


'''simple docstring'''



from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand


def    lowerCamelCase__   ( A     :    Namespace  ):




						'''simple docstring'''






						return ConvertCommand(
						    args.model_type   ,     args.tf_checkpoint   ,     args.pytorch_dump_output   ,     args.config   ,     args.finetuning_task_name  )


_lowercase    :    Any			     =  """
transformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires
TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.
"""






class  UpperCamelCase__(     lowerCAmelCase ):




				@staticmethod
				def   a__(	lowerCAmelCase    :				ArgumentParser   )->  Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			parser.add_parser(
										    '''convert'''   ,					help='''CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints.'''   ,					)
										train_parser.add_argument('''--model_type'''   ,					type=lowerCAmelCase   ,					required=lowerCAmelCase   ,					help='''Model\'s type.'''   )
										train_parser.add_argument(
										    '''--tf_checkpoint'''   ,					type=lowerCAmelCase   ,					required=lowerCAmelCase   ,					help='''TensorFlow checkpoint path or folder.'''   )
										train_parser.add_argument(
										    '''--pytorch_dump_output'''   ,					type=lowerCAmelCase   ,					required=lowerCAmelCase   ,					help='''Path to the PyTorch saved model output.'''   )
										train_parser.add_argument('''--config'''   ,					type=lowerCAmelCase   ,					default=''''''   ,					help='''Configuration file path or folder.'''   )
										train_parser.add_argument(
										    '''--finetuning_task_name'''   ,					type=lowerCAmelCase   ,					default=lowerCAmelCase   ,					help='''Optional fine-tuning task name if the TF model was a finetuned model.'''   ,					)
										train_parser.set_defaults(func=lowerCAmelCase   )




				def __init__(	self    :				str   ,					lowerCAmelCase    :				str   ,					lowerCAmelCase    :				str   ,					lowerCAmelCase    :				str   ,					lowerCAmelCase    :				str   ,					lowerCAmelCase    :				str   ,					*lowerCAmelCase    :				Optional[int]   ,					)->  Optional[int]:

										"""simple docstring"""




										UpperCAmelCase         =			logging.get_logger('''transformers-cli/converting'''   )

										self._logger.info(F"""Loading model {model_type}"""   )
										UpperCAmelCase         =			model_type
										UpperCAmelCase         =			tf_checkpoint
										UpperCAmelCase         =			pytorch_dump_output
										UpperCAmelCase         =			config
										UpperCAmelCase         =			finetuning_task_name





				def   a__(	self    :				Dict   )->  Union[str, Any]:

										"""simple docstring"""




										if self._model_type == "albert":
																try:
																						from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
																						    convert_tf_checkpoint_to_pytorch,
																						)
																except ImportError:
																						raise ImportError(lowerCAmelCase   )

																convert_tf_checkpoint_to_pytorch(self._tf_checkpoint   ,					self._config   ,					self._pytorch_dump_output   )
										elif self._model_type == "bert":
																try:
																						from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
																						    convert_tf_checkpoint_to_pytorch,
																						)
																except ImportError:
																						raise ImportError(lowerCAmelCase   )

																convert_tf_checkpoint_to_pytorch(self._tf_checkpoint   ,					self._config   ,					self._pytorch_dump_output   )
										elif self._model_type == "funnel":
																try:
																						from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
																						    convert_tf_checkpoint_to_pytorch,
																						)
																except ImportError:
																						raise ImportError(lowerCAmelCase   )

																convert_tf_checkpoint_to_pytorch(self._tf_checkpoint   ,					self._config   ,					self._pytorch_dump_output   )
										elif self._model_type == "t5":
																try:
																						from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
																except ImportError:
																						raise ImportError(lowerCAmelCase   )

																convert_tf_checkpoint_to_pytorch(self._tf_checkpoint   ,					self._config   ,					self._pytorch_dump_output   )
										elif self._model_type == "gpt":
																from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
																    convert_openai_checkpoint_to_pytorch,
																)

																convert_openai_checkpoint_to_pytorch(self._tf_checkpoint   ,					self._config   ,					self._pytorch_dump_output   )
										elif self._model_type == "transfo_xl":
																try:
																						from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
																						    convert_transfo_xl_checkpoint_to_pytorch,
																						)
																except ImportError:
																						raise ImportError(lowerCAmelCase   )

																if "ckpt" in self._tf_checkpoint.lower():
																						UpperCAmelCase         =			self._tf_checkpoint
																						UpperCAmelCase         =			''''''
																else:
																						UpperCAmelCase         =			self._tf_checkpoint
																						UpperCAmelCase         =			''''''
																convert_transfo_xl_checkpoint_to_pytorch(
																    lowerCAmelCase   ,					self._config   ,					self._pytorch_dump_output   ,					lowerCAmelCase   )
										elif self._model_type == "gpt2":
																try:
																						from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
																						    convert_gpta_checkpoint_to_pytorch,
																						)
																except ImportError:
																						raise ImportError(lowerCAmelCase   )

																convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint   ,					self._config   ,					self._pytorch_dump_output   )
										elif self._model_type == "xlnet":
																try:
																						from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
																						    convert_xlnet_checkpoint_to_pytorch,
																						)
																except ImportError:
																						raise ImportError(lowerCAmelCase   )

