





'''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       UpperCamelCase__  :







	def __init__(				self			:   Union[str, Any] , lowerCamelCase			:   Union[str, Any] , lowerCamelCase			:   Any=1_3 , lowerCamelCase			:   Any=7 , lowerCamelCase			:   Optional[int]=True , lowerCamelCase			:   Optional[Any]=True , lowerCamelCase			:   Optional[int]=True , lowerCamelCase			:   str=True , lowerCamelCase			:   Dict=True , lowerCamelCase			:   Any=False , lowerCamelCase			:   List[Any]=False , lowerCamelCase			:   str=False , lowerCamelCase			:   Any=2 , lowerCamelCase			:   Dict=9_9 , lowerCamelCase			:   int=0 , lowerCamelCase			:   int=3_2 , lowerCamelCase			:   Optional[Any]=5 , lowerCamelCase			:   Dict=4 , lowerCamelCase			:   Optional[Any]=0.1 , lowerCamelCase			:   List[str]=0.1 , lowerCamelCase			:   Dict=5_1_2 , lowerCamelCase			:   str=2 , lowerCamelCase			:   Dict=0.02 , lowerCamelCase			:   List[str]=2 , lowerCamelCase			:   str=4 , lowerCamelCase			:   List[Any]="last" , lowerCamelCase			:   str=True , lowerCamelCase			:   List[Any]=None , lowerCamelCase			:   str=0 , ):




				'''simple docstring'''
				a__     = parent
				a__     = batch_size
				a__     = seq_length
				a__     = is_training
				a__     = use_input_lengths
				a__     = use_token_type_ids
				a__     = use_labels
				a__     = gelu_activation
				a__     = sinusoidal_embeddings
				a__     = causal
				a__     = asm
				a__     = n_langs
				a__     = vocab_size
				a__     = n_special
				a__     = hidden_size
				a__     = num_hidden_layers
				a__     = num_attention_heads
				a__     = hidden_dropout_prob
				a__     = attention_probs_dropout_prob
				a__     = max_position_embeddings
				a__     = type_sequence_label_size
				a__     = initializer_range
				a__     = num_labels
				a__     = num_choices
				a__     = summary_type
				a__     = use_proj
				a__     = scope
				a__     = bos_token_id







	def 						__a   (				self			:   Optional[int]		):




				'''simple docstring'''
				a__     = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size		)
				a__     = random_attention_mask([self.batch_size, self.seq_length]		)

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

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

				a__     = None
				a__     = None
				a__     = None
				if self.use_labels:
							a__     = ids_tensor([self.batch_size] , self.type_sequence_label_size		)
							a__     = ids_tensor([self.batch_size, self.seq_length] , self.num_labels		)
							a__     = ids_tensor([self.batch_size] , 2		).float()
							a__     = ids_tensor([self.batch_size] , self.num_choices		)

				a__     = self.get_config()

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







	def 						__a   (				self			:   List[str]		):




				'''simple docstring'''
				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 						__a   (				self			:   Union[str, Any] , lowerCamelCase			:   List[Any] , lowerCamelCase			:   Optional[Any] , lowerCamelCase			:   Dict , lowerCamelCase			:   int , lowerCamelCase			:   Tuple , lowerCamelCase			:   Optional[Any] , lowerCamelCase			:   Optional[Any] , lowerCamelCase			:   List[str] , lowerCamelCase			:   Dict , ):




				'''simple docstring'''
				a__     = XLMModel(config=lowerCamelCase		)
				model.to(lowerCamelCase		)
				model.eval()
				a__     = model(lowerCamelCase , lengths=lowerCamelCase , langs=lowerCamelCase		)
				a__     = model(lowerCamelCase , langs=lowerCamelCase		)
				a__     = model(lowerCamelCase		)
				self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size)		)







	def 						__a   (				self			:   Union[str, Any] , lowerCamelCase			:   Any , lowerCamelCase			:   List[Any] , lowerCamelCase			:   str , lowerCamelCase			:   str , lowerCamelCase			:   Optional[Any] , lowerCamelCase			:   str , lowerCamelCase			:   Union[str, Any] , lowerCamelCase			:   Union[str, Any] , lowerCamelCase			:   List[Any] , ):




				'''simple docstring'''
				a__     = XLMWithLMHeadModel(lowerCamelCase		)
				model.to(lowerCamelCase		)
				model.eval()

				a__     = 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 						__a   (				self			:   Any , lowerCamelCase			:   Optional[Any] , lowerCamelCase			:   Dict , lowerCamelCase			:   str , lowerCamelCase			:   Optional[int] , lowerCamelCase			:   List[str] , lowerCamelCase			:   Optional[int] , lowerCamelCase			:   str , lowerCamelCase			:   Tuple , lowerCamelCase			:   Optional[Any] , ):




