
import flax.linen as nn
import jax.numpy as jnp

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD

class     _UpperCamelCase			(   nn.Module					):






			'''simple docstring'''

			__UpperCamelCase :  int
			__UpperCamelCase :  int
			__UpperCamelCase :  float    =	0.0
			__UpperCamelCase :  int    =	1
			__UpperCamelCase :  int    =	1
			__UpperCamelCase :  bool    =	True
			__UpperCamelCase :  bool    =	False
			__UpperCamelCase :  bool    =	False
			__UpperCamelCase :  bool    =	False
			__UpperCamelCase :  jnp.dtype    =	jnp.floataa

			def  lowerCAmelCase__		(    self	:    List[str] ):
							UpperCamelCase_:							List[str]		=  []
							UpperCamelCase_:							Optional[Any]		=  []

							for i in range(self.num_layers ):
											UpperCamelCase_:							Optional[Any]		=  self.in_channels if i == 0 else self.out_channels

											UpperCamelCase_:							Union[str, Any]		=  FlaxResnetBlockaD(
											    in_channels=snake_case_  ,		out_channels=self.out_channels  ,		dropout_prob=self.dropout  ,		dtype=self.dtype  ,		)
											resnets.append(snake_case_ )

											UpperCamelCase_:							str		=  FlaxTransformeraDModel(
											    in_channels=self.out_channels  ,		n_heads=self.num_attention_heads  ,		d_head=self.out_channels // self.num_attention_heads  ,		depth=1  ,		use_linear_projection=self.use_linear_projection  ,		only_cross_attention=self.only_cross_attention  ,		use_memory_efficient_attention=self.use_memory_efficient_attention  ,		dtype=self.dtype  ,		)
											attentions.append(snake_case_ )

							UpperCamelCase_:							List[Any]		=  resnets
							UpperCamelCase_:							List[str]		=  attentions

							if self.add_downsample:
											UpperCamelCase_:							Union[str, Any]		=  FlaxDownsampleaD(self.out_channels  ,		dtype=self.dtype )




			def __call__(    self	:    Tuple  ,		snake_case_	:    Dict  ,		snake_case_	:    List[str]  ,		snake_case_	:    Tuple  ,		snake_case_	:    Tuple=True ):
							UpperCamelCase_:							List[Any]		=  ()

							for resnet, attn in zip(self.resnets  ,		self.attentions ):
											UpperCamelCase_:							Optional[int]		=  resnet(snake_case_  ,		snake_case_  ,		deterministic=snake_case_ )
											UpperCamelCase_:							List[str]		=  attn(snake_case_  ,		snake_case_  ,		deterministic=snake_case_ )
											output_states += (hidden_states,)

							if self.add_downsample:
											UpperCamelCase_:							Tuple		=  self.downsamplers_a(snake_case_ )
											output_states += (hidden_states,)

							return hidden_states, output_states




class     _UpperCamelCase			(   nn.Module					):






			'''simple docstring'''

			__UpperCamelCase :  int
			__UpperCamelCase :  int
			__UpperCamelCase :  float    =	0.0
			__UpperCamelCase :  int    =	1
			__UpperCamelCase :  bool    =	True
			__UpperCamelCase :  jnp.dtype    =	jnp.floataa

			def  lowerCAmelCase__		(    self	:    Dict ):
							UpperCamelCase_:							Optional[int]		=  []

							for i in range(self.num_layers ):
											UpperCamelCase_:							Tuple		=  self.in_channels if i == 0 else self.out_channels

											UpperCamelCase_:							int		=  FlaxResnetBlockaD(
											    in_channels=snake_case_  ,		out_channels=self.out_channels  ,		dropout_prob=self.dropout  ,		dtype=self.dtype  ,		)
											resnets.append(snake_case_ )
							UpperCamelCase_:							Any		=  resnets

							if self.add_downsample:
											UpperCamelCase_:							List[str]		=  FlaxDownsampleaD(self.out_channels  ,		dtype=self.dtype )




			def __call__(    self	:    str  ,		snake_case_	:    List[str]  ,		snake_case_	:    int  ,		snake_case_	:    int=True ):
							UpperCamelCase_:							Any		=  ()

							for resnet in self.resnets:
											UpperCamelCase_:							int		=  resnet(snake_case_  ,		snake_case_  ,		deterministic=snake_case_ )
											output_states += (hidden_states,)

							if self.add_downsample:
											UpperCamelCase_:							Any		=  self.downsamplers_a(snake_case_ )
											output_states += (hidden_states,)

							return hidden_states, output_states




class     _UpperCamelCase			(   nn.Module					):






