
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 		__magic_name__				( lowerCAmelCase_						,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     _UpperCAmelCase  (					self		):
    torch.manual_seed(0		)
    _lowerCamelCase      =						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      =						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      =						TaFilmDecoder(
        input_dims=1_28	,		targets_length=2_56	,		max_decoder_noise_time=2_00_00.0	,		d_model=7_68	,		num_layers=1	,		num_heads=1	,		d_kv=4	,		d_ff=20_48	,		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     _UpperCAmelCase  (					self	,		a__	,		a__=0		):
    if str(__UpperCamelCase		).startswith('''mps'''		):
      _lowerCamelCase      =						torch.manual_seed(__UpperCamelCase		)
    else:
      _lowerCamelCase      =						torch.Generator(device=__UpperCamelCase		).manual_seed(__UpperCamelCase		)
    _lowerCamelCase      =						{
        "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		):
    _lowerCamelCase      =						"cpu"  # ensure determinism for the device-dependent torch.Generator
    _lowerCamelCase      =						self.get_dummy_components()
    _lowerCamelCase      =						SpectrogramDiffusionPipeline(**__UpperCamelCase		)
    _lowerCamelCase      =						pipe.to(__UpperCamelCase		)
    pipe.set_progress_bar_config(disable=__UpperCamelCase		)

    _lowerCamelCase      =						self.get_dummy_inputs(__UpperCamelCase		)
    _lowerCamelCase      =						pipe(**__UpperCamelCase		)
    _lowerCamelCase      =						output.audios

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

    assert mel_slice.shape == (3, 3)
    _lowerCamelCase      =						np.array(
        [-11.512925, -4.788215, -0.46172905, -2.051715, -10.539147, -10.970963, -9.091634, 4.0, 4.0]		)
    assert np.abs(mel_slice.flatten() - expected_slice		).max() < 1E-2


  @skip_mps
  def     _UpperCAmelCase  (					self		):
    return super().test_save_load_local()


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


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


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


  def     _UpperCAmelCase  (					self		):
    pass


  def     _UpperCAmelCase  (					self		):
    pass


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







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


  """simple docstring"""





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


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

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

    _lowerCamelCase      =						pipe.to(__UpperCamelCase		)
    pipe.set_progress_bar_config(disable=__UpperCamelCase		)

    def callback(a__	,		a__		):
      # decode mel to audio
      _lowerCamelCase      =						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      =						MidiProcessor()
    _lowerCamelCase      =						processor(__UpperCamelCase		)

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


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						torch_device

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

    _lowerCamelCase      =						processor(__UpperCamelCase		)
    # just run two denoising loops
    _lowerCamelCase      =						input_tokens[:2]

    _lowerCamelCase      =						torch.manual_seed(0		)
    _lowerCamelCase      =						pipe(__UpperCamelCase	,		num_inference_steps=2	,		generator=__UpperCamelCase		)

    _lowerCamelCase      =						output.audios[0]

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


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						torch_device

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

    _lowerCamelCase      =						MidiProcessor()

    _lowerCamelCase      =						processor(__UpperCamelCase		)

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

    _lowerCamelCase      =						torch.manual_seed(0		)
    _lowerCamelCase      =						pipe(__UpperCamelCase	,		num_inference_steps=1_00	,		generator=__UpperCamelCase		)

    _lowerCamelCase      =						output.audios[0]
    assert abs(np.abs(__UpperCamelCase		).sum() - 9389.1111		) < 5E-2






"""simple docstring"""




import argparse
import collections
import importlib.util
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
A__   :						List[str] 							=     """src/diffusers"""
A__   :						List[str] 							=     """docs/source/en"""
A__   :						List[Any] 							=     """."""



def 				a__			(					lowerCAmelCase						:							List[str]							,     lowerCAmelCase						:							Any							,     lowerCAmelCase						:							List[Any]		):






     '''simple docstring'''
     with open(lowerCAmelCase							,     "r"							,     encoding="utf-8"							,     newline="\n"		) as f:
          UpperCAmelCase__				:			List[str]							=    f.readlines()
     # Find the start prompt.
     UpperCAmelCase__				:			Union[str, Any]							=    0
     while not lines[start_index].startswith(lowerCAmelCase		):
          start_index += 1
     start_index += 1

     UpperCAmelCase__				:			List[Any]							=    start_index
     while not lines[end_index].startswith(lowerCAmelCase		):
          end_index += 1
     end_index -= 1

     while len(lines[start_index]		) <= 1:
          start_index += 1
     while len(lines[end_index]		) <= 1:
          end_index -= 1
     end_index += 1
     return "".join(lines[start_index:end_index]		), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
A__   :						Tuple 							=     """Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
A__   :						Any 							=     re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
A__   :						Tuple 							=     re.compile(R"""Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
A__   :						List[Any] 							=     re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# This is to make sure the diffusers module imported is the one in the repo.
A__   :						List[Any] 							=     importlib.util.spec_from_file_location(
    """diffusers""",
    os.path.join(TRANSFORMERS_PATH, """__init__.py"""),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
A__   :						Dict 							=     spec.loader.load_module()



def 				a__			(					lowerCAmelCase						:							List[str]		):






     '''simple docstring'''
     UpperCAmelCase__				:			Dict							=    re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"							,     lowerCAmelCase		)
     return [m.group(0		) for m in matches]



def 				a__			(					lowerCAmelCase						:							Tuple							,     lowerCAmelCase						:							Union[str, Any]		):






     '''simple docstring'''
     UpperCAmelCase__				:			List[Any]							=    2 if text == "✅" or text == "❌" else len(lowerCAmelCase		)
     UpperCAmelCase__				:			Any							=    (width - text_length) // 2
     UpperCAmelCase__				:			List[Any]							=    width - text_length - left_indent
     return " " * left_indent + text + " " * right_indent



def 				a__			(					):






