






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

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
a						:			Optional[Any]     	=					"https://storage.googleapis.com/cvdf-datasets/mnist/"






def 							lowerCamelCase__ (       __lowerCamelCase     :	str						):
  __UpperCAmelCase				:      Optional[int]	 =    numpy.dtype(numpy.uintaa						).newbyteorder(""">"""						)
  return numpy.frombuffer(bytestream.read(4						)    ,						dtype=__lowerCamelCase						)[0]






@deprecated(__lowerCamelCase    ,						"""Please use tf.data to implement this functionality."""						)
def 							lowerCamelCase__ (       __lowerCamelCase     :	Optional[int]						):
  print("""Extracting"""    ,						f.name						)
  with gzip.GzipFile(fileobj=__lowerCamelCase						) as bytestream:
    __UpperCAmelCase				:      Union[str, Any]	 =    _readaa(__lowerCamelCase						)
    if magic != 2051:
      raise ValueError(
          """Invalid magic number %d in MNIST image file: %s""" % (magic, f.name)						)
    __UpperCAmelCase				:      Any	 =    _readaa(__lowerCamelCase						)
    __UpperCAmelCase				:      Union[str, Any]	 =    _readaa(__lowerCamelCase						)
    __UpperCAmelCase				:      List[Any]	 =    _readaa(__lowerCamelCase						)
    __UpperCAmelCase				:      Optional[int]	 =    bytestream.read(rows * cols * num_images						)
    __UpperCAmelCase				:      Union[str, Any]	 =    numpy.frombuffer(__lowerCamelCase    ,						dtype=numpy.uinta						)
    __UpperCAmelCase				:      Union[str, Any]	 =    data.reshape(__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						1						)
    return data






@deprecated(__lowerCamelCase    ,						"""Please use tf.one_hot on tensors."""						)
def 							lowerCamelCase__ (       __lowerCamelCase     :	Optional[int]    ,						__lowerCamelCase     :	List[Any]						):
  __UpperCAmelCase				:      Any	 =    labels_dense.shape[0]
  __UpperCAmelCase				:      List[str]	 =    numpy.arange(__lowerCamelCase						) * num_classes
  __UpperCAmelCase				:      Union[str, Any]	 =    numpy.zeros((num_labels, num_classes)						)
  __UpperCAmelCase				:      List[Any]	 =    1
  return labels_one_hot






@deprecated(__lowerCamelCase    ,						"""Please use tf.data to implement this functionality."""						)
def 							lowerCamelCase__ (       __lowerCamelCase     :	Optional[Any]    ,						__lowerCamelCase     :	Optional[Any]=False    ,						__lowerCamelCase     :	Optional[int]=10						):
  print("""Extracting"""    ,						f.name						)
  with gzip.GzipFile(fileobj=__lowerCamelCase						) as bytestream:
    __UpperCAmelCase				:      str	 =    _readaa(__lowerCamelCase						)
    if magic != 2049:
      raise ValueError(
          """Invalid magic number %d in MNIST label file: %s""" % (magic, f.name)						)
    __UpperCAmelCase				:      str	 =    _readaa(__lowerCamelCase						)
    __UpperCAmelCase				:      Optional[int]	 =    bytestream.read(__lowerCamelCase						)
    __UpperCAmelCase				:      List[Any]	 =    numpy.frombuffer(__lowerCamelCase    ,						dtype=numpy.uinta						)
    if one_hot:
      return _dense_to_one_hot(__lowerCamelCase    ,						__lowerCamelCase						)
    return labels
class        a       :



      """simple docstring"""


