from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase							=       {
    "configuration_layoutlm": ["LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMConfig", "LayoutLMOnnxConfig"],
    "tokenization_layoutlm": ["LayoutLMTokenizer"],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase							=       ["LayoutLMTokenizerFast"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase							=       [
         "LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
         "LayoutLMForMaskedLM",
         "LayoutLMForSequenceClassification",
         "LayoutLMForTokenClassification",
         "LayoutLMForQuestionAnswering",
         "LayoutLMModel",
         "LayoutLMPreTrainedModel",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase							=       [
         "TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFLayoutLMForMaskedLM",
         "TFLayoutLMForSequenceClassification",
         "TFLayoutLMForTokenClassification",
         "TFLayoutLMForQuestionAnswering",
         "TFLayoutLMMainLayer",
         "TFLayoutLMModel",
         "TFLayoutLMPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
     from .tokenization_layoutlm import LayoutLMTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_layoutlm import (
              LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
              LayoutLMForMaskedLM,
              LayoutLMForQuestionAnswering,
              LayoutLMForSequenceClassification,
              LayoutLMForTokenClassification,
              LayoutLMModel,
              LayoutLMPreTrainedModel,
          )
     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_layoutlm import (
              TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFLayoutLMForMaskedLM,
              TFLayoutLMForQuestionAnswering,
              TFLayoutLMForSequenceClassification,
              TFLayoutLMForTokenClassification,
              TFLayoutLMMainLayer,
              TFLayoutLMModel,
              TFLayoutLMPreTrainedModel,
          )

else:
     import sys

     UpperCAmelCase							=       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




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()


UpperCAmelCase							=       "./tests/fixtures/elise_format0.mid"



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



   def 					UpperCAmelCase__       (				self   :   Tuple   )							->     Optional[Any]:




         '''simple docstring'''




         torch.manual_seed(0   )
         snake_case_     :			List[Any]       =       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_     :			Any       =       TaFilmDecoder(
             input_dims=1_28			,	targets_length=2_56			,	max_decoder_noise_time=2_0000.0			,	d_model=7_68			,	num_layers=1			,	num_heads=1			,	d_kv=4			,	d_ff=20_48			,	dropout_rate=0.1			,	)

         snake_case_     :			List[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 					UpperCAmelCase__       (				self   :   Dict			,	A__   :   int			,	A__   :   Dict=0   )							->     Tuple:




         '''simple docstring'''




         if str(A__   ).startswith("mps"   ):
               snake_case_     :			List[Any]       =       torch.manual_seed(A__   )
         else:
               snake_case_     :			List[Any]       =       torch.Generator(device=A__   ).manual_seed(A__   )
         snake_case_     :			Optional[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 					UpperCAmelCase__       (				self   :   Union[str, Any]   )							->     str:




         '''simple docstring'''




         snake_case_     :			List[Any]       =       "cpu"  # ensure determinism for the device-dependent torch.Generator
         snake_case_     :			List[Any]       =       self.get_dummy_components()
         snake_case_     :			str       =       SpectrogramDiffusionPipeline(**A__   )
         snake_case_     :			Optional[int]       =       pipe.to(A__   )
         pipe.set_progress_bar_config(disable=A__   )

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

         snake_case_     :			List[Any]       =       mel[0, -3:, -3:]

         assert mel_slice.shape == (3, 3)
         snake_case_     :			Any       =       np.array(
             [-11.51_2925, -4.78_8215, -0.4617_2905, -2.05_1715, -10.53_9147, -10.97_0963, -9.09_1634, 4.0, 4.0]   )
         assert np.abs(mel_slice.flatten() - expected_slice   ).max() < 1E-2



   @skip_mps
   def 					UpperCAmelCase__       (				self   :   Any   )							->     Tuple:




         '''simple docstring'''




         return super().test_save_load_local()



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




         '''simple docstring'''




         return super().test_dict_tuple_outputs_equivalent()



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




         '''simple docstring'''




         return super().test_save_load_optional_components()



   @skip_mps
   def 					UpperCAmelCase__       (				self   :   Optional[int]   )							->     List[str]:




         '''simple docstring'''




         return super().test_attention_slicing_forward_pass()



   def 					UpperCAmelCase__       (				self   :   List[str]   )							->     List[Any]:




         '''simple docstring'''




         pass



   def 					UpperCAmelCase__       (				self   :   Dict   )							->     str:




         '''simple docstring'''




         pass



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




         '''simple docstring'''




         return super().test_progress_bar()



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



   def 					UpperCAmelCase__       (				self   :   Union[str, Any]   )							->     List[Any]:




         '''simple docstring'''




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



   def 					UpperCAmelCase__       (				self   :   int   )							->     Dict:




         '''simple docstring'''




         snake_case_     :			Optional[int]       =       torch_device

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

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

         def callback(A__   :   Dict			,	A__   :   Optional[Any]   ):
               # decode mel to audio
               snake_case_     :			str       =       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_     :			List[str]       =       MidiProcessor()
         snake_case_     :			Dict       =       processor(A__   )

         snake_case_     :			Optional[Any]       =       input_tokens[:3]
         snake_case_     :			Dict       =       torch.manual_seed(0   )
         pipe(A__			,	num_inference_steps=5			,	generator=A__			,	callback=A__			,	output_type="mel"   )



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




         '''simple docstring'''




         snake_case_     :			Any       =       torch_device

         snake_case_     :			List[Any]       =       SpectrogramDiffusionPipeline.from_pretrained("google/music-spectrogram-diffusion"   )
         snake_case_     :			Any       =       pipe.to(A__   )
         pipe.set_progress_bar_config(disable=A__   )
         snake_case_     :			Any       =       MidiProcessor()

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

         snake_case_     :			Tuple       =       torch.manual_seed(0   )
         snake_case_     :			int       =       pipe(A__			,	num_inference_steps=2			,	generator=A__   )

         snake_case_     :			int       =       output.audios[0]

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



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




         '''simple docstring'''




         snake_case_     :			Union[str, Any]       =       torch_device

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

         snake_case_     :			int       =       MidiProcessor()

         snake_case_     :			int       =       processor(A__   )

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

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

         snake_case_     :			List[str]       =       output.audios[0]
         assert abs(np.abs(A__   ).sum() - 9389.1111   ) < 5E-2



