
'''simple docstring'''





import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib import Path
from urllib.parse import urlparse
from zipfile import ZipFile, is_zipfile

import cva
import numpy as np
import requests
import wget
from filelock import FileLock
from PIL import Image
from tqdm.auto import tqdm
from yaml import Loader, dump, load


try:
 import torch

 a__		: str  							=							True
except ImportError:
 a__		: List[Any]  							=							False


try:
 from torch.hub import _get_torch_home

 a__		: Union[str, Any]  							=							_get_torch_home()
except ImportError:
 a__		: Optional[int]  							=							os.path.expanduser(
     os.getenv("TORCH_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "torch"))
 )

a__		: Optional[int]  							=							os.path.join(torch_cache_home, "transformers")

a__		: Optional[int]  							=							"https://cdn.huggingface.co"
a__		: Optional[Any]  							=							"https://s3.amazonaws.com/models.huggingface.co/bert"
a__		: Any  							=							"/".join(str(Path(__file__).resolve()).split("/")[:-1])
a__		: Optional[Any]  							=							os.path.join(PATH, "config.yaml")
a__		: Dict  							=							os.path.join(PATH, "attributes.txt")
a__		: Optional[int]  							=							os.path.join(PATH, "objects.txt")
a__		: Dict  							=							os.getenv("PYTORCH_PRETRAINED_BERT_CACHE", default_cache_path)
a__		: Dict  							=							os.getenv("PYTORCH_TRANSFORMERS_CACHE", PYTORCH_PRETRAINED_BERT_CACHE)
a__		: str  							=							os.getenv("TRANSFORMERS_CACHE", PYTORCH_TRANSFORMERS_CACHE)
a__		: Any  							=							"pytorch_model.bin"
a__		: List[str]  							=							"config.yaml"





def 							snake_case				(		UpperCAmelCase=OBJECTS   ,	UpperCAmelCase=ATTRIBUTES						)->      Optional[Any]:







      """simple docstring"""
      __A													=      []
      with open(UpperCAmelCase						) as f:
            for object in f.readlines():
                  vg_classes.append(object.split(','						)[0].lower().strip()						)

      __A													=      []
      with open(UpperCAmelCase						) as f:
            for object in f.readlines():
                  vg_attrs.append(object.split(','						)[0].lower().strip()						)
      return vg_classes, vg_attrs





def 							snake_case				(		UpperCAmelCase						)->      List[str]:







      """simple docstring"""
      __A													=      OrderedDict()
      with open(UpperCAmelCase   ,	'rb'						) as f:
            __A													=      pkl.load(UpperCAmelCase						)['model']
      for k in copy.deepcopy(list(ckp.keys()						)						):
            __A													=      ckp.pop(UpperCAmelCase						)
            if isinstance(UpperCAmelCase   ,	np.ndarray						):
                  __A													=      torch.tensor(UpperCAmelCase						)
            else:
                  assert isinstance(UpperCAmelCase   ,	torch.tensor						), type(UpperCAmelCase						)
            __A													=      v
      return r


class 			UpperCamelCase__	:
 UpperCAmelCase__     :     List[str]    					=       {}






 def __init__(      self	:Tuple						,					_A	:dict						,					_A	:str = "root"						,					_A	:int=0      )					->      Tuple:







       '''simple docstring'''



       __A													=      name
       __A													=      level
       __A													=      {}
       for k, v in dictionary.items():
             if v is None:
                   raise ValueError()
             __A													=      copy.deepcopy(_A      )
             __A													=      copy.deepcopy(_A      )
             if isinstance(_A						,					_A      ):
                   __A													=      Config(_A						,					name=_A						,					level=level + 1      )
             __A													=      v
             setattr(self						,					_A						,					_A      )

       __A													=      d






 def __repr__(      self	:Dict      )					->      List[str]:







       '''simple docstring'''



       return str(list((self._pointer.keys())      )      )






 def __setattr__(      self	:int						,					_A	:Any						,					_A	:int      )					->      int:







       '''simple docstring'''



