





"""simple docstring"""
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import patch

import pyarrow as pa
import pytest
import requests
from packaging import version

from datasets import config


if config.PY_VERSION < version.parse("3.8"):
  import importlib_metadata
else:
  import importlib.metadata as importlib_metadata



def 					__lowerCamelCase       (							a_					:		Union[str, Any]			,       a_					:		int=False							)      ->				Optional[int]:
      try:
            __SCREAMING_SNAKE_CASE       :List[Any]		    =  os.environ[key]
      except KeyError:
            # KEY isn't set, default to `default`.
            __SCREAMING_SNAKE_CASE       :Dict		    =  default
      else:
            # KEY is set, convert it to True or False.
            try:
                  __SCREAMING_SNAKE_CASE       :Dict		    =  strtobool(a_							)
            except ValueError:
                  # More values are supported, but let's keep the message simple.
                  raise ValueError(f'''If set, {key} must be yes or no.'''							)
      return _value


lowerCamelCase_       =   parse_flag_from_env("RUN_SLOW", default=False)
lowerCamelCase_       =   parse_flag_from_env("RUN_REMOTE", default=False)
lowerCamelCase_       =   parse_flag_from_env("RUN_LOCAL", default=True)
lowerCamelCase_       =   parse_flag_from_env("RUN_PACKAGED", default=True)

# Compression
lowerCamelCase_       =   pytest.mark.skipif(not config.LZ4_AVAILABLE, reason="test requires lz4")
lowerCamelCase_       =   pytest.mark.skipif(not config.PY7ZR_AVAILABLE, reason="test requires py7zr")
lowerCamelCase_       =   pytest.mark.skipif(not config.ZSTANDARD_AVAILABLE, reason="test requires zstandard")

# Audio
lowerCamelCase_       =   pytest.mark.skipif(
    # On Windows and OS X, soundfile installs sndfile
    find_spec("soundfile") is None or version.parse(importlib_metadata.version("soundfile")) < version.parse("0.12.0"),
    reason="test requires sndfile>=0.12.1: 'pip install \"soundfile>=0.12.1\"'; ",
)

# Beam
lowerCamelCase_       =   pytest.mark.skipif(
    not config.BEAM_AVAILABLE or config.DILL_VERSION >= version.parse("0.3.2"),
    reason="test requires apache-beam and a compatible dill version",
)

# Dill-cloudpickle compatibility
lowerCamelCase_       =   pytest.mark.skipif(
    config.DILL_VERSION <= version.parse("0.3.2"),
    reason="test requires dill>0.3.2 for cloudpickle compatibility",
)

# Windows
lowerCamelCase_       =   pytest.mark.skipif(
    sys.platform == "win32",
    reason="test should not be run on Windows",
)



def 					__lowerCamelCase       (							a_					:		List[Any]							)      ->				int:
      try:
            import faiss  # noqa
      except ImportError:
            __SCREAMING_SNAKE_CASE       :List[Any]		    =  unittest.skip('''test requires faiss'''							)(a_							)
      return test_case



def 					__lowerCamelCase       (							a_					:		int							)      ->				Optional[Any]:
      try:
            import regex  # noqa
      except ImportError:
            __SCREAMING_SNAKE_CASE       :Tuple		    =  unittest.skip('''test requires regex'''							)(a_							)
      return test_case



def 					__lowerCamelCase       (							a_					:		Optional[int]							)      ->				Any:
      try:
            import elasticsearch  # noqa
      except ImportError:
            __SCREAMING_SNAKE_CASE       :Dict		    =  unittest.skip('''test requires elasticsearch'''							)(a_							)
      return test_case



def 					__lowerCamelCase       (							a_					:		Dict							)      ->				List[Any]:
      try:
            import sqlalchemy  # noqa
      except ImportError:
            __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  unittest.skip('''test requires sqlalchemy'''							)(a_							)
      return test_case



def 					__lowerCamelCase       (							a_					:		List[Any]							)      ->				int:
      if not config.TORCH_AVAILABLE:
            __SCREAMING_SNAKE_CASE       :int		    =  unittest.skip('''test requires PyTorch'''							)(a_							)
      return test_case



def 					__lowerCamelCase       (							a_					:		Any							)      ->				int:
      if not config.TF_AVAILABLE:
            __SCREAMING_SNAKE_CASE       :Optional[int]		    =  unittest.skip('''test requires TensorFlow'''							)(a_							)
      return test_case



