'''simple docstring'''

import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
lowerCAmelCase_ : Union[str, Any]						  =      logging.get_logger("transformers.models.speecht5")


def __a (	__lowerCamelCase			:						Any ,		__lowerCamelCase			:						str ,		__lowerCamelCase			:						Optional[Any]				)	->							Any:
      '''simple docstring'''


      hf_model.apply_weight_norm()

      lowercase_     =      checkpoint["input_conv.weight_g"]
      lowercase_     =      checkpoint["input_conv.weight_v"]
      lowercase_     =      checkpoint["input_conv.bias"]

      for i in range(len(config.upsample_rates				)				):
            lowercase_     =      checkpoint[f'upsamples.{i}.1.weight_g']
            lowercase_     =      checkpoint[f'upsamples.{i}.1.weight_v']
            lowercase_     =      checkpoint[f'upsamples.{i}.1.bias']

      for i in range(len(config.upsample_rates				) * len(config.resblock_kernel_sizes				)				):
            for j in range(len(config.resblock_dilation_sizes				)				):
                  lowercase_     =      checkpoint[f'blocks.{i}.convs1.{j}.1.weight_g']
                  lowercase_     =      checkpoint[f'blocks.{i}.convs1.{j}.1.weight_v']
                  lowercase_     =      checkpoint[f'blocks.{i}.convs1.{j}.1.bias']

                  lowercase_     =      checkpoint[f'blocks.{i}.convs2.{j}.1.weight_g']
                  lowercase_     =      checkpoint[f'blocks.{i}.convs2.{j}.1.weight_v']
                  lowercase_     =      checkpoint[f'blocks.{i}.convs2.{j}.1.bias']

      lowercase_     =      checkpoint["output_conv.1.weight_g"]
      lowercase_     =      checkpoint["output_conv.1.weight_v"]
      lowercase_     =      checkpoint["output_conv.1.bias"]

      hf_model.remove_weight_norm()


@torch.no_grad()
def __a (	__lowerCamelCase			:						int ,		__lowerCamelCase			:						int ,		__lowerCamelCase			:						List[str] ,		__lowerCamelCase			:						Optional[int]=None ,		__lowerCamelCase			:						List[str]=None ,		)	->							str:
      '''simple docstring'''


      if config_path is not None:
            lowercase_     =      SpeechTaHifiGanConfig.from_pretrained(__lowerCamelCase				)
      else:
            lowercase_     =      SpeechTaHifiGanConfig()

      lowercase_     =      SpeechTaHifiGan(__lowerCamelCase				)

      lowercase_     =      torch.load(__lowerCamelCase				)
      load_weights(orig_checkpoint["model"]["generator"] ,		__lowerCamelCase ,		__lowerCamelCase				)

      lowercase_     =      np.load(__lowerCamelCase				)
      lowercase_     =      stats[0].reshape(-1				)
      lowercase_     =      stats[1].reshape(-1				)
      lowercase_     =      torch.from_numpy(__lowerCamelCase				).float()
      lowercase_     =      torch.from_numpy(__lowerCamelCase				).float()

      model.save_pretrained(__lowerCamelCase				)

      if repo_id:
            print("Pushing to the hub..."				)
            model.push_to_hub(__lowerCamelCase				)


if __name__ == "__main__":
       lowerCAmelCase_ : Any						  =      argparse.ArgumentParser()
       parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to original checkpoint")
       parser.add_argument("--stats_path", required=True, default=None, type=str, help="Path to stats.npy file")
       parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
       parser.add_argument(
           "--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
       )
       parser.add_argument(
           "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
       )

       lowerCAmelCase_ : Optional[Any]						  =      parser.parse_args()
       convert_hifigan_checkpoint(
           args.checkpoint_path,
           args.stats_path,
           args.pytorch_dump_folder_path,
           args.config_path,
           args.push_to_hub,
       )







