




'''simple docstring'''


from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm
def 		snake_case_   (UpperCamelCase	:  np.ndarray			,						UpperCamelCase	:  np.ndarray				):





							'''simple docstring'''


							return math.sqrt(sum(pow(a - b			,						2				) for a, b in zip(UpperCamelCase			,						UpperCamelCase				)				)				)
def 		snake_case_   (UpperCamelCase	:  np.ndarray			,						UpperCamelCase	:  np.ndarray				):





							'''simple docstring'''


							if dataset.ndim != value_array.ndim:
														_a     					=    (
														    '''Wrong input data\'s dimensions... '''
														    f'dataset : {dataset.ndim}, value_array : {value_array.ndim}'
														)
														raise ValueError(UpperCamelCase				)

							try:
														if dataset.shape[1] != value_array.shape[1]:
																					_a     					=    (
																					    '''Wrong input data\'s shape... '''
																					    f'dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}'
																					)
																					raise ValueError(UpperCamelCase				)
							except IndexError:
														if dataset.ndim != value_array.ndim:
																					raise TypeError('''Wrong shape'''				)

							if dataset.dtype != value_array.dtype:
														_a     					=    (
														    '''Input data have different datatype... '''
														    f'dataset : {dataset.dtype}, value_array : {value_array.dtype}'
														)
														raise TypeError(UpperCamelCase				)

							_a     					=    []

							for value in value_array:
														_a     					=    euclidean(UpperCamelCase			,						dataset[0]				)
														_a     					=    dataset[0].tolist()

														for dataset_value in dataset[1:]:
																					_a     					=    euclidean(UpperCamelCase			,						UpperCamelCase				)

																					if dist > temp_dist:
																												_a     					=    temp_dist
																												_a     					=    dataset_value.tolist()

														answer.append([vector, dist]				)

							return answer
def 		snake_case_   (UpperCamelCase	:  np.ndarray			,						UpperCamelCase	:  np.ndarray				):





							'''simple docstring'''


							return np.dot(UpperCamelCase			,						UpperCamelCase				) / (norm(UpperCamelCase				) * norm(UpperCamelCase				))


if __name__ == "__main__":
							import doctest

							doctest.testmod()







'''simple docstring'''


from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand
def 		snake_case_   (UpperCamelCase	:  Namespace				):





							'''simple docstring'''


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


_snake_case				:				List[Any]       							=		'\ntransformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires\nTensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.\n'

class       A   (		_a   ):







							@staticmethod
							def        __lowerCAmelCase      (  lowerCAmelCase_	:  ArgumentParser )      ->							Any:


														"""simple docstring"""


														_a     					=    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	:  int							,      lowerCAmelCase_	:  str							,      lowerCAmelCase_	:  str							,      lowerCAmelCase_	:  str							,      lowerCAmelCase_	:  str							,      lowerCAmelCase_	:  str							,      *lowerCAmelCase_	:  Optional[Any]							,      )      ->							List[str]:


														"""simple docstring"""


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

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







							def        __lowerCAmelCase      (  self	:  Dict )      ->							Dict:


														"""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():
																												_a     					=    self._tf_checkpoint
																												_a     					=    ''''''
																					else:
																												_a     					=    self._tf_checkpoint
																												_a     					=    ''''''
																					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]''' )

