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 							_SCREAMING_SNAKE_CASE:
   def __init__(    self							,     lowerCAmelCase_							,     )	->Dict:
        a  :    Tuple				=						parent
        a  :    Any				=						13
        a  :    Optional[Any]				=						7
        a  :    Optional[Any]				=						True
        a  :    Any				=						True
        a  :    str				=						True
        a  :    Dict				=						True
        a  :    int				=						True
        a  :    List[Any]				=						False
        a  :    Any				=						False
        a  :    Dict				=						False
        a  :    Optional[int]				=						2
        a  :    Tuple				=						99
        a  :    Union[str, Any]				=						0
        a  :    Optional[int]				=						32
        a  :    Dict				=						2
        a  :    Optional[int]				=						4
        a  :    Optional[Any]				=						0.1
        a  :    Union[str, Any]				=						0.1
        a  :    str				=						5_12
        a  :    Any				=						16
        a  :    List[str]				=						2
        a  :    Optional[int]				=						0.02
        a  :    int				=						3
        a  :    Optional[int]				=						4
        a  :    Any				=						'''last'''
        a  :    int				=						True
        a  :    List[Any]				=						None
        a  :    int				=						0
   def _lowercase(    self    )	->Optional[Any]:
        a  :    List[Any]				=						ids_tensor([self.batch_size, self.seq_length]							,     self.vocab_size    )
        a  :    int				=						random_attention_mask([self.batch_size, self.seq_length]							,     dtype=tf.floataa    )

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

        a  :    List[Any]				=						None
        if self.use_token_type_ids:
             a  :    Union[str, Any]				=						ids_tensor([self.batch_size, self.seq_length]							,     self.n_langs    )

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

        a  :    Optional[Any]				=						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 _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     )	->Optional[int]:
        a  :    Optional[Any]				=						TFXLMModel(config=lowerCAmelCase_    )
        a  :    List[str]				=						{'''input_ids''': input_ids, '''lengths''': input_lengths, '''langs''': token_type_ids}
        a  :    Any				=						model(lowerCAmelCase_    )

        a  :    int				=						[input_ids, input_mask]
        a  :    str				=						model(lowerCAmelCase_    )
        self.parent.assertEqual(result.last_hidden_state.shape							,     (self.batch_size, self.seq_length, self.hidden_size)    )
   def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     )	->List[str]:
        a  :    Optional[int]				=						TFXLMWithLMHeadModel(lowerCAmelCase_    )

        a  :    Optional[Any]				=						{'''input_ids''': input_ids, '''lengths''': input_lengths, '''langs''': token_type_ids}
        a  :    List[str]				=						model(lowerCAmelCase_    )

        a  :    Optional[Any]				=						outputs

        self.parent.assertEqual(result.logits.shape							,     (self.batch_size, self.seq_length, self.vocab_size)    )
   def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     )	->Any:
        a  :    int				=						TFXLMForQuestionAnsweringSimple(lowerCAmelCase_    )

        a  :    Dict				=						{'''input_ids''': input_ids, '''lengths''': input_lengths}

        a  :    Dict				=						model(lowerCAmelCase_    )

        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							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     )	->Optional[int]:
        a  :    str				=						TFXLMForSequenceClassification(lowerCAmelCase_    )

        a  :    Dict				=						{'''input_ids''': input_ids, '''lengths''': input_lengths}

        a  :    Optional[Any]				=						model(lowerCAmelCase_    )

        self.parent.assertEqual(result.logits.shape							,     (self.batch_size, self.type_sequence_label_size)    )
   def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     )	->Tuple:
        a  :    Optional[int]				=						self.num_labels
        a  :    Dict				=						TFXLMForTokenClassification(config=lowerCAmelCase_    )
        a  :    Any				=						{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
        a  :    Tuple				=						model(lowerCAmelCase_    )
        self.parent.assertEqual(result.logits.shape							,     (self.batch_size, self.seq_length, self.num_labels)    )
   def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     )	->List[str]:
        a  :    Tuple				=						self.num_choices
        a  :    List[Any]				=						TFXLMForMultipleChoice(config=lowerCAmelCase_    )
        a  :    List[Any]				=						tf.tile(tf.expand_dims(lowerCAmelCase_							,     1    )							,     (1, self.num_choices, 1)    )
        a  :    Tuple				=						tf.tile(tf.expand_dims(lowerCAmelCase_							,     1    )							,     (1, self.num_choices, 1)    )
        a  :    List[str]				=						tf.tile(tf.expand_dims(lowerCAmelCase_							,     1    )							,     (1, self.num_choices, 1)    )
        a  :    Any				=						{
            '''input_ids''': multiple_choice_inputs_ids,
            '''attention_mask''': multiple_choice_input_mask,
            '''token_type_ids''': multiple_choice_token_type_ids,
        }
        a  :    Dict				=						model(lowerCAmelCase_    )
        self.parent.assertEqual(result.logits.shape							,     (self.batch_size, self.num_choices)    )


