_=3	,    lowercase_=10_24	,    lowercase_=16	,    lowercase_="gelu"	,    lowercase_=1E-06	,    lowercase_=0.0	,    lowercase_=1E-10	,    lowercase_=True	,    lowercase_=4.0	,    lowercase_=True	,    lowercase_=True	,    lowercase_=14	,    lowercase_=[2, 5, 8, 11]	,    lowercase_=1_28	,    lowercase_=None	,    **lowercase_	,    ):
     super().__init__(**lowercase_)

     snake_case_	:      Any            =  hidden_size
     snake_case_	:      List[str]            =  output_channels
     snake_case_	:      int            =  num_hidden_layers
     snake_case_	:      Union[str, Any]            =  num_attention_heads
     snake_case_	:      Optional[int]            =  num_channels
     snake_case_	:      Union[str, Any]            =  image_size
     snake_case_	:      Any            =  patch_size
     snake_case_	:      Dict            =  hidden_act
     snake_case_	:      Any            =  layer_norm_eps
     snake_case_	:      str            =  attention_dropout
     snake_case_	:      Tuple            =  initializer_range
     snake_case_	:      List[Any]            =  qkv_bias
     snake_case_	:      Optional[Any]            =  mlp_ratio
     snake_case_	:      List[Any]            =  use_abs_pos
     snake_case_	:      Dict            =  use_rel_pos
     snake_case_	:      Optional[int]            =  window_size
     snake_case_	:      Tuple            =  global_attn_indexes
     snake_case_	:      List[str]            =  num_pos_feats
     snake_case_	:      Tuple            =  int(hidden_size * mlp_ratio) if mlp_dim is None else mlp_dim






class 				UpperCAmelCase_			(		snake_case__   ):
 UpperCAmelCase_						      =  """sam"""
 UpperCAmelCase_						      =  True





 def __init__(      self	,    lowercase_=None	,    lowercase_=None	,    lowercase_=None	,    lowercase_=0.02	,    **lowercase_	,    ):
     super().__init__(**lowercase_)
     snake_case_	:      List[str]            =  vision_config if vision_config is not None else {}
     snake_case_	:      Optional[Any]            =  prompt_encoder_config if prompt_encoder_config is not None else {}
     snake_case_	:      Optional[int]            =  mask_decoder_config if mask_decoder_config is not None else {}

     if isinstance(lowercase_	,    lowercase_):
         snake_case_	:      List[str]            =  vision_config.to_dict()
     if isinstance(lowercase_	,    lowercase_):
         snake_case_	:      Dict            =  prompt_encoder_config.to_dict()
     if isinstance(lowercase_	,    lowercase_):
         snake_case_	:      Any            =  mask_decoder_config.to_dict()

     snake_case_	:      Union[str, Any]            =  SamVisionConfig(**lowercase_)
     snake_case_	:      List[str]            =  SamPromptEncoderConfig(**lowercase_)
     snake_case_	:      Dict            =  SamMaskDecoderConfig(**lowercase_)
     snake_case_	:      int            =  initializer_range





 def     snake_case__						(      self):
     snake_case_	:      Optional[int]            =  copy.deepcopy(self.__dict__)
     snake_case_	:      int            =  self.vision_config.to_dict()
     snake_case_	:      Optional[int]            =  self.prompt_encoder_config.to_dict()
     snake_case_	:      Any            =  self.mask_decoder_config.to_dict()
     snake_case_	:      Any            =  self.__class__.model_type
     return output






'''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 				UpperCAmelCase_			(		snake_case__      ,  unittest.TestCase   ):
 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):
     torch.manual_seed(0)
     snake_case_	:      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"	,    )

     snake_case_	:      int            =  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"	,    )

     snake_case_	:      Tuple            =  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	,    )

     snake_case_	:      Union[str, Any]            =  DDPMScheduler()

     snake_case_	:      List[Any]            =  {
         "notes_encoder": notes_encoder.eval(),
         "continuous_encoder": continuous_encoder.eval(),
         "decoder": decoder.eval(),
         "scheduler": scheduler,
         "melgan": None,
     }
     return components





