






import unittest

from transformers import XLMConfig, is_torch_available
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 (
     XLMForMultipleChoice,
     XLMForQuestionAnswering,
     XLMForQuestionAnsweringSimple,
     XLMForSequenceClassification,
     XLMForTokenClassification,
     XLMModel,
     XLMWithLMHeadModel,
 )
 from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST


class 						_a       :






  """simple docstring"""





  def __init__(     self		,		_UpperCAmelCase		,		_UpperCAmelCase=13		,		_UpperCAmelCase=7		,		_UpperCAmelCase=True		,		_UpperCAmelCase=True		,		_UpperCAmelCase=True		,		_UpperCAmelCase=True		,		_UpperCAmelCase=True		,		_UpperCAmelCase=False		,		_UpperCAmelCase=False		,		_UpperCAmelCase=False		,		_UpperCAmelCase=2		,		_UpperCAmelCase=99		,		_UpperCAmelCase=0		,		_UpperCAmelCase=32		,		_UpperCAmelCase=5		,		_UpperCAmelCase=4		,		_UpperCAmelCase=0.1		,		_UpperCAmelCase=0.1		,		_UpperCAmelCase=512		,		_UpperCAmelCase=2		,		_UpperCAmelCase=0.0_2		,		_UpperCAmelCase=2		,		_UpperCAmelCase=4		,		_UpperCAmelCase="last"		,		_UpperCAmelCase=True		,		_UpperCAmelCase=None		,		_UpperCAmelCase=0		,		)			-> Dict:
     UpperCamelCase_									=     parent
     UpperCamelCase_									=     batch_size
     UpperCamelCase_									=     seq_length
     UpperCamelCase_									=     is_training
     UpperCamelCase_									=     use_input_lengths
     UpperCamelCase_									=     use_token_type_ids
     UpperCamelCase_									=     use_labels
     UpperCamelCase_									=     gelu_activation
     UpperCamelCase_									=     sinusoidal_embeddings
     UpperCamelCase_									=     causal
     UpperCamelCase_									=     asm
     UpperCamelCase_									=     n_langs
     UpperCamelCase_									=     vocab_size
     UpperCamelCase_									=     n_special
     UpperCamelCase_									=     hidden_size
     UpperCamelCase_									=     num_hidden_layers
     UpperCamelCase_									=     num_attention_heads
     UpperCamelCase_									=     hidden_dropout_prob
     UpperCamelCase_									=     attention_probs_dropout_prob
     UpperCamelCase_									=     max_position_embeddings
     UpperCamelCase_									=     type_sequence_label_size
     UpperCamelCase_									=     initializer_range
     UpperCamelCase_									=     num_labels
     UpperCamelCase_									=     num_choices
     UpperCamelCase_									=     summary_type
     UpperCamelCase_									=     use_proj
     UpperCamelCase_									=     scope
     UpperCamelCase_									=     bos_token_id




  def    _UpperCAmelCase     (     self )			-> Optional[Any]:
     UpperCamelCase_									=     ids_tensor([self.batch_size, self.seq_length]		,		self.vocab_size )
     UpperCamelCase_									=     random_attention_mask([self.batch_size, self.seq_length] )

     UpperCamelCase_									=     None
     if self.use_input_lengths:
        UpperCamelCase_									=     (
            ids_tensor([self.batch_size]		,		vocab_size=2 ) + self.seq_length - 2
        )  # small variation of seq_length

     UpperCamelCase_									=     None
     if self.use_token_type_ids:
        UpperCamelCase_									=     ids_tensor([self.batch_size, self.seq_length]		,		self.n_langs )

     UpperCamelCase_									=     None
     UpperCamelCase_									=     None
     UpperCamelCase_									=     None
     if self.use_labels:
        UpperCamelCase_									=     ids_tensor([self.batch_size]		,		self.type_sequence_label_size )
        UpperCamelCase_									=     ids_tensor([self.batch_size, self.seq_length]		,		self.num_labels )
        UpperCamelCase_									=     ids_tensor([self.batch_size]		,		2 ).float()
        UpperCamelCase_									=     ids_tensor([self.batch_size]		,		self.num_choices )

