


'''simple docstring'''






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   __snake_case			:







    """simple docstring"""



    def __init__(  self       :    Optional[int]       ,		lowerCamelCase       :    List[Any]       ,		lowerCamelCase       :    Tuple=13       ,		lowerCamelCase       :    int=7       ,		lowerCamelCase       :    List[Any]=True       ,		lowerCamelCase       :    List[str]=True       ,		lowerCamelCase       :    str=True       ,		lowerCamelCase       :    Tuple=True       ,		lowerCamelCase       :    Optional[Any]=True       ,		lowerCamelCase       :    Any=False       ,		lowerCamelCase       :    Optional[int]=False       ,		lowerCamelCase       :    int=False       ,		lowerCamelCase       :    Any=2       ,		lowerCamelCase       :    Union[str, Any]=99       ,		lowerCamelCase       :    Optional[Any]=0       ,		lowerCamelCase       :    Optional[int]=32       ,		lowerCamelCase       :    Tuple=5       ,		lowerCamelCase       :    Any=4       ,		lowerCamelCase       :    Tuple=0.1       ,		lowerCamelCase       :    Union[str, Any]=0.1       ,		lowerCamelCase       :    str=5_12       ,		lowerCamelCase       :    str=2       ,		lowerCamelCase       :    Optional[Any]=0.02       ,		lowerCamelCase       :    List[str]=2       ,		lowerCamelCase       :    List[Any]=4       ,		lowerCamelCase       :    Tuple="last"       ,		lowerCamelCase       :    List[str]=True       ,		lowerCamelCase       :    Dict=None       ,		lowerCamelCase       :    List[str]=0       ,		)  ->    Optional[int]:
      lowerCAmelCase_     : List[Any]     =  parent
      lowerCAmelCase_     : Optional[int]     =  batch_size
      lowerCAmelCase_     : List[str]     =  seq_length
      lowerCAmelCase_     : Union[str, Any]     =  is_training
      lowerCAmelCase_     : int     =  use_input_lengths
      lowerCAmelCase_     : Optional[int]     =  use_token_type_ids
      lowerCAmelCase_     : Tuple     =  use_labels
      lowerCAmelCase_     : Optional[int]     =  gelu_activation
      lowerCAmelCase_     : Union[str, Any]     =  sinusoidal_embeddings
      lowerCAmelCase_     : Optional[Any]     =  causal
      lowerCAmelCase_     : Optional[int]     =  asm
      lowerCAmelCase_     : Optional[int]     =  n_langs
      lowerCAmelCase_     : List[str]     =  vocab_size
      lowerCAmelCase_     : Any     =  n_special
      lowerCAmelCase_     : str     =  hidden_size
      lowerCAmelCase_     : List[Any]     =  num_hidden_layers
      lowerCAmelCase_     : str     =  num_attention_heads
      lowerCAmelCase_     : Dict     =  hidden_dropout_prob
      lowerCAmelCase_     : Optional[Any]     =  attention_probs_dropout_prob
      lowerCAmelCase_     : Dict     =  max_position_embeddings
      lowerCAmelCase_     : int     =  type_sequence_label_size
      lowerCAmelCase_     : Tuple     =  initializer_range
      lowerCAmelCase_     : Any     =  num_labels
      lowerCAmelCase_     : str     =  num_choices
      lowerCAmelCase_     : Tuple     =  summary_type
      lowerCAmelCase_     : Any     =  use_proj
      lowerCAmelCase_     : int     =  scope
      lowerCAmelCase_     : str     =  bos_token_id

    def __lowercase  (  self       :    int						)  ->    Optional[Any]:
      lowerCAmelCase_     : Optional[Any]     =  ids_tensor([self.batch_size, self.seq_length]       ,		self.vocab_size						)
      lowerCAmelCase_     : str     =  random_attention_mask([self.batch_size, self.seq_length]						)