     '''simple docstring'''
     UpperCAmelCase__				:			int							=    diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
     UpperCAmelCase__				:			Optional[int]							=    {
         name: config_mapping_names[code]
         for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
         if code in config_mapping_names
     }
     UpperCAmelCase__				:			Optional[int]							=    {name: config.replace("ConfigMixin"							,     ""		) for name, config in model_name_to_config.items()}

     # Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
     UpperCAmelCase__				:			str							=    collections.defaultdict(lowerCAmelCase		)
     UpperCAmelCase__				:			Optional[int]							=    collections.defaultdict(lowerCAmelCase		)
     UpperCAmelCase__				:			str							=    collections.defaultdict(lowerCAmelCase		)
     UpperCAmelCase__				:			str							=    collections.defaultdict(lowerCAmelCase		)
     UpperCAmelCase__				:			Union[str, Any]							=    collections.defaultdict(lowerCAmelCase		)

     # Let's lookup through all diffusers object (once).
     for attr_name in dir(lowerCAmelCase		):
          UpperCAmelCase__				:			List[str]							=    None
          if attr_name.endswith("Tokenizer"		):
               UpperCAmelCase__				:			Union[str, Any]							=    slow_tokenizers
               UpperCAmelCase__				:			Optional[Any]							=    attr_name[:-9]
          elif attr_name.endswith("TokenizerFast"		):
               UpperCAmelCase__				:			List[str]							=    fast_tokenizers
               UpperCAmelCase__				:			Union[str, Any]							=    attr_name[:-13]
          elif _re_tf_models.match(lowerCAmelCase		) is not None:
               UpperCAmelCase__				:			Union[str, Any]							=    tf_models
               UpperCAmelCase__				:			Optional[int]							=    _re_tf_models.match(lowerCAmelCase		).groups()[0]
          elif _re_flax_models.match(lowerCAmelCase		) is not None:
               UpperCAmelCase__				:			List[Any]							=    flax_models
               UpperCAmelCase__				:			Any							=    _re_flax_models.match(lowerCAmelCase		).groups()[0]
          elif _re_pt_models.match(lowerCAmelCase		) is not None:
               UpperCAmelCase__				:			str							=    pt_models
               UpperCAmelCase__				:			List[str]							=    _re_pt_models.match(lowerCAmelCase		).groups()[0]

          if lookup_dict is not None:
               while len(lowerCAmelCase		) > 0:
                    if attr_name in model_name_to_prefix.values():
                         UpperCAmelCase__				:			Optional[Any]							=    True
                         break
                    # Try again after removing the last word in the name
                    UpperCAmelCase__				:			Union[str, Any]							=    "".join(camel_case_split(lowerCAmelCase		)[:-1]		)

    # Let's build that table!
     UpperCAmelCase__				:			Tuple							=    list(model_name_to_config.keys()		)
     model_names.sort(key=str.lower		)
     UpperCAmelCase__				:			Dict							=    ["Model", "Tokenizer slow", "Tokenizer fast", "PyTorch support", "TensorFlow support", "Flax Support"]
     # We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
     UpperCAmelCase__				:			List[str]							=    [len(lowerCAmelCase		) + 2 for c in columns]
     UpperCAmelCase__				:			Union[str, Any]							=    max([len(lowerCAmelCase		) for name in model_names]		) + 2

     # Build the table per se
     UpperCAmelCase__				:			str							=    "|" + "|".join([_center_text(lowerCAmelCase							,     lowerCAmelCase		) for c, w in zip(lowerCAmelCase							,     lowerCAmelCase		)]		) + "|\n"
     # Use ":-----:" format to center-aligned table cell texts
     table += "|" + "|".join([":" + "-" * (w - 2) + ":" for w in widths]		) + "|\n"

     UpperCAmelCase__				:			List[Any]							=    {True: "✅", False: "❌"}
     for name in model_names:
          UpperCAmelCase__				:			Optional[Any]							=    model_name_to_prefix[name]
          UpperCAmelCase__				:			List[Any]							=    [
              name,
              check[slow_tokenizers[prefix]],
              check[fast_tokenizers[prefix]],
              check[pt_models[prefix]],
              check[tf_models[prefix]],
              check[flax_models[prefix]],
          ]
          table += "|" + "|".join([_center_text(lowerCAmelCase							,     lowerCAmelCase		) for l, w in zip(lowerCAmelCase							,     lowerCAmelCase		)]		) + "|\n"
     return table



def 				a__			(					lowerCAmelCase						:							Union[str, Any]=False		):






     '''simple docstring'''
     UpperCAmelCase__				,						UpperCAmelCase__				,						UpperCAmelCase__				,						UpperCAmelCase__				:			int							=    _find_text_in_file(
         filename=os.path.join(lowerCAmelCase							,     "index.mdx"		)							,     start_prompt="<!--This table is updated automatically from the auto modules"							,     end_prompt="<!-- End table-->"							,     )
     UpperCAmelCase__				:			Optional[Any]							=    get_model_table_from_auto_modules()

     if current_table != new_table:
          if overwrite:
               with open(os.path.join(lowerCAmelCase							,     "index.mdx"		)							,     "w"							,     encoding="utf-8"							,     newline="\n"		) as f:
                    f.writelines(lines[:start_index] + [new_table] + lines[end_index:]		)
          else:
               raise ValueError(
                   "The model table in the `index.mdx` has not been updated. Run `make fix-copies` to fix this."		)


if __name__ == "__main__":
    A__   :						Union[str, Any] 							=     argparse.ArgumentParser()
    parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
    A__   :						str 							=     parser.parse_args()

    check_model_table(args.fix_and_overwrite)



