

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 				lowerCamelCase  :

			def __init__(   self			:  str      ,			__UpperCAmelCase			:  Dict      ,			)				->      Tuple:
						SCREAMING_SNAKE_CASE__									= parent
						SCREAMING_SNAKE_CASE__									= 1_3
						SCREAMING_SNAKE_CASE__									= 7
						SCREAMING_SNAKE_CASE__									= True
						SCREAMING_SNAKE_CASE__									= True
						SCREAMING_SNAKE_CASE__									= True
						SCREAMING_SNAKE_CASE__									= True
						SCREAMING_SNAKE_CASE__									= True
						SCREAMING_SNAKE_CASE__									= False
						SCREAMING_SNAKE_CASE__									= False
						SCREAMING_SNAKE_CASE__									= False
						SCREAMING_SNAKE_CASE__									= 2
						SCREAMING_SNAKE_CASE__									= 9_9
						SCREAMING_SNAKE_CASE__									= 0
						SCREAMING_SNAKE_CASE__									= 3_2
						SCREAMING_SNAKE_CASE__									= 2
						SCREAMING_SNAKE_CASE__									= 4
						SCREAMING_SNAKE_CASE__									= 0.1
						SCREAMING_SNAKE_CASE__									= 0.1
						SCREAMING_SNAKE_CASE__									= 5_1_2
						SCREAMING_SNAKE_CASE__									= 1_6
						SCREAMING_SNAKE_CASE__									= 2
						SCREAMING_SNAKE_CASE__									= 0.02
						SCREAMING_SNAKE_CASE__									= 3
						SCREAMING_SNAKE_CASE__									= 4
						SCREAMING_SNAKE_CASE__									= """last"""
						SCREAMING_SNAKE_CASE__									= True
						SCREAMING_SNAKE_CASE__									= None
						SCREAMING_SNAKE_CASE__									= 0

			def    SCREAMING_SNAKE_CASE			(   self			:  Optional[int]    )				->      str:
						SCREAMING_SNAKE_CASE__									= ids_tensor([self.batch_size, self.seq_length]      ,			self.vocab_size    )
						SCREAMING_SNAKE_CASE__									= random_attention_mask([self.batch_size, self.seq_length]      ,			dtype=tf.floataa    )

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

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

						SCREAMING_SNAKE_CASE__									= None
						SCREAMING_SNAKE_CASE__									= None
						SCREAMING_SNAKE_CASE__									= None
						if self.use_labels:
									SCREAMING_SNAKE_CASE__									= ids_tensor([self.batch_size]      ,			self.type_sequence_label_size    )
									SCREAMING_SNAKE_CASE__									= ids_tensor([self.batch_size, self.seq_length]      ,			self.num_labels    )
									SCREAMING_SNAKE_CASE__									= ids_tensor([self.batch_size]      ,			2      ,			dtype=tf.floataa    )
									SCREAMING_SNAKE_CASE__									= ids_tensor([self.batch_size]      ,			self.num_choices    )

						SCREAMING_SNAKE_CASE__									= 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    SCREAMING_SNAKE_CASE			(   self			:  Dict      ,			__UpperCAmelCase			:  Optional[int]      ,			__UpperCAmelCase			:  Any      ,			__UpperCAmelCase			:  Optional[Any]      ,			__UpperCAmelCase			:  int      ,			__UpperCAmelCase			:  Optional[Any]      ,			__UpperCAmelCase			:  Any      ,			__UpperCAmelCase			:  Dict      ,			__UpperCAmelCase			:  Any      ,			__UpperCAmelCase			:  int      ,			)				->      Optional[Any]:
						SCREAMING_SNAKE_CASE__									= TFXLMModel(config=__UpperCAmelCase    )
						SCREAMING_SNAKE_CASE__									= {"""input_ids""": input_ids, """lengths""": input_lengths, """langs""": token_type_ids}
						SCREAMING_SNAKE_CASE__									= model(__UpperCAmelCase    )