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

      lowerCAmelCase_     : Optional[int]     =  None
      if self.use_token_type_ids:
        lowerCAmelCase_     : Tuple     =  ids_tensor([self.batch_size, self.seq_length]       ,		self.n_langs						)

      lowerCAmelCase_     : str     =  None
      lowerCAmelCase_     : List[str]     =  None
      lowerCAmelCase_     : Optional[Any]     =  None
      if self.use_labels:
        lowerCAmelCase_     : Union[str, Any]     =  ids_tensor([self.batch_size]       ,		self.type_sequence_label_size						)
        lowerCAmelCase_     : Optional[Any]     =  ids_tensor([self.batch_size, self.seq_length]       ,		self.num_labels						)
        lowerCAmelCase_     : Optional[int]     =  ids_tensor([self.batch_size]       ,		2						).float()
        lowerCAmelCase_     : Any     =  ids_tensor([self.batch_size]       ,		self.num_choices						)

      lowerCAmelCase_     : Optional[int]     =  self.get_config()

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

    def __lowercase  (  self       :    int						)  ->    Tuple:
      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 __lowercase  (  self       :    List[str]       ,		lowerCamelCase       :    int       ,		lowerCamelCase       :    str       ,		lowerCamelCase       :    Any       ,		lowerCamelCase       :    str       ,		lowerCamelCase       :    Any       ,		lowerCamelCase       :    Union[str, Any]       ,		lowerCamelCase       :    Optional[int]       ,		lowerCamelCase       :    Dict       ,		lowerCamelCase       :    str       ,		)  ->    Any:
      lowerCAmelCase_     : Dict     =  XLMModel(config=lowerCamelCase						)
      model.to(lowerCamelCase						)
      model.eval()
      lowerCAmelCase_     : List[str]     =  model(lowerCamelCase       ,		lengths=lowerCamelCase       ,		langs=lowerCamelCase						)
      lowerCAmelCase_     : Union[str, Any]     =  model(lowerCamelCase       ,		langs=lowerCamelCase						)
      lowerCAmelCase_     : Union[str, Any]     =  model(lowerCamelCase						)
      self.parent.assertEqual(result.last_hidden_state.shape       ,		(self.batch_size, self.seq_length, self.hidden_size)						)

    def __lowercase  (  self       :    Tuple       ,		lowerCamelCase       :    Tuple       ,		lowerCamelCase       :    int       ,		lowerCamelCase       :    Optional[Any]       ,		lowerCamelCase       :    Union[str, Any]       ,		lowerCamelCase       :    int       ,		lowerCamelCase       :    List[Any]       ,		lowerCamelCase       :    Optional[Any]       ,		lowerCamelCase       :    Optional[Any]       ,		lowerCamelCase       :    List[Any]       ,		)  ->    Dict:
      lowerCAmelCase_     : Optional[int]     =  XLMWithLMHeadModel(lowerCamelCase						)
      model.to(lowerCamelCase						)
      model.eval()

      lowerCAmelCase_     : List[str]     =  model(lowerCamelCase       ,		token_type_ids=lowerCamelCase       ,		labels=lowerCamelCase						)
      self.parent.assertEqual(result.loss.shape       ,		()						)
      self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.seq_length, self.vocab_size)						)

    def __lowercase  (  self       :    Tuple       ,		lowerCamelCase       :    List[Any]       ,		lowerCamelCase       :    int       ,		lowerCamelCase       :    str       ,		lowerCamelCase       :    Any       ,		lowerCamelCase       :    List[Any]       ,		lowerCamelCase       :    str       ,		lowerCamelCase       :    List[Any]       ,		lowerCamelCase       :    Union[str, Any]       ,		lowerCamelCase       :    Optional[int]       ,		)  ->    List[Any]:
      lowerCAmelCase_     : Any     =  XLMForQuestionAnsweringSimple(lowerCamelCase						)
      model.to(lowerCamelCase						)
      model.eval()

      lowerCAmelCase_     : List[Any]     =  model(lowerCamelCase						)