 def     snake_case__						(      self	,    lowercase_	,    lowercase_=0):
     if str(lowercase_).startswith("mps"):
         snake_case_	:      Dict            =  torch.manual_seed(lowercase_)
     else:
         snake_case_	:      Dict            =  torch.Generator(device=lowercase_).manual_seed(lowercase_)
     snake_case_	:      int            =  {
         "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     snake_case__						(      self):
     snake_case_	:      List[Any]            =  "cpu"  # ensure determinism for the device-dependent torch.Generator
     snake_case_	:      List[Any]            =  self.get_dummy_components()
     snake_case_	:      Tuple            =  SpectrogramDiffusionPipeline(**lowercase_)
     snake_case_	:      Tuple            =  pipe.to(lowercase_)
     pipe.set_progress_bar_config(disable=lowercase_)

     snake_case_	:      List[str]            =  self.get_dummy_inputs(lowercase_)
     snake_case_	:      Any            =  pipe(**lowercase_)
     snake_case_	:      str            =  output.audios

     snake_case_	:      str            =  mel[0, -3:, -3:]

     assert mel_slice.shape == (3, 3)
     snake_case_	:      Any            =  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):
     return super().test_save_load_local()





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





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





 @skip_mps
 def     snake_case__						(      self):
     return super().test_attention_slicing_forward_pass()





 def     snake_case__						(      self):
     pass





 def     snake_case__						(      self):
     pass





 @skip_mps
 def     snake_case__						(      self):
     return super().test_progress_bar()






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





 def     snake_case__						(      self):
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()





 def     snake_case__						(      self):
     # TODO - test that pipeline can decode tokens in a callback
     # so that music can be played live
     snake_case_	:      Any            =  torch_device

     snake_case_	:      str            =  SpectrogramDiffusionPipeline.from_pretrained("google/music-spectrogram-diffusion")
     snake_case_	:      Tuple            =  pipe.melgan
     snake_case_	:      List[str]            =  None

     snake_case_	:      int            =  pipe.to(lowercase_)
     pipe.set_progress_bar_config(disable=lowercase_)

     def callback(lowercase_	,    lowercase_):
         # decode mel to audio
         snake_case_	:      Dict            =  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

     snake_case_	:      Tuple            =  MidiProcessor()
     snake_case_	:      int            =  processor(lowercase_)

     snake_case_	:      int            =  input_tokens[:3]
     snake_case_	:      Union[str, Any]            =  torch.manual_seed(0)
     pipe(lowercase_	,    num_inference_steps=5	,    generator=lowercase_	,    callback=lowercase_	,    output_type="mel")





 def     snake_case__						(      self):
     snake_case_	:      Any            =  torch_device

     snake_case_	:      Optional[Any]            =  SpectrogramDiffusionPipeline.from_pretrained("google/music-spectrogram-diffusion")
     snake_case_	:      Optional[int]            =  pipe.to(lowercase_)
     pipe.set_progress_bar_config(disable=lowercase_)
     snake_case_	:      Union[str, Any]            =  MidiProcessor()

     snake_case_	:      List[str]            =  processor(lowercase_)
     # just run two denoising loops
     snake_case_	:      int            =  input_tokens[:2]

     snake_case_	:      Optional[Any]            =  torch.manual_seed(0)
     snake_case_	:      Tuple            =  pipe(lowercase_	,    num_inference_steps=2	,    generator=lowercase_)

     snake_case_	:      Optional[Any]            =  output.audios[0]

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





 def     snake_case__						(      self):
     snake_case_	:      Union[str, Any]            =  torch_device

     snake_case_	:      Tuple            =  SpectrogramDiffusionPipeline.from_pretrained("google/music-spectrogram-diffusion")
     snake_case_	:      Optional[Any]            =  pipe.to(lowercase_)
     pipe.set_progress_bar_config(disable=lowercase_)

     snake_case_	:      Tuple            =  MidiProcessor()

     snake_case_	:      int            =  processor(lowercase_)

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

     snake_case_	:      Optional[Any]            =  torch.manual_seed(0)
     snake_case_	:      Tuple            =  pipe(lowercase_	,    num_inference_steps=1_00	,    generator=lowercase_)

     snake_case_	:      Tuple            =  output.audios[0]
     assert abs(np.abs(lowercase_).sum() - 9_389.1_111) < 5E-2




