


import unittest

from parameterized import parameterized

from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer







class       lowerCamelCase__   :





     def __init__(self		,     UpperCAmelCase		,     UpperCAmelCase=1_3		,     UpperCAmelCase=7		,     UpperCAmelCase=True		,     UpperCAmelCase=True		,     UpperCAmelCase=False		,     UpperCAmelCase=True		,     UpperCAmelCase=9_9		,     UpperCAmelCase=3_2		,     UpperCAmelCase=5		,     UpperCAmelCase=4		,     UpperCAmelCase=3_7		,     UpperCAmelCase="gelu"		,     UpperCAmelCase=0.1		,     UpperCAmelCase=0.1		,     UpperCAmelCase=5_1_2		,     UpperCAmelCase=1_6		,     UpperCAmelCase=2		,     UpperCAmelCase=0.02		,     UpperCAmelCase=3		,     UpperCAmelCase=4		,     UpperCAmelCase=None		,     )     ->   Optional[int]:
      _lowercase							      =parent
      _lowercase							      =batch_size
      _lowercase							      =seq_length
      _lowercase							      =is_training
      _lowercase							      =use_input_mask
      _lowercase							      =use_token_type_ids
      _lowercase							      =use_labels
      _lowercase							      =vocab_size
      _lowercase							      =hidden_size
      _lowercase							      =num_hidden_layers
      _lowercase							      =num_attention_heads
      _lowercase							      =intermediate_size
      _lowercase							      =hidden_act
      _lowercase							      =hidden_dropout_prob
      _lowercase							      =attention_probs_dropout_prob
      _lowercase							      =max_position_embeddings
      _lowercase							      =type_vocab_size
      _lowercase							      =type_sequence_label_size
      _lowercase							      =initializer_range
      _lowercase							      =num_labels
      _lowercase							      =num_choices
      _lowercase							      =scope





     def      __A					(self       )     ->   Optional[int]:
      _lowercase							      =ids_tensor([self.batch_size, self.seq_length]		,     self.vocab_size       )

      _lowercase							      =None
      if self.use_input_mask:
       _lowercase							      =random_attention_mask([self.batch_size, self.seq_length]       )

      _lowercase							      =None
      if self.use_token_type_ids:
       _lowercase							      =ids_tensor([self.batch_size, self.seq_length]		,     self.type_vocab_size       )

      _lowercase							      =None
      _lowercase							      =None
      _lowercase							      =None
      if self.use_labels:
       _lowercase							      =ids_tensor([self.batch_size]		,     self.type_sequence_label_size       )
       _lowercase							      =ids_tensor([self.batch_size, self.seq_length]		,     self.num_labels       )
       _lowercase							      =ids_tensor([self.batch_size]		,     self.num_choices       )

      _lowercase							      =self.get_config()

      return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels





     def      __A					(self       )     ->   Optional[Any]:
      return LlamaConfig(
          vocab_size=self.vocab_size		,     hidden_size=self.hidden_size		,     num_hidden_layers=self.num_hidden_layers		,     num_attention_heads=self.num_attention_heads		,     intermediate_size=self.intermediate_size		,     hidden_act=self.hidden_act		,     hidden_dropout_prob=self.hidden_dropout_prob		,     attention_probs_dropout_prob=self.attention_probs_dropout_prob		,     max_position_embeddings=self.max_position_embeddings		,     type_vocab_size=self.type_vocab_size		,     is_decoder=UpperCAmelCase		,     initializer_range=self.initializer_range		,     )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   List[Any]:
      _lowercase							      =LlamaModel(config=UpperCAmelCase       )
      model.to(UpperCAmelCase       )
      model.eval()
      _lowercase							      =model(UpperCAmelCase		,     attention_mask=UpperCAmelCase       )
      _lowercase							      =model(UpperCAmelCase       )
      self.parent.assertEqual(result.last_hidden_state.shape		,     (self.batch_size, self.seq_length, self.hidden_size)       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     )     ->   Any:
      _lowercase							      =True
      _lowercase							      =LlamaModel(UpperCAmelCase       )
      model.to(UpperCAmelCase       )
      model.eval()
      _lowercase							      =model(
          UpperCAmelCase		,     attention_mask=UpperCAmelCase		,     encoder_hidden_states=UpperCAmelCase		,     encoder_attention_mask=UpperCAmelCase		,     )
      _lowercase							      =model(
          UpperCAmelCase		,     attention_mask=UpperCAmelCase		,     encoder_hidden_states=UpperCAmelCase		,     )
      _lowercase							      =model(UpperCAmelCase		,     attention_mask=UpperCAmelCase       )
      self.parent.assertEqual(result.last_hidden_state.shape		,     (self.batch_size, self.seq_length, self.hidden_size)       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     )     ->   Union[str, Any]:
      _lowercase							      =LlamaForCausalLM(config=UpperCAmelCase       )
      model.to(UpperCAmelCase       )
      model.eval()
      _lowercase							      =model(UpperCAmelCase		,     attention_mask=UpperCAmelCase		,     labels=UpperCAmelCase       )
      self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.seq_length, self.vocab_size)       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     )     ->   List[Any]:
      _lowercase							      =True
      _lowercase							      =True
      _lowercase							      =LlamaForCausalLM(config=UpperCAmelCase       )
      model.to(UpperCAmelCase       )
      model.eval()