      lowerCAmelCase_     : Optional[Any]     =  model(lowerCamelCase       ,		start_positions=lowerCamelCase       ,		end_positions=lowerCamelCase						)
      lowerCAmelCase_     : int     =  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 __lowercase  (  self       :    List[Any]       ,		lowerCamelCase       :    int       ,		lowerCamelCase       :    List[str]       ,		lowerCamelCase       :    List[str]       ,		lowerCamelCase       :    Optional[int]       ,		lowerCamelCase       :    Optional[int]       ,		lowerCamelCase       :    str       ,		lowerCamelCase       :    Dict       ,		lowerCamelCase       :    Tuple       ,		lowerCamelCase       :    Dict       ,		)  ->    List[str]:
      lowerCAmelCase_     : Optional[int]     =  XLMForQuestionAnswering(lowerCamelCase						)
      model.to(lowerCamelCase						)
      model.eval()

      lowerCAmelCase_     : Tuple     =  model(lowerCamelCase						)

      lowerCAmelCase_     : Optional[int]     =  model(
          lowerCamelCase       ,		start_positions=lowerCamelCase       ,		end_positions=lowerCamelCase       ,		cls_index=lowerCamelCase       ,		is_impossible=lowerCamelCase       ,		p_mask=lowerCamelCase       ,		)

      lowerCAmelCase_     : Any     =  model(
          lowerCamelCase       ,		start_positions=lowerCamelCase       ,		end_positions=lowerCamelCase       ,		cls_index=lowerCamelCase       ,		is_impossible=lowerCamelCase       ,		)

      ((lowerCAmelCase_),				)     : Tuple     =  result_with_labels.to_tuple()

      lowerCAmelCase_     : Tuple     =  model(lowerCamelCase       ,		start_positions=lowerCamelCase       ,		end_positions=lowerCamelCase						)

      ((lowerCAmelCase_),				)     : Optional[Any]     =  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 __lowercase  (  self       :    Any       ,		lowerCamelCase       :    List[str]       ,		lowerCamelCase       :    List[Any]       ,		lowerCamelCase       :    List[str]       ,		lowerCamelCase       :    str       ,		lowerCamelCase       :    Optional[int]       ,		lowerCamelCase       :    Optional[Any]       ,		lowerCamelCase       :    Optional[int]       ,		lowerCamelCase       :    str       ,		lowerCamelCase       :    Optional[int]       ,		)  ->    List[Any]:
      lowerCAmelCase_     : Any     =  XLMForSequenceClassification(lowerCamelCase						)
      model.to(lowerCamelCase						)
      model.eval()

      lowerCAmelCase_     : Optional[Any]     =  model(lowerCamelCase						)
      lowerCAmelCase_     : Dict     =  model(lowerCamelCase       ,		labels=lowerCamelCase						)
      self.parent.assertEqual(result.loss.shape       ,		()						)
      self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.type_sequence_label_size)						)

    def __lowercase  (  self       :    Optional[Any]       ,		lowerCamelCase       :    List[str]       ,		lowerCamelCase       :    Union[str, Any]       ,		lowerCamelCase       :    Union[str, Any]       ,		lowerCamelCase       :    str       ,		lowerCamelCase       :    List[str]       ,		lowerCamelCase       :    Union[str, Any]       ,		lowerCamelCase       :    Optional[int]       ,		lowerCamelCase       :    Tuple       ,		lowerCamelCase       :    int       ,		)  ->    Tuple:
      lowerCAmelCase_     : Any     =  self.num_labels
      lowerCAmelCase_     : Any     =  XLMForTokenClassification(lowerCamelCase						)
      model.to(lowerCamelCase						)
      model.eval()

      lowerCAmelCase_     : Dict     =  model(lowerCamelCase       ,		attention_mask=lowerCamelCase       ,		labels=lowerCamelCase						)
      self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.seq_length, self.num_labels)						)

