





import argparse
import math
import os
from copy import deepcopy

import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel


__UpperCAmelCase       :   int				       =							{
    "gwf-440k": {
        "url": "https://model-server.zqevans2.workers.dev/gwf-440k.ckpt",
        "sample_rate": 48_000,
        "sample_size": 65_536,
    },
    "jmann-small-190k": {
        "url": "https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt",
        "sample_rate": 48_000,
        "sample_size": 65_536,
    },
    "jmann-large-580k": {
        "url": "https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt",
        "sample_rate": 48_000,
        "sample_size": 131_072,
    },
    "maestro-uncond-150k": {
        "url": "https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt",
        "sample_rate": 16_000,
        "sample_size": 65_536,
    },
    "unlocked-uncond-250k": {
        "url": "https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt",
        "sample_rate": 16_000,
        "sample_size": 65_536,
    },
    "honk-140k": {
        "url": "https://model-server.zqevans2.workers.dev/honk-140k.ckpt",
        "sample_rate": 16_000,
        "sample_size": 65_536,
    },
}







def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)   ->    Optional[int]:
  return torch.atana(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__) / math.pi * 2







def 			A__							(    SCREAMING_SNAKE_CASE__)   ->    Tuple:
  __snake_case:						List[Any]   =						torch.sin(t * math.pi / 2) ** 2
  __snake_case:						int   =						(1 - sigma**2) ** 0.5
  return alpha_sigma_to_t(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       pass







class     __snake_case       (							nn.Module			):





       '''simple docstring'''







       def __init__(  self :    Dict							,				A :    Tuple		):
         super().__init__()

         __snake_case:						Union[str, Any]   =						DiffusionAttnUnetaD(A							,				n_attn_layers=4		)
         __snake_case:						List[Any]   =						deepcopy(self.diffusion		)
         __snake_case:						str   =						torch.quasirandom.SobolEngine(1							,				scramble=A		)







def 			A__							(    SCREAMING_SNAKE_CASE__)   ->    Optional[int]:
  __snake_case:						str   =						MODELS_MAP[model_name]["""url"""]
  os.system(F'''wget {url} ./''')

  return F'''./{model_name}.ckpt'''


__UpperCAmelCase       :   Any				       =							{
    "1": "resnets.0",
    "2": "attentions.0",
    "3": "resnets.1",
    "4": "attentions.1",
    "5": "resnets.2",
    "6": "attentions.2",
}
__UpperCAmelCase       :   Tuple				       =							{
    "8": "resnets.0",
    "9": "attentions.0",
    "10": "resnets.1",
    "11": "attentions.1",
    "12": "resnets.2",
    "13": "attentions.2",
}
__UpperCAmelCase       :   Optional[Any]				       =							{
    "1": "resnets.0",
    "2": "attentions.0",
    "3": "resnets.1",
    "4": "attentions.1",
    "5": "resnets.2",
    "6": "attentions.2",
    "8": "resnets.3",
    "9": "attentions.3",
    "10": "resnets.4",
    "11": "attentions.4",
    "12": "resnets.5",
    "13": "attentions.5",
}
__UpperCAmelCase       :   Optional[Any]				       =							{
    "0": "resnets.0",
    "1": "resnets.1",
    "2": "resnets.2",
    "4": "resnets.0",
    "5": "resnets.1",
    "6": "resnets.2",
}

__UpperCAmelCase       :   Optional[int]				       =							{
    "skip": "conv_skip",
    "main.0": "conv_1",
    "main.1": "group_norm_1",
    "main.3": "conv_2",
    "main.4": "group_norm_2",
}

__UpperCAmelCase       :   Any				       =							{
    "norm": "group_norm",
    "qkv_proj": ["query", "key", "value"],
    "out_proj": ["proj_attn"],
}







def 			A__							(    SCREAMING_SNAKE_CASE__)   ->    Any:
  if name.startswith("""skip"""):
    return name.replace("""skip"""					,					RES_CONV_MAP["""skip"""])

  # name has to be of format main.{digit}
  if not name.startswith("""main."""):
    raise ValueError(F'''ResConvBlock error with {name}''')

  return name.replace(name[:6]					,					RES_CONV_MAP[name[:6]])







def 			A__							(    SCREAMING_SNAKE_CASE__)   ->    int:
  for key, value in ATTN_MAP.items():
    if name.startswith(SCREAMING_SNAKE_CASE__) and not isinstance(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__):
      return name.replace(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)
    elif name.startswith(SCREAMING_SNAKE_CASE__):
      return [name.replace(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__) for v in value]
  raise ValueError(F'''Attn error with {name}''')







def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__=13)   ->    Dict:
  __snake_case:						int   =						input_string

  if string.split(""".""")[0] == "timestep_embed":
    return string.replace("""timestep_embed"""					,					"""time_proj""")