def 					__lowerCamelCase       (							a_					:		int							)      ->				str:
      if not config.JAX_AVAILABLE:
            __SCREAMING_SNAKE_CASE       :Optional[int]		    =  unittest.skip('''test requires JAX'''							)(a_							)
      return test_case



def 					__lowerCamelCase       (							a_					:		Union[str, Any]							)      ->				Optional[Any]:
      if not config.PIL_AVAILABLE:
            __SCREAMING_SNAKE_CASE       :Tuple		    =  unittest.skip('''test requires Pillow'''							)(a_							)
      return test_case



def 					__lowerCamelCase       (							a_					:		Union[str, Any]							)      ->				Dict:
      try:
            import transformers  # noqa F401
      except ImportError:
            return unittest.skip('''test requires transformers'''							)(a_							)
      else:
            return test_case



def 					__lowerCamelCase       (							a_					:		Optional[int]							)      ->				Union[str, Any]:
      try:
            import tiktoken  # noqa F401
      except ImportError:
            return unittest.skip('''test requires tiktoken'''							)(a_							)
      else:
            return test_case



def 					__lowerCamelCase       (							a_					:		Union[str, Any]							)      ->				Tuple:
      try:
            import spacy  # noqa F401
      except ImportError:
            return unittest.skip('''test requires spacy'''							)(a_							)
      else:
            return test_case



def 					__lowerCamelCase       (							a_					:		Optional[Any]							)      ->				Union[str, Any]:

      def _require_spacy_model(a_					:		Dict							):
            try:
                  import spacy  # noqa F401

                  spacy.load(a_							)
            except ImportError:
                  return unittest.skip('''test requires spacy'''							)(a_							)
            except OSError:
                  return unittest.skip('''test requires spacy model \'{}\''''.format(a_							)							)(a_							)
            else:
                  return test_case

      return _require_spacy_model



def 					__lowerCamelCase       (							a_					:		Optional[Any]							)      ->				Optional[int]:
      try:
            import pyspark  # noqa F401
      except ImportError:
            return unittest.skip('''test requires pyspark'''							)(a_							)
      else:
            return test_case



def 					__lowerCamelCase       (							a_					:		Union[str, Any]							)      ->				Union[str, Any]:
      try:
            import joblibspark  # noqa F401
      except ImportError:
            return unittest.skip('''test requires joblibspark'''							)(a_							)
      else:
            return test_case



def 					__lowerCamelCase       (							a_					:		int							)      ->				Dict:
      if not _run_slow_tests or _run_slow_tests == 0:
            __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  unittest.skip('''test is slow'''							)(a_							)
      return test_case



def 					__lowerCamelCase       (							a_					:		Any							)      ->				int:
      if not _run_local_tests or _run_local_tests == 0:
            __SCREAMING_SNAKE_CASE       :str		    =  unittest.skip('''test is local'''							)(a_							)
      return test_case



def 					__lowerCamelCase       (							a_					:		List[str]							)      ->				str:
      if not _run_packaged_tests or _run_packaged_tests == 0:
            __SCREAMING_SNAKE_CASE       :List[str]		    =  unittest.skip('''test is packaged'''							)(a_							)
      return test_case



def 					__lowerCamelCase       (							a_					:		str							)      ->				Dict:
      if not _run_remote_tests or _run_remote_tests == 0:
            __SCREAMING_SNAKE_CASE       :List[Any]		    =  unittest.skip('''test requires remote'''							)(a_							)
      return test_case



def 					__lowerCamelCase       (							*a_					:		int							)      ->				Optional[int]:
      def decorate(cls					:		Optional[int]							):
            for name, fn in cls.__dict__.items():
                  if callable(a_							) and name.startswith('''test'''							):
                        for decorator in decorators:
                              __SCREAMING_SNAKE_CASE       :Tuple		    =  decorator(a_							)
                        setattr(cls			,       a_			,       a_							)
            return cls

      return decorate






class  _SCREAMING_SNAKE_CASE(       A  ):
       pass






class  _SCREAMING_SNAKE_CASE(       A  ):
       SCREAMING_SNAKE_CASE_       :				List[str]     						=  0
       SCREAMING_SNAKE_CASE_       :				Dict     						=  1
       SCREAMING_SNAKE_CASE_       :				Optional[Any]     						=  2