     UpperCamelCase_									=     self.get_config()

     return (
         config,
         input_ids,
         token_type_ids,
         input_lengths,
         sequence_labels,
         token_labels,
         is_impossible_labels,
         choice_labels,
         input_mask,
     )




  def    _UpperCAmelCase     (     self )			-> Any:
     return XLMConfig(
         vocab_size=self.vocab_size		,		n_special=self.n_special		,		emb_dim=self.hidden_size		,		n_layers=self.num_hidden_layers		,		n_heads=self.num_attention_heads		,		dropout=self.hidden_dropout_prob		,		attention_dropout=self.attention_probs_dropout_prob		,		gelu_activation=self.gelu_activation		,		sinusoidal_embeddings=self.sinusoidal_embeddings		,		asm=self.asm		,		causal=self.causal		,		n_langs=self.n_langs		,		max_position_embeddings=self.max_position_embeddings		,		initializer_range=self.initializer_range		,		summary_type=self.summary_type		,		use_proj=self.use_proj		,		num_labels=self.num_labels		,		bos_token_id=self.bos_token_id		,		)




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		)			-> List[str]:
     UpperCamelCase_									=     XLMModel(config=snake_case_ )
     model.to(snake_case_ )
     model.eval()
     UpperCamelCase_									=     model(snake_case_		,		lengths=snake_case_		,		langs=snake_case_ )
     UpperCamelCase_									=     model(snake_case_		,		langs=snake_case_ )
     UpperCamelCase_									=     model(snake_case_ )
     self.parent.assertEqual(result.last_hidden_state.shape		,		(self.batch_size, self.seq_length, self.hidden_size) )




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		)			-> List[str]:
     UpperCamelCase_									=     XLMWithLMHeadModel(snake_case_ )
     model.to(snake_case_ )
     model.eval()

     UpperCamelCase_									=     model(snake_case_		,		token_type_ids=snake_case_		,		labels=snake_case_ )
     self.parent.assertEqual(result.loss.shape		,		() )
     self.parent.assertEqual(result.logits.shape		,		(self.batch_size, self.seq_length, self.vocab_size) )




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		)			-> int:
     UpperCamelCase_									=     XLMForQuestionAnsweringSimple(snake_case_ )
     model.to(snake_case_ )
     model.eval()

     UpperCamelCase_									=     model(snake_case_ )

     UpperCamelCase_									=     model(snake_case_		,		start_positions=snake_case_		,		end_positions=snake_case_ )
     UpperCamelCase_									=     outputs
     self.parent.assertEqual(result.start_logits.shape		,		(self.batch_size, self.seq_length) )
     self.parent.assertEqual(result.end_logits.shape		,		(self.batch_size, self.seq_length) )




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		)			-> Union[str, Any]:
     UpperCamelCase_									=     XLMForQuestionAnswering(snake_case_ )
     model.to(snake_case_ )
     model.eval()

     UpperCamelCase_									=     model(snake_case_ )

     UpperCamelCase_									=     model(
         snake_case_		,		start_positions=snake_case_		,		end_positions=snake_case_		,		cls_index=snake_case_		,		is_impossible=snake_case_		,		p_mask=snake_case_		,		)

     UpperCamelCase_									=     model(
         snake_case_		,		start_positions=snake_case_		,		end_positions=snake_case_		,		cls_index=snake_case_		,		is_impossible=snake_case_		,		)

     (UpperCamelCase_					)									=     result_with_labels.to_tuple()

     UpperCamelCase_									=     model(snake_case_		,		start_positions=snake_case_		,		end_positions=snake_case_ )

     (UpperCamelCase_					)									=     result_with_labels.to_tuple()

     self.parent.assertEqual(result_with_labels.loss.shape		,		() )
     self.parent.assertEqual(result.start_top_log_probs.shape		,		(self.batch_size, model.config.start_n_top) )
     self.parent.assertEqual(result.start_top_index.shape		,		(self.batch_size, model.config.start_n_top) )
     self.parent.assertEqual(
         result.end_top_log_probs.shape		,		(self.batch_size, model.config.start_n_top * model.config.end_n_top) )
     self.parent.assertEqual(
         result.end_top_index.shape		,		(self.batch_size, model.config.start_n_top * model.config.end_n_top) )
     self.parent.assertEqual(result.cls_logits.shape		,		(self.batch_size,) )




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		)			-> Optional[int]:
     UpperCamelCase_									=     XLMForSequenceClassification(snake_case_ )
     model.to(snake_case_ )
     model.eval()