      # first forward pass
      _lowercase							      =model(
          UpperCAmelCase		,     attention_mask=UpperCAmelCase		,     encoder_hidden_states=UpperCAmelCase		,     encoder_attention_mask=UpperCAmelCase		,     use_cache=UpperCAmelCase		,     )
      _lowercase							      =outputs.past_key_values

      # create hypothetical multiple next token and extent to next_input_ids
      _lowercase							      =ids_tensor((self.batch_size, 3)		,     config.vocab_size       )
      _lowercase							      =ids_tensor((self.batch_size, 3)		,     vocab_size=2       )

      # append to next input_ids and
      _lowercase							      =torch.cat([input_ids, next_tokens]		,     dim=-1       )
      _lowercase							      =torch.cat([input_mask, next_mask]		,     dim=-1       )

      _lowercase							      =model(
          UpperCAmelCase		,     attention_mask=UpperCAmelCase		,     encoder_hidden_states=UpperCAmelCase		,     encoder_attention_mask=UpperCAmelCase		,     output_hidden_states=UpperCAmelCase		,     )['''hidden_states'''][0]
      _lowercase							      =model(
          UpperCAmelCase		,     attention_mask=UpperCAmelCase		,     encoder_hidden_states=UpperCAmelCase		,     encoder_attention_mask=UpperCAmelCase		,     past_key_values=UpperCAmelCase		,     output_hidden_states=UpperCAmelCase		,     )['''hidden_states'''][0]

      # select random slice
      _lowercase							      =ids_tensor((1,)		,     output_from_past.shape[-1]       ).item()
      _lowercase							      =output_from_no_past[:, -3:, random_slice_idx].detach()
      _lowercase							      =output_from_past[:, :, random_slice_idx].detach()

      self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]       )

      # test that outputs are equal for slice
      self.parent.assertTrue(torch.allclose(UpperCAmelCase		,     UpperCAmelCase		,     atol=1e-3       )       )





     def      __A					(self       )     ->   Any:
      _lowercase							      =self.prepare_config_and_inputs()
      (
          (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    
      )							      =config_and_inputs
      _lowercase							      ={'''input_ids''': input_ids, '''attention_mask''': input_mask}
      return config, inputs_dict







@require_torch
class       lowerCamelCase__   (       lowerCAmelCase   ,							lowerCAmelCase   ,							lowerCAmelCase   ,							unittest.TestCase):
     SCREAMING_SNAKE_CASE__      			=			(LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
     SCREAMING_SNAKE_CASE__      			=			(LlamaForCausalLM,) if is_torch_available() else ()
     SCREAMING_SNAKE_CASE__      			=			(
         {
             '''feature-extraction''': LlamaModel,
             '''text-classification''': LlamaForSequenceClassification,
             '''text-generation''': LlamaForCausalLM,
             '''zero-shot''': LlamaForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     SCREAMING_SNAKE_CASE__      			=			False
     SCREAMING_SNAKE_CASE__      			=			False





     def      __A					(self       )     ->   Optional[int]:
      _lowercase							      =LlamaModelTester(self       )
      _lowercase							      =ConfigTester(self		,     config_class=UpperCAmelCase		,     hidden_size=3_7       )





     def      __A					(self       )     ->   Dict:
      self.config_tester.run_common_tests()





     def      __A					(self       )     ->   Union[str, Any]:
      _lowercase							      =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*UpperCAmelCase       )





     def      __A					(self       )     ->   List[Any]:
      _lowercase							      =self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
       _lowercase							      =type
       self.model_tester.create_and_check_model(*UpperCAmelCase       )