						SCREAMING_SNAKE_CASE__									= [input_ids, input_mask]
						SCREAMING_SNAKE_CASE__									= model(__UpperCAmelCase    )
						self.parent.assertEqual(result.last_hidden_state.shape      ,			(self.batch_size, self.seq_length, self.hidden_size)    )

			def    SCREAMING_SNAKE_CASE			(   self			:  Optional[Any]      ,			__UpperCAmelCase			:  List[str]      ,			__UpperCAmelCase			:  Dict      ,			__UpperCAmelCase			:  Any      ,			__UpperCAmelCase			:  str      ,			__UpperCAmelCase			:  List[Any]      ,			__UpperCAmelCase			:  Optional[int]      ,			__UpperCAmelCase			:  Optional[int]      ,			__UpperCAmelCase			:  List[Any]      ,			__UpperCAmelCase			:  int      ,			)				->      Dict:
						SCREAMING_SNAKE_CASE__									= TFXLMWithLMHeadModel(__UpperCAmelCase    )

						SCREAMING_SNAKE_CASE__									= {"""input_ids""": input_ids, """lengths""": input_lengths, """langs""": token_type_ids}
						SCREAMING_SNAKE_CASE__									= model(__UpperCAmelCase    )

						SCREAMING_SNAKE_CASE__									= outputs

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

			def    SCREAMING_SNAKE_CASE			(   self			:  Dict      ,			__UpperCAmelCase			:  Optional[Any]      ,			__UpperCAmelCase			:  Optional[Any]      ,			__UpperCAmelCase			:  int      ,			__UpperCAmelCase			:  Optional[int]      ,			__UpperCAmelCase			:  Tuple      ,			__UpperCAmelCase			:  Union[str, Any]      ,			__UpperCAmelCase			:  List[Any]      ,			__UpperCAmelCase			:  List[str]      ,			__UpperCAmelCase			:  Optional[int]      ,			)				->      List[Any]:
						SCREAMING_SNAKE_CASE__									= TFXLMForQuestionAnsweringSimple(__UpperCAmelCase    )

						SCREAMING_SNAKE_CASE__									= {"""input_ids""": input_ids, """lengths""": input_lengths}

						SCREAMING_SNAKE_CASE__									= model(__UpperCAmelCase    )

						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    SCREAMING_SNAKE_CASE			(   self			:  Dict      ,			__UpperCAmelCase			:  int      ,			__UpperCAmelCase			:  int      ,			__UpperCAmelCase			:  Optional[Any]      ,			__UpperCAmelCase			:  str      ,			__UpperCAmelCase			:  Any      ,			__UpperCAmelCase			:  int      ,			__UpperCAmelCase			:  Any      ,			__UpperCAmelCase			:  Dict      ,			__UpperCAmelCase			:  Optional[Any]      ,			)				->      int:
						SCREAMING_SNAKE_CASE__									= TFXLMForSequenceClassification(__UpperCAmelCase    )

						SCREAMING_SNAKE_CASE__									= {"""input_ids""": input_ids, """lengths""": input_lengths}

						SCREAMING_SNAKE_CASE__									= model(__UpperCAmelCase    )

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

			def    SCREAMING_SNAKE_CASE			(   self			:  str      ,			__UpperCAmelCase			:  List[Any]      ,			__UpperCAmelCase			:  Tuple      ,			__UpperCAmelCase			:  str      ,			__UpperCAmelCase			:  Optional[int]      ,			__UpperCAmelCase			:  Dict      ,			__UpperCAmelCase			:  Union[str, Any]      ,			__UpperCAmelCase			:  int      ,			__UpperCAmelCase			:  str      ,			__UpperCAmelCase			:  List[Any]      ,			)				->      str:
						SCREAMING_SNAKE_CASE__									= self.num_labels
						SCREAMING_SNAKE_CASE__									= TFXLMForTokenClassification(config=__UpperCAmelCase    )
						SCREAMING_SNAKE_CASE__									= {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
						SCREAMING_SNAKE_CASE__									= model(__UpperCAmelCase    )
						self.parent.assertEqual(result.logits.shape      ,			(self.batch_size, self.seq_length, self.num_labels)    )