@contextmanager
def 					__lowerCamelCase       (							a_					:		int=OfflineSimulationMode.CONNECTION_FAILS			,       a_					:		Any=1e-16							)      ->				Tuple:
      __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  requests.Session().request

      def timeout_request(a_					:		Optional[Any]			,       a_					:		Dict			,       a_					:		int			,       **a_					:		Optional[Any]							):
            # Change the url to an invalid url so that the connection hangs
            __SCREAMING_SNAKE_CASE       :Dict		    =  '''https://10.255.255.1'''
            if kwargs.get('''timeout'''							) is None:
                  raise RequestWouldHangIndefinitelyError(
                      f'''Tried a call to {url} in offline mode with no timeout set. Please set a timeout.'''							)
            __SCREAMING_SNAKE_CASE       :List[Any]		    =  timeout
            try:
                  return online_request(a_			,       a_			,       **a_							)
            except Exception as e:
                  # The following changes in the error are just here to make the offline timeout error prettier
                  __SCREAMING_SNAKE_CASE       :str		    =  url
                  __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  e.args[0]
                  __SCREAMING_SNAKE_CASE       :Dict		    =  (max_retry_error.args[0].replace('''10.255.255.1'''			,       f'''OfflineMock[{url}]'''							),)
                  __SCREAMING_SNAKE_CASE       :Dict		    =  (max_retry_error,)
                  raise

      def raise_connection_error(a_					:		List[Any]			,       a_					:		Union[str, Any]			,       **a_					:		List[Any]							):
            raise requests.ConnectionError('''Offline mode is enabled.'''			,       request=a_							)

      if mode is OfflineSimulationMode.CONNECTION_FAILS:
            with patch('''requests.Session.send'''			,       a_							):
                  yield
      elif mode is OfflineSimulationMode.CONNECTION_TIMES_OUT:
            # inspired from https://stackoverflow.com/a/904609
            with patch('''requests.Session.request'''			,       a_							):
                  yield
      elif mode is OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1:
            with patch('''datasets.config.HF_DATASETS_OFFLINE'''			,       a_							):
                  yield
      else:
            raise ValueError('''Please use a value from the OfflineSimulationMode enum.'''							)



@contextmanager
def 					__lowerCamelCase       (							*a_					:		int			,       **a_					:		List[Any]							)      ->				str:
      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  str(Path().resolve()							)
      with tempfile.TemporaryDirectory(*a_			,       **a_							) as tmp_dir:
            try:
                  os.chdir(a_							)
                  yield
            finally:
                  os.chdir(a_							)



@contextmanager
def 					__lowerCamelCase       (							)      ->				Any:
      import gc

      gc.collect()
      __SCREAMING_SNAKE_CASE       :str		    =  pa.total_allocated_bytes()
      yield
      assert pa.total_allocated_bytes() - previous_allocated_memory > 0, "Arrow memory didn't increase."



@contextmanager
def 					__lowerCamelCase       (							)      ->				int:
      import gc

      gc.collect()
      __SCREAMING_SNAKE_CASE       :int		    =  pa.total_allocated_bytes()
      yield
      assert pa.total_allocated_bytes() - previous_allocated_memory <= 0, "Arrow memory wasn't expected to increase."



def 					__lowerCamelCase       (							a_					:		List[str]			,       a_					:		str							)      ->				Dict:
      return deepcopy(a_							).integers(0			,       1_00			,       10							).tolist() == deepcopy(a_							).integers(0			,       1_00			,       10							).tolist()



def 					__lowerCamelCase       (							a_					:		Tuple							)      ->				List[Any]:
      import decorator
      from requests.exceptions import HTTPError

      def _wrapper(a_					:		List[str]			,       *a_					:		Any			,       **a_					:		int							):
            try:
                  return func(*a_			,       **a_							)
            except HTTPError as err:
                  if str(a_							).startswith('''500'''							) or str(a_							).startswith('''502'''							):
                        pytest.xfail(str(a_							)							)
                  raise err

      return decorator.decorator(_wrapper			,       a_							)






class  _SCREAMING_SNAKE_CASE:




       def __init__(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				List[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Tuple		    =  returncode
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  stdout
             __SCREAMING_SNAKE_CASE       :Tuple		    =  stderr




async def 					__lowerCamelCase       (							a_					:		Optional[int]			,       a_					:		List[str]							)      ->				Tuple:
      while True:
            __SCREAMING_SNAKE_CASE       :str		    =  await stream.readline()
            if line:
                  callback(a_							)
            else:
                  break



async def 					__lowerCamelCase       (							a_					:		List[str]			,       a_					:		str=None			,       a_					:		Union[str, Any]=None			,       a_					:		Tuple=None			,       a_					:		int=False			,       a_					:		Optional[int]=False							)      ->				_RunOutput:
      if echo:
            print('''\nRunning: '''			,       ''' '''.join(a_							)							)