			'''simple docstring'''

			__UpperCamelCase :  int
			__UpperCamelCase :  int
			__UpperCamelCase :  int
			__UpperCamelCase :  float    =	0.0
			__UpperCamelCase :  int    =	1
			__UpperCamelCase :  int    =	1
			__UpperCamelCase :  bool    =	True
			__UpperCamelCase :  bool    =	False
			__UpperCamelCase :  bool    =	False
			__UpperCamelCase :  bool    =	False
			__UpperCamelCase :  jnp.dtype    =	jnp.floataa

			def  lowerCAmelCase__		(    self	:    Tuple ):
							UpperCamelCase_:							str		=  []
							UpperCamelCase_:							Dict		=  []

							for i in range(self.num_layers ):
											UpperCamelCase_:							Dict		=  self.in_channels if (i == self.num_layers - 1) else self.out_channels
											UpperCamelCase_:							Tuple		=  self.prev_output_channel if i == 0 else self.out_channels

											UpperCamelCase_:							Optional[Any]		=  FlaxResnetBlockaD(
											    in_channels=resnet_in_channels + res_skip_channels  ,		out_channels=self.out_channels  ,		dropout_prob=self.dropout  ,		dtype=self.dtype  ,		)
											resnets.append(snake_case_ )

											UpperCamelCase_:							Optional[int]		=  FlaxTransformeraDModel(
											    in_channels=self.out_channels  ,		n_heads=self.num_attention_heads  ,		d_head=self.out_channels // self.num_attention_heads  ,		depth=1  ,		use_linear_projection=self.use_linear_projection  ,		only_cross_attention=self.only_cross_attention  ,		use_memory_efficient_attention=self.use_memory_efficient_attention  ,		dtype=self.dtype  ,		)
											attentions.append(snake_case_ )

							UpperCamelCase_:							Optional[Any]		=  resnets
							UpperCamelCase_:							List[Any]		=  attentions

							if self.add_upsample:
											UpperCamelCase_:							Union[str, Any]		=  FlaxUpsampleaD(self.out_channels  ,		dtype=self.dtype )




			def __call__(    self	:    Union[str, Any]  ,		snake_case_	:    Union[str, Any]  ,		snake_case_	:    Optional[Any]  ,		snake_case_	:    Tuple  ,		snake_case_	:    int  ,		snake_case_	:    int=True ):
							for resnet, attn in zip(self.resnets  ,		self.attentions ):
											# pop res hidden states
											UpperCamelCase_:							Optional[Any]		=  res_hidden_states_tuple[-1]
											UpperCamelCase_:							Dict		=  res_hidden_states_tuple[:-1]
											UpperCamelCase_:							List[str]		=  jnp.concatenate((hidden_states, res_hidden_states)  ,		axis=-1 )

											UpperCamelCase_:							Optional[Any]		=  resnet(snake_case_  ,		snake_case_  ,		deterministic=snake_case_ )
											UpperCamelCase_:							str		=  attn(snake_case_  ,		snake_case_  ,		deterministic=snake_case_ )

							if self.add_upsample:
											UpperCamelCase_:							str		=  self.upsamplers_a(snake_case_ )

							return hidden_states




class     _UpperCamelCase			(   nn.Module					):






			'''simple docstring'''

			__UpperCamelCase :  int
			__UpperCamelCase :  int
			__UpperCamelCase :  int
			__UpperCamelCase :  float    =	0.0
			__UpperCamelCase :  int    =	1
			__UpperCamelCase :  bool    =	True
			__UpperCamelCase :  jnp.dtype    =	jnp.floataa

			def  lowerCAmelCase__		(    self	:    Union[str, Any] ):
							UpperCamelCase_:							Optional[Any]		=  []

							for i in range(self.num_layers ):
											UpperCamelCase_:							List[str]		=  self.in_channels if (i == self.num_layers - 1) else self.out_channels
											UpperCamelCase_:							List[str]		=  self.prev_output_channel if i == 0 else self.out_channels