      @deprecated(
          __lowercase						, """Please use alternatives such as official/mnist/_DataSet.py"""
          """ from tensorflow/models."""						, )
      def __init__( self   :     Optional[int]						, __lowercase   :     Union[str, Any]						, __lowercase   :     List[Any]						, __lowercase   :     int=False						, __lowercase   :     List[Any]=False						, __lowercase   :     List[Any]=dtypes.floataa						, __lowercase   :     int=True						, __lowercase   :     Union[str, Any]=None						, )				-> List[Any]:
        __UpperCAmelCase ,					__UpperCAmelCase				:      str	 =    random_seed.get_seed(__lowercase   )
        # If op level seed is not set, use whatever graph level seed is returned
        numpy.random.seed(seeda if seed is None else seeda   )
        __UpperCAmelCase				:      Union[str, Any]	 =    dtypes.as_dtype(__lowercase   ).base_dtype
        if dtype not in (dtypes.uinta, dtypes.floataa):
          raise TypeError("""Invalid image dtype %r, expected uint8 or float32""" % dtype   )
        if fake_data:
          __UpperCAmelCase				:      int	 =    10000
          __UpperCAmelCase				:      Dict	 =    one_hot
        else:
          assert (
              images.shape[0] == labels.shape[0]
          ), f"""images.shape: {images.shape} labels.shape: {labels.shape}"""
          __UpperCAmelCase				:      Dict	 =    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
            __UpperCAmelCase				:      Any	 =    images.reshape(
                images.shape[0]						, images.shape[1] * images.shape[2]   )
          if dtype == dtypes.floataa:
            # Convert from [0, 255] -> [0.0, 1.0].
            __UpperCAmelCase				:      Any	 =    images.astype(numpy.floataa   )
            __UpperCAmelCase				:      Union[str, Any]	 =    numpy.multiply(__lowercase						, 1.0 / 255.0   )
        __UpperCAmelCase				:      str	 =    images
        __UpperCAmelCase				:      Dict	 =    labels
        __UpperCAmelCase				:      int	 =    0
        __UpperCAmelCase				:      int	 =    0
      @property
      def       UpperCAmelCase ( self   :     Dict   )				-> Union[str, Any]:
        return self._images
      @property
      def       UpperCAmelCase ( self   :     Any   )				-> List[str]:
        return self._labels
      @property
      def       UpperCAmelCase ( self   :     Tuple   )				-> str:
        return self._num_examples
      @property
      def       UpperCAmelCase ( self   :     List[str]   )				-> List[Any]:
        return self._epochs_completed




      def       UpperCAmelCase ( self   :     Any						, __lowercase   :     Tuple						, __lowercase   :     List[str]=False						, __lowercase   :     str=True   )				-> List[str]:
        if fake_data:
          __UpperCAmelCase				:      Any	 =    [1] * 784
          __UpperCAmelCase				:      Any	 =    [1] + [0] * 9 if self.one_hot else 0
          return (
              [fake_image for _ in range(__lowercase   )],
              [fake_label for _ in range(__lowercase   )],
          )
        __UpperCAmelCase				:      Dict	 =    self._index_in_epoch
        # Shuffle for the first epoch
        if self._epochs_completed == 0 and start == 0 and shuffle:
          __UpperCAmelCase				:      str	 =    numpy.arange(self._num_examples   )
          numpy.random.shuffle(__lowercase   )
          __UpperCAmelCase				:      int	 =    self.images[perma]
          __UpperCAmelCase				:      List[str]	 =    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
          __UpperCAmelCase				:      Tuple	 =    self._num_examples - start
          __UpperCAmelCase				:      int	 =    self._images[start : self._num_examples]
          __UpperCAmelCase				:      Optional[int]	 =    self._labels[start : self._num_examples]
          # Shuffle the data
          if shuffle:
            __UpperCAmelCase				:      Union[str, Any]	 =    numpy.arange(self._num_examples   )
            numpy.random.shuffle(__lowercase   )
            __UpperCAmelCase				:      str	 =    self.images[perm]
            __UpperCAmelCase				:      List[str]	 =    self.labels[perm]
          # Start next epoch
          __UpperCAmelCase				:      List[Any]	 =    0
          __UpperCAmelCase				:      Optional[Any]	 =    batch_size - rest_num_examples
          __UpperCAmelCase				:      Any	 =    self._index_in_epoch
          __UpperCAmelCase				:      List[str]	 =    self._images[start:end]
          __UpperCAmelCase				:      Optional[int]	 =    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
          __UpperCAmelCase				:      Optional[int]	 =    self._index_in_epoch
          return self._images[start:end], self._labels[start:end]