     def      __A					(self       )     ->   Union[str, Any]:
      _lowercase      ,    _lowercase							      =self.model_tester.prepare_config_and_inputs_for_common()
      _lowercase							      =3
      _lowercase							      =input_dict['''input_ids''']
      _lowercase							      =input_ids.ne(1       ).to(UpperCAmelCase       )
      _lowercase							      =ids_tensor([self.model_tester.batch_size]		,     self.model_tester.type_sequence_label_size       )
      _lowercase							      =LlamaForSequenceClassification(UpperCAmelCase       )
      model.to(UpperCAmelCase       )
      model.eval()
      _lowercase							      =model(UpperCAmelCase		,     attention_mask=UpperCAmelCase		,     labels=UpperCAmelCase       )
      self.assertEqual(result.logits.shape		,     (self.model_tester.batch_size, self.model_tester.num_labels)       )





     def      __A					(self       )     ->   str:
      _lowercase      ,    _lowercase							      =self.model_tester.prepare_config_and_inputs_for_common()
      _lowercase							      =3
      _lowercase							      ='''single_label_classification'''
      _lowercase							      =input_dict['''input_ids''']
      _lowercase							      =input_ids.ne(1       ).to(UpperCAmelCase       )
      _lowercase							      =ids_tensor([self.model_tester.batch_size]		,     self.model_tester.type_sequence_label_size       )
      _lowercase							      =LlamaForSequenceClassification(UpperCAmelCase       )
      model.to(UpperCAmelCase       )
      model.eval()
      _lowercase							      =model(UpperCAmelCase		,     attention_mask=UpperCAmelCase		,     labels=UpperCAmelCase       )
      self.assertEqual(result.logits.shape		,     (self.model_tester.batch_size, self.model_tester.num_labels)       )





     def      __A					(self       )     ->   Dict:
      _lowercase      ,    _lowercase							      =self.model_tester.prepare_config_and_inputs_for_common()
      _lowercase							      =3
      _lowercase							      ='''multi_label_classification'''
      _lowercase							      =input_dict['''input_ids''']
      _lowercase							      =input_ids.ne(1       ).to(UpperCAmelCase       )
      _lowercase							      =ids_tensor(
          [self.model_tester.batch_size, config.num_labels]		,     self.model_tester.type_sequence_label_size       ).to(torch.float       )
      _lowercase							      =LlamaForSequenceClassification(UpperCAmelCase       )
      model.to(UpperCAmelCase       )
      model.eval()
      _lowercase							      =model(UpperCAmelCase		,     attention_mask=UpperCAmelCase		,     labels=UpperCAmelCase       )
      self.assertEqual(result.logits.shape		,     (self.model_tester.batch_size, self.model_tester.num_labels)       )





     @unittest.skip('''LLaMA buffers include complex numbers, which breaks this test'''       )
     def      __A					(self       )     ->   Dict:
      pass





     @parameterized.expand([('''linear''',), ('''dynamic''',)]       )
     def      __A					(self		,     UpperCAmelCase       )     ->   Optional[Any]:
      _lowercase      ,    _lowercase							      =self.model_tester.prepare_config_and_inputs_for_common()
      _lowercase							      =ids_tensor([1, 1_0]		,     config.vocab_size       )
      _lowercase							      =ids_tensor([1, int(config.max_position_embeddings * 1.5       )]		,     config.vocab_size       )

      set_seed(4_2       )  # Fixed seed at init time so the two models get the same random weights
      _lowercase							      =LlamaModel(UpperCAmelCase       )
      original_model.to(UpperCAmelCase       )
      original_model.eval()
      _lowercase							      =original_model(UpperCAmelCase       ).last_hidden_state
      _lowercase							      =original_model(UpperCAmelCase       ).last_hidden_state

      set_seed(4_2       )  # Fixed seed at init time so the two models get the same random weights
      _lowercase							      ={'''type''': scaling_type, '''factor''': 10.0}
      _lowercase							      =LlamaModel(UpperCAmelCase       )
      scaled_model.to(UpperCAmelCase       )
      scaled_model.eval()
      _lowercase							      =scaled_model(UpperCAmelCase       ).last_hidden_state
      _lowercase							      =scaled_model(UpperCAmelCase       ).last_hidden_state

      # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
      # maximum sequence length, so the outputs for the short input should match.
      if scaling_type == "dynamic":
       self.assertTrue(torch.allclose(UpperCAmelCase		,     UpperCAmelCase		,     atol=1e-5       )       )
      else:
       self.assertFalse(torch.allclose(UpperCAmelCase		,     UpperCAmelCase		,     atol=1e-5       )       )