      __SCREAMING_SNAKE_CASE       :str		    =  await asyncio.create_subprocess_exec(
          cmd[0]			,       *cmd[1:]			,       stdin=a_			,       stdout=asyncio.subprocess.PIPE			,       stderr=asyncio.subprocess.PIPE			,       env=a_			,       )

      # note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
      # https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
      #
      # If it starts hanging, will need to switch to the following code. The problem is that no data
      # will be seen until it's done and if it hangs for example there will be no debug info.
      # out, err = await p.communicate()
      # return _RunOutput(p.returncode, out, err)

      __SCREAMING_SNAKE_CASE       :List[Any]		    =  []
      __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  []

      def tee(a_					:		List[str]			,       a_					:		List[str]			,       a_					:		Any			,       a_					:		int=""							):
            __SCREAMING_SNAKE_CASE       :Tuple		    =  line.decode('''utf-8'''							).rstrip()
            sink.append(a_							)
            if not quiet:
                  print(a_			,       a_			,       file=a_							)

    # XXX: the timeout doesn't seem to make any difference here
      await asyncio.wait(
          [
              _read_stream(p.stdout			,       lambda a_							: tee(a_			,       a_			,       sys.stdout			,       label='''stdout:'''							)							),
              _read_stream(p.stderr			,       lambda a_							: tee(a_			,       a_			,       sys.stderr			,       label='''stderr:'''							)							),
          ]			,       timeout=a_			,       )
      return _RunOutput(await p.wait()			,       a_			,       a_							)



def 					__lowerCamelCase       (							a_					:		Optional[Any]			,       a_					:		Optional[int]=None			,       a_					:		int=None			,       a_					:		Any=1_80			,       a_					:		Optional[Any]=False			,       a_					:		int=True							)      ->				_RunOutput:
      __SCREAMING_SNAKE_CASE       :Tuple		    =  asyncio.get_event_loop()
      __SCREAMING_SNAKE_CASE       :Tuple		    =  loop.run_until_complete(
          _stream_subprocess(a_			,       env=a_			,       stdin=a_			,       timeout=a_			,       quiet=a_			,       echo=a_							)							)

      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  ''' '''.join(a_							)
      if result.returncode > 0:
            __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  '''\n'''.join(result.stderr							)
            raise RuntimeError(
                f'''\'{cmd_str}\' failed with returncode {result.returncode}\n\n'''
                f'''The combined stderr from workers follows:\n{stderr}'''							)

      # check that the subprocess actually did run and produced some output, should the test rely on
      # the remote side to do the testing
      if not result.stdout and not result.stderr:
            raise RuntimeError(f'''\'{cmd_str}\' produced no output.'''							)

      return result



def 					__lowerCamelCase       (							)      ->				Tuple:
      __SCREAMING_SNAKE_CASE       :List[Any]		    =  os.environ.get('''PYTEST_XDIST_WORKER'''			,       '''gw0'''							)
      __SCREAMING_SNAKE_CASE       :Dict		    =  re.sub(r'''^gw'''			,       ''''''			,       a_			,       0			,       re.M							)
      return int(a_							)







def 					__lowerCamelCase       (							)      ->				Any:
      __SCREAMING_SNAKE_CASE       :Tuple		    =  2_95_00
      __SCREAMING_SNAKE_CASE       :int		    =  pytest_xdist_worker_id()
      return port + uniq_delta