@deprecated(__lowerCamelCase    ,						"""Please write your own downloading logic."""						)
def 							lowerCamelCase__ (       __lowerCamelCase     :	Tuple    ,						__lowerCamelCase     :	List[str]    ,						__lowerCamelCase     :	int						):
  if not gfile.Exists(__lowerCamelCase						):
    gfile.MakeDirs(__lowerCamelCase						)
  __UpperCAmelCase				:      List[Any]	 =    os.path.join(__lowerCamelCase    ,						__lowerCamelCase						)
  if not gfile.Exists(__lowerCamelCase						):
    urllib.request.urlretrieve(__lowerCamelCase    ,						__lowerCamelCase						)  # noqa: S310
    with gfile.GFile(__lowerCamelCase						) as f:
      __UpperCAmelCase				:      str	 =    f.size()
    print("""Successfully downloaded"""    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						"""bytes."""						)
  return filepath







@deprecated(
    __lowerCamelCase    ,						"""Please use alternatives such as:""" """ tensorflow_datasets.load('mnist')"""						)
def 							lowerCamelCase__ (       __lowerCamelCase     :	Tuple    ,						__lowerCamelCase     :	int=False    ,						__lowerCamelCase     :	Any=False    ,						__lowerCamelCase     :	Dict=dtypes.floataa    ,						__lowerCamelCase     :	Optional[int]=True    ,						__lowerCamelCase     :	Union[str, Any]=5000    ,						__lowerCamelCase     :	Dict=None    ,						__lowerCamelCase     :	Union[str, Any]=DEFAULT_SOURCE_URL    ,						):
  if fake_data:

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

    __UpperCAmelCase				:      Union[str, Any]	 =    fake()
    __UpperCAmelCase				:      Optional[Any]	 =    fake()
    __UpperCAmelCase				:      Optional[int]	 =    fake()
    return _Datasets(train=__lowerCamelCase    ,						validation=__lowerCamelCase    ,						test=__lowerCamelCase						)

  if not source_url:  # empty string check
    __UpperCAmelCase				:      Tuple	 =    DEFAULT_SOURCE_URL

  __UpperCAmelCase				:      Tuple	 =    """train-images-idx3-ubyte.gz"""
  __UpperCAmelCase				:      Union[str, Any]	 =    """train-labels-idx1-ubyte.gz"""
  __UpperCAmelCase				:      Optional[int]	 =    """t10k-images-idx3-ubyte.gz"""
  __UpperCAmelCase				:      List[str]	 =    """t10k-labels-idx1-ubyte.gz"""

  __UpperCAmelCase				:      Optional[int]	 =    _maybe_download(
      __lowerCamelCase    ,						__lowerCamelCase    ,						source_url + train_images_file						)
  with gfile.Open(__lowerCamelCase    ,						"""rb"""						) as f:
    __UpperCAmelCase				:      Dict	 =    _extract_images(__lowerCamelCase						)

  __UpperCAmelCase				:      Tuple	 =    _maybe_download(
      __lowerCamelCase    ,						__lowerCamelCase    ,						source_url + train_labels_file						)
  with gfile.Open(__lowerCamelCase    ,						"""rb"""						) as f:
    __UpperCAmelCase				:      Optional[Any]	 =    _extract_labels(__lowerCamelCase    ,						one_hot=__lowerCamelCase						)

  __UpperCAmelCase				:      Dict	 =    _maybe_download(
      __lowerCamelCase    ,						__lowerCamelCase    ,						source_url + test_images_file						)
  with gfile.Open(__lowerCamelCase    ,						"""rb"""						) as f:
    __UpperCAmelCase				:      Optional[int]	 =    _extract_images(__lowerCamelCase						)