      # The output should be different for long inputs
      self.assertFalse(torch.allclose(UpperCAmelCase		,     UpperCAmelCase		,     atol=1e-5       )       )







@require_torch
class       lowerCamelCase__   (       unittest.TestCase):





     @unittest.skip('''Logits are not exactly the same, once we fix the instabalities somehow, will update!'''       )
     @slow
     def      __A					(self       )     ->   Tuple:
      _lowercase							      =[1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
      _lowercase							      =LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-7b-hf'''		,     device_map='''auto'''       )
      _lowercase							      =model(torch.tensor([input_ids]       )       )
      # Expected mean on dim = -1
      _lowercase							      =torch.tensor([[-6.6550, -4.1227, -4.9859, -3.2406, 0.8262, -3.0033, 1.2964, -3.3699]]       )
      torch.testing.assert_close(out.mean(-1       )		,     UpperCAmelCase		,     atol=1e-2		,     rtol=1e-2       )
      # slicing logits[0, 0, 0:30]
      # fmt: off
      _lowercase							      =torch.tensor([-12.8281, -7.4453, -0.4639, -8.0625, -7.2500, -8.0000, -6.4883, -7.7695, -7.8438, -7.0312, -6.2188, -7.1328, -1.8496, 1.9961, -8.6250, -6.7227, -12.8281, -6.9492, -7.0742, -7.7852, -7.5820, -7.9062, -6.9375, -7.9805, -8.3438, -8.1562, -8.0469, -7.6250, -7.7422, -7.3398,]       )
      # fmt: on
      torch.testing.assert_close(out[0, 0, :3_0]		,     UpperCAmelCase		,     atol=1e-5		,     rtol=1e-5       )





     @unittest.skip('''Logits are not exactly the same, once we fix the instabalities somehow, will update!'''       )
     @slow
     def      __A					(self       )     ->   List[str]:
      _lowercase							      =[1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
      _lowercase							      =LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-13b-hf'''		,     device_map='''auto'''       )
      _lowercase							      =model(torch.tensor(UpperCAmelCase       )       )
      # Expected mean on dim = -1
      _lowercase							      =torch.tensor([[-2.0622, -1.2794, -1.1638, -0.9788, -1.4603, -1.0238, -1.7893, -1.4411]]       )
      torch.testing.assert_close(out.mean(-1       )		,     UpperCAmelCase		,     atol=1e-2		,     rtol=1e-2       )
      # slicing logits[0, 0, 0:30]
      # fmt: off
      _lowercase							      =torch.tensor([-8.1406, -8.0547, 2.7461, -1.2344, -0.1448, -1.8262, -1.0020, -1.8154, -1.6895, -1.8516, -2.3574, -0.9277, 3.7598, 6.5742, -1.2998, -0.1177, -8.1406, -2.9688, -2.9199, -3.1699, -3.5254, -2.3555, -2.7988, -3.4141, -2.8262, -4.5195, -3.3379, -3.3164, -2.7832, -3.0273]       )
      # fmt: on
      torch.testing.assert_close(out[0, 0, :3_0]		,     UpperCAmelCase		,     atol=1e-5		,     rtol=1e-5       )





     @unittest.skip('''Logits are not exactly the same, once we fix the instabalities somehow, will update!'''       )
     @slow
     def      __A					(self       )     ->   int:
      _lowercase							      =[1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
      _lowercase							      =LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-13b-chat-hf'''		,     device_map='''auto'''       )
      _lowercase							      =model(torch.tensor(UpperCAmelCase       )       )
      # Expected mean on dim = -1
      _lowercase							      =torch.tensor([[-0.8562, -1.8520, -0.7551, -0.4162, -1.5161, -1.2038, -2.4823, -2.3254]]       )
      torch.testing.assert_close(out.mean(-1       )		,     UpperCAmelCase		,     atol=1e-2		,     rtol=1e-2       )
      # slicing logits[0, 0, 0:30]
      # fmt: off
      _lowercase							      =torch.tensor([-2.2227, 4.8828, 0.9023, -0.4578, -0.7871, -0.1033, -0.6221, -0.5786, -0.7803, -1.0674, -1.2920, -0.1570, 0.8008, 2.0723, -0.9497, 0.2771, -2.2227, -0.7612, -1.4346, -1.2061, -1.6426, -0.3000, -0.7139, -1.1934, -1.8691, -1.6973, -1.5947, -1.2705, -0.3523, -0.5513]       )
      # fmt: on
      torch.testing.assert_close(out.mean(-1       )		,     UpperCAmelCase		,     atol=1e-2		,     rtol=1e-2       )