											UpperCamelCase_:							str		=  FlaxResnetBlockaD(
											    in_channels=resnet_in_channels + res_skip_channels  ,		out_channels=self.out_channels  ,		dropout_prob=self.dropout  ,		dtype=self.dtype  ,		)
											resnets.append(snake_case_ )

							UpperCamelCase_:							str		=  resnets

							if self.add_upsample:
											UpperCamelCase_:							List[Any]		=  FlaxUpsampleaD(self.out_channels  ,		dtype=self.dtype )




			def __call__(    self	:    int  ,		snake_case_	:    Union[str, Any]  ,		snake_case_	:    str  ,		snake_case_	:    int  ,		snake_case_	:    List[str]=True ):
							for resnet in self.resnets:
											# pop res hidden states
											UpperCamelCase_:							Optional[Any]		=  res_hidden_states_tuple[-1]
											UpperCamelCase_:							List[str]		=  res_hidden_states_tuple[:-1]
											UpperCamelCase_:							List[Any]		=  jnp.concatenate((hidden_states, res_hidden_states)  ,		axis=-1 )

											UpperCamelCase_:							Optional[Any]		=  resnet(snake_case_  ,		snake_case_  ,		deterministic=snake_case_ )

							if self.add_upsample:
											UpperCamelCase_:							Tuple		=  self.upsamplers_a(snake_case_ )

							return hidden_states




class     _UpperCamelCase			(   nn.Module					):






			'''simple docstring'''

			__UpperCamelCase :  int
			__UpperCamelCase :  float    =	0.0
			__UpperCamelCase :  int    =	1
			__UpperCamelCase :  int    =	1
			__UpperCamelCase :  bool    =	False
			__UpperCamelCase :  bool    =	False
			__UpperCamelCase :  jnp.dtype    =	jnp.floataa

			def  lowerCAmelCase__		(    self	:    Any ):
							# there is always at least one resnet
							UpperCamelCase_:							Optional[int]		=  [
							    FlaxResnetBlockaD(
							        in_channels=self.in_channels  ,		out_channels=self.in_channels  ,		dropout_prob=self.dropout  ,		dtype=self.dtype  ,		)
							]

							UpperCamelCase_:							List[str]		=  []

							for _ in range(self.num_layers ):
											UpperCamelCase_:							str		=  FlaxTransformeraDModel(
											    in_channels=self.in_channels  ,		n_heads=self.num_attention_heads  ,		d_head=self.in_channels // self.num_attention_heads  ,		depth=1  ,		use_linear_projection=self.use_linear_projection  ,		use_memory_efficient_attention=self.use_memory_efficient_attention  ,		dtype=self.dtype  ,		)
											attentions.append(snake_case_ )

											UpperCamelCase_:							Optional[Any]		=  FlaxResnetBlockaD(
											    in_channels=self.in_channels  ,		out_channels=self.in_channels  ,		dropout_prob=self.dropout  ,		dtype=self.dtype  ,		)
											resnets.append(snake_case_ )

							UpperCamelCase_:							Optional[Any]		=  resnets
							UpperCamelCase_:							str		=  attentions




			def __call__(    self	:    Tuple  ,		snake_case_	:    Optional[Any]  ,		snake_case_	:    int  ,		snake_case_	:    List[Any]  ,		snake_case_	:    int=True ):
							UpperCamelCase_:							str		=  self.resnets[0](snake_case_  ,		snake_case_ )
							for attn, resnet in zip(self.attentions  ,		self.resnets[1:] ):
											UpperCamelCase_:							List[str]		=  attn(snake_case_  ,		snake_case_  ,		deterministic=snake_case_ )
											UpperCamelCase_:							List[Any]		=  resnet(snake_case_  ,		snake_case_  ,		deterministic=snake_case_ )