  __UpperCAmelCase				:      Optional[int]	 =    _maybe_download(
      __lowerCamelCase    ,						__lowerCamelCase    ,						source_url + test_labels_file						)
  with gfile.Open(__lowerCamelCase    ,						"""rb"""						) as f:
    __UpperCAmelCase				:      List[str]	 =    _extract_labels(__lowerCamelCase    ,						one_hot=__lowerCamelCase						)

  if not 0 <= validation_size <= len(__lowerCamelCase						):
    __UpperCAmelCase				:      List[Any]	 =    (
        """Validation size should be between 0 and """
        f"""{len(__lowerCamelCase						)}. Received: {validation_size}."""
    )
    raise ValueError(__lowerCamelCase						)

  __UpperCAmelCase				:      Dict	 =    train_images[:validation_size]
  __UpperCAmelCase				:      Dict	 =    train_labels[:validation_size]
  __UpperCAmelCase				:      Dict	 =    train_images[validation_size:]
  __UpperCAmelCase				:      Optional[int]	 =    train_labels[validation_size:]

  __UpperCAmelCase				:      Tuple	 =    {"""dtype""": dtype, """reshape""": reshape, """seed""": seed}

  __UpperCAmelCase				:      Tuple	 =    _DataSet(__lowerCamelCase    ,						__lowerCamelCase    ,						**__lowerCamelCase						)
  __UpperCAmelCase				:      List[str]	 =    _DataSet(__lowerCamelCase    ,						__lowerCamelCase    ,						**__lowerCamelCase						)
  __UpperCAmelCase				:      Optional[int]	 =    _DataSet(__lowerCamelCase    ,						__lowerCamelCase    ,						**__lowerCamelCase						)

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









"""simple docstring"""





from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


_UpperCamelCase					      =   logging.get_logger(__name__)

_UpperCamelCase					      =   {
    'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json',
    # See all Marian models at https://huggingface.co/models?filter=marian
}
class 		SCREAMING_SNAKE_CASE_				(     snake_case__ ):
				"""simple docstring"""
				__snake_case :							Tuple									=		"""marian"""
				__snake_case :							Any									=		["""past_key_values"""]
				__snake_case :							Optional[Any]									=		{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}




				def __init__(  self	:List[Any]    ,     __lowercase	:Any=5_8101    ,     __lowercase	:Tuple=None    ,     __lowercase	:Union[str, Any]=1024    ,     __lowercase	:Dict=12    ,     __lowercase	:int=4096    ,     __lowercase	:int=16    ,     __lowercase	:List[Any]=12    ,     __lowercase	:Dict=4096    ,     __lowercase	:Dict=16    ,     __lowercase	:Tuple=0.0    ,     __lowercase	:Tuple=0.0    ,     __lowercase	:List[Any]=True    ,     __lowercase	:int=True    ,     __lowercase	:Tuple="gelu"    ,     __lowercase	:str=1024    ,     __lowercase	:Optional[int]=0.1    ,     __lowercase	:List[str]=0.0    ,     __lowercase	:Union[str, Any]=0.0    ,     __lowercase	:Dict=0.02    ,     __lowercase	:Tuple=5_8100    ,     __lowercase	:Optional[Any]=False    ,     __lowercase	:int=5_8100    ,     __lowercase	:Any=0    ,     __lowercase	:str=0    ,     __lowercase	:str=True    ,     **__lowercase	:Any    ,     ):
											__lowerCamelCase   :   List[Any]       =vocab_size
											__lowerCamelCase   :   Optional[int]       =decoder_vocab_size or vocab_size
											__lowerCamelCase   :   Tuple       =max_position_embeddings
											__lowerCamelCase   :   List[Any]       =d_model
											__lowerCamelCase   :   Any       =encoder_ffn_dim
											__lowerCamelCase   :   str       =encoder_layers
											__lowerCamelCase   :   List[str]       =encoder_attention_heads
											__lowerCamelCase   :   str       =decoder_ffn_dim
											__lowerCamelCase   :   Tuple       =decoder_layers
											__lowerCamelCase   :   Any       =decoder_attention_heads
											__lowerCamelCase   :   List[Any]       =dropout
											__lowerCamelCase   :   Any       =attention_dropout
											__lowerCamelCase   :   Union[str, Any]       =activation_dropout
											__lowerCamelCase   :   Optional[int]       =activation_function
											__lowerCamelCase   :   Dict       =init_std
											__lowerCamelCase   :   List[Any]       =encoder_layerdrop
											__lowerCamelCase   :   Optional[Any]       =decoder_layerdrop
											__lowerCamelCase   :   Any       =use_cache
											__lowerCamelCase   :   Any       =encoder_layers
											__lowerCamelCase   :   Optional[int]       =scale_embedding  # scale factor will be sqrt(d_model) if True
											__lowerCamelCase   :   int       =share_encoder_decoder_embeddings
											super().__init__(
											    pad_token_id=__lowercase    ,     eos_token_id=__lowercase    ,     is_encoder_decoder=__lowercase    ,     decoder_start_token_id=__lowercase    ,     forced_eos_token_id=__lowercase    ,     **__lowercase    ,     )