     UpperCamelCase_									=     model(snake_case_ )
     UpperCamelCase_									=     model(snake_case_		,		labels=snake_case_ )
     self.parent.assertEqual(result.loss.shape		,		() )
     self.parent.assertEqual(result.logits.shape		,		(self.batch_size, self.type_sequence_label_size) )




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		)			-> int:
     UpperCamelCase_									=     self.num_labels
     UpperCamelCase_									=     XLMForTokenClassification(snake_case_ )
     model.to(snake_case_ )
     model.eval()

     UpperCamelCase_									=     model(snake_case_		,		attention_mask=snake_case_		,		labels=snake_case_ )
     self.parent.assertEqual(result.logits.shape		,		(self.batch_size, self.seq_length, self.num_labels) )




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		)			-> Optional[Any]:
     UpperCamelCase_									=     self.num_choices
     UpperCamelCase_									=     XLMForMultipleChoice(config=snake_case_ )
     model.to(snake_case_ )
     model.eval()
     UpperCamelCase_									=     input_ids.unsqueeze(1 ).expand(-1		,		self.num_choices		,		-1 ).contiguous()
     UpperCamelCase_									=     token_type_ids.unsqueeze(1 ).expand(-1		,		self.num_choices		,		-1 ).contiguous()
     UpperCamelCase_									=     input_mask.unsqueeze(1 ).expand(-1		,		self.num_choices		,		-1 ).contiguous()
     UpperCamelCase_									=     model(
         snake_case_		,		attention_mask=snake_case_		,		token_type_ids=snake_case_		,		labels=snake_case_		,		)
     self.parent.assertEqual(result.logits.shape		,		(self.batch_size, self.num_choices) )




  def    _UpperCAmelCase     (     self )			-> int:
     UpperCamelCase_									=     self.prepare_config_and_inputs()
     (
         UpperCamelCase_					
     )									=     config_and_inputs
     UpperCamelCase_									=     {"input_ids": input_ids, "token_type_ids": token_type_ids, "lengths": input_lengths}
     return config, inputs_dict




@require_torch
class 						_a       (  _snake_case						,       _snake_case						,       _snake_case						,       unittest.TestCase    ):






  """simple docstring"""





  A_						=   (
      (
          XLMModel,
          XLMWithLMHeadModel,
          XLMForQuestionAnswering,
          XLMForSequenceClassification,
          XLMForQuestionAnsweringSimple,
          XLMForTokenClassification,
          XLMForMultipleChoice,
      )
      if is_torch_available()
      else ()
  )
  A_						=   (
      (XLMWithLMHeadModel,) if is_torch_available() else ()
  )  # TODO (PVP): Check other models whether language generation is also applicable
  A_						=   (
      {
          """feature-extraction""": XLMModel,
          """fill-mask""": XLMWithLMHeadModel,
          """question-answering""": XLMForQuestionAnsweringSimple,
          """text-classification""": XLMForSequenceClassification,
          """text-generation""": XLMWithLMHeadModel,
          """token-classification""": XLMForTokenClassification,
          """zero-shot""": XLMForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase )			-> List[Any]:
     if (
         pipeline_test_casse_name == "QAPipelineTests"
         and tokenizer_name is not None
         and not tokenizer_name.endswith('Fast' )
     ):
        # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
        # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
        # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
        return True

     return False




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase=False )			-> str:
     UpperCamelCase_									=     super()._prepare_for_class(snake_case_		,		snake_case_		,		return_labels=snake_case_ )

     if return_labels:
        if model_class.__name__ == "XLMForQuestionAnswering":
           UpperCamelCase_									=     torch.zeros(
               self.model_tester.batch_size		,		dtype=torch.long		,		device=snake_case_ )
           UpperCamelCase_									=     torch.zeros(
               self.model_tester.batch_size		,		dtype=torch.long		,		device=snake_case_ )

     return inputs_dict




  def    _UpperCAmelCase     (     self )			-> str:
     UpperCamelCase_									=     XLMModelTester(self )
     UpperCamelCase_									=     ConfigTester(self		,		config_class=snake_case_		,		emb_dim=37 )




  def    _UpperCAmelCase     (     self )			-> Any:
     self.config_tester.run_common_tests()




  def    _UpperCAmelCase     (     self )			-> Optional[Any]:
     UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_xlm_model(*snake_case_ )




