
"""simple docstring"""
import collections
import gzip
import os
import urllib

import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated

_a      :			List[Any]					=							collections.namedtuple("""_Datasets""", ["""train""", """validation""", """test"""])

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
_a      :			Union[str, Any]					=							"""https://storage.googleapis.com/cvdf-datasets/mnist/"""
def 				a__   (   a   :				Dict		):

  """simple docstring"""







  _snake_case     :						Any						=   numpy.dtype(numpy.uintaa		).newbyteorder(">"		)
  return numpy.frombuffer(bytestream.read(4		)       ,     dtype=a		)[0]
@deprecated(a       ,     "Please use tf.data to implement this functionality."		)
def 				a__   (   a   :				Any		):

  """simple docstring"""







  print("Extracting"       ,     f.name		)
  with gzip.GzipFile(fileobj=a		) as bytestream:
    _snake_case     :						Any						=   _readaa(a		)
    if magic != 2_051:
      raise ValueError(
          "Invalid magic number %d in MNIST image file: %s" % (magic, f.name)		)
    _snake_case     :						Optional[int]						=   _readaa(a		)
    _snake_case     :						Union[str, Any]						=   _readaa(a		)
    _snake_case     :						str						=   _readaa(a		)
    _snake_case     :						List[str]						=   bytestream.read(rows * cols * num_images		)
    _snake_case     :						List[str]						=   numpy.frombuffer(a       ,     dtype=numpy.uinta		)
    _snake_case     :						Any						=   data.reshape(a       ,     a       ,     a       ,     1		)
    return data
@deprecated(a       ,     "Please use tf.one_hot on tensors."		)
def 				a__   (   a   :				Dict       ,     a   :				Optional[Any]		):

  """simple docstring"""







  _snake_case     :						List[Any]						=   labels_dense.shape[0]
  _snake_case     :						Optional[Any]						=   numpy.arange(a		) * num_classes
  _snake_case     :						int						=   numpy.zeros((num_labels, num_classes)		)
  _snake_case     :						List[str]						=   1
  return labels_one_hot
@deprecated(a       ,     "Please use tf.data to implement this functionality."		)
def 				a__   (   a   :				Optional[int]       ,     a   :				Any=False       ,     a   :				List[str]=10		):

  """simple docstring"""







  print("Extracting"       ,     f.name		)
  with gzip.GzipFile(fileobj=a		) as bytestream:
    _snake_case     :						Tuple						=   _readaa(a		)
    if magic != 2_049:
      raise ValueError(
          "Invalid magic number %d in MNIST label file: %s" % (magic, f.name)		)
    _snake_case     :						int						=   _readaa(a		)
    _snake_case     :						Tuple						=   bytestream.read(a		)
    _snake_case     :						Dict						=   numpy.frombuffer(a       ,     dtype=numpy.uinta		)
    if one_hot:
      return _dense_to_one_hot(a       ,     a		)
    return labels






class      _UpperCAmelCase				:
    @deprecated(
        snake_case_						,    "Please use alternatives such as official/mnist/_DataSet.py"
        " from tensorflow/models."						,    )
    def __init__(    self						,    snake_case_						,    snake_case_						,    snake_case_=False						,    snake_case_=False						,    snake_case_=dtypes.floataa						,    snake_case_=True						,    snake_case_=None						,    ):
      _snake_case     :						Tuple						=   random_seed.get_seed(snake_case_     )
      # If op level seed is not set, use whatever graph level seed is returned
      numpy.random.seed(seeda if seed is None else seeda     )
      _snake_case     :						Optional[int]						=   dtypes.as_dtype(snake_case_     ).base_dtype
      if dtype not in (dtypes.uinta, dtypes.floataa):
        raise TypeError("Invalid image dtype %r, expected uint8 or float32" % dtype     )
      if fake_data:
        _snake_case     :						List[Any]						=   1_00_00
        _snake_case     :						Optional[Any]						=   one_hot
      else:
        assert (
            images.shape[0] == labels.shape[0]
        ), F'images.shape: {images.shape} labels.shape: {labels.shape}'
        _snake_case     :						List[Any]						=   images.shape[0]