							return hidden_states








import argparse
import os
from pathlib import Path

import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download

from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
    BarkCoarseConfig,
    BarkConfig,
    BarkFineConfig,
    BarkSemanticConfig,
)
from transformers.models.bark.generation_configuration_bark import (
    BarkCoarseGenerationConfig,
    BarkFineGenerationConfig,
    BarkGenerationConfig,
    BarkSemanticGenerationConfig,
)
from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase_  :	Union[str, Any] 				=					logging.get_logger(__name__)

set_seed(7_70)


lowerCamelCase_  :	str 				=					{
    """c_attn""": """att_proj""",
    """c_proj""": """out_proj""",
    """c_fc""": """in_proj""",
    """transformer.""": """""",
    """h.""": """layers.""",
    """ln_1""": """layernorm_1""",
    """ln_2""": """layernorm_2""",
    """ln_f""": """layernorm_final""",
    """wpe""": """position_embeds_layer""",
    """wte""": """input_embeds_layer""",
}


lowerCamelCase_  :	Any 				=					{
    """text_small""": {
        """repo_id""": """suno/bark""",
        """file_name""": """text.pt""",
    },
    """coarse_small""": {
        """repo_id""": """suno/bark""",
        """file_name""": """coarse.pt""",
    },
    """fine_small""": {
        """repo_id""": """suno/bark""",
        """file_name""": """fine.pt""",
    },
    """text""": {
        """repo_id""": """suno/bark""",
        """file_name""": """text_2.pt""",
    },
    """coarse""": {
        """repo_id""": """suno/bark""",
        """file_name""": """coarse_2.pt""",
    },
    """fine""": {
        """repo_id""": """suno/bark""",
        """file_name""": """fine_2.pt""",
    },
}

lowerCamelCase_  :	str 				=					os.path.dirname(os.path.abspath(__file__))
lowerCamelCase_  :	Any 				=					os.path.join(os.path.expanduser("""~"""), """.cache""")
lowerCamelCase_  :	Dict 				=					os.path.join(os.getenv("""XDG_CACHE_HOME""", default_cache_dir), """suno""", """bark_v0""")







def     A__   (  lowerCamelCase       ,      lowerCamelCase=False  )					->		int:
				UpperCamelCase_:							Union[str, Any]		=  model_type
				if use_small:
								key += "_small"
				return os.path.join(lowerCamelCase       ,      REMOTE_MODEL_PATHS[key]["""file_name"""]  )







def     A__   (  lowerCamelCase       ,      lowerCamelCase  )					->		Tuple:
				os.makedirs(lowerCamelCase       ,      exist_ok=lowerCamelCase  )
				hf_hub_download(repo_id=lowerCamelCase       ,      filename=lowerCamelCase       ,      local_dir=lowerCamelCase  )







def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase=False       ,      lowerCamelCase="text"  )					->		Optional[int]:
				if model_type == "text":
								UpperCamelCase_:							str		=  BarkSemanticModel
								UpperCamelCase_:							Dict		=  BarkSemanticConfig
								UpperCamelCase_:							int		=  BarkSemanticGenerationConfig
				elif model_type == "coarse":
								UpperCamelCase_:							str		=  BarkCoarseModel
								UpperCamelCase_:							int		=  BarkCoarseConfig
								UpperCamelCase_:							Any		=  BarkCoarseGenerationConfig
				elif model_type == "fine":
								UpperCamelCase_:							Optional[Any]		=  BarkFineModel
								UpperCamelCase_:							int		=  BarkFineConfig
								UpperCamelCase_:							Dict		=  BarkFineGenerationConfig
				else:
								raise NotImplementedError()
				UpperCamelCase_:							str		=  F'''{model_type}_small''' if use_small else model_type
				UpperCamelCase_:							List[Any]		=  REMOTE_MODEL_PATHS[model_key]
				if not os.path.exists(lowerCamelCase  ):
								logger.info(F'''{model_type} model not found, downloading into `{CACHE_DIR}`.'''  )
								_download(model_info["""repo_id"""]       ,      model_info["""file_name"""]  )
				UpperCamelCase_:							int		=  torch.load(lowerCamelCase       ,      map_location=lowerCamelCase  )
				# this is a hack
				UpperCamelCase_:							Tuple		=  checkpoint["""model_args"""]
				if "input_vocab_size" not in model_args:
								UpperCamelCase_:							int		=  model_args["""vocab_size"""]
								UpperCamelCase_:							Optional[int]		=  model_args["""vocab_size"""]
								del model_args["vocab_size"]