class 		SCREAMING_SNAKE_CASE_				(     snake_case__ ):
				"""simple docstring"""




				@property
				# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
				def        __lowercase	(  self	:int      ):
											if self.task in ["default", "seq2seq-lm"]:
																		__lowerCamelCase   :   Tuple       =OrderedDict(
																		    [
																		        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
																		        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
																		    ]      )

																		if self.use_past:
																									__lowerCamelCase   :   Optional[Any]       ={0: '''batch'''}
																									__lowerCamelCase   :   Any       ={0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
																		else:
																									__lowerCamelCase   :   Optional[Any]       ={0: '''batch''', 1: '''decoder_sequence'''}
																									__lowerCamelCase   :   List[Any]       ={0: '''batch''', 1: '''decoder_sequence'''}

																		if self.use_past:
																									self.fill_with_past_key_values_(__lowercase    ,     direction='''inputs'''      )
											elif self.task == "causal-lm":
																		# TODO: figure this case out.
																		__lowerCamelCase   :   Any       =OrderedDict(
																		    [
																		        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
																		        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
																		    ]      )
																		if self.use_past:
																									__lowerCamelCase     ,    __lowerCamelCase   :   str       =self.num_layers
																									for i in range(__lowercase      ):
																																__lowerCamelCase   :   Optional[int]       ={0: '''batch''', 2: '''past_sequence + sequence'''}
																																__lowerCamelCase   :   List[str]       ={0: '''batch''', 2: '''past_sequence + sequence'''}
											else:
																		__lowerCamelCase   :   List[str]       =OrderedDict(
																		    [
																		        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
																		        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
																		        ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
																		        ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
																		    ]      )

											return common_inputs




				@property
				# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
				def        __lowercase	(  self	:Optional[int]      ):
											if self.task in ["default", "seq2seq-lm"]:
																		__lowerCamelCase   :   Tuple       =super().outputs
											else:
																		__lowerCamelCase   :   List[str]       =super(__lowercase    ,     self      ).outputs
																		if self.use_past:
																									__lowerCamelCase     ,    __lowerCamelCase   :   int       =self.num_layers
																									for i in range(__lowercase      ):
																																__lowerCamelCase   :   Any       ={0: '''batch''', 2: '''past_sequence + sequence'''}
																																__lowerCamelCase   :   Optional[int]       ={0: '''batch''', 2: '''past_sequence + sequence'''}
											return common_outputs




				def        __lowercase	(  self	:str    ,     __lowercase	:PreTrainedTokenizer    ,     __lowercase	:int = -1    ,     __lowercase	:int = -1    ,     __lowercase	:bool = False    ,     __lowercase	:Optional[TensorType] = None    ,     ):
											__lowerCamelCase   :   List[str]       =self._generate_dummy_inputs_for_encoder_and_decoder(
											    __lowercase    ,     __lowercase    ,     __lowercase    ,     __lowercase    ,     __lowercase      )

