






"""simple docstring"""
from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


lowerCamelCase_							      = logging.get_logger(__name__)







class      UpperCamelCase_			(__A	):
	__magic_name__       =							['''input_values''', '''padding_mask''']





	def __init__( self   :					Dict      ,      lowerCAmelCase_   :					int = 1      ,      lowerCAmelCase_   :					int = 24_000      ,      lowerCAmelCase_   :					float = 0.0      ,      lowerCAmelCase_   :					float = None      ,      lowerCAmelCase_   :					float = None      ,      **lowerCAmelCase_   :					List[Any]      ,      )		->     Optional[int]:
				super().__init__(feature_size=lowerCAmelCase_      ,      sampling_rate=lowerCAmelCase_      ,      padding_value=lowerCAmelCase_      ,      **lowerCAmelCase_		)
				UpperCAmelCase_	:					Tuple     	=   chunk_length_s
				UpperCAmelCase_	:					Union[str, Any]     	=   overlap





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					Any		)		->     Optional[int]:
				if self.chunk_length_s is None:
							return None
				else:
							return int(self.chunk_length_s * self.sampling_rate		)





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					Any		)		->     Optional[int]:
				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		)		)





	def __call__( self   :					int      ,      lowerCAmelCase_   :					Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]      ,      lowerCAmelCase_   :					Optional[Union[bool, str, PaddingStrategy]] = None      ,      lowerCAmelCase_   :					Optional[bool] = False      ,      lowerCAmelCase_   :					Optional[int] = None      ,      lowerCAmelCase_   :					Optional[Union[str, TensorType]] = None      ,      lowerCAmelCase_   :					Optional[int] = None      ,      )		->     BatchFeature:
				if sampling_rate is not None:
							if sampling_rate != self.sampling_rate:
										raise ValueError(
										    f"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
										    f""" {self.sampling_rate}. Please make sure that the provided audio input was sampled with"""
										    f""" {self.sampling_rate} and not {sampling_rate}."""		)
				else:
							logger.warning(
							    "It is strongly recommended to pass the `sampling_rate` argument to this function. "
							    "Failing to do so can result in silent errors that might be hard to debug."		)

				if padding and truncation:
							raise ValueError("Both padding and truncation were set. Make sure you only set one."		)
				elif padding is None:
							# by default let's pad the inputs
							UpperCAmelCase_	:					Tuple     	=   True

				UpperCAmelCase_	:					Union[str, Any]     	=   bool(
				    isinstance(lowerCAmelCase_      ,      (list, tuple)		) and (isinstance(raw_audio[0]      ,      (np.ndarray, tuple, list)		))		)

				if is_batched:
							UpperCAmelCase_	:					List[str]     	=   [np.asarray(lowerCAmelCase_      ,      dtype=np.floataa		).T for audio in raw_audio]
				elif not is_batched and not isinstance(lowerCAmelCase_      ,      np.ndarray		):
							UpperCAmelCase_	:					str     	=   np.asarray(lowerCAmelCase_      ,      dtype=np.floataa		)
				elif isinstance(lowerCAmelCase_      ,      np.ndarray		) and raw_audio.dtype is np.dtype(np.floataa		):
							UpperCAmelCase_	:					str     	=   raw_audio.astype(np.floataa		)

				# always return batch
				if not is_batched:
							UpperCAmelCase_	:					List[str]     	=   [np.asarray(lowerCAmelCase_		).T]

				# verify inputs are valid
				for idx, example in enumerate(lowerCAmelCase_		):
							if example.ndim > 2:
										raise ValueError(f"""Expected input shape (channels, length) but got shape {example.shape}"""		)
							if self.feature_size == 1 and example.ndim != 1:
										raise ValueError(f"""Expected mono audio but example has {example.shape[-1]} channels"""		)
							if self.feature_size == 2 and example.shape[-1] != 2:
										raise ValueError(f"""Expected stereo audio but example has {example.shape[-1]} channels"""		)

				UpperCAmelCase_	:					Optional[Any]     	=   None
				UpperCAmelCase_	:					List[str]     	=   BatchFeature({"input_values": raw_audio}		)
				if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
							if truncation:
										UpperCAmelCase_	:					Optional[int]     	=   min(array.shape[0] for array in raw_audio		)
										UpperCAmelCase_	:					Optional[Any]     	=   int(np.floor(max_length / self.chunk_stride		)		)
										UpperCAmelCase_	:					Optional[int]     	=   (nb_step - 1) * self.chunk_stride + self.chunk_length
							elif padding:
										UpperCAmelCase_	:					int     	=   max(array.shape[0] for array in raw_audio		)
										UpperCAmelCase_	:					Dict     	=   int(np.ceil(max_length / self.chunk_stride		)		)
										UpperCAmelCase_	:					Optional[Any]     	=   (nb_step - 1) * self.chunk_stride + self.chunk_length
										UpperCAmelCase_	:					Tuple     	=   "max_length"
							else:
										UpperCAmelCase_	:					Optional[int]     	=   input_values

        # normal padding on batch
				if padded_inputs is None:
							UpperCAmelCase_	:					List[Any]     	=   self.pad(
							    lowerCAmelCase_      ,      max_length=lowerCAmelCase_      ,      truncation=lowerCAmelCase_      ,      padding=lowerCAmelCase_      ,      return_attention_mask=lowerCAmelCase_      ,      )
							if padding:
										UpperCAmelCase_	:					Any     	=   padded_inputs.pop("attention_mask"		)

				UpperCAmelCase_	:					List[Any]     	=   []
				for example in padded_inputs.pop("input_values"		):
							if self.feature_size == 1:
										UpperCAmelCase_	:					List[str]     	=   example[..., None]
							input_values.append(example.T		)

				UpperCAmelCase_	:					Dict     	=   input_values
				if return_tensors is not None:
							UpperCAmelCase_	:					Optional[int]     	=   padded_inputs.convert_to_tensors(lowerCAmelCase_		)

				return padded_inputs











"""simple docstring"""
from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand

def 							snake_case (  A__		):
			return ConvertCommand(
			    args.model_type      ,args.tf_checkpoint      ,args.pytorch_dump_output      ,args.config      ,args.finetuning_task_name		)


lowerCamelCase_							      = '''
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_			(__A	):





	@staticmethod
	def 	_SCREAMING_SNAKE_CASE ( lowerCAmelCase_   :					ArgumentParser		)		->     Optional[Any]:
				UpperCAmelCase_	:					Any     	=   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_   :					Tuple      ,      )		->     int:
				UpperCAmelCase_	:					Dict     	=   logging.get_logger("transformers-cli/converting"		)

				self._logger.info(f"""Loading model {model_type}"""		)
				UpperCAmelCase_	:					Optional[int]     	=   model_type
				UpperCAmelCase_	:					Union[str, Any]     	=   tf_checkpoint
				UpperCAmelCase_	:					Tuple     	=   pytorch_dump_output
				UpperCAmelCase_	:					Tuple     	=   config
				UpperCAmelCase_	:					List[str]     	=   finetuning_task_name





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple		)		->     List[Any]:
				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_	:					Union[str, Any]     	=   self._tf_checkpoint
										UpperCAmelCase_	:					List[str]     	=   ""
							else:
										UpperCAmelCase_	:					Any     	=   self._tf_checkpoint
										UpperCAmelCase_	:					Any     	=   ""
							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]"		)