				# convert Bark model arguments to HF Bark model arguments
				UpperCamelCase_:							Tuple		=  model_args.pop("""n_head"""  )
				UpperCamelCase_:							Dict		=  model_args.pop("""n_embd"""  )
				UpperCamelCase_:							List[str]		=  model_args.pop("""n_layer"""  )

				UpperCamelCase_:							Optional[Any]		=  ConfigClass(**checkpoint["""model_args"""]  )
				UpperCamelCase_:							Optional[Any]		=  ModelClass(config=lowerCamelCase  )
				UpperCamelCase_:							List[Any]		=  GenerationConfigClass()

				UpperCamelCase_:							Optional[Any]		=  model_generation_config
				UpperCamelCase_:							Optional[int]		=  checkpoint["""model"""]
				# fixup checkpoint
				UpperCamelCase_:							Dict		=  """_orig_mod."""
				for k, v in list(state_dict.items()  ):
								if k.startswith(lowerCamelCase  ):
												# replace part of the key with corresponding layer name in HF implementation
												UpperCamelCase_:							Optional[int]		=  k[len(lowerCamelCase  ) :]
												for old_layer_name in new_layer_name_dict:
																UpperCamelCase_:							Dict		=  new_k.replace(lowerCamelCase       ,      new_layer_name_dict[old_layer_name]  )

												UpperCamelCase_:							List[str]		=  state_dict.pop(lowerCamelCase  )

				UpperCamelCase_:							Optional[int]		=  set(state_dict.keys()  ) - set(model.state_dict().keys()  )
				UpperCamelCase_:							Dict		=  {k for k in extra_keys if not k.endswith(""".attn.bias"""  )}
				UpperCamelCase_:							Optional[Any]		=  set(model.state_dict().keys()  ) - set(state_dict.keys()  )
				UpperCamelCase_:							Union[str, Any]		=  {k for k in missing_keys if not k.endswith(""".attn.bias"""  )}
				if len(lowerCamelCase  ) != 0:
								raise ValueError(F'''extra keys found: {extra_keys}'''  )
				if len(lowerCamelCase  ) != 0:
								raise ValueError(F'''missing keys: {missing_keys}'''  )
				model.load_state_dict(lowerCamelCase       ,      strict=lowerCamelCase  )
				UpperCamelCase_:							str		=  model.num_parameters(exclude_embeddings=lowerCamelCase  )
				UpperCamelCase_:							int		=  checkpoint["""best_val_loss"""].item()
				logger.info(F'''model loaded: {round(n_params/1E6       ,      1  )}M params, {round(lowerCamelCase       ,      3  )} loss'''  )
				model.eval()
				model.to(lowerCamelCase  )
				del checkpoint, state_dict

				return model







def     A__   (  lowerCamelCase       ,      lowerCamelCase=False       ,      lowerCamelCase="text"  )					->		Any:
				if model_type not in ("text", "coarse", "fine"):
								raise NotImplementedError()

				UpperCamelCase_:							Union[str, Any]		=  """cpu"""  # do conversion on cpu

				UpperCamelCase_:							int		=  _get_ckpt_path(lowerCamelCase       ,      use_small=lowerCamelCase  )
				UpperCamelCase_:							Dict		=  _load_model(lowerCamelCase       ,      lowerCamelCase       ,      model_type=lowerCamelCase       ,      use_small=lowerCamelCase  )

				# load bark initial model
				UpperCamelCase_:							List[Any]		=  _bark_load_model(lowerCamelCase       ,      """cpu"""       ,      model_type=lowerCamelCase       ,      use_small=lowerCamelCase  )

				if model_type == "text":
								UpperCamelCase_:							Tuple		=  bark_model["""model"""]

				if model.num_parameters(exclude_embeddings=lowerCamelCase  ) != bark_model.get_num_params():
								raise ValueError("""initial and new models don't have the same number of parameters"""  )