     @unittest.skip(
         '''Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test'''       )
     @slow
     def      __A					(self       )     ->   Dict:
      _lowercase							      =[1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
      _lowercase							      =LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-70b-hf'''		,     device_map='''auto'''       )
      _lowercase							      =model(torch.tensor(UpperCAmelCase       )       )

      _lowercase							      =torch.tensor(
          [[-4.2327, -3.3360, -4.6665, -4.7631, -1.8180, -3.4170, -1.4211, -3.1810]]		,     dtype=torch.floataa       )
      torch.testing.assert_close(out.mean(-1       )		,     UpperCAmelCase		,     atol=1e-2		,     rtol=1e-2       )
      # fmt: off
      _lowercase							      =torch.tensor([-9.4922, -3.9551, 1.7998, -5.6758, -5.1055, -5.8984, -4.8320, -6.8086, -6.5391, -5.6172, -5.5820, -5.5352, 1.7881, 3.6289, -6.5117, -3.4785, -9.5000, -6.0352, -6.8125, -6.0195, -6.6836, -5.4727, -6.2812, -6.0391, -7.3398, -7.4297, -7.4844, -6.5820, -5.8789, -5.5312]       )
      # fmt: on
      torch.testing.assert_close(out[0, 0, :3_0]		,     UpperCAmelCase		,     atol=1e-5		,     rtol=1e-5       )





     @unittest.skip('''Model is curently gated'''       )
     @slow
     def      __A					(self       )     ->   int:
      _lowercase							      ='''Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the "princi'''
      _lowercase							      ='''Simply put, the theory of relativity states that '''
      _lowercase							      =LlamaTokenizer.from_pretrained('''meta-llama/Llama-2-13b-chat-hf'''       )
      _lowercase							      =tokenizer.encode(UpperCAmelCase		,     return_tensors='''pt'''       )
      _lowercase							      =LlamaForCausalLM.from_pretrained(
          '''meta-llama/Llama-2-13b-chat-hf'''		,     device_map='''sequential'''		,     use_safetensors=UpperCAmelCase       )

      # greedy generation outputs
      _lowercase							      =model.generate(UpperCAmelCase		,     max_new_tokens=6_4		,     top_p=UpperCAmelCase		,     temperature=1		,     do_sample=UpperCAmelCase       )
      _lowercase							      =tokenizer.decode(generated_ids[0]		,     skip_special_tokens=UpperCAmelCase       )
      self.assertEqual(UpperCAmelCase		,     UpperCAmelCase       )





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCAmelCase__   	=							{
    '''configuration_xlm''': ['''XLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMConfig''', '''XLMOnnxConfig'''],
    '''tokenization_xlm''': ['''XLMTokenizer'''],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__   	=							[
      '''XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''XLMForMultipleChoice''',
      '''XLMForQuestionAnswering''',
      '''XLMForQuestionAnsweringSimple''',
      '''XLMForSequenceClassification''',
      '''XLMForTokenClassification''',
      '''XLMModel''',
      '''XLMPreTrainedModel''',
      '''XLMWithLMHeadModel''',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__   	=							[
      '''TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TFXLMForMultipleChoice''',
      '''TFXLMForQuestionAnsweringSimple''',
      '''TFXLMForSequenceClassification''',
      '''TFXLMForTokenClassification''',
      '''TFXLMMainLayer''',
      '''TFXLMModel''',
      '''TFXLMPreTrainedModel''',
      '''TFXLMWithLMHeadModel''',
  ]


if TYPE_CHECKING:
  from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
  from .tokenization_xlm import XLMTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_xlm import (
        XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        XLMForMultipleChoice,
        XLMForQuestionAnswering,
        XLMForQuestionAnsweringSimple,
        XLMForSequenceClassification,
        XLMForTokenClassification,
        XLMModel,
        XLMPreTrainedModel,
        XLMWithLMHeadModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_xlm import (
        TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFXLMForMultipleChoice,
        TFXLMForQuestionAnsweringSimple,
        TFXLMForSequenceClassification,
        TFXLMForTokenClassification,
        TFXLMMainLayer,
        TFXLMModel,
        TFXLMPreTrainedModel,
        TFXLMWithLMHeadModel,
    )

else:
  import sys

  UpperCAmelCase__   	=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