			def    SCREAMING_SNAKE_CASE			(   self			:  Dict      ,			__UpperCAmelCase			:  str      ,			__UpperCAmelCase			:  str      ,			__UpperCAmelCase			:  Optional[int]      ,			__UpperCAmelCase			:  List[str]      ,			__UpperCAmelCase			:  Union[str, Any]      ,			__UpperCAmelCase			:  Optional[int]      ,			__UpperCAmelCase			:  List[Any]      ,			__UpperCAmelCase			:  Optional[int]      ,			__UpperCAmelCase			:  List[str]      ,			)				->      Union[str, Any]:
						SCREAMING_SNAKE_CASE__									= self.num_choices
						SCREAMING_SNAKE_CASE__									= TFXLMForMultipleChoice(config=__UpperCAmelCase    )
						SCREAMING_SNAKE_CASE__									= tf.tile(tf.expand_dims(__UpperCAmelCase      ,			1    )      ,			(1, self.num_choices, 1)    )
						SCREAMING_SNAKE_CASE__									= tf.tile(tf.expand_dims(__UpperCAmelCase      ,			1    )      ,			(1, self.num_choices, 1)    )
						SCREAMING_SNAKE_CASE__									= tf.tile(tf.expand_dims(__UpperCAmelCase      ,			1    )      ,			(1, self.num_choices, 1)    )
						SCREAMING_SNAKE_CASE__									= {
						    """input_ids""": multiple_choice_inputs_ids,
						    """attention_mask""": multiple_choice_input_mask,
						    """token_type_ids""": multiple_choice_token_type_ids,
						}
						SCREAMING_SNAKE_CASE__									= model(__UpperCAmelCase    )
						self.parent.assertEqual(result.logits.shape      ,			(self.batch_size, self.num_choices)    )





			def    SCREAMING_SNAKE_CASE			(   self			:  int    )				->      str:
						SCREAMING_SNAKE_CASE__									= self.prepare_config_and_inputs()
						(
						    (
						    SCREAMING_SNAKE_CASE__						
						)							,						(
						    SCREAMING_SNAKE_CASE__						
						)							,						(
						    SCREAMING_SNAKE_CASE__						
						)							,						(
						    SCREAMING_SNAKE_CASE__						
						)							,						(
						    SCREAMING_SNAKE_CASE__						
						)							,						(
						    SCREAMING_SNAKE_CASE__						
						)							,						(
						    SCREAMING_SNAKE_CASE__						
						)							,						(
						    SCREAMING_SNAKE_CASE__						
						)							,						(
						    SCREAMING_SNAKE_CASE__						
						)							,												
						)									= config_and_inputs
						SCREAMING_SNAKE_CASE__									= {
						    """input_ids""": input_ids,
						    """token_type_ids""": token_type_ids,
						    """langs""": token_type_ids,
						    """lengths""": input_lengths,
						}
						return config, inputs_dict





@require_tf
class 				lowerCamelCase  (A__     ,A__     ,unittest.TestCase     ):
			lowerCamelCase__				:						List[str]   						=							(
			    (
			        TFXLMModel,
			        TFXLMWithLMHeadModel,
			        TFXLMForSequenceClassification,
			        TFXLMForQuestionAnsweringSimple,
			        TFXLMForTokenClassification,
			        TFXLMForMultipleChoice,
			    )
			    if is_tf_available()
			    else ()
			)
			lowerCamelCase__				:						str   						=							(
			    (TFXLMWithLMHeadModel,) if is_tf_available() else ()
			)  # TODO (PVP): Check other models whether language generation is also applicable
			lowerCamelCase__				:						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 {}
			)
			lowerCamelCase__				:						Dict   						=							False
			lowerCamelCase__				:						Any   						=							False