       __A													=      val
       __A													=      val
       __A													=      key.split('.'      )
       __A													=      len(_A      ) - 1
       __A													=      self._pointer
       if len(_A      ) > 1:
             for i, l in enumerate(_A      ):
                   if hasattr(self						,					_A      ) and isinstance(getattr(self						,					_A      )						,					_A      ):
                         setattr(getattr(self						,					_A      )						,					'.'.join(levels[i:]      )						,					_A      )
                   if l == last_level:
                         __A													=      val
                   else:
                         __A													=      pointer[l]






 def 						lowercase_    (      self	:Dict      )					->      int:







       '''simple docstring'''



       return self._pointer






 def 						lowercase_    (      self	:Optional[Any]						,					_A	:List[str]						,					_A	:str      )					->      Tuple:







       '''simple docstring'''



       with open(F'{file_name}'						,					'w'      ) as stream:
             dump(_A						,					_A      )






 def 						lowercase_    (      self	:str						,					_A	:str						,					_A	:Dict      )					->      str:







       '''simple docstring'''



       with open(F'{file_name}'						,					'w'      ) as stream:
             json.dump(_A						,					_A      )






 @staticmethod
 def 						lowercase_    (      _A	:str      )					->      Tuple:







       '''simple docstring'''



       with open(_A      ) as stream:
             __A													=      load(_A						,					Loader=_A      )
       return data






 def __str__(      self	:int      )					->      Union[str, Any]:







       '''simple docstring'''



       __A													=      '    '
       if self._name != "root":
             __A													=      F'{t * (self._level-1)}{self._name}:\n'
       else:
             __A													=      ''
       __A													=      self._level
       for i, (k, v) in enumerate(self._pointer.items()      ):
             if isinstance(_A						,					_A      ):
                   r += F'{t * (self._level)}{v}\n'
                   self._level += 1
             else:
                   r += F'{t * (self._level)}{k}: {v} ({type(_A      ).__name__})\n'
             __A													=      level
       return r[:-1]






 @classmethod
 def 						lowercase_    (      cls	:Dict						,					_A	:str						,					**_A	:int      )					->      Optional[Any]:







       '''simple docstring'''



       __A													=      cls.get_config_dict(_A						,					**_A      )
       return cls(_A      )






 @classmethod
 def 						lowercase_    (      cls	:Union[str, Any]						,					_A	:str						,					**_A	:int      )					->      Any:







       '''simple docstring'''



       __A													=      kwargs.pop('cache_dir'						,					_A      )
       __A													=      kwargs.pop('force_download'						,					_A      )
       __A													=      kwargs.pop('resume_download'						,					_A      )
       __A													=      kwargs.pop('proxies'						,					_A      )
       __A													=      kwargs.pop('local_files_only'						,					_A      )

       if os.path.isdir(_A      ):
             __A													=      os.path.join(_A						,					_A      )
       elif os.path.isfile(_A      ) or is_remote_url(_A      ):
             __A													=      pretrained_model_name_or_path
       else:
             __A													=      hf_bucket_url(_A						,					filename=_A						,					use_cdn=_A      )

       try:
             # Load from URL or cache if already cached
             __A													=      cached_path(
                 _A						,					cache_dir=_A						,					force_download=_A						,					proxies=_A						,					resume_download=_A						,					local_files_only=_A						,					)
             # Load config dict
             if resolved_config_file is None:
                   raise EnvironmentError

             __A													=      Config.load_yaml(_A      )

       except EnvironmentError:
             __A													=      'Can\'t load config for'
             raise EnvironmentError(_A      )

       if resolved_config_file == config_file:
             print('loading configuration file from path'      )
       else:
             print('loading configuration file cache'      )

       return Config.load_yaml(_A      ), kwargs





def 							snake_case				(		UpperCAmelCase						)->      Tuple:







      """simple docstring"""
      __A													=      torch.load('dump.pt'   ,	map_location=in_tensor.device						)
      __A													=      in_tensor.numpy()
      __A													=      out_tensor.numpy()[0]
      print(na.shape   ,	na[0, 0, :5]						)
      print(na.shape   ,	na[0, 0, :5]						)
      assert np.allclose(UpperCAmelCase   ,	UpperCAmelCase   ,	rtol=0.01   ,	atol=0.1						), (
          f'{sum([1 for x in np.isclose(UpperCAmelCase   ,	UpperCAmelCase   ,	rtol=0.01   ,	atol=0.1						).flatten() if x is False]						)/len(na.flatten()						)*1_0_0:.4f} %'
          " element-wise mismatch"
      )
      raise Exception('tensors are all good'						)

      # Hugging face functions below





def 							snake_case				(		UpperCAmelCase						)->      str:







      """simple docstring"""
      __A													=      urlparse(UpperCAmelCase						)
      return parsed.scheme in ("http", "https")





def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase=True						)->      str:







      """simple docstring"""
      __A													=      CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
      __A													=      '/' not in model_id
      if legacy_format:
            return f'{endpoint}/{model_id}-{filename}'
      else:
            return f'{endpoint}/{model_id}/{filename}'





def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase=None   ,	UpperCAmelCase=0   ,	UpperCAmelCase=None   ,	)->      Optional[Any]:







      """simple docstring"""
      __A													=      'python/{}'.format(sys.version.split()[0]						)
      if _torch_available:
            ua += "; torch/{}".format(torch.__version__						)
      if isinstance(UpperCAmelCase   ,	UpperCAmelCase						):
            ua += "; " + "; ".join('{}/{}'.format(UpperCAmelCase   ,	UpperCAmelCase						) for k, v in user_agent.items()						)
      elif isinstance(UpperCAmelCase   ,	UpperCAmelCase						):
            ua += "; " + user_agent
      __A													=      {'user-agent': ua}
      if resume_size > 0:
            __A													=      'bytes=%d-' % (resume_size,)
      __A													=      requests.get(UpperCAmelCase   ,	stream=UpperCAmelCase   ,	proxies=UpperCAmelCase   ,	headers=UpperCAmelCase						)
      if response.status_code == 4_1_6:  # Range not satisfiable
            return
      __A													=      response.headers.get('Content-Length'						)
      __A													=      resume_size + int(UpperCAmelCase						) if content_length is not None else None
      __A													=      tqdm(
          unit='B'   ,	unit_scale=UpperCAmelCase   ,	total=UpperCAmelCase   ,	initial=UpperCAmelCase   ,	desc='Downloading'   ,	)
      for chunk in response.iter_content(chunk_size=1_0_2_4						):
            if chunk:  # filter out keep-alive new chunks
                  progress.update(len(UpperCAmelCase						)						)
                  temp_file.write(UpperCAmelCase						)
      progress.close()





def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase=None   ,	UpperCAmelCase=False   ,	UpperCAmelCase=None   ,	UpperCAmelCase=1_0   ,	UpperCAmelCase=False   ,	UpperCAmelCase=None   ,	UpperCAmelCase=False   ,	)->      Dict:







      """simple docstring"""






      if cache_dir is None:
            __A													=      TRANSFORMERS_CACHE
      if isinstance(UpperCAmelCase   ,	UpperCAmelCase						):
            __A													=      str(UpperCAmelCase						)

      os.makedirs(UpperCAmelCase   ,	exist_ok=UpperCAmelCase						)

      __A													=      None
      if not local_files_only:
            try:
                  __A													=      requests.head(UpperCAmelCase   ,	allow_redirects=UpperCAmelCase   ,	proxies=UpperCAmelCase   ,	timeout=UpperCAmelCase						)
                  if response.status_code == 2_0_0:
                        __A													=      response.headers.get('ETag'						)
            except (EnvironmentError, requests.exceptions.Timeout):
                  # etag is already None
                  pass