											# Generate decoder inputs
											__lowerCamelCase   :   Optional[Any]       =seq_length if not self.use_past else 1
											__lowerCamelCase   :   List[Any]       =self._generate_dummy_inputs_for_encoder_and_decoder(
											    __lowercase    ,     __lowercase    ,     __lowercase    ,     __lowercase    ,     __lowercase      )
											__lowerCamelCase   :   Dict       ={f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
											__lowerCamelCase   :   str       =dict(**__lowercase    ,     **__lowercase      )

											if self.use_past:
																		if not is_torch_available():
																									raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''      )
																		else:
																									import torch
																		__lowerCamelCase     ,    __lowerCamelCase   :   int       =common_inputs['''input_ids'''].shape
																		__lowerCamelCase   :   Optional[Any]       =common_inputs['''decoder_input_ids'''].shape[1]
																		__lowerCamelCase     ,    __lowerCamelCase   :   Optional[int]       =self.num_attention_heads
																		__lowerCamelCase   :   Any       =(
																		    batch,
																		    num_encoder_attention_heads,
																		    encoder_seq_length,
																		    self._config.hidden_size // num_encoder_attention_heads,
																		)
																		__lowerCamelCase   :   Dict       =decoder_seq_length + 3
																		__lowerCamelCase   :   Tuple       =(
																		    batch,
																		    num_decoder_attention_heads,
																		    decoder_past_length,
																		    self._config.hidden_size // num_decoder_attention_heads,
																		)

																		__lowerCamelCase   :   Optional[int]       =torch.cat(
																		    [common_inputs['''decoder_attention_mask'''], torch.ones(__lowercase    ,     __lowercase      )]    ,     dim=1      )

																		__lowerCamelCase   :   Any       =[]
																		# If the number of encoder and decoder layers are present in the model configuration, both are considered
																		__lowerCamelCase     ,    __lowerCamelCase   :   str       =self.num_layers
																		__lowerCamelCase   :   List[Any]       =min(__lowercase    ,     __lowercase      )
																		__lowerCamelCase   :   int       =max(__lowercase    ,     __lowercase      ) - min_num_layers
																		__lowerCamelCase   :   Any       ='''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

																		for _ in range(__lowercase      ):
																									common_inputs["past_key_values"].append(
																									    (
																									        torch.zeros(__lowercase      ),
																									        torch.zeros(__lowercase      ),
																									        torch.zeros(__lowercase      ),
																									        torch.zeros(__lowercase      ),
																									    )      )
																		# TODO: test this.
																		__lowerCamelCase   :   Dict       =encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
																		for _ in range(__lowercase    ,     __lowercase      ):
																									common_inputs["past_key_values"].append((torch.zeros(__lowercase      ), torch.zeros(__lowercase      ))      )
											return common_inputs




				def        __lowercase	(  self	:Dict    ,     __lowercase	:PreTrainedTokenizer    ,     __lowercase	:int = -1    ,     __lowercase	:int = -1    ,     __lowercase	:bool = False    ,     __lowercase	:Optional[TensorType] = None    ,     ):
											__lowerCamelCase   :   List[str]       =self._generate_dummy_inputs_for_encoder_and_decoder(
											    __lowercase    ,     __lowercase    ,     __lowercase    ,     __lowercase    ,     __lowercase      )

											if self.use_past:
																		if not is_torch_available():
																									raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''      )
																		else:
																									import torch
																		__lowerCamelCase     ,    __lowerCamelCase   :   int       =common_inputs['''input_ids'''].shape
																		# Not using the same length for past_key_values
																		__lowerCamelCase   :   str       =seqlen + 2
																		__lowerCamelCase     ,    __lowerCamelCase   :   List[str]       =self.num_layers
																		__lowerCamelCase     ,    __lowerCamelCase   :   Optional[Any]       =self.num_attention_heads
																		__lowerCamelCase   :   List[str]       =(
																		    batch,
																		    num_encoder_attention_heads,
																		    past_key_values_length,
																		    self._config.hidden_size // num_encoder_attention_heads,
																		)