'''simple docstring'''

import inspect
import os

import torch
from transformers import AutoModel
from transformers.testing_utils import mockenv_context
from transformers.trainer_utils import set_seed

import accelerate
from accelerate.accelerator import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils.testing import (
    AccelerateTestCase,
    TempDirTestCase,
    execute_subprocess_async,
    require_cuda,
    require_fsdp,
    require_multi_gpu,
    slow,
)
from accelerate.utils.constants import (
    FSDP_AUTO_WRAP_POLICY,
    FSDP_BACKWARD_PREFETCH,
    FSDP_SHARDING_STRATEGY,
    FSDP_STATE_DICT_TYPE,
)
from accelerate.utils.dataclasses import FullyShardedDataParallelPlugin
from accelerate.utils.other import patch_environment


set_seed(42)

lowerCAmelCase_ : Optional[Any]						  =      "bert-base-cased"
lowerCAmelCase_ : Any						  =      "fp16"
lowerCAmelCase_ : Union[str, Any]						  =      "bf16"
lowerCAmelCase_ : List[Any]						  =      [FPaa, BFaa]





@require_fsdp
@require_cuda
class 							lowercase						(						__lowerCamelCase ):


   def __UpperCAmelCase       (   self				:	str)      ->					Union[str, Any]:
         super().setUp()

         lowercase_     =      dict(
             ACCELERATE_USE_FSDP="true"   ,		MASTER_ADDR="localhost"   ,		MASTER_PORT="10999"   ,		RANK="0"   ,		LOCAL_RANK="0"   ,		WORLD_SIZE="1"   ,		)


   def __UpperCAmelCase       (   self				:	Optional[Any])      ->					List[Any]:
         from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy

         for i, strategy in enumerate(__lowerCAmelCase):
               lowercase_     =      self.dist_env.copy()
               lowercase_     =      F'{i + 1}'
               lowercase_     =      strategy
               with mockenv_context(**__lowerCAmelCase):
                     lowercase_     =      FullyShardedDataParallelPlugin()
                     self.assertEqual(fsdp_plugin.sharding_strategy   ,		ShardingStrategy(i + 1))


   def __UpperCAmelCase       (   self				:	Union[str, Any])      ->					List[str]:
         from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch

         for i, prefetch_policy in enumerate(__lowerCAmelCase):
               lowercase_     =      self.dist_env.copy()
               lowercase_     =      prefetch_policy
               with mockenv_context(**__lowerCAmelCase):
                     lowercase_     =      FullyShardedDataParallelPlugin()
                     if prefetch_policy == "NO_PREFETCH":
                           self.assertIsNone(fsdp_plugin.backward_prefetch)
                     else:
                           self.assertEqual(fsdp_plugin.backward_prefetch   ,		BackwardPrefetch(i + 1))


   def __UpperCAmelCase       (   self				:	Dict)      ->					List[str]:
         from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType

         for i, state_dict_type in enumerate(__lowerCAmelCase):
               lowercase_     =      self.dist_env.copy()
               lowercase_     =      state_dict_type
               with mockenv_context(**__lowerCAmelCase):
                     lowercase_     =      FullyShardedDataParallelPlugin()
                     self.assertEqual(fsdp_plugin.state_dict_type   ,		StateDictType(i + 1))
                     if state_dict_type == "FULL_STATE_DICT":
                           self.assertTrue(fsdp_plugin.state_dict_config.offload_to_cpu)
                           self.assertTrue(fsdp_plugin.state_dict_config.ranka_only)


   def __UpperCAmelCase       (   self				:	Optional[int])      ->					List[Any]:
         lowercase_     =      AutoModel.from_pretrained(__lowerCAmelCase)
         for policy in FSDP_AUTO_WRAP_POLICY:
               lowercase_     =      self.dist_env.copy()
               lowercase_     =      policy
               if policy == "TRANSFORMER_BASED_WRAP":
                     lowercase_     =      "BertLayer"
               elif policy == "SIZE_BASED_WRAP":
                     lowercase_     =      "2000"
               with mockenv_context(**__lowerCAmelCase):
                     lowercase_     =      FullyShardedDataParallelPlugin()
                     fsdp_plugin.set_auto_wrap_policy(__lowerCAmelCase)
                     if policy == "NO_WRAP":
                           self.assertIsNone(fsdp_plugin.auto_wrap_policy)
                     else:
                           self.assertIsNotNone(fsdp_plugin.auto_wrap_policy)

         lowercase_     =      self.dist_env.copy()
         lowercase_     =      "TRANSFORMER_BASED_WRAP"
         lowercase_     =      "T5Layer"
         with mockenv_context(**__lowerCAmelCase):
               lowercase_     =      FullyShardedDataParallelPlugin()
               with self.assertRaises(__lowerCAmelCase) as cm:
                     fsdp_plugin.set_auto_wrap_policy(__lowerCAmelCase)
               self.assertTrue("Could not find the transformer layer class to wrap in the model." in str(cm.exception))