				'''simple docstring'''
				a__     = XLMForQuestionAnsweringSimple(lowerCamelCase		)
				model.to(lowerCamelCase		)
				model.eval()

				a__     = model(lowerCamelCase		)

				a__     = model(lowerCamelCase , start_positions=lowerCamelCase , end_positions=lowerCamelCase		)
				a__     = 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 						__a   (				self			:   int , lowerCamelCase			:   Any , lowerCamelCase			:   Tuple , lowerCamelCase			:   Optional[Any] , lowerCamelCase			:   Optional[Any] , lowerCamelCase			:   str , lowerCamelCase			:   Optional[Any] , lowerCamelCase			:   int , lowerCamelCase			:   List[str] , lowerCamelCase			:   Union[str, Any] , ):




				'''simple docstring'''
				a__     = XLMForQuestionAnswering(lowerCamelCase		)
				model.to(lowerCamelCase		)
				model.eval()

				a__     = model(lowerCamelCase		)

				a__     = model(
				    lowerCamelCase , start_positions=lowerCamelCase , end_positions=lowerCamelCase , cls_index=lowerCamelCase , is_impossible=lowerCamelCase , p_mask=lowerCamelCase , )

				a__     = model(
				    lowerCamelCase , start_positions=lowerCamelCase , end_positions=lowerCamelCase , cls_index=lowerCamelCase , is_impossible=lowerCamelCase , )

				((a__)   ,				)     = result_with_labels.to_tuple()

				a__     = model(lowerCamelCase , start_positions=lowerCamelCase , end_positions=lowerCamelCase		)

				((a__)   ,				)     = 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 						__a   (				self			:   Tuple , lowerCamelCase			:   Tuple , lowerCamelCase			:   Optional[int] , lowerCamelCase			:   Optional[int] , lowerCamelCase			:   List[str] , lowerCamelCase			:   str , lowerCamelCase			:   str , lowerCamelCase			:   str , lowerCamelCase			:   Union[str, Any] , lowerCamelCase			:   List[Any] , ):




				'''simple docstring'''
				a__     = XLMForSequenceClassification(lowerCamelCase		)
				model.to(lowerCamelCase		)
				model.eval()

				a__     = model(lowerCamelCase		)
				a__     = 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 						__a   (				self			:   Any , lowerCamelCase			:   Any , lowerCamelCase			:   List[str] , lowerCamelCase			:   Optional[int] , lowerCamelCase			:   Dict , lowerCamelCase			:   List[str] , lowerCamelCase			:   List[Any] , lowerCamelCase			:   Dict , lowerCamelCase			:   List[Any] , lowerCamelCase			:   Optional[Any] , ):




				'''simple docstring'''
				a__     = self.num_labels
				a__     = XLMForTokenClassification(lowerCamelCase		)
				model.to(lowerCamelCase		)
				model.eval()

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







	def 						__a   (				self			:   Union[str, Any] , lowerCamelCase			:   int , lowerCamelCase			:   Any , lowerCamelCase			:   Optional[int] , lowerCamelCase			:   Optional[Any] , lowerCamelCase			:   str , lowerCamelCase			:   List[Any] , lowerCamelCase			:   List[str] , lowerCamelCase			:   int , lowerCamelCase			:   Dict , ):




				'''simple docstring'''
				a__     = self.num_choices
				a__     = XLMForMultipleChoice(config=lowerCamelCase		)
				model.to(lowerCamelCase		)
				model.eval()
				a__     = input_ids.unsqueeze(1		).expand(-1 , self.num_choices , -1		).contiguous()
				a__     = token_type_ids.unsqueeze(1		).expand(-1 , self.num_choices , -1		).contiguous()
				a__     = input_mask.unsqueeze(1		).expand(-1 , self.num_choices , -1		).contiguous()
				a__     = model(
				    lowerCamelCase , attention_mask=lowerCamelCase , token_type_ids=lowerCamelCase , labels=lowerCamelCase , )
				self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices)		)







	def 						__a   (				self			:   Any		):




				'''simple docstring'''
				a__     = self.prepare_config_and_inputs()
				(
				    (
				    a__
				)   ,				(
				    a__
				)   ,				(
				    a__
				)   ,				(
				    a__
				)   ,				(
				    a__
				)   ,				(
				    a__
				)   ,				(
				    a__
				)   ,				(
				    a__
				)   ,				(
				    a__
				)   ,				
				)     = config_and_inputs
				a__     = {"input_ids": input_ids, "token_type_ids": token_type_ids, "lengths": input_lengths}
				return config, inputs_dict