																		__lowerCamelCase   :   Any       =common_inputs['''attention_mask'''].dtype
																		__lowerCamelCase   :   Optional[int]       =torch.cat(
																		    [common_inputs['''attention_mask'''], torch.ones(__lowercase    ,     __lowercase    ,     dtype=__lowercase      )]    ,     dim=1      )
																		__lowerCamelCase   :   List[str]       =[
																		    (torch.zeros(__lowercase      ), torch.zeros(__lowercase      )) for _ in range(__lowercase      )
																		]
											return common_inputs




				def        __lowercase	(  self	:int    ,     __lowercase	:PreTrainedTokenizer    ,     __lowercase	:int = -1    ,     __lowercase	:int = -1    ,     __lowercase	:bool = False    ,     __lowercase	:Optional[TensorType] = None    ,     ):
											# Copied from OnnxConfig.generate_dummy_inputs
											# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
											# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
											__lowerCamelCase   :   Union[str, Any]       =compute_effective_axis_dimension(
											    __lowercase    ,     fixed_dimension=OnnxConfig.default_fixed_batch    ,     num_token_to_add=0      )

											# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
											__lowerCamelCase   :   Optional[int]       =tokenizer.num_special_tokens_to_add(__lowercase      )
											__lowerCamelCase   :   Optional[int]       =compute_effective_axis_dimension(
											    __lowercase    ,     fixed_dimension=OnnxConfig.default_fixed_sequence    ,     num_token_to_add=__lowercase      )

											# Generate dummy inputs according to compute batch and sequence
											__lowerCamelCase   :   Any       =[''' '''.join([tokenizer.unk_token]      ) * seq_length] * batch_size
											__lowerCamelCase   :   Union[str, Any]       =dict(tokenizer(__lowercase    ,     return_tensors=__lowercase      )      )
											return common_inputs




				def        __lowercase	(  self	:Tuple    ,     __lowercase	:PreTrainedTokenizer    ,     __lowercase	:int = -1    ,     __lowercase	:int = -1    ,     __lowercase	:bool = False    ,     __lowercase	:Optional[TensorType] = None    ,     ):
											if self.task in ["default", "seq2seq-lm"]:
																		__lowerCamelCase   :   int       =self._generate_dummy_inputs_for_default_and_seqaseq_lm(
																		    __lowercase    ,     batch_size=__lowercase    ,     seq_length=__lowercase    ,     is_pair=__lowercase    ,     framework=__lowercase      )

											else:
																		__lowerCamelCase   :   List[Any]       =self._generate_dummy_inputs_for_causal_lm(
																		    __lowercase    ,     batch_size=__lowercase    ,     seq_length=__lowercase    ,     is_pair=__lowercase    ,     framework=__lowercase      )

											return common_inputs




				def        __lowercase	(  self	:Optional[int]    ,     __lowercase	:Tuple    ,     __lowercase	:Tuple    ,     __lowercase	:Dict    ,     __lowercase	:List[str]      ):
											if self.task in ["default", "seq2seq-lm"]:
																		__lowerCamelCase   :   str       =super()._flatten_past_key_values_(__lowercase    ,     __lowercase    ,     __lowercase    ,     __lowercase      )
											else:
																		__lowerCamelCase   :   Optional[Any]       =super(__lowercase    ,     self      )._flatten_past_key_values_(
																		    __lowercase    ,     __lowercase    ,     __lowercase    ,     __lowercase      )




				@property
				def        __lowercase	(  self	:List[str]      ):
											return 1e-4