    def __lowercase  (  self       :    Optional[int]       ,		lowerCamelCase       :    Optional[int]       ,		lowerCamelCase       :    List[Any]       ,		lowerCamelCase       :    Union[str, Any]       ,		lowerCamelCase       :    Optional[Any]       ,		lowerCamelCase       :    Tuple       ,		lowerCamelCase       :    List[Any]       ,		lowerCamelCase       :    List[str]       ,		lowerCamelCase       :    Any       ,		lowerCamelCase       :    Any       ,		)  ->    Any:
      lowerCAmelCase_     : List[Any]     =  self.num_choices
      lowerCAmelCase_     : str     =  XLMForMultipleChoice(config=lowerCamelCase						)
      model.to(lowerCamelCase						)
      model.eval()
      lowerCAmelCase_     : List[Any]     =  input_ids.unsqueeze(1						).expand(-1       ,		self.num_choices       ,		-1						).contiguous()
      lowerCAmelCase_     : Dict     =  token_type_ids.unsqueeze(1						).expand(-1       ,		self.num_choices       ,		-1						).contiguous()
      lowerCAmelCase_     : Union[str, Any]     =  input_mask.unsqueeze(1						).expand(-1       ,		self.num_choices       ,		-1						).contiguous()
      lowerCAmelCase_     : int     =  model(
          lowerCamelCase       ,		attention_mask=lowerCamelCase       ,		token_type_ids=lowerCamelCase       ,		labels=lowerCamelCase       ,		)
      self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.num_choices)						)





    def __lowercase  (  self       :    int						)  ->    Optional[Any]:
      lowerCAmelCase_     : List[str]     =  self.prepare_config_and_inputs()
      (
          (
          lowerCAmelCase_
      ),				(
          lowerCAmelCase_
      ),				(
          lowerCAmelCase_
      ),				(
          lowerCAmelCase_
      ),				(
          lowerCAmelCase_
      ),				(
          lowerCAmelCase_
      ),				(
          lowerCAmelCase_
      ),				(
          lowerCAmelCase_
      ),				(
          lowerCAmelCase_
      ),				
      )     : List[Any]     =  config_and_inputs
      lowerCAmelCase_     : Optional[Any]     =  {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """lengths""": input_lengths}
      return config, inputs_dict







@require_torch
class   __snake_case			(  _SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,unittest.TestCase):







    """simple docstring"""



    lowercase  					=   (
        (
            XLMModel,
            XLMWithLMHeadModel,
            XLMForQuestionAnswering,
            XLMForSequenceClassification,
            XLMForQuestionAnsweringSimple,
            XLMForTokenClassification,
            XLMForMultipleChoice,
        )
        if is_torch_available()
        else ()
    )
    lowercase  					=   (
        (XLMWithLMHeadModel,) if is_torch_available() else ()
    )  # TODO (PVP): Check other models whether language generation is also applicable
    lowercase  					=   (
        {
            '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 __lowercase  (  self       :    Tuple       ,		lowerCamelCase       :    List[Any]       ,		lowerCamelCase       :    Union[str, Any]       ,		lowerCamelCase       :    List[str]       ,		lowerCamelCase       :    Optional[int]       ,		lowerCamelCase       :    Tuple						)  ->    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 __lowercase  (  self       :    Optional[int]       ,		lowerCamelCase       :    List[Any]       ,		lowerCamelCase       :    Dict       ,		lowerCamelCase       :    List[Any]=False						)  ->    Optional[Any]:
      lowerCAmelCase_     : Optional[Any]     =  super()._prepare_for_class(lowerCamelCase       ,		lowerCamelCase       ,		return_labels=lowerCamelCase						)

      if return_labels:
        if model_class.__name__ == "XLMForQuestionAnswering":
          lowerCAmelCase_     : Tuple     =  torch.zeros(
              self.model_tester.batch_size       ,		dtype=torch.long       ,		device=lowerCamelCase						)
          lowerCAmelCase_     : List[str]     =  torch.zeros(
              self.model_tester.batch_size       ,		dtype=torch.long       ,		device=lowerCamelCase						)

      return inputs_dict

    def __lowercase  (  self       :    List[Any]						)  ->    Optional[int]:
      lowerCAmelCase_     : Union[str, Any]     =  XLMModelTester(self						)
      lowerCAmelCase_     : Optional[Any]     =  ConfigTester(self       ,		config_class=lowerCamelCase       ,		emb_dim=37						)

    def __lowercase  (  self       :    Union[str, Any]						)  ->    Any:
      self.config_tester.run_common_tests()

    def __lowercase  (  self       :    Optional[Any]						)  ->    Union[str, Any]:
      lowerCAmelCase_     : Any     =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_xlm_model(*lowerCamelCase						)