        # Convert shape from [num examples, rows, columns, depth]
        # to [num examples, rows*columns] (assuming depth == 1)
        if reshape:
          assert images.shape[3] == 1
          _snake_case     :						Any						=   images.reshape(
              images.shape[0]						,    images.shape[1] * images.shape[2]     )
        if dtype == dtypes.floataa:
          # Convert from [0, 255] -> [0.0, 1.0].
          _snake_case     :						Dict						=   images.astype(numpy.floataa     )
          _snake_case     :						Optional[Any]						=   numpy.multiply(snake_case_						,    1.0 / 255.0     )
      _snake_case     :						Optional[int]						=   images
      _snake_case     :						List[str]						=   labels
      _snake_case     :						Any						=   0
      _snake_case     :						Tuple						=   0
    @property
    def 					lowerCamelCase__	(    self     ):
      return self._images
    @property
    def 					lowerCamelCase__	(    self     ):
      return self._labels
    @property
    def 					lowerCamelCase__	(    self     ):
      return self._num_examples
    @property
    def 					lowerCamelCase__	(    self     ):
      return self._epochs_completed


    def 					lowerCamelCase__	(    self						,    snake_case_						,    snake_case_=False						,    snake_case_=True     ):
      if fake_data:
        _snake_case     :						Union[str, Any]						=   [1] * 7_84
        _snake_case     :						Dict						=   [1] + [0] * 9 if self.one_hot else 0
        return (
            [fake_image for _ in range(snake_case_     )],
            [fake_label for _ in range(snake_case_     )],
        )
      _snake_case     :						Optional[Any]						=   self._index_in_epoch
      # Shuffle for the first epoch
      if self._epochs_completed == 0 and start == 0 and shuffle:
        _snake_case     :						List[Any]						=   numpy.arange(self._num_examples     )
        numpy.random.shuffle(snake_case_     )
        _snake_case     :						Dict						=   self.images[perma]
        _snake_case     :						Union[str, Any]						=   self.labels[perma]
      # Go to the next epoch
      if start + batch_size > self._num_examples:
        # Finished epoch
        self._epochs_completed += 1
        # Get the rest examples in this epoch
        _snake_case     :						str						=   self._num_examples - start
        _snake_case     :						Optional[Any]						=   self._images[start : self._num_examples]
        _snake_case     :						Union[str, Any]						=   self._labels[start : self._num_examples]
        # Shuffle the data
        if shuffle:
          _snake_case     :						Optional[Any]						=   numpy.arange(self._num_examples     )
          numpy.random.shuffle(snake_case_     )
          _snake_case     :						Union[str, Any]						=   self.images[perm]
          _snake_case     :						Dict						=   self.labels[perm]
        # Start next epoch
        _snake_case     :						Union[str, Any]						=   0
        _snake_case     :						int						=   batch_size - rest_num_examples
        _snake_case     :						Dict						=   self._index_in_epoch
        _snake_case     :						List[Any]						=   self._images[start:end]
        _snake_case     :						Dict						=   self._labels[start:end]
        return (
            numpy.concatenate((images_rest_part, images_new_part)						,    axis=0     ),
            numpy.concatenate((labels_rest_part, labels_new_part)						,    axis=0     ),
        )
      else:
        self._index_in_epoch += batch_size
        _snake_case     :						Optional[int]						=   self._index_in_epoch
        return self._images[start:end], self._labels[start:end]


@deprecated(a       ,     "Please write your own downloading logic."		)
def 				a__   (   a   :				List[Any]       ,     a   :				int       ,     a   :				Any		):

  """simple docstring"""







  if not gfile.Exists(a		):
    gfile.MakeDirs(a		)
  _snake_case     :						Optional[int]						=   os.path.join(a       ,     a		)
  if not gfile.Exists(a		):
    urllib.request.urlretrieve(a       ,     a		)  # noqa: S310
    with gfile.GFile(a		) as f:
      _snake_case     :						Any						=   f.size()
    print("Successfully downloaded"       ,     a       ,     a       ,     "bytes."		)
  return filepath



@deprecated(
    a       ,     "Please use alternatives such as:" " tensorflow_datasets.load('mnist')"		)
def 				a__   (   a   :				Optional[Any]       ,     a   :				Optional[Any]=False       ,     a   :				Tuple=False       ,     a   :				str=dtypes.floataa       ,     a   :				Union[str, Any]=True       ,     a   :				Union[str, Any]=5_000       ,     a   :				Union[str, Any]=None       ,     a   :				Optional[int]=DEFAULT_SOURCE_URL       ,     ):

  """simple docstring"""







  if fake_data:

    def fake():
      return _DataSet(
          []       ,     []       ,     fake_data=a       ,     one_hot=a       ,     dtype=a       ,     seed=a		)

    _snake_case     :						int						=   fake()
    _snake_case     :						Any						=   fake()
    _snake_case     :						Optional[int]						=   fake()
    return _Datasets(train=a       ,     validation=a       ,     test=a		)

  if not source_url:  # empty string check
    _snake_case     :						Optional[int]						=   DEFAULT_SOURCE_URL

