


from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import (
       TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
       TFXLMForMultipleChoice,
       TFXLMForQuestionAnsweringSimple,
       TFXLMForSequenceClassification,
       TFXLMForTokenClassification,
       TFXLMModel,
       TFXLMWithLMHeadModel,
       XLMConfig,
   )
class      UpperCAmelCase_ :

   '''simple docstring'''




   def __init__(		self						,    __A						,    ):






     """simple docstring"""





     lowerCamelCase			: Union[str, Any]   =    parent
     lowerCamelCase			: Union[str, Any]   =    13
     lowerCamelCase			: str   =    7
     lowerCamelCase			: Union[str, Any]   =    True
     lowerCamelCase			: int   =    True
     lowerCamelCase			: str   =    True
     lowerCamelCase			: List[Any]   =    True
     lowerCamelCase			: Tuple   =    True
     lowerCamelCase			: int   =    False
     lowerCamelCase			: int   =    False
     lowerCamelCase			: Dict   =    False
     lowerCamelCase			: Optional[Any]   =    2
     lowerCamelCase			: Any   =    99
     lowerCamelCase			: int   =    0
     lowerCamelCase			: Dict   =    32
     lowerCamelCase			: int   =    2
     lowerCamelCase			: List[str]   =    4
     lowerCamelCase			: Optional[Any]   =    0.1
     lowerCamelCase			: Any   =    0.1
     lowerCamelCase			: List[str]   =    512
     lowerCamelCase			: Dict   =    16
     lowerCamelCase			: Optional[int]   =    2
     lowerCamelCase			: str   =    0.02
     lowerCamelCase			: List[Any]   =    3
     lowerCamelCase			: Tuple   =    4
     lowerCamelCase			: Optional[int]   =    "last"
     lowerCamelCase			: int   =    True
     lowerCamelCase			: List[str]   =    None
     lowerCamelCase			: str   =    0






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: List[str]   =    ids_tensor([self.batch_size, self.seq_length]						,    self.vocab_size			)
     lowerCamelCase			: Dict   =    random_attention_mask([self.batch_size, self.seq_length]						,    dtype=tf.floataa			)

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

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

     lowerCamelCase			: str   =    None
     lowerCamelCase			: List[Any]   =    None
     lowerCamelCase			: Any   =    None
     if self.use_labels:
       lowerCamelCase			: List[str]   =    ids_tensor([self.batch_size]						,    self.type_sequence_label_size			)
       lowerCamelCase			: Dict   =    ids_tensor([self.batch_size, self.seq_length]						,    self.num_labels			)
       lowerCamelCase			: int   =    ids_tensor([self.batch_size]						,    2						,    dtype=tf.floataa			)
       lowerCamelCase			: List[Any]   =    ids_tensor([self.batch_size]						,    self.num_choices			)

     lowerCamelCase			: Optional[int]   =    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						,    bos_token_id=self.bos_token_id						,    )

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






   def _snake_case   (		self						,    __A						,    __A						,    __A						,    __A						,    __A						,    __A						,    __A						,    __A						,    __A						,    ):






     """simple docstring"""





     lowerCamelCase			: Union[str, Any]   =    TFXLMModel(config=__A			)
     lowerCamelCase			: str   =    {"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
     lowerCamelCase			: Optional[int]   =    model(__A			)

     lowerCamelCase			: Union[str, Any]   =    [input_ids, input_mask]
     lowerCamelCase			: List[str]   =    model(__A			)
     self.parent.assertEqual(result.last_hidden_state.shape						,    (self.batch_size, self.seq_length, self.hidden_size)			)






   def _snake_case   (		self						,    __A						,    __A						,    __A						,    __A						,    __A						,    __A						,    __A						,    __A						,    __A						,    ):






     """simple docstring"""





     lowerCamelCase			: Dict   =    TFXLMWithLMHeadModel(__A			)

     lowerCamelCase			: List[str]   =    {"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
     lowerCamelCase			: Union[str, Any]   =    model(__A			)

     lowerCamelCase			: Any   =    outputs

     self.parent.assertEqual(result.logits.shape						,    (self.batch_size, self.seq_length, self.vocab_size)			)






   def _snake_case   (		self						,    __A						,    __A						,    __A						,    __A						,    __A						,    __A						,    __A						,    __A						,    __A						,    ):






     """simple docstring"""





     lowerCamelCase			: List[Any]   =    TFXLMForQuestionAnsweringSimple(__A			)

     lowerCamelCase			: Dict   =    {"input_ids": input_ids, "lengths": input_lengths}

     lowerCamelCase			: Any   =    model(__A			)

     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 _snake_case   (		self						,    __A						,    __A						,    __A						,    __A						,    __A						,    __A						,    __A						,    __A						,    __A						,    ):






     """simple docstring"""





     lowerCamelCase			: str   =    TFXLMForSequenceClassification(__A			)

     lowerCamelCase			: Dict   =    {"input_ids": input_ids, "lengths": input_lengths}

     lowerCamelCase			: Optional[int]   =    model(__A			)

     self.parent.assertEqual(result.logits.shape						,    (self.batch_size, self.type_sequence_label_size)			)






   def _snake_case   (		self						,    __A						,    __A						,    __A						,    __A						,    __A						,    __A						,    __A						,    __A						,    __A						,    ):






     """simple docstring"""





     lowerCamelCase			: str   =    self.num_labels
     lowerCamelCase			: Tuple   =    TFXLMForTokenClassification(config=__A			)
     lowerCamelCase			: Dict   =    {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
     lowerCamelCase			: Dict   =    model(__A			)
     self.parent.assertEqual(result.logits.shape						,    (self.batch_size, self.seq_length, self.num_labels)			)






   def _snake_case   (		self						,    __A						,    __A						,    __A						,    __A						,    __A						,    __A						,    __A						,    __A						,    __A						,    ):