    def __lowercase  (  self       :    Optional[Any]						)  ->    Tuple:
      lowerCAmelCase_     : Optional[int]     =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_xlm_lm_head(*lowerCamelCase						)

    def __lowercase  (  self       :    List[Any]						)  ->    int:
      lowerCAmelCase_     : List[str]     =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_xlm_simple_qa(*lowerCamelCase						)

    def __lowercase  (  self       :    List[Any]						)  ->    Dict:
      lowerCAmelCase_     : List[str]     =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_xlm_qa(*lowerCamelCase						)

    def __lowercase  (  self       :    Optional[Any]						)  ->    Any:
      lowerCAmelCase_     : str     =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_xlm_sequence_classif(*lowerCamelCase						)

    def __lowercase  (  self       :    Dict						)  ->    str:
      lowerCAmelCase_     : int     =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_xlm_token_classif(*lowerCamelCase						)

    def __lowercase  (  self       :    Any						)  ->    str:
      lowerCAmelCase_     : str     =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_xlm_for_multiple_choice(*lowerCamelCase						)

    def __lowercase  (  self       :    Tuple       ,		lowerCamelCase       :    Dict       ,		lowerCamelCase       :    Dict       ,		lowerCamelCase       :    Union[str, Any]       ,		lowerCamelCase       :    str       ,		lowerCamelCase       :    Optional[int]       ,		lowerCamelCase       :    int=False       ,		lowerCamelCase       :    Any=1						)  ->    Any:
      self.assertIsInstance(lowerCamelCase       ,		lowerCamelCase						)
      self.assertListEqual(
          [isinstance(lowerCamelCase       ,		lowerCamelCase						) for iter_attentions in attentions]       ,		[True] * len(lowerCamelCase						)						)
      self.assertEqual(len(lowerCamelCase						)       ,		(max_length - min_length) * num_beam_groups						)

      for idx, iter_attentions in enumerate(lowerCamelCase						):
        # adds PAD dummy token
        lowerCAmelCase_     : Union[str, Any]     =  min_length + idx + 1
        lowerCAmelCase_     : str     =  min_length + idx + 1

        lowerCAmelCase_     : str     =  (
            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(lowerCamelCase						)						)

    def __lowercase  (  self       :    int       ,		lowerCamelCase       :    Tuple       ,		lowerCamelCase       :    Optional[Any]       ,		lowerCamelCase       :    Any       ,		lowerCamelCase       :    Any       ,		lowerCamelCase       :    str       ,		lowerCamelCase       :    List[Any]=False       ,		lowerCamelCase       :    str=1						)  ->    Union[str, Any]:
      self.assertIsInstance(lowerCamelCase       ,		lowerCamelCase						)
      self.assertListEqual(
          [isinstance(lowerCamelCase       ,		lowerCamelCase						) for iter_hidden_states in hidden_states]       ,		[True] * len(lowerCamelCase						)       ,		)
      self.assertEqual(len(lowerCamelCase						)       ,		(max_length - min_length) * num_beam_groups						)

      for idx, iter_hidden_states in enumerate(lowerCamelCase						):
        # adds PAD dummy token
        lowerCAmelCase_     : List[Any]     =  min_length + idx + 1
        lowerCAmelCase_     : List[Any]     =  (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(lowerCamelCase						)       ,		)
      pass





    @slow
    def __lowercase  (  self       :    Any						)  ->    List[Any]:
      for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        lowerCAmelCase_     : Dict     =  XLMModel.from_pretrained(lowerCamelCase						)
        self.assertIsNotNone(lowerCamelCase						)







@require_torch
class   __snake_case			(  unittest.TestCase):







    """simple docstring"""