																convert_xlnet_checkpoint_to_pytorch(
																    self._tf_checkpoint   ,					self._config   ,					self._pytorch_dump_output   ,					self._finetuning_task_name   )
										elif self._model_type == "xlm":
																from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
																    convert_xlm_checkpoint_to_pytorch,
																)

																convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint   ,					self._pytorch_dump_output   )
										elif self._model_type == "lxmert":
																from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
																    convert_lxmert_checkpoint_to_pytorch,
																)

																convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint   ,					self._pytorch_dump_output   )
										elif self._model_type == "rembert":
																from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
																    convert_rembert_tf_checkpoint_to_pytorch,
																)

																convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint   ,					self._config   ,					self._pytorch_dump_output   )
										else:
																raise ValueError(
																    '''--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]'''   )









'''simple docstring'''



import math
from typing import Optional

import numpy as np

from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

_lowercase    :    List[str]			     =  {
    """facebook/encodec_24khz""": """https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json""",
    """facebook/encodec_48khz""": """https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json""",
}






class  UpperCamelCase__(     lowerCAmelCase ):
				__magic_name__       :   List[str]							= "encodec"




				def __init__(	self    :				List[str]   ,					lowerCAmelCase    :				int=[1.5, 3.0, 6.0, 12.0, 24.0]   ,					lowerCAmelCase    :				Tuple=24000   ,					lowerCAmelCase    :				List[Any]=1   ,					lowerCAmelCase    :				Union[str, Any]=False   ,					lowerCAmelCase    :				str=None   ,					lowerCAmelCase    :				Dict=None   ,					lowerCAmelCase    :				str=128   ,					lowerCAmelCase    :				Any=32   ,					lowerCAmelCase    :				Any=1   ,					lowerCAmelCase    :				List[Any]=[8, 5, 4, 2]   ,					lowerCAmelCase    :				Union[str, Any]="weight_norm"   ,					lowerCAmelCase    :				str=7   ,					lowerCAmelCase    :				Optional[int]=7   ,					lowerCAmelCase    :				Any=3   ,					lowerCAmelCase    :				Tuple=2   ,					lowerCAmelCase    :				Optional[Any]=True   ,					lowerCAmelCase    :				List[str]="reflect"   ,					lowerCAmelCase    :				Optional[int]=2   ,					lowerCAmelCase    :				List[Any]=2   ,					lowerCAmelCase    :				Union[str, Any]=1.0   ,					lowerCAmelCase    :				Optional[Any]=1024   ,					lowerCAmelCase    :				Optional[Any]=None   ,					lowerCAmelCase    :				str=True   ,					**lowerCAmelCase    :				str   ,					)->  List[str]:

										"""simple docstring"""




										UpperCAmelCase         =			target_bandwidths
										UpperCAmelCase         =			sampling_rate
										UpperCAmelCase         =			audio_channels
										UpperCAmelCase         =			normalize
										UpperCAmelCase         =			chunk_length_s
										UpperCAmelCase         =			overlap
										UpperCAmelCase         =			hidden_size
										UpperCAmelCase         =			num_filters
										UpperCAmelCase         =			num_residual_layers
										UpperCAmelCase         =			upsampling_ratios
										UpperCAmelCase         =			norm_type
										UpperCAmelCase         =			kernel_size
										UpperCAmelCase         =			last_kernel_size
										UpperCAmelCase         =			residual_kernel_size
										UpperCAmelCase         =			dilation_growth_rate
										UpperCAmelCase         =			use_causal_conv
										UpperCAmelCase         =			pad_mode
										UpperCAmelCase         =			compress
										UpperCAmelCase         =			num_lstm_layers
										UpperCAmelCase         =			trim_right_ratio
										UpperCAmelCase         =			codebook_size
										UpperCAmelCase         =			codebook_dim if codebook_dim is not None else hidden_size
										UpperCAmelCase         =			use_conv_shortcut

										if self.norm_type not in ["weight_norm", "time_group_norm"]:
																raise ValueError(
																    F"""self.norm_type must be one of `\"weight_norm\"`, `\"time_group_norm\"`), got {self.norm_type}"""   )

										super().__init__(**lowerCAmelCase   )




				@property
				def   a__(	self    :				str   )->  Optional[int]:

										"""simple docstring"""




										if self.chunk_length_s is None:
																return None
										else:
																return int(self.chunk_length_s * self.sampling_rate   )




				@property
				def   a__(	self    :				List[str]   )->  Optional[int]:

										"""simple docstring"""




										if self.chunk_length_s is None or self.overlap is None:
																return None
										else:
																return max(1   ,					int((1.0 - self.overlap) * self.chunk_length   )   )




				@property
				def   a__(	self    :				List[Any]   )->  int:

										"""simple docstring"""




										UpperCAmelCase         =			np.prod(self.upsampling_ratios   )
										return math.ceil(self.sampling_rate / hop_length   )





				@property
				def   a__(	self    :				int   )->  int:

										"""simple docstring"""




										return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10)   )