         lowercase_     =      self.dist_env.copy()
         lowercase_     =      "SIZE_BASED_WRAP"
         lowercase_     =      "0"
         with mockenv_context(**__lowerCAmelCase):
               lowercase_     =      FullyShardedDataParallelPlugin()
               fsdp_plugin.set_auto_wrap_policy(__lowerCAmelCase)
               self.assertIsNone(fsdp_plugin.auto_wrap_policy)


   def __UpperCAmelCase       (   self				:	Union[str, Any])      ->					int:
         from torch.distributed.fsdp.fully_sharded_data_parallel import MixedPrecision
         from torch.distributed.fsdp.sharded_grad_scaler import ShardedGradScaler

         for mp_dtype in dtypes:
               lowercase_     =      self.dist_env.copy()
               lowercase_     =      mp_dtype
               with mockenv_context(**__lowerCAmelCase):
                     lowercase_     =      Accelerator()
                     if mp_dtype == "fp16":
                           lowercase_     =      torch.floataa
                     elif mp_dtype == "bf16":
                           lowercase_     =      torch.bfloataa
                     lowercase_     =      MixedPrecision(param_dtype=__lowerCAmelCase   ,		reduce_dtype=__lowerCAmelCase   ,		buffer_dtype=__lowerCAmelCase)
                     self.assertEqual(accelerator.state.fsdp_plugin.mixed_precision_policy   ,		__lowerCAmelCase)
                     if mp_dtype == FPaa:
                           self.assertTrue(isinstance(accelerator.scaler   ,		__lowerCAmelCase))
                     elif mp_dtype == BFaa:
                           self.assertIsNone(accelerator.scaler)
                     AcceleratorState._reset_state(__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	List[str])      ->					Dict:
         from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload

         for flag in [True, False]:
               lowercase_     =      self.dist_env.copy()
               lowercase_     =      str(__lowerCAmelCase).lower()
               with mockenv_context(**__lowerCAmelCase):
                     lowercase_     =      FullyShardedDataParallelPlugin()
                     self.assertEqual(fsdp_plugin.cpu_offload   ,		CPUOffload(offload_params=__lowerCAmelCase))





@require_fsdp
@require_multi_gpu
@slow
class 							lowercase						(						__lowerCamelCase ):


   def __UpperCAmelCase       (   self				:	Optional[int])      ->					str:
         super().setUp()
         lowercase_     =      0.82
         lowercase_     =      [
             "fsdp_shard_grad_op_transformer_based_wrap",
             "fsdp_full_shard_transformer_based_wrap",
         ]
         lowercase_     =      {
             "multi_gpu_fp16": 3200,
             "fsdp_shard_grad_op_transformer_based_wrap_fp16": 2000,
             "fsdp_full_shard_transformer_based_wrap_fp16": 1900,
             # Disabling below test as it overwhelms the RAM memory usage
             # on CI self-hosted runner leading to tests getting killed.
             # "fsdp_full_shard_cpu_offload_transformer_based_wrap_fp32": 1500,  # fp16 was leading to indefinite hang
         }
         lowercase_     =      160
         lowercase_     =      160

         lowercase_     =      inspect.getfile(accelerate.test_utils)
         lowercase_     =      os.path.sep.join(mod_file.split(os.path.sep)[:-1] + ["scripts", "external_deps"])


   def __UpperCAmelCase       (   self				:	Optional[Any])      ->					Optional[Any]:
         lowercase_     =      os.path.join(self.test_scripts_folder   ,		"test_performance.py")
         lowercase_     =      ["accelerate", "launch", "--num_processes=2", "--num_machines=1", "--machine_rank=0", "--use_fsdp"]
         for config in self.performance_configs:
               lowercase_     =      cmd.copy()
               for i, strategy in enumerate(__lowerCAmelCase):
                     if strategy.lower() in config:
                           cmd_config.append(F'--fsdp_sharding_strategy={i+1}')
                           break

               if "fp32" in config:
                     cmd_config.append("--mixed_precision=no")
               else:
                     cmd_config.append("--mixed_precision=fp16")

               if "cpu_offload" in config:
                     cmd_config.append("--fsdp_offload_params=True")

               for policy in FSDP_AUTO_WRAP_POLICY:
                     if policy.lower() in config:
                           cmd_config.append(F'--fsdp_auto_wrap_policy={policy}')
                           break