      __A													=      url_to_filename(UpperCAmelCase   ,	UpperCAmelCase						)

      # get cache path to put the file
      __A													=      os.path.join(UpperCAmelCase   ,	UpperCAmelCase						)

      # etag is None = we don't have a connection, or url doesn't exist, or is otherwise inaccessible.
      # try to get the last downloaded one
      if etag is None:
            if os.path.exists(UpperCAmelCase						):
                  return cache_path
            else:
                  __A													=      [
                      file
                      for file in fnmatch.filter(os.listdir(UpperCAmelCase						)   ,	filename + '.*'						)
                      if not file.endswith('.json'						) and not file.endswith('.lock'						)
                  ]
                  if len(UpperCAmelCase						) > 0:
                        return os.path.join(UpperCAmelCase   ,	matching_files[-1]						)
                  else:
                        # If files cannot be found and local_files_only=True,
                        # the models might've been found if local_files_only=False
                        # Notify the user about that
                        if local_files_only:
                              raise ValueError(
                                  'Cannot find the requested files in the cached path and outgoing traffic has been'
                                  ' disabled. To enable model look-ups and downloads online, set \'local_files_only\''
                                  ' to False.'						)
                        return None

                        # From now on, etag is not None.
      if os.path.exists(UpperCAmelCase						) and not force_download:
            return cache_path

      # Prevent parallel downloads of the same file with a lock.
      __A													=      cache_path + '.lock'
      with FileLock(UpperCAmelCase						):
            # If the download just completed while the lock was activated.
            if os.path.exists(UpperCAmelCase						) and not force_download:
                  # Even if returning early like here, the lock will be released.
                  return cache_path

            if resume_download:
                  __A													=      cache_path + '.incomplete'

                  @contextmanager
                  def _resumable_file_manager():
                        with open(UpperCAmelCase   ,	'a+b'						) as f:
                              yield f

                  __A													=      _resumable_file_manager
                  if os.path.exists(UpperCAmelCase						):
                        __A													=      os.stat(UpperCAmelCase						).st_size
                  else:
                        __A													=      0
            else:
                  __A													=      partial(tempfile.NamedTemporaryFile   ,	dir=UpperCAmelCase   ,	delete=UpperCAmelCase						)
                  __A													=      0

            # Download to temporary file, then copy to cache dir once finished.
            # Otherwise you get corrupt cache entries if the download gets interrupted.
            with temp_file_manager() as temp_file:
                  print(
                      '%s not found in cache or force_download set to True, downloading to %s'   ,	UpperCAmelCase   ,	temp_file.name   ,	)

                  http_get(
                      UpperCAmelCase   ,	UpperCAmelCase   ,	proxies=UpperCAmelCase   ,	resume_size=UpperCAmelCase   ,	user_agent=UpperCAmelCase   ,	)

            os.replace(temp_file.name   ,	UpperCAmelCase						)

            __A													=      {'url': url, 'etag': etag}
            __A													=      cache_path + '.json'
            with open(UpperCAmelCase   ,	'w'						) as meta_file:
                  json.dump(UpperCAmelCase   ,	UpperCAmelCase						)

      return cache_path





def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase=None						)->      str:







      """simple docstring"""
      __A													=      url.encode('utf-8'						)
      __A													=      shaaaa(UpperCAmelCase						)
      __A													=      url_hash.hexdigest()

      if etag:
            __A													=      etag.encode('utf-8'						)
            __A													=      shaaaa(UpperCAmelCase						)
            filename += "." + etag_hash.hexdigest()

      if url.endswith('.h5'						):
            filename += ".h5"

      return filename





def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase=None   ,	UpperCAmelCase=False   ,	UpperCAmelCase=None   ,	UpperCAmelCase=False   ,	UpperCAmelCase=None   ,	UpperCAmelCase=False   ,	UpperCAmelCase=False   ,	UpperCAmelCase=False   ,	)->      Dict:







      """simple docstring"""






      if cache_dir is None:
            __A													=      TRANSFORMERS_CACHE
      if isinstance(UpperCAmelCase   ,	UpperCAmelCase						):
            __A													=      str(UpperCAmelCase						)
      if isinstance(UpperCAmelCase   ,	UpperCAmelCase						):
            __A													=      str(UpperCAmelCase						)

      if is_remote_url(UpperCAmelCase						):
            # URL, so get it from the cache (downloading if necessary)
            __A													=      get_from_cache(
                UpperCAmelCase   ,	cache_dir=UpperCAmelCase   ,	force_download=UpperCAmelCase   ,	proxies=UpperCAmelCase   ,	resume_download=UpperCAmelCase   ,	user_agent=UpperCAmelCase   ,	local_files_only=UpperCAmelCase   ,	)
      elif os.path.exists(UpperCAmelCase						):
            # File, and it exists.
            __A													=      url_or_filename
      elif urlparse(UpperCAmelCase						).scheme == "":
            # File, but it doesn't exist.
            raise EnvironmentError('file {} not found'.format(UpperCAmelCase						)						)
      else:
            # Something unknown
            raise ValueError('unable to parse {} as a URL or as a local path'.format(UpperCAmelCase						)						)

      if extract_compressed_file:
            if not is_zipfile(UpperCAmelCase						) and not tarfile.is_tarfile(UpperCAmelCase						):
                  return output_path

            # Path where we extract compressed archives
            # We avoid '.' in dir name and add "-extracted" at the end: "./model.zip" => "./model-zip-extracted/"
            __A													=      os.path.split(UpperCAmelCase						)
            __A													=      output_file.replace('.'   ,	'-'						) + '-extracted'
            __A													=      os.path.join(UpperCAmelCase   ,	UpperCAmelCase						)

            if os.path.isdir(UpperCAmelCase						) and os.listdir(UpperCAmelCase						) and not force_extract:
                  return output_path_extracted

            # Prevent parallel extractions
            __A													=      output_path + '.lock'
            with FileLock(UpperCAmelCase						):
                  shutil.rmtree(UpperCAmelCase   ,	ignore_errors=UpperCAmelCase						)
                  os.makedirs(UpperCAmelCase						)
                  if is_zipfile(UpperCAmelCase						):
                        with ZipFile(UpperCAmelCase   ,	'r'						) as zip_file:
                              zip_file.extractall(UpperCAmelCase						)
                              zip_file.close()
                  elif tarfile.is_tarfile(UpperCAmelCase						):
                        __A													=      tarfile.open(UpperCAmelCase						)
                        tar_file.extractall(UpperCAmelCase						)
                        tar_file.close()
                  else:
                        raise EnvironmentError('Archive format of {} could not be identified'.format(UpperCAmelCase						)						)

            return output_path_extracted

      return output_path





def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase=","						)->      Any:







      """simple docstring"""
      assert isinstance(UpperCAmelCase   ,	UpperCAmelCase						)
      if os.path.isfile(UpperCAmelCase						):
            with open(UpperCAmelCase						) as f:
                  __A													=      eval(f.read()						)
      else:
            __A													=      requests.get(UpperCAmelCase						)
            try:
                  __A													=      requests.json()
            except Exception:
                  __A													=      req.content.decode()
                  assert data is not None, "could not connect"
                  try:
                        __A													=      eval(UpperCAmelCase						)
                  except Exception:
                        __A													=      data.split('\n'						)
            req.close()
      return data





def 							snake_case				(		UpperCAmelCase						)->      Union[str, Any]:







      """simple docstring"""
      __A													=      requests.get(UpperCAmelCase						)
      __A													=      np.array(Image.open(BytesIO(response.content						)						)						)
      return img





def 							snake_case				(		UpperCAmelCase						)->      List[str]:







      """simple docstring"""
      __A													=      url.split('/'						)[-1]
      if fn not in os.listdir(os.getcwd()						):
            wget.download(UpperCAmelCase						)
      with open(UpperCAmelCase   ,	'rb'						) as stream:
            __A													=      pkl.load(UpperCAmelCase						)
      __A													=      weights.pop('model'						)
      __A													=      {}
      for k, v in model.items():
            __A													=      torch.from_numpy(UpperCAmelCase						)
            if "running_var" in k:
                  __A													=      torch.tensor([0]						)
                  __A													=      k.replace('running_var'   ,	'num_batches_tracked'						)
                  __A													=      zero
      return new





def 							snake_case				(		)->      Any:







      """simple docstring"""
      print(f'{os.path.abspath(os.path.join(UpperCAmelCase   ,	os.pardir						)						)}/demo.ipynb'						)





def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase="RGB"						)->      Optional[int]:







      """simple docstring"""
      assert isinstance(UpperCAmelCase   ,	UpperCAmelCase						)
      if os.path.isfile(UpperCAmelCase						):
            __A													=      cva.imread(UpperCAmelCase						)
      else:
            __A													=      get_image_from_url(UpperCAmelCase						)
            assert img is not None, f'could not connect to: {im}'
      __A													=      cva.cvtColor(UpperCAmelCase   ,	cva.COLOR_BGR2RGB						)
      if input_format == "RGB":
            __A													=      img[:, :, ::-1]
      return img





def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase=1						)->      str:







      """simple docstring"""
      return (images[i : i + batch] for i in range(0   ,	len(UpperCAmelCase						)   ,	UpperCAmelCase						))









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

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


_a									=  logging.get_logger(__name__)

_a									=  {
    '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/config.json''',
    # See all M2M100 models at https://huggingface.co/models?filter=m2m_100
}






class 				A_					(						snake_case__   ):
     _lowercase     :	List[Any]        =      'm2m_100'
     _lowercase     :	str        =      ['past_key_values']
     _lowercase     :	List[Any]        =      {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}


     def __init__(				self     :						int     ,    UpperCAmelCase     :						Any=1_2_8_1_1_2     ,    UpperCAmelCase     :						Optional[int]=1_0_2_4     ,    UpperCAmelCase     :						Tuple=1_2     ,    UpperCAmelCase     :						List[str]=4_0_9_6     ,    UpperCAmelCase     :						Dict=1_6     ,    UpperCAmelCase     :						int=1_2     ,    UpperCAmelCase     :						int=4_0_9_6     ,    UpperCAmelCase     :						List[str]=1_6     ,    UpperCAmelCase     :						Optional[Any]=0.05     ,    UpperCAmelCase     :						Dict=0.05     ,    UpperCAmelCase     :						Union[str, Any]=True     ,    UpperCAmelCase     :						Dict=True     ,    UpperCAmelCase     :						List[str]="relu"     ,    UpperCAmelCase     :						Dict=1_0_2_4     ,    UpperCAmelCase     :						str=0.1     ,    UpperCAmelCase     :						Dict=0.1     ,    UpperCAmelCase     :						List[str]=0.0     ,    UpperCAmelCase     :						Union[str, Any]=0.02     ,    UpperCAmelCase     :						Optional[Any]=2     ,    UpperCAmelCase     :						str=True     ,    UpperCAmelCase     :						Optional[int]=1     ,    UpperCAmelCase     :						List[Any]=0     ,    UpperCAmelCase     :						Union[str, Any]=2     ,    **UpperCAmelCase     :						Union[str, Any]     ,    )				->      Tuple:
      __lowerCAmelCase:  Optional[Any]        = vocab_size
      __lowerCAmelCase:  Dict        = max_position_embeddings
      __lowerCAmelCase:  Tuple        = d_model
      __lowerCAmelCase:  List[str]        = encoder_ffn_dim
      __lowerCAmelCase:  Any        = encoder_layers
      __lowerCAmelCase:  int        = encoder_attention_heads
      __lowerCAmelCase:  str        = decoder_ffn_dim
      __lowerCAmelCase:  Tuple        = decoder_layers
      __lowerCAmelCase:  Tuple        = decoder_attention_heads
      __lowerCAmelCase:  Any        = dropout
      __lowerCAmelCase:  Optional[int]        = attention_dropout
      __lowerCAmelCase:  Tuple        = activation_dropout
      __lowerCAmelCase:  Optional[Any]        = activation_function
      __lowerCAmelCase:  Union[str, Any]        = init_std
      __lowerCAmelCase:  List[str]        = encoder_layerdrop
      __lowerCAmelCase:  Optional[Any]        = decoder_layerdrop
      __lowerCAmelCase:  Dict        = use_cache
      __lowerCAmelCase:  Any        = encoder_layers
      __lowerCAmelCase:  Tuple        = scale_embedding  # scale factor will be sqrt(d_model) if True

      super().__init__(
          pad_token_id=UpperCAmelCase     ,    bos_token_id=UpperCAmelCase     ,    eos_token_id=UpperCAmelCase     ,    is_encoder_decoder=UpperCAmelCase     ,    decoder_start_token_id=UpperCAmelCase     ,    **UpperCAmelCase     ,    )







class 				A_					(						snake_case__   ):