"""simple docstring"""
import warnings
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


lowerCamelCase_       =		logging.get_logger(__name__)

lowerCamelCase_       =		{
    "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/config.json",
    # See all BART models at https://huggingface.co/models?filter=bart
}






class  _SCREAMING_SNAKE_CASE(       A  ):
       SCREAMING_SNAKE_CASE_       :				str     						=  '''bart'''
       SCREAMING_SNAKE_CASE_       :				str     						=  ['''past_key_values''']
       SCREAMING_SNAKE_CASE_       :				str     						=  {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}




       def __init__(						self		,SCREAMING_SNAKE_CASE__=5_02_65		,SCREAMING_SNAKE_CASE__=10_24		,SCREAMING_SNAKE_CASE__=12		,SCREAMING_SNAKE_CASE__=40_96		,SCREAMING_SNAKE_CASE__=16		,SCREAMING_SNAKE_CASE__=12		,SCREAMING_SNAKE_CASE__=40_96		,SCREAMING_SNAKE_CASE__=16		,SCREAMING_SNAKE_CASE__=0.0		,SCREAMING_SNAKE_CASE__=0.0		,SCREAMING_SNAKE_CASE__="gelu"		,SCREAMING_SNAKE_CASE__=10_24		,SCREAMING_SNAKE_CASE__=0.1		,SCREAMING_SNAKE_CASE__=0.0		,SCREAMING_SNAKE_CASE__=0.0		,SCREAMING_SNAKE_CASE__=0.0_2		,SCREAMING_SNAKE_CASE__=0.0		,SCREAMING_SNAKE_CASE__=False		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=3		,SCREAMING_SNAKE_CASE__=1		,SCREAMING_SNAKE_CASE__=0		,SCREAMING_SNAKE_CASE__=2		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=2		,SCREAMING_SNAKE_CASE__=2		,**SCREAMING_SNAKE_CASE__		,)      ->				int:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :str		    =  vocab_size
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  max_position_embeddings
             __SCREAMING_SNAKE_CASE       :Any		    =  d_model
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  encoder_ffn_dim
             __SCREAMING_SNAKE_CASE       :List[str]		    =  encoder_layers
             __SCREAMING_SNAKE_CASE       :Tuple		    =  encoder_attention_heads
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  decoder_ffn_dim
             __SCREAMING_SNAKE_CASE       :Any		    =  decoder_layers
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  decoder_attention_heads
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  dropout
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  attention_dropout
             __SCREAMING_SNAKE_CASE       :Dict		    =  activation_dropout
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  activation_function
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  init_std
             __SCREAMING_SNAKE_CASE       :int		    =  encoder_layerdrop
             __SCREAMING_SNAKE_CASE       :Any		    =  decoder_layerdrop
             __SCREAMING_SNAKE_CASE       :str		    =  classifier_dropout
             __SCREAMING_SNAKE_CASE       :List[str]		    =  use_cache
             __SCREAMING_SNAKE_CASE       :List[str]		    =  encoder_layers
             __SCREAMING_SNAKE_CASE       :Tuple		    =  scale_embedding  # scale factor will be sqrt(d_model) if True

             super().__init__(
                 num_labels=SCREAMING_SNAKE_CASE__		,pad_token_id=SCREAMING_SNAKE_CASE__		,bos_token_id=SCREAMING_SNAKE_CASE__		,eos_token_id=SCREAMING_SNAKE_CASE__		,is_encoder_decoder=SCREAMING_SNAKE_CASE__		,decoder_start_token_id=SCREAMING_SNAKE_CASE__		,forced_eos_token_id=SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__		,)

             # ensure backward compatibility for BART CNN models
             if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated'''		,SCREAMING_SNAKE_CASE__							):
                   __SCREAMING_SNAKE_CASE       :str		    =  self.bos_token_id
                   warnings.warn(
                       f'''Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '''
                       '''The config can simply be saved and uploaded again to be fixed.'''							)






class  _SCREAMING_SNAKE_CASE(       A  ):




       @property
       def    _UpperCamelCase					(						self							)      ->				Mapping[str, Mapping[int, str]]:





             """simple docstring"""






             if self.task in ["default", "seq2seq-lm"]:
                   __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  OrderedDict(
                       [
                           ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                           ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                       ]							)

                   if self.use_past:
                         __SCREAMING_SNAKE_CASE       :int		    =  {0: '''batch'''}
                         __SCREAMING_SNAKE_CASE       :int		    =  {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
                   else:
                         __SCREAMING_SNAKE_CASE       :Tuple		    =  {0: '''batch''', 1: '''decoder_sequence'''}
                         __SCREAMING_SNAKE_CASE       :Any		    =  {0: '''batch''', 1: '''decoder_sequence'''}