  __snake_case:						List[Any]   =						0
  if string.startswith("""net.3."""):
    depth += 1
    __snake_case:						Union[str, Any]   =						string[6:]
  elif string.startswith("""net."""):
    __snake_case:						int   =						string[4:]

  while string.startswith("""main.7."""):
    depth += 1
    __snake_case:						Any   =						string[7:]

  if string.startswith("""main."""):
    __snake_case:						Union[str, Any]   =						string[5:]

  # mid block
  if string[:2].isdigit():
    __snake_case:						Tuple   =						string[:2]
    __snake_case:						Union[str, Any]   =						string[2:]
  else:
    __snake_case:						Union[str, Any]   =						string[0]
    __snake_case:						int   =						string[1:]

  if depth == max_depth:
    __snake_case:						Dict   =						MID_NUM_TO_LAYER[layer_num]
    __snake_case:						Optional[Any]   =						"""mid_block"""
  elif depth > 0 and int(SCREAMING_SNAKE_CASE__) < 7:
    __snake_case:						List[str]   =						DOWN_NUM_TO_LAYER[layer_num]
    __snake_case:						Dict   =						F'''down_blocks.{depth}'''
  elif depth > 0 and int(SCREAMING_SNAKE_CASE__) > 7:
    __snake_case:						List[Any]   =						UP_NUM_TO_LAYER[layer_num]
    __snake_case:						Any   =						F'''up_blocks.{max_depth - depth - 1}'''
  elif depth == 0:
    __snake_case:						List[Any]   =						DEPTH_0_TO_LAYER[layer_num]
    __snake_case:						int   =						F'''up_blocks.{max_depth - 1}''' if int(SCREAMING_SNAKE_CASE__) > 3 else """down_blocks.0"""

  if not string_left.startswith("""."""):
    raise ValueError(F'''Naming error with {input_string} and string_left: {string_left}.''')

  __snake_case:						int   =						string_left[1:]

  if "resnets" in new_layer:
    __snake_case:						List[Any]   =						convert_resconv_naming(SCREAMING_SNAKE_CASE__)
  elif "attentions" in new_layer:
    __snake_case:						str   =						convert_attn_naming(SCREAMING_SNAKE_CASE__)
    __snake_case:						List[Any]   =						new_string_left

  if not isinstance(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__):
    __snake_case:						List[str]   =						prefix + """.""" + new_layer + """.""" + string_left
  else:
    __snake_case:						str   =						[prefix + """.""" + new_layer + """.""" + s for s in string_left]
  return new_string







def 			A__							(    SCREAMING_SNAKE_CASE__)   ->    Tuple:
  __snake_case:						Union[str, Any]   =						{}
  for k, v in state_dict.items():
    if k.endswith("""kernel"""):
      # up- and downsample layers, don't have trainable weights
      continue

    __snake_case:						str   =						rename(SCREAMING_SNAKE_CASE__)

    # check if we need to transform from Conv => Linear for attention
    if isinstance(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__):
      __snake_case:						Union[str, Any]   =						transform_conv_attns(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)
    else:
      __snake_case:						int   =						v

  return new_state_dict







def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)   ->    List[Any]:
  if len(SCREAMING_SNAKE_CASE__) == 1:
    if len(v.shape) == 3:
      # weight
      __snake_case:						List[str]   =						v[:, :, 0]
    else:
      # bias
      __snake_case:						Dict   =						v
  else:
    # qkv matrices
    __snake_case:						Tuple   =						v.shape[0]
    __snake_case:						List[Any]   =						trippled_shape // 3
    for i in range(3):
      if len(v.shape) == 3:
        __snake_case:						int   =						v[i * single_shape : (i + 1) * single_shape, :, 0]
      else:
        __snake_case:						int   =						v[i * single_shape : (i + 1) * single_shape]
  return new_state_dict







def 			A__							(    SCREAMING_SNAKE_CASE__)   ->    int:
  __snake_case:						str   =						torch.device("""cuda""" if torch.cuda.is_available() else """cpu""")

  __snake_case:						List[str]   =						args.model_path.split("""/""")[-1].split(""".""")[0]
  if not os.path.isfile(args.model_path):
    assert (
        model_name == args.model_path
    ), F'''Make sure to provide one of the official model names {MODELS_MAP.keys()}'''
    __snake_case:						int   =						download(SCREAMING_SNAKE_CASE__)