@require_torch
class       UpperCamelCase__  (		__lowerCAmelCase	,__lowerCAmelCase	,__lowerCAmelCase	,unittest.TestCase     ):
	lowerCAmelCase__					:			Dict								=						(
	    (
	        XLMModel,
	        XLMWithLMHeadModel,
	        XLMForQuestionAnswering,
	        XLMForSequenceClassification,
	        XLMForQuestionAnsweringSimple,
	        XLMForTokenClassification,
	        XLMForMultipleChoice,
	    )
	    if is_torch_available()
	    else ()
	)
	lowerCAmelCase__					:			Any								=						(
	    (XLMWithLMHeadModel,) if is_torch_available() else ()
	)  # TODO (PVP): Check other models whether language generation is also applicable
	lowerCAmelCase__					:			Optional[Any]								=						(
	    {
	        "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 						__a   (				self			:   Tuple , lowerCamelCase			:   Optional[int] , lowerCamelCase			:   List[Any] , lowerCamelCase			:   int , lowerCamelCase			:   Tuple , lowerCamelCase			:   str		):




				'''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 						__a   (				self			:   str , lowerCamelCase			:   Tuple , lowerCamelCase			:   Union[str, Any] , lowerCamelCase			:   int=False		):




				'''simple docstring'''
				a__     = super()._prepare_for_class(lowerCamelCase , lowerCamelCase , return_labels=lowerCamelCase		)

				if return_labels:
							if model_class.__name__ == "XLMForQuestionAnswering":
										a__     = torch.zeros(
										    self.model_tester.batch_size , dtype=torch.long , device=lowerCamelCase		)
										a__     = torch.zeros(
										    self.model_tester.batch_size , dtype=torch.long , device=lowerCamelCase		)

				return inputs_dict







	def 						__a   (				self			:   Tuple		):




				'''simple docstring'''
				a__     = XLMModelTester(self		)
				a__     = ConfigTester(self , config_class=lowerCamelCase , emb_dim=3_7		)







	def 						__a   (				self			:   Tuple		):




				'''simple docstring'''
				self.config_tester.run_common_tests()







	def 						__a   (				self			:   List[str]		):




				'''simple docstring'''
				a__     = self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_xlm_model(*lowerCamelCase		)







	def 						__a   (				self			:   List[Any]		):




				'''simple docstring'''
				a__     = self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_xlm_lm_head(*lowerCamelCase		)







	def 						__a   (				self			:   Dict		):




				'''simple docstring'''
				a__     = self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_xlm_simple_qa(*lowerCamelCase		)







	def 						__a   (				self			:   Tuple		):




				'''simple docstring'''
				a__     = self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_xlm_qa(*lowerCamelCase		)







	def 						__a   (				self			:   Optional[int]		):




				'''simple docstring'''
				a__     = self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_xlm_sequence_classif(*lowerCamelCase		)







	def 						__a   (				self			:   Optional[Any]		):




				'''simple docstring'''
				a__     = self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_xlm_token_classif(*lowerCamelCase		)







	def 						__a   (				self			:   Tuple		):




				'''simple docstring'''
				a__     = self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_xlm_for_multiple_choice(*lowerCamelCase		)







	def 						__a   (				self			:   Optional[Any] , lowerCamelCase			:   Dict , lowerCamelCase			:   int , lowerCamelCase			:   Any , lowerCamelCase			:   Tuple , lowerCamelCase			:   Any , lowerCamelCase			:   List[Any]=False , lowerCamelCase			:   Union[str, Any]=1		):




				'''simple docstring'''
				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
							a__     = min_length + idx + 1
							a__     = min_length + idx + 1

							a__     = (
							    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 						__a   (				self			:   str , lowerCamelCase			:   Any , lowerCamelCase			:   List[Any] , lowerCamelCase			:   List[str] , lowerCamelCase			:   str , lowerCamelCase			:   Tuple , lowerCamelCase			:   Dict=False , lowerCamelCase			:   int=1		):




				'''simple docstring'''
				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
							a__     = min_length + idx + 1
							a__     = (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 						__a   (				self			:   Optional[int]		):




				'''simple docstring'''
				for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							a__     = XLMModel.from_pretrained(lowerCamelCase		)
							self.assertIsNotNone(lowerCamelCase		)






@require_torch
class       UpperCamelCase__  (		unittest.TestCase     ):







	@slow
	def 						__a   (				self			:   Tuple		):




				'''simple docstring'''
				a__     = XLMWithLMHeadModel.from_pretrained("xlm-mlm-en-2048"		)
				model.to(lowerCamelCase		)
				a__     = torch.tensor([[1_4, 4_4_7]] , dtype=torch.long , device=lowerCamelCase		)  # the president
				a__     = [
				    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
				a__     = model.generate(lowerCamelCase , do_sample=lowerCamelCase		)
				self.assertListEqual(output_ids[0].cpu().numpy().tolist() , lowerCamelCase		)










'''simple docstring'''


def    _lowerCamelCase  (__lowerCamelCase  :     int = 400_0000     )   ->    int:
			a__     = [0, 1]
			a__     = 0
			while fib[i] <= n:
						fib.append(fib[i] + fib[i + 1]     )
						if fib[i + 2] > n:
									break
						i += 1
			a__     = 0
			for j in range(len(__lowerCamelCase     ) - 1     ):
						if fib[j] % 2 == 0:
									total += fib[j]

			return total


if __name__ == "__main__":
		print(f"""{solution() = }""")