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





             """simple docstring"""






             if self.task in ["default", "seq2seq-lm"]:
                   __SCREAMING_SNAKE_CASE       :str		    =  super().outputs
             else:
                   __SCREAMING_SNAKE_CASE       :List[str]		    =  super(SCREAMING_SNAKE_CASE__		,self							).outputs
                   if self.use_past:
                         __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE       :Dict		    =  self.num_layers
                         for i in range(SCREAMING_SNAKE_CASE__							):
                               __SCREAMING_SNAKE_CASE       :str		    =  {0: '''batch''', 2: '''past_sequence + sequence'''}
                               __SCREAMING_SNAKE_CASE       :Any		    =  {0: '''batch''', 2: '''past_sequence + sequence'''}
             return common_outputs




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = -1		,SCREAMING_SNAKE_CASE__ = -1		,SCREAMING_SNAKE_CASE__ = False		,SCREAMING_SNAKE_CASE__ = None		,)      ->				Mapping[str, Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :int		    =  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)

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

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

                   __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  torch.cat(
                       [common_inputs['''decoder_attention_mask'''], torch.ones(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)]		,dim=1							)

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

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




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = -1		,SCREAMING_SNAKE_CASE__ = -1		,SCREAMING_SNAKE_CASE__ = False		,SCREAMING_SNAKE_CASE__ = None		,)      ->				Mapping[str, Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Dict		    =  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)

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

                   __SCREAMING_SNAKE_CASE       :Tuple		    =  common_inputs['''attention_mask'''].dtype
                   __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  torch.cat(
                       [common_inputs['''attention_mask'''], torch.ones(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,dtype=SCREAMING_SNAKE_CASE__							)]		,dim=1							)
                   __SCREAMING_SNAKE_CASE       :str		    =  [
                       (torch.zeros(SCREAMING_SNAKE_CASE__							), torch.zeros(SCREAMING_SNAKE_CASE__							)) for _ in range(SCREAMING_SNAKE_CASE__							)
                   ]
             return common_inputs




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = -1		,SCREAMING_SNAKE_CASE__ = -1		,SCREAMING_SNAKE_CASE__ = False		,SCREAMING_SNAKE_CASE__ = None		,)      ->				Mapping[str, Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :List[Any]		    =  compute_effective_axis_dimension(
                 SCREAMING_SNAKE_CASE__		,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
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  tokenizer.num_special_tokens_to_add(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  compute_effective_axis_dimension(
                 SCREAMING_SNAKE_CASE__		,fixed_dimension=OnnxConfig.default_fixed_sequence		,num_token_to_add=SCREAMING_SNAKE_CASE__							)

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




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = -1		,SCREAMING_SNAKE_CASE__ = -1		,SCREAMING_SNAKE_CASE__ = False		,SCREAMING_SNAKE_CASE__ = None		,)      ->				Mapping[str, Any]:





             """simple docstring"""






             if self.task in ["default", "seq2seq-lm"]:
                   __SCREAMING_SNAKE_CASE       :str		    =  self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                       SCREAMING_SNAKE_CASE__		,batch_size=SCREAMING_SNAKE_CASE__		,seq_length=SCREAMING_SNAKE_CASE__		,is_pair=SCREAMING_SNAKE_CASE__		,framework=SCREAMING_SNAKE_CASE__							)

             elif self.task == "causal-lm":
                   __SCREAMING_SNAKE_CASE       :int		    =  self._generate_dummy_inputs_for_causal_lm(
                       SCREAMING_SNAKE_CASE__		,batch_size=SCREAMING_SNAKE_CASE__		,seq_length=SCREAMING_SNAKE_CASE__		,is_pair=SCREAMING_SNAKE_CASE__		,framework=SCREAMING_SNAKE_CASE__							)
             else:
                   __SCREAMING_SNAKE_CASE       :int		    =  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                       SCREAMING_SNAKE_CASE__		,batch_size=SCREAMING_SNAKE_CASE__		,seq_length=SCREAMING_SNAKE_CASE__		,is_pair=SCREAMING_SNAKE_CASE__		,framework=SCREAMING_SNAKE_CASE__							)

             return common_inputs




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				str:





             """simple docstring"""






             if self.task in ["default", "seq2seq-lm"]:
                   __SCREAMING_SNAKE_CASE       :Dict		    =  super()._flatten_past_key_values_(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)
             else:
                   __SCREAMING_SNAKE_CASE       :Dict		    =  super(SCREAMING_SNAKE_CASE__		,self							)._flatten_past_key_values_(
                       SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)