  def    _UpperCAmelCase     (     self )			-> int:
     UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_xlm_lm_head(*snake_case_ )




  def    _UpperCAmelCase     (     self )			-> Tuple:
     UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_xlm_simple_qa(*snake_case_ )




  def    _UpperCAmelCase     (     self )			-> str:
     UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_xlm_qa(*snake_case_ )




  def    _UpperCAmelCase     (     self )			-> Tuple:
     UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_xlm_sequence_classif(*snake_case_ )




  def    _UpperCAmelCase     (     self )			-> Any:
     UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_xlm_token_classif(*snake_case_ )




  def    _UpperCAmelCase     (     self )			-> List[Any]:
     UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_xlm_for_multiple_choice(*snake_case_ )




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase=False		,		_UpperCAmelCase=1 )			-> Optional[int]:
     self.assertIsInstance(snake_case_		,		snake_case_ )
     self.assertListEqual(
         [isinstance(snake_case_		,		snake_case_ ) for iter_attentions in attentions]		,		[True] * len(snake_case_ ) )
     self.assertEqual(len(snake_case_ )		,		(max_length - min_length) * num_beam_groups )

     for idx, iter_attentions in enumerate(snake_case_ ):
        # adds PAD dummy token
        UpperCamelCase_									=     min_length + idx + 1
        UpperCamelCase_									=     min_length + idx + 1

        UpperCamelCase_									=     (
            batch_size * num_beam_groups,
            config.num_attention_heads,
            tgt_len,
            src_len,
        )
        # check attn size
        self.assertListEqual(
            [layer_attention.shape for layer_attention in iter_attentions]		,		[expected_shape] * len(snake_case_ ) )




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase=False		,		_UpperCAmelCase=1 )			-> Dict:
     self.assertIsInstance(snake_case_		,		snake_case_ )
     self.assertListEqual(
         [isinstance(snake_case_		,		snake_case_ ) for iter_hidden_states in hidden_states]		,		[True] * len(snake_case_ )		,		)
     self.assertEqual(len(snake_case_ )		,		(max_length - min_length) * num_beam_groups )

     for idx, iter_hidden_states in enumerate(snake_case_ ):
        # adds PAD dummy token
        UpperCamelCase_									=     min_length + idx + 1
        UpperCamelCase_									=     (batch_size * num_beam_groups, seq_len, config.hidden_size)
        # check hidden size
        self.assertListEqual(
            [layer_hidden_states.shape for layer_hidden_states in iter_hidden_states]		,		[expected_shape] * len(snake_case_ )		,		)
     pass




  @slow
  def    _UpperCAmelCase     (     self )			-> str:
     for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        UpperCamelCase_									=     XLMModel.from_pretrained(snake_case_ )
        self.assertIsNotNone(snake_case_ )




@require_torch
class 						_a       (  unittest.TestCase    ):






  """simple docstring"""





  @slow
  def    _UpperCAmelCase     (     self )			-> Optional[Any]:
     UpperCamelCase_									=     XLMWithLMHeadModel.from_pretrained('xlm-mlm-en-2048' )
     model.to(snake_case_ )
     UpperCamelCase_									=     torch.tensor([[14, 447]]		,		dtype=torch.long		,		device=snake_case_ )  # the president
     UpperCamelCase_									=     [
         14,
         447,
         14,
         447,
         14,
         447,
         14,
         447,
         14,
         447,
         14,
         447,
         14,
         447,
         14,
         447,
         14,
         447,
         14,
         447,
     ]  # the president the president the president the president the president the president the president the president the president the president
     # TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
     UpperCamelCase_									=     model.generate(snake_case_		,		do_sample=snake_case_ )
     self.assertListEqual(output_ids[0].cpu().numpy().tolist()		,		snake_case_ )













import os
from math import logaa






def     _snake_case    (__lowercase = "base_exp.txt"):
   UpperCamelCase_									=     0
   UpperCamelCase_									=     0
   for i, line in enumerate(open(os.path.join(os.path.dirname(__lowercase)    ,      __lowercase))):
      UpperCamelCase_		,      UpperCamelCase_									=     list(map(__lowercase    ,      line.split(',')))
      if x * logaa(__lowercase) > largest:
         UpperCamelCase_									=     x * logaa(__lowercase)
         UpperCamelCase_									=     i + 1
   return result


if __name__ == "__main__":
 print(solution())