    @slow
    def __lowercase  (  self       :    Dict						)  ->    Tuple:
      lowerCAmelCase_     : Optional[Any]     =  XLMWithLMHeadModel.from_pretrained("""xlm-mlm-en-2048"""						)
      model.to(lowerCamelCase						)
      lowerCAmelCase_     : List[Any]     =  torch.tensor([[14, 4_47]]       ,		dtype=torch.long       ,		device=lowerCamelCase						)  # the president
      lowerCAmelCase_     : List[str]     =  [
          14,
          4_47,
          14,
          4_47,
          14,
          4_47,
          14,
          4_47,
          14,
          4_47,
          14,
          4_47,
          14,
          4_47,
          14,
          4_47,
          14,
          4_47,
          14,
          4_47,
      ]  # 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
      lowerCAmelCase_     : str     =  model.generate(lowerCamelCase       ,		do_sample=lowerCamelCase						)
      self.assertListEqual(output_ids[0].cpu().numpy().tolist()       ,		lowerCamelCase						)







'''simple docstring'''






import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeqaSeqLM,
    AutoTokenizer,
    HfArgumentParser,
    MBartTokenizer,
    MBartTokenizerFast,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    SeqaSeqDataCollator,
    SeqaSeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


__A   :    str											= logging.getLogger(__name__)






@dataclass
class   __snake_case			:







    """simple docstring"""



    lowercase  					=   field(
        metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'})
    lowercase  					=   field(
        default=_SCREAMING_SNAKE_CASE      ,metadata={'help': 'Pretrained config name or path if not the same as model_name'})
    lowercase  					=   field(
        default=_SCREAMING_SNAKE_CASE      ,metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'})
    lowercase  					=   field(
        default=_SCREAMING_SNAKE_CASE      ,metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}      ,)
    lowercase  					=   field(default=_SCREAMING_SNAKE_CASE      ,metadata={'help': 'Whether tp freeze the encoder.'})
    lowercase  					=   field(default=_SCREAMING_SNAKE_CASE      ,metadata={'help': 'Whether  to freeze the embeddings.'})







@dataclass
class   __snake_case			:







    """simple docstring"""



    lowercase  					=   field(
        metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'})
    lowercase  					=   field(
        default='summarization'      ,metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'}      ,)
    lowercase  					=   field(
        default=10_24      ,metadata={
            'help': (
                'The maximum total input sequence length after tokenization. Sequences longer '
                'than this will be truncated, sequences shorter will be padded.'
            )
        }      ,)
    lowercase  					=   field(
        default=1_28      ,metadata={
            'help': (
                'The maximum total sequence length for target text after tokenization. Sequences longer '
                'than this will be truncated, sequences shorter will be padded.'
            )
        }      ,)
    lowercase  					=   field(
        default=1_42      ,metadata={
            'help': (
                'The maximum total sequence length for validation target text after tokenization. Sequences longer '
                'than this will be truncated, sequences shorter will be padded. '
                'This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '
                'during ``evaluate`` and ``predict``.'
            )
        }      ,)
    lowercase  					=   field(
        default=1_42      ,metadata={
            'help': (
                'The maximum total sequence length for test target text after tokenization. Sequences longer '
                'than this will be truncated, sequences shorter will be padded.'
            )
        }      ,)
    lowercase  					=   field(default=-1      ,metadata={'help': '# training examples. -1 means use all.'})
    lowercase  					=   field(default=-1      ,metadata={'help': '# validation examples. -1 means use all.'})
    lowercase  					=   field(default=-1      ,metadata={'help': '# test examples. -1 means use all.'})
    lowercase  					=   field(default=_SCREAMING_SNAKE_CASE      ,metadata={'help': 'Source language id for translation.'})
    lowercase  					=   field(default=_SCREAMING_SNAKE_CASE      ,metadata={'help': 'Target language id for translation.'})
    lowercase  					=   field(default=_SCREAMING_SNAKE_CASE      ,metadata={'help': '# num_beams to use for evaluation.'})
    lowercase  					=   field(
        default=_SCREAMING_SNAKE_CASE      ,metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'}      ,)







def 							UpperCamelCase_     (       A__   :  Tuple				,       A__   :  List[str]				,       A__   :  str   ):
  '''simple docstring'''





  logger.info(f'***** {split} metrics *****'   )
  for key in sorted(metrics.keys()   ):
    logger.info(f'  {key} = {metrics[key]}'   )
  save_json(A__				,       os.path.join(A__				,       f'{split}_results.json'   )   )







def 							UpperCamelCase_     (       ):
  '''simple docstring'''





  lowerCAmelCase_     : Dict     =  HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments)   )

  if len(sys.argv   ) == 2 and sys.argv[1].endswith(""".json"""   ):
    # If we pass only one argument to the script and it's the path to a json file,
    # let's parse it to get our arguments.
    lowerCAmelCase_,				lowerCAmelCase_,				lowerCAmelCase_     : Optional[int]     =  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
  else:
    lowerCAmelCase_,				lowerCAmelCase_,				lowerCAmelCase_     : Union[str, Any]     =  parser.parse_args_into_dataclasses()

  check_output_dir(A__   )