  __snake_case:						Tuple   =						MODELS_MAP[model_name]["""sample_rate"""]
  __snake_case:						Optional[int]   =						MODELS_MAP[model_name]["""sample_size"""]

  __snake_case:						Optional[int]   =						Object()
  __snake_case:						Union[str, Any]   =						sample_size
  __snake_case:						Optional[int]   =						sample_rate
  __snake_case:						Dict   =						0

  __snake_case:						Union[str, Any]   =						UNetaDModel(sample_size=SCREAMING_SNAKE_CASE__					,					sample_rate=SCREAMING_SNAKE_CASE__)
  __snake_case:						List[str]   =						diffusers_model.state_dict()

  __snake_case:						int   =						DiffusionUncond(SCREAMING_SNAKE_CASE__)
  orig_model.load_state_dict(torch.load(args.model_path					,					map_location=SCREAMING_SNAKE_CASE__)["""state_dict"""])
  __snake_case:						Any   =						orig_model.diffusion_ema.eval()
  __snake_case:						str   =						orig_model.state_dict()
  __snake_case:						Dict   =						rename_orig_weights(SCREAMING_SNAKE_CASE__)

  __snake_case:						int   =						set(renamed_state_dict.keys()) - set(diffusers_state_dict.keys())
  __snake_case:						str   =						set(diffusers_state_dict.keys()) - set(renamed_state_dict.keys())

  assert len(SCREAMING_SNAKE_CASE__) == 0, F'''Problem with {renamed_minus_diffusers}'''
  assert all(k.endswith("""kernel""") for k in list(SCREAMING_SNAKE_CASE__)), F'''Problem with {diffusers_minus_renamed}'''

  for key, value in renamed_state_dict.items():
    assert (
        diffusers_state_dict[key].squeeze().shape == value.squeeze().shape
    ), F'''Shape for {key} doesn\'t match. Diffusers: {diffusers_state_dict[key].shape} vs. {value.shape}'''
    if key == "time_proj.weight":
      __snake_case:						str   =						value.squeeze()

    __snake_case:						Union[str, Any]   =						value

  diffusers_model.load_state_dict(SCREAMING_SNAKE_CASE__)

  __snake_case:						Union[str, Any]   =						100
  __snake_case:						List[Any]   =						33

  __snake_case:						Union[str, Any]   =						IPNDMScheduler(num_train_timesteps=SCREAMING_SNAKE_CASE__)

  __snake_case:						List[Any]   =						torch.manual_seed(SCREAMING_SNAKE_CASE__)
  __snake_case:						Tuple   =						torch.randn([1, 2, config.sample_size]					,					generator=SCREAMING_SNAKE_CASE__).to(SCREAMING_SNAKE_CASE__)

  __snake_case:						Tuple   =						torch.linspace(1					,					0					,					steps + 1					,					device=SCREAMING_SNAKE_CASE__)[:-1]
  __snake_case:						Union[str, Any]   =						get_crash_schedule(SCREAMING_SNAKE_CASE__)

  __snake_case:						List[Any]   =						DanceDiffusionPipeline(unet=SCREAMING_SNAKE_CASE__					,					scheduler=SCREAMING_SNAKE_CASE__)

  __snake_case:						List[str]   =						torch.manual_seed(33)
  __snake_case:						str   =						pipe(num_inference_steps=SCREAMING_SNAKE_CASE__					,					generator=SCREAMING_SNAKE_CASE__).audios

  __snake_case:						str   =						sampling.iplms_sample(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					{})
  __snake_case:						Union[str, Any]   =						generated.clamp(-1					,					1)

  __snake_case:						Optional[Any]   =						(generated - audio).abs().sum()
  __snake_case:						Union[str, Any]   =						(generated - audio).abs().max()

  if args.save:
    pipe.save_pretrained(args.checkpoint_path)

  print("""Diff sum"""					,					SCREAMING_SNAKE_CASE__)
  print("""Diff max"""					,					SCREAMING_SNAKE_CASE__)

  assert diff_max < 1e-3, F'''Diff max: {diff_max} is too much :-/'''

  print(F'''Conversion for {model_name} successful!''')


if __name__ == "__main__":
      __UpperCAmelCase       :   int				       =							argparse.ArgumentParser()

      parser.add_argument("--model_path", default=None, type=str, required=True, help="Path to the model to convert.")
      parser.add_argument(
          "--save", default=True, type=bool, required=False, help="Whether to save the converted model or not."
      )
      parser.add_argument("--checkpoint_path", default=None, type=str, required=True, help="Path to the output model.")
      __UpperCAmelCase       :   List[Any]				       =							parser.parse_args()

      main(args)













import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
      import tensorflow as tf

      from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments







@require_tf
class     __snake_case       (							unittest.TestCase			):