   def _lowercase(    self    )	->Tuple:
        a  :    Optional[Any]				=						self.prepare_config_and_inputs()
        (
            a							
        )  :    Union[str, Any]				=						config_and_inputs
        a  :    Dict				=						{
            '''input_ids''': input_ids,
            '''token_type_ids''': token_type_ids,
            '''langs''': token_type_ids,
            '''lengths''': input_lengths,
        }
        return config, inputs_dict






@require_tf
class 							_SCREAMING_SNAKE_CASE(   snake_case__       ,      snake_case__       ,      unittest.TestCase	):
   UpperCamelCase_				:						Optional[Any] 			=   (
       (
           TFXLMModel,
           TFXLMWithLMHeadModel,
           TFXLMForSequenceClassification,
           TFXLMForQuestionAnsweringSimple,
           TFXLMForTokenClassification,
           TFXLMForMultipleChoice,
       )
       if is_tf_available()
       else ()
   )
   UpperCamelCase_				:						List[Any] 			=   (
       (TFXLMWithLMHeadModel,) if is_tf_available() else ()
   )  # TODO (PVP): Check other models whether language generation is also applicable
   UpperCamelCase_				:						List[str] 			=   (
       {
           '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 {}
   )
   UpperCamelCase_				:						Optional[Any] 			=   False
   UpperCamelCase_				:						int 			=   False
   def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_    )	->int:
        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    )	->int:
        a  :    Any				=						TFXLMModelTester(self    )
        a  :    List[Any]				=						ConfigTester(self							,     config_class=lowerCAmelCase_							,     emb_dim=37    )
   def _lowercase(    self    )	->Any:
        self.config_tester.run_common_tests()
   def _lowercase(    self    )	->Tuple:
        a  :    Tuple				=						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_xlm_model(*lowerCAmelCase_    )
   def _lowercase(    self    )	->Any:
        a  :    Any				=						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_xlm_lm_head(*lowerCAmelCase_    )
   def _lowercase(    self    )	->str:
        a  :    Tuple				=						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_xlm_qa(*lowerCAmelCase_    )
   def _lowercase(    self    )	->Optional[Any]:
        a  :    Tuple				=						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_xlm_sequence_classif(*lowerCAmelCase_    )
   def _lowercase(    self    )	->str:
        a  :    str				=						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_xlm_for_token_classification(*lowerCAmelCase_    )
   def _lowercase(    self    )	->Optional[int]:
        a  :    List[Any]				=						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_xlm_for_multiple_choice(*lowerCAmelCase_    )


   @slow
   def _lowercase(    self    )	->Optional[int]:
        for model_name in TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             a  :    Any				=						TFXLMModel.from_pretrained(lowerCAmelCase_    )
             self.assertIsNotNone(lowerCAmelCase_    )






@require_tf
class 							_SCREAMING_SNAKE_CASE(   unittest.TestCase	):
   @slow
   def _lowercase(    self    )	->Union[str, Any]:
        a  :    str				=						TFXLMWithLMHeadModel.from_pretrained('''xlm-mlm-en-2048'''    )
        a  :    Optional[int]				=						tf.convert_to_tensor([[14, 4_47]]							,     dtype=tf.intaa    )  # the president
        a  :    Dict				=						[
            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
        a  :    Optional[Any]				=						model.generate(lowerCAmelCase_							,     do_sample=lowerCAmelCase_    )
        self.assertListEqual(output_ids[0].numpy().tolist()							,     lowerCAmelCase_    )
def 	UpperCAmelCase       (		lowerCAmelCase_       )	->	int:
     return 1 if input_a == 0 else 0







def 	UpperCAmelCase       (		)	->	None:
     assert not_gate(0       ) == 1
     assert not_gate(1       ) == 0


if __name__ == "__main__":
  print(not_gate(0))
  print(not_gate(1))