  # Setup logging
  logging.basicConfig(
      format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""				,       datefmt="""%m/%d/%Y %H:%M:%S"""				,       level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN				,       )
  logger.warning(
      """Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"""				,       training_args.local_rank				,       training_args.device				,       training_args.n_gpu				,       bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED   )				,       training_args.fpaa				,       )
  transformers.utils.logging.enable_default_handler()
  transformers.utils.logging.enable_explicit_format()
  # Set the verbosity to info of the Transformers logger (on main process only):
  if is_main_process(training_args.local_rank   ):
    transformers.utils.logging.set_verbosity_info()
  logger.info("""Training/evaluation parameters %s"""				,       A__   )

  # Set seed
  set_seed(training_args.seed   )

  # Load pretrained model and tokenizer
  #
  # Distributed training:
  # The .from_pretrained methods guarantee that only one local process can concurrently
  # download model & vocab.

  lowerCAmelCase_     : List[Any]     =  AutoConfig.from_pretrained(
      model_args.config_name if model_args.config_name else model_args.model_name_or_path				,       cache_dir=model_args.cache_dir				,       )

  lowerCAmelCase_     : List[Any]     =  ("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
  for p in extra_model_params:
    if getattr(A__				,       A__				,       A__   ):
      assert hasattr(A__				,       A__   ), f'({config.__class__.__name__}) doesn\'t have a `{p}` attribute'
      setattr(A__				,       A__				,       getattr(A__				,       A__   )   )

  lowerCAmelCase_     : Any     =  AutoTokenizer.from_pretrained(
      model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path				,       cache_dir=model_args.cache_dir				,       )
  lowerCAmelCase_     : Union[str, Any]     =  AutoModelForSeqaSeqLM.from_pretrained(
      model_args.model_name_or_path				,       from_tf=""".ckpt""" in model_args.model_name_or_path				,       config=A__				,       cache_dir=model_args.cache_dir				,       )

  # use task specific params
  use_task_specific_params(A__				,       data_args.task   )

  # set num_beams for evaluation
  if data_args.eval_beams is None:
    lowerCAmelCase_     : List[Any]     =  model.config.num_beams

  # set decoder_start_token_id for MBart
  if model.config.decoder_start_token_id is None and isinstance(A__				,       (MBartTokenizer, MBartTokenizerFast)   ):
    assert (
        data_args.tgt_lang is not None and data_args.src_lang is not None
    ), "mBart requires --tgt_lang and --src_lang"
    if isinstance(A__				,       A__   ):
      lowerCAmelCase_     : List[str]     =  tokenizer.lang_code_to_id[data_args.tgt_lang]
    else:
      lowerCAmelCase_     : int     =  tokenizer.convert_tokens_to_ids(data_args.tgt_lang   )

  if model_args.freeze_embeds:
    freeze_embeds(A__   )
  if model_args.freeze_encoder:
    freeze_params(model.get_encoder()   )
    assert_all_frozen(model.get_encoder()   )