       '''simple docstring'''







       def 							UpperCAmelCase__ (  self :    Any							,				A :    Union[str, Any]		):
         for model_result in results.values():
           for batch_size, sequence_length in zip(model_result["""bs"""]							,				model_result["""ss"""]		):
             __snake_case:						Dict   =						model_result["""result"""][batch_size][sequence_length]
             self.assertIsNotNone(A		)




       def 							UpperCAmelCase__ (  self :    List[Any]		):
         __snake_case:						List[Any]   =						"""sshleifer/tiny-gpt2"""
         __snake_case:						Union[str, Any]   =						TensorFlowBenchmarkArguments(
             models=[MODEL_ID]							,				training=A							,				inference=A							,				sequence_lengths=[8]							,				batch_sizes=[1]							,				eager_mode=A							,				multi_process=A							,				)
         __snake_case:						Dict   =						TensorFlowBenchmark(A		)
         __snake_case:						List[str]   =						benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result		)
         self.check_results_dict_not_empty(results.memory_inference_result		)




       def 							UpperCAmelCase__ (  self :    Optional[int]		):
         __snake_case:						Optional[int]   =						"""sgugger/tiny-distilbert-classification"""
         __snake_case:						str   =						TensorFlowBenchmarkArguments(
             models=[MODEL_ID]							,				training=A							,				inference=A							,				sequence_lengths=[8]							,				batch_sizes=[1]							,				multi_process=A							,				only_pretrain_model=A							,				)
         __snake_case:						Any   =						TensorFlowBenchmark(A		)
         __snake_case:						List[Any]   =						benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result		)
         self.check_results_dict_not_empty(results.memory_inference_result		)




       def 							UpperCAmelCase__ (  self :    int		):
         __snake_case:						Dict   =						"""sshleifer/tiny-gpt2"""
         __snake_case:						Union[str, Any]   =						TensorFlowBenchmarkArguments(
             models=[MODEL_ID]							,				training=A							,				inference=A							,				sequence_lengths=[8]							,				batch_sizes=[1]							,				multi_process=A							,				)
         __snake_case:						int   =						TensorFlowBenchmark(A		)
         __snake_case:						str   =						benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result		)
         self.check_results_dict_not_empty(results.memory_inference_result		)




       def 							UpperCAmelCase__ (  self :    List[Any]		):
         __snake_case:						List[Any]   =						"""sshleifer/tiny-gpt2"""
         __snake_case:						int   =						AutoConfig.from_pretrained(A		)
         __snake_case:						Optional[Any]   =						TensorFlowBenchmarkArguments(
             models=[MODEL_ID]							,				training=A							,				inference=A							,				sequence_lengths=[8]							,				batch_sizes=[1]							,				eager_mode=A							,				multi_process=A							,				)
         __snake_case:						Union[str, Any]   =						TensorFlowBenchmark(A							,				[config]		)
         __snake_case:						Tuple   =						benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result		)
         self.check_results_dict_not_empty(results.memory_inference_result		)




       def 							UpperCAmelCase__ (  self :    Any		):
         __snake_case:						Optional[int]   =						"""sshleifer/tiny-gpt2"""
         __snake_case:						Tuple   =						AutoConfig.from_pretrained(A		)
         __snake_case:						Union[str, Any]   =						TensorFlowBenchmarkArguments(
             models=[MODEL_ID]							,				training=A							,				inference=A							,				sequence_lengths=[8]							,				batch_sizes=[1]							,				multi_process=A							,				)
         __snake_case:						str   =						TensorFlowBenchmark(A							,				[config]		)
         __snake_case:						str   =						benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result		)
         self.check_results_dict_not_empty(results.memory_inference_result		)




       def 							UpperCAmelCase__ (  self :    Any		):
         __snake_case:						Tuple   =						"""sshleifer/tiny-gpt2"""
         __snake_case:						str   =						TensorFlowBenchmarkArguments(
             models=[MODEL_ID]							,				training=A							,				inference=A							,				sequence_lengths=[8]							,				batch_sizes=[1]							,				multi_process=A							,				)
         __snake_case:						Tuple   =						TensorFlowBenchmark(A		)
         __snake_case:						Tuple   =						benchmark.run()
         self.check_results_dict_not_empty(results.time_train_result		)
         self.check_results_dict_not_empty(results.memory_train_result		)