     @property
     def     UpperCAmelCase    (				self     :						List[Any]   )				->      Mapping[str, Mapping[int, str]]:
      __lowerCAmelCase:  Union[str, Any]        = OrderedDict(
          [
              ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
              ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
          ]   )

      if self.use_past:
       __lowerCAmelCase:  str        = {0: 'batch'}
       __lowerCAmelCase:  Union[str, Any]        = {0: 'batch', 1: 'past_decoder_sequence + sequence'}
      else:
       __lowerCAmelCase:  Any        = {0: 'batch', 1: 'decoder_sequence'}
       __lowerCAmelCase:  Dict        = {0: 'batch', 1: 'decoder_sequence'}

      if self.use_past:
       self.fill_with_past_key_values_(UpperCAmelCase     ,    direction='inputs'   )
      return common_inputs


     def     UpperCAmelCase    (				self     :						Tuple     ,    UpperCAmelCase     :						PreTrainedTokenizer     ,    UpperCAmelCase     :						int = -1     ,    UpperCAmelCase     :						int = -1     ,    UpperCAmelCase     :						bool = False     ,    UpperCAmelCase     :						Optional[TensorType] = None     ,    )				->      Mapping[str, Any]:
      # 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:  List[str]        = compute_effective_axis_dimension(
          UpperCAmelCase     ,    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:  Tuple        = tokenizer.num_special_tokens_to_add(UpperCAmelCase   )
      __lowerCAmelCase:  List[str]        = compute_effective_axis_dimension(
          UpperCAmelCase     ,    fixed_dimension=OnnxConfig.default_fixed_sequence     ,    num_token_to_add=UpperCAmelCase   )

      # Generate dummy inputs according to compute batch and sequence
      __lowerCAmelCase:  int        = [' '.join([tokenizer.unk_token]   ) * seq_length] * batch_size
      __lowerCAmelCase:  Optional[int]        = dict(tokenizer(UpperCAmelCase     ,    return_tensors=UpperCAmelCase   )   )
      return common_inputs


     def     UpperCAmelCase    (				self     :						str     ,    UpperCAmelCase     :						PreTrainedTokenizer     ,    UpperCAmelCase     :						int = -1     ,    UpperCAmelCase     :						int = -1     ,    UpperCAmelCase     :						bool = False     ,    UpperCAmelCase     :						Optional[TensorType] = None     ,    )				->      Mapping[str, Any]:
      __lowerCAmelCase:  Optional[int]        = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          UpperCAmelCase     ,    UpperCAmelCase     ,    UpperCAmelCase     ,    UpperCAmelCase     ,    UpperCAmelCase   )

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

      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:  List[Any]        = common_inputs['input_ids'].shape
       __lowerCAmelCase:  int        = common_inputs['decoder_input_ids'].shape[1]
       __lowerCAmelCase      ,				__lowerCAmelCase:  str        = self.num_attention_heads
       __lowerCAmelCase:  str        = (
           batch,
           num_encoder_attention_heads,
           encoder_seq_length,
           self._config.hidden_size // num_encoder_attention_heads,
       )
       __lowerCAmelCase:  Optional[int]        = decoder_seq_length + 3
       __lowerCAmelCase:  List[str]        = (
           batch,
           num_decoder_attention_heads,
           decoder_past_length,
           self._config.hidden_size // num_decoder_attention_heads,
       )

       __lowerCAmelCase:  Dict        = torch.cat(
           [common_inputs['decoder_attention_mask'], torch.ones(UpperCAmelCase     ,    UpperCAmelCase   )]     ,    dim=1   )

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

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


     _lowercase     :	int        =      _generate_dummy_inputs_for_default_and_seqaseq_lm