     """simple docstring"""





     lowerCamelCase			: str   =    self.num_choices
     lowerCamelCase			: Any   =    TFXLMForMultipleChoice(config=__A			)
     lowerCamelCase			: str   =    tf.tile(tf.expand_dims(__A						,    1			)						,    (1, self.num_choices, 1)			)
     lowerCamelCase			: Optional[int]   =    tf.tile(tf.expand_dims(__A						,    1			)						,    (1, self.num_choices, 1)			)
     lowerCamelCase			: int   =    tf.tile(tf.expand_dims(__A						,    1			)						,    (1, self.num_choices, 1)			)
     lowerCamelCase			: Tuple   =    {
         "input_ids": multiple_choice_inputs_ids,
         "attention_mask": multiple_choice_input_mask,
         "token_type_ids": multiple_choice_token_type_ids,
     }
     lowerCamelCase			: Union[str, Any]   =    model(__A			)
     self.parent.assertEqual(result.logits.shape						,    (self.batch_size, self.num_choices)			)






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: Optional[int]   =    self.prepare_config_and_inputs()
     (
         (
         lowerCamelCase
     )   ,     (
         lowerCamelCase
     )   ,     (
         lowerCamelCase
     )   ,     (
         lowerCamelCase
     )   ,     (
         lowerCamelCase
     )   ,     (
         lowerCamelCase
     )   ,     (
         lowerCamelCase
     )   ,     (
         lowerCamelCase
     )   ,     (
         lowerCamelCase
     )   ,     
     )			: Tuple   =    config_and_inputs
     lowerCamelCase			: Tuple   =    {
         "input_ids": input_ids,
         "token_type_ids": token_type_ids,
         "langs": token_type_ids,
         "lengths": input_lengths,
     }
     return config, inputs_dict



@require_tf
class      UpperCAmelCase_ ( UpperCamelCase		,			UpperCamelCase		,			unittest.TestCase   ):

   '''simple docstring'''




   __A			:				List[str]							=		(
       (
           TFXLMModel,
           TFXLMWithLMHeadModel,
           TFXLMForSequenceClassification,
           TFXLMForQuestionAnsweringSimple,
           TFXLMForTokenClassification,
           TFXLMForMultipleChoice,
       )
       if is_tf_available()
       else ()
   )
   __A			:				int							=		(
       (TFXLMWithLMHeadModel,) if is_tf_available() else ()
   )  # TODO (PVP): Check other models whether language generation is also applicable
   __A			:				List[Any]							=		(
       {
           "feature-extraction": TFXLMModel,
           "fill-mask": TFXLMWithLMHeadModel,
           "question-answering": TFXLMForQuestionAnsweringSimple,
           "text-classification": TFXLMForSequenceClassification,
           "text-generation": TFXLMWithLMHeadModel,
           "token-classification": TFXLMForTokenClassification,
           "zero-shot": TFXLMForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )
   __A			:				List[str]							=		False
   __A			:				str							=		False






   def _snake_case   (		self						,    __A						,    __A						,    __A						,    __A						,    __A			):






     """simple docstring"""





     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 _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: Dict   =    TFXLMModelTester(self			)
     lowerCamelCase			: Tuple   =    ConfigTester(self						,    config_class=__A						,    emb_dim=37			)






   def _snake_case   (		self			):






     """simple docstring"""





     self.config_tester.run_common_tests()






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: Optional[int]   =    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_xlm_model(*__A			)






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: List[Any]   =    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_xlm_lm_head(*__A			)






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: int   =    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_xlm_qa(*__A			)






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: List[str]   =    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_xlm_sequence_classif(*__A			)






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: List[Any]   =    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_xlm_for_token_classification(*__A			)






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: Union[str, Any]   =    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_xlm_for_multiple_choice(*__A			)






   @slow
   def _snake_case   (		self			):






     """simple docstring"""





     for model_name in TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       lowerCamelCase			: Any   =    TFXLMModel.from_pretrained(__A			)
       self.assertIsNotNone(__A			)



@require_tf
class      UpperCAmelCase_ ( unittest.TestCase   ):

   '''simple docstring'''




   @slow
   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: List[Any]   =    TFXLMWithLMHeadModel.from_pretrained("xlm-mlm-en-2048"			)
     lowerCamelCase			: List[Any]   =    tf.convert_to_tensor([[14, 447]]						,    dtype=tf.intaa			)  # the president
     lowerCamelCase			: Dict   =    [
         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
     lowerCamelCase			: Union[str, Any]   =    model.generate(__A						,    do_sample=__A			)
     self.assertListEqual(output_ids[0].numpy().tolist()						,    __A			)







def 		lowercase_(  SCREAMING_SNAKE_CASE_     ):


  '''simple docstring'''

  if number != int(SCREAMING_SNAKE_CASE_     ):
    raise ValueError("factorial() only accepts integral values"     )
  if number < 0:
    raise ValueError("factorial() not defined for negative values"     )
  lowerCamelCase			: Optional[Any]   =    1
  for i in range(1				,						number + 1     ):
    value *= i
  return value






def 		lowercase_(  SCREAMING_SNAKE_CASE_     ):


  '''simple docstring'''

  if not isinstance(SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_     ):
    raise ValueError("factorial() only accepts integral values"     )
  if n < 0:
    raise ValueError("factorial() not defined for negative values"     )
  return 1 if n == 0 or n == 1 else n * factorial(n - 1     )


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   _snake_case					=					int(input('''Enter a positive integer: ''').strip() or 0)
   print(f'''factorial{n} is {factorial(n)}''')