               if policy == "TRANSFORMER_BASED_WRAP":
                     cmd_config.append("--fsdp_transformer_layer_cls_to_wrap=BertLayer")
               elif policy == "SIZE_BASED_WRAP":
                     cmd_config.append("--fsdp_min_num_params=2000")

               cmd_config.extend(
                   [
                       self.test_file_path,
                       F'--output_dir={self.tmpdir}',
                       F'--performance_lower_bound={self.performance_lower_bound}',
                   ])
               with patch_environment(omp_num_threads=1):
                     execute_subprocess_async(__lowerCAmelCase   ,		env=os.environ.copy())


   def __UpperCAmelCase       (   self				:	Dict)      ->					Dict:
         lowercase_     =      os.path.join(self.test_scripts_folder   ,		"test_checkpointing.py")
         lowercase_     =      [
             "accelerate",
             "launch",
             "--num_processes=2",
             "--num_machines=1",
             "--machine_rank=0",
             "--use_fsdp",
             "--mixed_precision=fp16",
             "--fsdp_transformer_layer_cls_to_wrap=BertLayer",
         ]

         for i, strategy in enumerate(__lowerCAmelCase):
               lowercase_     =      cmd.copy()
               cmd_config.append(F'--fsdp_sharding_strategy={i+1}')
               if strategy != "FULL_SHARD":
                     continue
               lowercase_     =      len(__lowerCAmelCase)
               for state_dict_type in FSDP_STATE_DICT_TYPE:
                     lowercase_     =      cmd_config[:state_dict_config_index]
                     cmd_config.append(F'--fsdp_state_dict_type={state_dict_type}')
                     cmd_config.extend(
                         [
                             self.test_file_path,
                             F'--output_dir={self.tmpdir}',
                             "--partial_train_epoch=1",
                         ])
                     with patch_environment(omp_num_threads=1):
                           execute_subprocess_async(__lowerCAmelCase   ,		env=os.environ.copy())

                     lowercase_     =      cmd_config[:-1]
                     lowercase_     =      os.path.join(self.tmpdir   ,		"epoch_0")
                     cmd_config.extend(
                         [
                             F'--resume_from_checkpoint={resume_from_checkpoint}',
                         ])
                     with patch_environment(omp_num_threads=1):
                           execute_subprocess_async(__lowerCAmelCase   ,		env=os.environ.copy())


   def __UpperCAmelCase       (   self				:	Optional[int])      ->					int:
         lowercase_     =      os.path.join(self.test_scripts_folder   ,		"test_peak_memory_usage.py")
         lowercase_     =      [
             "accelerate",
             "launch",
             "--num_processes=2",
             "--num_machines=1",
             "--machine_rank=0",
         ]
         for spec, peak_mem_upper_bound in self.peak_memory_usage_upper_bound.items():
               lowercase_     =      cmd.copy()
               if "fp16" in spec:
                     cmd_config.extend(["--mixed_precision=fp16"])
               else:
                     cmd_config.extend(["--mixed_precision=no"])

               if "multi_gpu" in spec:
                     continue
               else:
                     cmd_config.extend(["--use_fsdp"])
                     for i, strategy in enumerate(__lowerCAmelCase):
                           if strategy.lower() in spec:
                                 cmd_config.append(F'--fsdp_sharding_strategy={i+1}')
                                 break

                     if "cpu_offload" in spec:
                           cmd_config.append("--fsdp_offload_params=True")

                     for policy in FSDP_AUTO_WRAP_POLICY:
                           if policy.lower() in spec:
                                 cmd_config.append(F'--fsdp_auto_wrap_policy={policy}')
                                 break

                     if policy == "TRANSFORMER_BASED_WRAP":
                           cmd_config.append("--fsdp_transformer_layer_cls_to_wrap=BertLayer")
                     elif policy == "SIZE_BASED_WRAP":
                           cmd_config.append("--fsdp_min_num_params=2000")

               cmd_config.extend(
                   [
                       self.test_file_path,
                       F'--output_dir={self.tmpdir}',
                       F'--peak_memory_upper_bound={peak_mem_upper_bound}',
                       F'--n_train={self.n_train}',
                       F'--n_val={self.n_val}',
                   ])
               with patch_environment(omp_num_threads=1):
                     execute_subprocess_async(__lowerCAmelCase   ,		env=os.environ.copy())