			def    SCREAMING_SNAKE_CASE			(   self			:  List[Any]      ,			__UpperCAmelCase			:  int      ,			__UpperCAmelCase			:  Tuple      ,			__UpperCAmelCase			:  Tuple      ,			__UpperCAmelCase			:  Optional[int]      ,			__UpperCAmelCase			:  List[str]    )				->      List[str]:
						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    SCREAMING_SNAKE_CASE			(   self			:  str    )				->      Optional[int]:
						SCREAMING_SNAKE_CASE__									= TFXLMModelTester(self    )
						SCREAMING_SNAKE_CASE__									= ConfigTester(self      ,			config_class=__UpperCAmelCase      ,			emb_dim=3_7    )

			def    SCREAMING_SNAKE_CASE			(   self			:  Optional[int]    )				->      Dict:
						self.config_tester.run_common_tests()

			def    SCREAMING_SNAKE_CASE			(   self			:  Dict    )				->      List[Any]:
						SCREAMING_SNAKE_CASE__									= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_model(*__UpperCAmelCase    )

			def    SCREAMING_SNAKE_CASE			(   self			:  Any    )				->      str:
						SCREAMING_SNAKE_CASE__									= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_lm_head(*__UpperCAmelCase    )

			def    SCREAMING_SNAKE_CASE			(   self			:  Optional[Any]    )				->      Optional[int]:
						SCREAMING_SNAKE_CASE__									= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_qa(*__UpperCAmelCase    )

			def    SCREAMING_SNAKE_CASE			(   self			:  Tuple    )				->      Any:
						SCREAMING_SNAKE_CASE__									= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_sequence_classif(*__UpperCAmelCase    )

			def    SCREAMING_SNAKE_CASE			(   self			:  str    )				->      Any:
						SCREAMING_SNAKE_CASE__									= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_for_token_classification(*__UpperCAmelCase    )

			def    SCREAMING_SNAKE_CASE			(   self			:  List[str]    )				->      Optional[int]:
						SCREAMING_SNAKE_CASE__									= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_for_multiple_choice(*__UpperCAmelCase    )





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





@require_tf
class 				lowerCamelCase  (unittest.TestCase     ):

			@slow
			def    SCREAMING_SNAKE_CASE			(   self			:  List[str]    )				->      Any:
						SCREAMING_SNAKE_CASE__									= TFXLMWithLMHeadModel.from_pretrained("""xlm-mlm-en-2048"""    )
						SCREAMING_SNAKE_CASE__									= tf.convert_to_tensor([[1_4, 4_4_7]]      ,			dtype=tf.intaa    )  # the president
						SCREAMING_SNAKE_CASE__									= [
						    1_4,
						    4_4_7,
						    1_4,
						    4_4_7,
						    1_4,
						    4_4_7,
						    1_4,
						    4_4_7,
						    1_4,
						    4_4_7,
						    1_4,
						    4_4_7,
						    1_4,
						    4_4_7,
						    1_4,
						    4_4_7,
						    1_4,
						    4_4_7,
						    1_4,
						    4_4_7,
						]  # 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
						SCREAMING_SNAKE_CASE__									= model.generate(__UpperCAmelCase      ,			do_sample=__UpperCAmelCase    )
						self.assertListEqual(output_ids[0].numpy().tolist()      ,			__UpperCAmelCase    )





"""simple docstring"""




from .hash_table import HashTable





class 				lowerCamelCase  (A__     ):

			def __init__(   self			:  List[str]      ,			*__UpperCAmelCase			:  Union[str, Any]      ,			**__UpperCAmelCase			:  Any    )				->      Optional[int]:
						super().__init__(*__UpperCAmelCase      ,			**__UpperCAmelCase    )





			def    SCREAMING_SNAKE_CASE			(   self			:  List[str]      ,			__UpperCAmelCase			:  int      ,			__UpperCAmelCase			:  Union[str, Any]=None    )				->      Optional[int]:
						SCREAMING_SNAKE_CASE__									= 1
						SCREAMING_SNAKE_CASE__									= self.hash_function(key + i * i    )

						while self.values[new_key] is not None and self.values[new_key] != key:
									i += 1
									SCREAMING_SNAKE_CASE__									= (
									    self.hash_function(key + i * i    )
									    if not self.balanced_factor() >= self.lim_charge
									    else None
									)

									if new_key is None:
												break

						return new_key


