



'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase_									=    logging.get_logger(__name__)

lowercase_									=    {
    '''xlm-mlm-en-2048''': '''https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json''',
    '''xlm-mlm-ende-1024''': '''https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json''',
    '''xlm-mlm-enfr-1024''': '''https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json''',
    '''xlm-mlm-enro-1024''': '''https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json''',
    '''xlm-mlm-tlm-xnli15-1024''': '''https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json''',
    '''xlm-mlm-xnli15-1024''': '''https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json''',
    '''xlm-clm-enfr-1024''': '''https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json''',
    '''xlm-clm-ende-1024''': '''https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json''',
    '''xlm-mlm-17-1280''': '''https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json''',
    '''xlm-mlm-100-1280''': '''https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json''',
}






class __A     (    A		):



 '''simple docstring'''







 __lowerCamelCase       :			List[str]		=			'xlm'
 __lowerCamelCase       :			Dict		=			{
     'hidden_size': 'emb_dim',
     'num_attention_heads': 'n_heads',
     'num_hidden_layers': 'n_layers',
     'n_words': 'vocab_size',  # For backward compatibility
 }



 def __init__(self					,  A=30_145					,  A=2_048					,  A=12					,  A=16					,  A=0.1					,  A=0.1					,  A=True					,  A=False					,  A=False					,  A=False					,  A=1					,  A=True					,  A=512					,  A=2_048**-0.5					,  A=1E-12					,  A=0.02					,  A=0					,  A=1					,  A=2					,  A=3					,  A=5					,  A=True					,  A="first"					,  A=True					,  A=None					,  A=True					,  A=0.1					,  A=5					,  A=5					,  A=0					,  A=0					,  A=2					,  A=0					,  **A					,  )       ->	Optional[Any]:


      """simple docstring"""



      _a           =    vocab_size
      _a           =    emb_dim
      _a           =    n_layers
      _a           =    n_heads
      _a           =    dropout
      _a           =    attention_dropout
      _a           =    gelu_activation
      _a           =    sinusoidal_embeddings
      _a           =    causal
      _a           =    asm
      _a           =    n_langs
      _a           =    use_lang_emb
      _a           =    layer_norm_eps
      _a           =    bos_index
      _a           =    eos_index
      _a           =    pad_index
      _a           =    unk_index
      _a           =    mask_index
      _a           =    is_encoder
      _a           =    max_position_embeddings
      _a           =    embed_init_std
      _a           =    init_std
      _a           =    summary_type
      _a           =    summary_use_proj
      _a           =    summary_activation
      _a           =    summary_proj_to_labels
      _a           =    summary_first_dropout
      _a           =    start_n_top
      _a           =    end_n_top
      _a           =    mask_token_id
      _a           =    lang_id

      if "n_words" in kwargs:
           _a           =    kwargs['''n_words''']

      super().__init__(pad_token_id=A					,  bos_token_id=A					,  **A			)






class __A     (    A		):



 '''simple docstring'''







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


      """simple docstring"""



      if self.task == "multiple-choice":
           _a           =    {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
      else:
           _a           =    {0: '''batch''', 1: '''sequence'''}
      return OrderedDict(
          [
              ('''input_ids''', dynamic_axis),
              ('''attention_mask''', dynamic_axis),
              ('''token_type_ids''', dynamic_axis),
          ]			)











import os
import sys
import unittest


__a:     Tuple   					=					os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
__a:     Tuple   					=					os.path.join(git_repo_path, '''src''', '''transformers''')

__a:     Optional[Any]   					=					'''
{0} = None
'''

__a:     List[Any]   					=					'''
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
'''


__a:     int   					=					'''
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
'''
class 		SCREAMING_SNAKE_CASE__	(  unittest.TestCase     ):

  '''simple docstring'''







  def  lowerCamelCase       (      self		:			Tuple							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    find_backend("""    _import_structure[\"models.albert\"].append(\"AlbertTokenizerFast\")"""							)
         self.assertIsNone(lowerCamelCase							)

         _UpperCAmelCase							=    find_backend("""    if not is_tokenizers_available():"""							)
         self.assertEqual(lowerCamelCase       ,				"""tokenizers"""							)

         _UpperCAmelCase							=    find_backend("""    if not is_tensorflow_text_available():"""							)
         self.assertEqual(lowerCamelCase       ,				"""tensorflow_text"""							)

         _UpperCAmelCase							=    find_backend("""    if not (is_sentencepiece_available() and is_tokenizers_available()):"""							)
         self.assertEqual(lowerCamelCase       ,				"""sentencepiece_and_tokenizers"""							)

         _UpperCAmelCase							=    find_backend(
             """    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"""							)
         self.assertEqual(lowerCamelCase       ,				"""sentencepiece_and_tensorflow_text"""							)

         _UpperCAmelCase							=    find_backend(
             """    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):"""							)
         self.assertEqual(lowerCamelCase       ,				"""sentencepiece_and_tokenizers_and_vision"""							)


  def  lowerCamelCase       (      self		:			int							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    read_init()
         # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
         self.assertIn("""torch"""       ,				lowerCamelCase							)
         self.assertIn("""tensorflow_text"""       ,				lowerCamelCase							)
         self.assertIn("""sentencepiece_and_tokenizers"""       ,				lowerCamelCase							)

         # Likewise, we can't assert on the exact content of a key
         self.assertIn("""BertModel"""       ,				objects["""torch"""]							)
         self.assertIn("""TFBertModel"""       ,				objects["""tf"""]							)
         self.assertIn("""FlaxBertModel"""       ,				objects["""flax"""]							)
         self.assertIn("""BertModel"""       ,				objects["""torch"""]							)
         self.assertIn("""TFBertTokenizer"""       ,				objects["""tensorflow_text"""]							)
         self.assertIn("""convert_slow_tokenizer"""       ,				objects["""sentencepiece_and_tokenizers"""]							)


  def  lowerCamelCase       (      self		:			int							)						->	Any:


         """simple docstring"""
         _UpperCAmelCase							=    create_dummy_object("""CONSTANT"""       ,				"""'torch'"""							)
         self.assertEqual(lowerCamelCase       ,				"""\nCONSTANT = None\n"""							)

         _UpperCAmelCase							=    create_dummy_object("""function"""       ,				"""'torch'"""							)
         self.assertEqual(
             lowerCamelCase       ,				"""\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n"""							)

         _UpperCAmelCase							=    """
class FakeClass(metaclass=DummyObject):
    _backends = 'torch'

    def __init__(self, *args, **kwargs):
        requires_backends(self, 'torch')
"""
         _UpperCAmelCase							=    create_dummy_object("""FakeClass"""       ,				"""'torch'"""							)
         self.assertEqual(lowerCamelCase       ,				lowerCamelCase							)





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


         """simple docstring"""
         _UpperCAmelCase							=    """# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends


CONSTANT = None


def function(*args, **kwargs):
    requires_backends(function, [\"torch\"])


class FakeClass(metaclass=DummyObject):
    _backends = [\"torch\"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, [\"torch\"])
"""
         _UpperCAmelCase							=    create_dummy_files({"""torch""": ["""CONSTANT""", """function""", """FakeClass"""]}							)
         self.assertEqual(dummy_files["""torch"""]       ,				lowerCamelCase							)