  lowerCAmelCase_     : Any     =  SeqaSeqDataset

  # Get datasets
  lowerCAmelCase_     : List[str]     =  (
      dataset_class(
          A__				,       type_path="""train"""				,       data_dir=data_args.data_dir				,       n_obs=data_args.n_train				,       max_target_length=data_args.max_target_length				,       max_source_length=data_args.max_source_length				,       prefix=model.config.prefix or """"""				,       )
      if training_args.do_train
      else None
  )
  lowerCAmelCase_     : List[Any]     =  (
      dataset_class(
          A__				,       type_path="""val"""				,       data_dir=data_args.data_dir				,       n_obs=data_args.n_val				,       max_target_length=data_args.val_max_target_length				,       max_source_length=data_args.max_source_length				,       prefix=model.config.prefix or """"""				,       )
      if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
      else None
  )
  lowerCAmelCase_     : List[Any]     =  (
      dataset_class(
          A__				,       type_path="""test"""				,       data_dir=data_args.data_dir				,       n_obs=data_args.n_test				,       max_target_length=data_args.test_max_target_length				,       max_source_length=data_args.max_source_length				,       prefix=model.config.prefix or """"""				,       )
      if training_args.do_predict
      else None
  )

  # Initialize our Trainer
  lowerCAmelCase_     : Dict     =  (
      build_compute_metrics_fn(data_args.task				,       A__   ) if training_args.predict_with_generate else None
  )
  lowerCAmelCase_     : str     =  SeqaSeqTrainer(
      model=A__				,       args=A__				,       data_args=A__				,       train_dataset=A__				,       eval_dataset=A__				,       data_collator=SeqaSeqDataCollator(
          A__				,       A__				,       model.config.decoder_start_token_id				,       training_args.tpu_num_cores   )				,       compute_metrics=A__				,       tokenizer=A__				,       )

  lowerCAmelCase_     : Dict     =  {}
  # Training
  if training_args.do_train:
    logger.info("""*** Train ***"""   )

    lowerCAmelCase_     : Optional[int]     =  trainer.train(
        model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path   ) else None   )
    lowerCAmelCase_     : Union[str, Any]     =  train_result.metrics
    lowerCAmelCase_     : Optional[int]     =  data_args.n_train

    trainer.save_model()  # this also saves the tokenizer

    if trainer.is_world_process_zero():
      handle_metrics("""train"""				,       A__				,       training_args.output_dir   )
      all_metrics.update(A__   )

      # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
      trainer.state.save_to_json(os.path.join(training_args.output_dir				,       """trainer_state.json"""   )   )

      # For convenience, we also re-save the tokenizer to the same directory,
      # so that you can share your model easily on huggingface.co/models =)
      tokenizer.save_pretrained(training_args.output_dir   )

    # Evaluation
  if training_args.do_eval:
    logger.info("""*** Evaluate ***"""   )

    lowerCAmelCase_     : Any     =  trainer.evaluate(metric_key_prefix="""val"""   )
    lowerCAmelCase_     : Optional[int]     =  data_args.n_val
    lowerCAmelCase_     : Any     =  round(metrics["""val_loss"""]				,       4   )

    if trainer.is_world_process_zero():
      handle_metrics("""val"""				,       A__				,       training_args.output_dir   )
      all_metrics.update(A__   )

  if training_args.do_predict:
    logger.info("""*** Predict ***"""   )

    lowerCAmelCase_     : Union[str, Any]     =  trainer.predict(test_dataset=A__				,       metric_key_prefix="""test"""   )
    lowerCAmelCase_     : Optional[int]     =  test_output.metrics
    lowerCAmelCase_     : List[Any]     =  data_args.n_test

    if trainer.is_world_process_zero():
      lowerCAmelCase_     : int     =  round(metrics["""test_loss"""]				,       4   )
      handle_metrics("""test"""				,       A__				,       training_args.output_dir   )
      all_metrics.update(A__   )

      if training_args.predict_with_generate:
        lowerCAmelCase_     : int     =  tokenizer.batch_decode(
            test_output.predictions				,       skip_special_tokens=A__				,       clean_up_tokenization_spaces=A__   )
        lowerCAmelCase_     : List[Any]     =  lmap(str.strip				,       A__   )
        write_txt_file(A__				,       os.path.join(training_args.output_dir				,       """test_generations.txt"""   )   )

  if trainer.is_world_process_zero():
    save_json(A__				,       os.path.join(training_args.output_dir				,       """all_results.json"""   )   )

  return all_metrics







def 							UpperCamelCase_     (       A__   :  Optional[int]   ):
  '''simple docstring'''





  main()


if __name__ == "__main__":
  main()