				# check if same output as the bark model
				UpperCamelCase_:							Optional[Any]		=  5
				UpperCamelCase_:							List[str]		=  10

				if model_type in ["text", "coarse"]:
								UpperCamelCase_:							int		=  torch.randint(2_56       ,      (batch_size, sequence_length)       ,      dtype=torch.int  )
								UpperCamelCase_:							Tuple		=  bark_model(lowerCamelCase  )[0]

								UpperCamelCase_:							Optional[Any]		=  model(lowerCamelCase  )

								# take last logits
								UpperCamelCase_:							Union[str, Any]		=  output_new_model_total.logits[:, [-1], :]

				else:
								UpperCamelCase_:							Tuple		=  3
								UpperCamelCase_:							List[Any]		=  8
								UpperCamelCase_:							List[str]		=  torch.randint(2_56       ,      (batch_size, sequence_length, n_codes_total)       ,      dtype=torch.int  )

								UpperCamelCase_:							int		=  model(lowerCamelCase       ,      lowerCamelCase  )
								UpperCamelCase_:							Any		=  bark_model(lowerCamelCase       ,      lowerCamelCase  )

								UpperCamelCase_:							Optional[int]		=  output_new_model_total.logits

				# output difference should come from the difference of self-attention implementation design
				if output_new_model.shape != output_old_model.shape:
								raise ValueError("""initial and new outputs don't have the same shape"""  )
				if (output_new_model - output_old_model).abs().max().item() > 1E-3:
								raise ValueError("""initial and new outputs are not equal"""  )

				Path(lowerCamelCase  ).mkdir(exist_ok=lowerCamelCase  )
				model.save_pretrained(lowerCamelCase  )







def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      )					->		str:
				UpperCamelCase_:							List[str]		=  os.path.join(lowerCamelCase       ,      lowerCamelCase  )

				UpperCamelCase_:							Optional[int]		=  BarkSemanticConfig.from_pretrained(os.path.join(lowerCamelCase       ,      """config.json"""  )  )
				UpperCamelCase_:							List[Any]		=  BarkCoarseConfig.from_pretrained(os.path.join(lowerCamelCase       ,      """config.json"""  )  )
				UpperCamelCase_:							Optional[int]		=  BarkFineConfig.from_pretrained(os.path.join(lowerCamelCase       ,      """config.json"""  )  )
				UpperCamelCase_:							Any		=  EncodecConfig.from_pretrained("""facebook/encodec_24khz"""  )

				UpperCamelCase_:							Optional[Any]		=  BarkSemanticModel.from_pretrained(lowerCamelCase  )
				UpperCamelCase_:							Tuple		=  BarkCoarseModel.from_pretrained(lowerCamelCase  )
				UpperCamelCase_:							List[str]		=  BarkFineModel.from_pretrained(lowerCamelCase  )
				UpperCamelCase_:							Tuple		=  EncodecModel.from_pretrained("""facebook/encodec_24khz"""  )

				UpperCamelCase_:							int		=  BarkConfig.from_sub_model_configs(
				    lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )

				UpperCamelCase_:							Optional[int]		=  BarkGenerationConfig.from_sub_model_configs(
				    semantic.generation_config       ,      coarseAcoustic.generation_config       ,      fineAcoustic.generation_config  )

				UpperCamelCase_:							Optional[Any]		=  BarkModel(lowerCamelCase  )

				UpperCamelCase_:							int		=  semantic
				UpperCamelCase_:							Tuple		=  coarseAcoustic
				UpperCamelCase_:							Optional[int]		=  fineAcoustic
				UpperCamelCase_:							Any		=  codec

				UpperCamelCase_:							Dict		=  bark_generation_config

				Path(lowerCamelCase  ).mkdir(exist_ok=lowerCamelCase  )
				bark.save_pretrained(lowerCamelCase       ,      repo_id=lowerCamelCase       ,      push_to_hub=lowerCamelCase  )


if __name__ == "__main__":
							lowerCamelCase_  :	List[Any] 				=					argparse.ArgumentParser()
							# Required parameters

							parser.add_argument("""model_type""", type=str, help="""text, coarse or fine.""")
							parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
							parser.add_argument("""--is_small""", action="""store_true""", help="""convert the small version instead of the large.""")

							lowerCamelCase_  :	Dict 				=					parser.parse_args()

							load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)






