

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   __lowercase				:
	def __init__(							self				,							A_				,							) ->List[str]:



							'''simple docstring'''





							__lowerCAmelCase :						List[Any] = parent
							__lowerCAmelCase :						Any = 13
							__lowerCAmelCase :						Dict = 7
							__lowerCAmelCase :						Dict = True
							__lowerCAmelCase :						Union[str, Any] = True
							__lowerCAmelCase :						str = True
							__lowerCAmelCase :						Union[str, Any] = True
							__lowerCAmelCase :						str = True
							__lowerCAmelCase :						Optional[Any] = False
							__lowerCAmelCase :						Optional[int] = False
							__lowerCAmelCase :						str = False
							__lowerCAmelCase :						str = 2
							__lowerCAmelCase :						Tuple = 99
							__lowerCAmelCase :						Optional[Any] = 0
							__lowerCAmelCase :						Union[str, Any] = 32
							__lowerCAmelCase :						List[Any] = 2
							__lowerCAmelCase :						str = 4
							__lowerCAmelCase :						int = 0.1
							__lowerCAmelCase :						str = 0.1
							__lowerCAmelCase :						Optional[Any] = 512
							__lowerCAmelCase :						int = 16
							__lowerCAmelCase :						Any = 2
							__lowerCAmelCase :						Dict = 0.02
							__lowerCAmelCase :						int = 3
							__lowerCAmelCase :						str = 4
							__lowerCAmelCase :						Union[str, Any] = 'last'
							__lowerCAmelCase :						Dict = True
							__lowerCAmelCase :						Dict = None
							__lowerCAmelCase :						List[str] = 0
	def      UpperCamelCase__  (							self ) ->Any:



							'''simple docstring'''





							__lowerCAmelCase :						Any = ids_tensor([self.batch_size, self.seq_length]				,							self.vocab_size )
							__lowerCAmelCase :						Optional[int] = random_attention_mask([self.batch_size, self.seq_length]				,							dtype=tf.floataa )

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

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

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

							__lowerCAmelCase :						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      UpperCamelCase__  (							self				,							A_				,							A_				,							A_				,							A_				,							A_				,							A_				,							A_				,							A_				,							A_				,							) ->List[str]:



							'''simple docstring'''





							__lowerCAmelCase :						Tuple = TFXLMModel(config=_A )
							__lowerCAmelCase :						Optional[int] = {'input_ids': input_ids, 'lengths': input_lengths, 'langs': token_type_ids}
							__lowerCAmelCase :						Any = model(_A )

							__lowerCAmelCase :						int = [input_ids, input_mask]
							__lowerCAmelCase :						Optional[Any] = model(_A )
							self.parent.assertEqual(result.last_hidden_state.shape				,							(self.batch_size, self.seq_length, self.hidden_size) )
	def      UpperCamelCase__  (							self				,							A_				,							A_				,							A_				,							A_				,							A_				,							A_				,							A_				,							A_				,							A_				,							) ->Optional[int]:



							'''simple docstring'''





							__lowerCAmelCase :						Any = TFXLMWithLMHeadModel(_A )

							__lowerCAmelCase :						Dict = {'input_ids': input_ids, 'lengths': input_lengths, 'langs': token_type_ids}
							__lowerCAmelCase :						str = model(_A )

							__lowerCAmelCase :						str = outputs

							self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.seq_length, self.vocab_size) )
	def      UpperCamelCase__  (							self				,							A_				,							A_				,							A_				,							A_				,							A_				,							A_				,							A_				,							A_				,							A_				,							) ->int:



							'''simple docstring'''





							__lowerCAmelCase :						Tuple = TFXLMForQuestionAnsweringSimple(_A )

							__lowerCAmelCase :						Tuple = {'input_ids': input_ids, 'lengths': input_lengths}

							__lowerCAmelCase :						Dict = 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      UpperCamelCase__  (							self				,							A_				,							A_				,							A_				,							A_				,							A_				,							A_				,							A_				,							A_				,							A_				,							) ->Union[str, Any]:



							'''simple docstring'''





							__lowerCAmelCase :						Optional[Any] = TFXLMForSequenceClassification(_A )

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

							__lowerCAmelCase :						int = model(_A )

							self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.type_sequence_label_size) )
	def      UpperCamelCase__  (							self				,							A_				,							A_				,							A_				,							A_				,							A_				,							A_				,							A_				,							A_				,							A_				,							) ->Any:



							'''simple docstring'''





							__lowerCAmelCase :						Any = self.num_labels
							__lowerCAmelCase :						Optional[Any] = TFXLMForTokenClassification(config=_A )
							__lowerCAmelCase :						List[Any] = {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
							__lowerCAmelCase :						Optional[int] = model(_A )
							self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.seq_length, self.num_labels) )
	def      UpperCamelCase__  (							self				,							A_				,							A_				,							A_				,							A_				,							A_				,							A_				,							A_				,							A_				,							A_				,							) ->int:



							'''simple docstring'''





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





	def      UpperCamelCase__  (							self ) ->Tuple:



							'''simple docstring'''





							__lowerCAmelCase :						Dict = self.prepare_config_and_inputs()
							(
							    __lowerCAmelCase    
							) :						List[str] = 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   __lowercase				(snake_case__		,       snake_case__		,       unittest.TestCase			):
	_UpperCamelCase        =  (
	    (
	        TFXLMModel,
	        TFXLMWithLMHeadModel,
	        TFXLMForSequenceClassification,
	        TFXLMForQuestionAnsweringSimple,
	        TFXLMForTokenClassification,
	        TFXLMForMultipleChoice,
	    )
	    if is_tf_available()
	    else ()
	)
	_UpperCamelCase        =  (
	    (TFXLMWithLMHeadModel,) if is_tf_available() else ()
	)  # TODO (PVP): Check other models whether language generation is also applicable
	_UpperCamelCase        =  (
	    {
	        '''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        =  False
	_UpperCamelCase        =  False
	def      UpperCamelCase__  (							self				,							A_				,							A_				,							A_				,							A_				,							A_ ) ->List[Any]:



							'''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      UpperCamelCase__  (							self ) ->List[str]:



							'''simple docstring'''





							__lowerCAmelCase :						Dict = TFXLMModelTester(self )
							__lowerCAmelCase :						str = ConfigTester(self				,							config_class=_A				,							emb_dim=37 )
	def      UpperCamelCase__  (							self ) ->List[str]:



							'''simple docstring'''





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



							'''simple docstring'''





							__lowerCAmelCase :						Union[str, Any] = self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_xlm_model(*_A )
	def      UpperCamelCase__  (							self ) ->int:



							'''simple docstring'''





							__lowerCAmelCase :						Tuple = self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_xlm_lm_head(*_A )
	def      UpperCamelCase__  (							self ) ->Dict:



							'''simple docstring'''





							__lowerCAmelCase :						Optional[Any] = self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_xlm_qa(*_A )
	def      UpperCamelCase__  (							self ) ->List[str]:



							'''simple docstring'''





							__lowerCAmelCase :						Dict = self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_xlm_sequence_classif(*_A )
	def      UpperCamelCase__  (							self ) ->Optional[Any]:



							'''simple docstring'''





							__lowerCAmelCase :						Union[str, Any] = self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_xlm_for_token_classification(*_A )
	def      UpperCamelCase__  (							self ) ->str:



							'''simple docstring'''





							__lowerCAmelCase :						Optional[int] = self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_xlm_for_multiple_choice(*_A )





	@slow
	def      UpperCamelCase__  (							self ) ->Optional[int]:



							'''simple docstring'''





							for model_name in TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													__lowerCAmelCase :						Tuple = TFXLMModel.from_pretrained(_A )
													self.assertIsNotNone(_A )







@require_tf
class   __lowercase				(unittest.TestCase			):
	@slow
	def      UpperCamelCase__  (							self ) ->Optional[int]:



							'''simple docstring'''





							__lowerCAmelCase :						Tuple = TFXLMWithLMHeadModel.from_pretrained('''xlm-mlm-en-2048''' )
							__lowerCAmelCase :						Dict = tf.convert_to_tensor([[14, 447]]				,							dtype=tf.intaa )  # the president
							__lowerCAmelCase :						int = [
							    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 :						str = model.generate(_A				,							do_sample=_A )
							self.assertListEqual(output_ids[0].numpy().tolist()				,							_A )





def 		_lowercase			(					lowercase__       ,     lowercase__						):
      if len(str(lowercase__						)						) == 1 or len(str(lowercase__						)						) == 1:
            return a * b

      __lowerCAmelCase :						Any = max(len(str(lowercase__						)						)       ,     len(str(lowercase__						)						)						)
      __lowerCAmelCase :						Optional[int] = ma // 2

      __lowerCAmelCase,							__lowerCAmelCase :						Tuple = divmod(lowercase__       ,     1_0**ma						)
      __lowerCAmelCase,							__lowerCAmelCase :						Any = divmod(lowercase__       ,     1_0**ma						)

      __lowerCAmelCase :						str = karatsuba(lowercase__       ,     lowercase__						)
      __lowerCAmelCase :						Optional[int] = karatsuba((aa + aa)       ,     (ba + ba)						)
      __lowerCAmelCase :						List[Any] = karatsuba(lowercase__       ,     lowercase__						)

      return (z * 1_0 ** (2 * ma)) + ((y - z - x) * 1_0 ** (ma)) + (x)







def 		_lowercase			(					):
      print(karatsuba(1_5_4_6_3       ,     2_3_4_8_9						)						)


if __name__ == "__main__":
 main()