  _snake_case     :						Tuple						=   "train-images-idx3-ubyte.gz"
  _snake_case     :						int						=   "train-labels-idx1-ubyte.gz"
  _snake_case     :						str						=   "t10k-images-idx3-ubyte.gz"
  _snake_case     :						int						=   "t10k-labels-idx1-ubyte.gz"

  _snake_case     :						Optional[int]						=   _maybe_download(
      a       ,     a       ,     source_url + train_images_file		)
  with gfile.Open(a       ,     "rb"		) as f:
    _snake_case     :						List[str]						=   _extract_images(a		)

  _snake_case     :						Union[str, Any]						=   _maybe_download(
      a       ,     a       ,     source_url + train_labels_file		)
  with gfile.Open(a       ,     "rb"		) as f:
    _snake_case     :						str						=   _extract_labels(a       ,     one_hot=a		)

  _snake_case     :						List[Any]						=   _maybe_download(
      a       ,     a       ,     source_url + test_images_file		)
  with gfile.Open(a       ,     "rb"		) as f:
    _snake_case     :						Optional[Any]						=   _extract_images(a		)

  _snake_case     :						str						=   _maybe_download(
      a       ,     a       ,     source_url + test_labels_file		)
  with gfile.Open(a       ,     "rb"		) as f:
    _snake_case     :						Union[str, Any]						=   _extract_labels(a       ,     one_hot=a		)

  if not 0 <= validation_size <= len(a		):
    _snake_case     :						Optional[int]						=   (
        "Validation size should be between 0 and "
        f'{len(a		)}. Received: {validation_size}.'
    )
    raise ValueError(a		)

  _snake_case     :						Any						=   train_images[:validation_size]
  _snake_case     :						List[Any]						=   train_labels[:validation_size]
  _snake_case     :						Dict						=   train_images[validation_size:]
  _snake_case     :						int						=   train_labels[validation_size:]

  _snake_case     :						List[Any]						=   {"dtype": dtype, "reshape": reshape, "seed": seed}

  _snake_case     :						Tuple						=   _DataSet(a       ,     a       ,     **a		)
  _snake_case     :						Tuple						=   _DataSet(a       ,     a       ,     **a		)
  _snake_case     :						Tuple						=   _DataSet(a       ,     a       ,     **a		)

  return _Datasets(train=a       ,     validation=a       ,     test=a		)



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

from ..utils import logging
from . import BaseTransformersCLICommand
def 				a__   (   a   :				Namespace		):

  """simple docstring"""







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


_a      :			int					=							"""
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				(					_snake_case):
    @staticmethod
    def 					lowerCamelCase__	(    snake_case_     ):
      _snake_case     :						Dict						=   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=snake_case_						,    required=snake_case_						,    help="Model's type."     )
      train_parser.add_argument(
          "--tf_checkpoint"						,    type=snake_case_						,    required=snake_case_						,    help="TensorFlow checkpoint path or folder."     )
      train_parser.add_argument(
          "--pytorch_dump_output"						,    type=snake_case_						,    required=snake_case_						,    help="Path to the PyTorch saved model output."     )
      train_parser.add_argument("--config"						,    type=snake_case_						,    default=""						,    help="Configuration file path or folder."     )
      train_parser.add_argument(
          "--finetuning_task_name"						,    type=snake_case_						,    default=snake_case_						,    help="Optional fine-tuning task name if the TF model was a finetuned model."						,    )
      train_parser.set_defaults(func=snake_case_     )
    def __init__(    self						,    snake_case_						,    snake_case_						,    snake_case_						,    snake_case_						,    snake_case_						,    *snake_case_						,    ):
      _snake_case     :						str						=   logging.get_logger("transformers-cli/converting"     )

      self._logger.info(F'Loading model {model_type}'     )
      _snake_case     :						Optional[int]						=   model_type
      _snake_case     :						Any						=   tf_checkpoint
      _snake_case     :						Optional[int]						=   pytorch_dump_output
      _snake_case     :						Tuple						=   config
      _snake_case     :						Tuple						=   finetuning_task_name


    def 					lowerCamelCase__	(    self     ):
      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(snake_case_     )

        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(snake_case_     )

        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(snake_case_     )

        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(snake_case_     )

        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(snake_case_     )

        if "ckpt" in self._tf_checkpoint.lower():
          _snake_case     :						int						=   self._tf_checkpoint
          _snake_case     :						Optional[Any]						=   ""
        else:
          _snake_case     :						Optional[int]						=   self._tf_checkpoint
          _snake_case     :						List[str]						=   ""
        convert_transfo_xl_checkpoint_to_pytorch(
            snake_case_						,    self._config						,    self._pytorch_dump_output						,    snake_case_     )
      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(snake_case_     )

        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(snake_case_     )

        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]"     )