       def 							UpperCAmelCase__ (  self :    int		):
         __snake_case:						Dict   =						"""sshleifer/tiny-gpt2"""
         __snake_case:						Union[str, Any]   =						AutoConfig.from_pretrained(A		)
         __snake_case:						Optional[int]   =						TensorFlowBenchmarkArguments(
             models=[MODEL_ID]							,				training=A							,				inference=A							,				sequence_lengths=[8]							,				batch_sizes=[1]							,				multi_process=A							,				)
         __snake_case:						List[str]   =						TensorFlowBenchmark(A							,				[config]		)
         __snake_case:						Dict   =						benchmark.run()
         self.check_results_dict_not_empty(results.time_train_result		)
         self.check_results_dict_not_empty(results.memory_train_result		)




       def 							UpperCAmelCase__ (  self :    List[str]		):
         __snake_case:						List[Any]   =						"""patrickvonplaten/t5-tiny-random"""
         __snake_case:						List[str]   =						AutoConfig.from_pretrained(A		)
         __snake_case:						List[str]   =						TensorFlowBenchmarkArguments(
             models=[MODEL_ID]							,				training=A							,				inference=A							,				sequence_lengths=[8]							,				batch_sizes=[1]							,				multi_process=A							,				)
         __snake_case:						Optional[int]   =						TensorFlowBenchmark(A							,				configs=[config]		)
         __snake_case:						Tuple   =						benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result		)
         self.check_results_dict_not_empty(results.memory_inference_result		)




       @unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices("""GPU"""		)		) == 0							,				"""Cannot do xla on CPU."""		)
       def 							UpperCAmelCase__ (  self :    Optional[Any]		):
         __snake_case:						Optional[Any]   =						"""sshleifer/tiny-gpt2"""
         __snake_case:						List[Any]   =						TensorFlowBenchmarkArguments(
             models=[MODEL_ID]							,				training=A							,				inference=A							,				sequence_lengths=[8]							,				batch_sizes=[1]							,				use_xla=A							,				multi_process=A							,				)
         __snake_case:						Union[str, Any]   =						TensorFlowBenchmark(A		)
         __snake_case:						List[Any]   =						benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result		)
         self.check_results_dict_not_empty(results.memory_inference_result		)




       def 							UpperCAmelCase__ (  self :    Union[str, Any]		):
         __snake_case:						List[str]   =						"""sshleifer/tiny-gpt2"""
         with tempfile.TemporaryDirectory() as tmp_dir:
           __snake_case:						int   =						TensorFlowBenchmarkArguments(
               models=[MODEL_ID]							,				inference=A							,				save_to_csv=A							,				sequence_lengths=[8]							,				batch_sizes=[1]							,				inference_time_csv_file=os.path.join(A							,				"""inf_time.csv"""		)							,				inference_memory_csv_file=os.path.join(A							,				"""inf_mem.csv"""		)							,				env_info_csv_file=os.path.join(A							,				"""env.csv"""		)							,				multi_process=A							,				)
           __snake_case:						Tuple   =						TensorFlowBenchmark(A		)
           benchmark.run()
           self.assertTrue(Path(os.path.join(A							,				"""inf_time.csv"""		)		).exists()		)
           self.assertTrue(Path(os.path.join(A							,				"""inf_mem.csv"""		)		).exists()		)
           self.assertTrue(Path(os.path.join(A							,				"""env.csv"""		)		).exists()		)






       def 							UpperCAmelCase__ (  self :    Optional[int]		):
         __snake_case:						Any   =						"""sshleifer/tiny-gpt2"""

         def _check_summary_is_not_empty(A :    Union[str, Any]		):
           self.assertTrue(hasattr(A							,				"""sequential"""		)		)
           self.assertTrue(hasattr(A							,				"""cumulative"""		)		)
           self.assertTrue(hasattr(A							,				"""current"""		)		)
           self.assertTrue(hasattr(A							,				"""total"""		)		)

         with tempfile.TemporaryDirectory() as tmp_dir:
           __snake_case:						Union[str, Any]   =						TensorFlowBenchmarkArguments(
               models=[MODEL_ID]							,				inference=A							,				sequence_lengths=[8]							,				batch_sizes=[1]							,				log_filename=os.path.join(A							,				"""log.txt"""		)							,				log_print=A							,				trace_memory_line_by_line=A							,				eager_mode=A							,				multi_process=A							,				)
           __snake_case:						Dict   =						TensorFlowBenchmark(A		)
           __snake_case:						List[str]   =						benchmark.run()
           _check_summary_is_not_empty(result.inference_summary		)
           self.assertTrue(Path(os.path.join(A							,				"""log.txt"""		)		).exists()		)






