
"""simple docstring"""


import os
import tempfile
import unittest

from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

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 (
		    FlaubertForMultipleChoice,
		    FlaubertForQuestionAnswering,
		    FlaubertForQuestionAnsweringSimple,
		    FlaubertForSequenceClassification,
		    FlaubertForTokenClassification,
		    FlaubertModel,
		    FlaubertWithLMHeadModel,
		)
		from transformers.models.flaubert.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST







class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						def __init__( self     :      List[Any]  ,A_     :      str  ,A_     :      Optional[int]=13  ,A_     :      Optional[int]=7  ,A_     :      List[str]=True  ,A_     :      Dict=True  ,A_     :      Dict=True  ,A_     :      Any=True  ,A_     :      Union[str, Any]=True  ,A_     :      Tuple=False  ,A_     :      List[str]=False  ,A_     :      List[str]=False  ,A_     :      int=2  ,A_     :      Dict=99  ,A_     :      Any=0  ,A_     :      Tuple=32  ,A_     :      int=5  ,A_     :      Optional[int]=4  ,A_     :      Tuple=0.1  ,A_     :      Optional[Any]=0.1  ,A_     :      Any=512  ,A_     :      Union[str, Any]=12  ,A_     :      str=2  ,A_     :      str=0.02  ,A_     :      str=3  ,A_     :      str=4  ,A_     :      Optional[int]="last"  ,A_     :      Dict=None  ,A_     :      Optional[int]=None  ,)     ->   int:
							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_vocab_size
							A						   =   type_sequence_label_size
							A						   =   initializer_range
							A						   =   num_labels
							A						   =   num_choices
							A						   =   summary_type
							A						   =   use_proj
							A						   =   scope


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   str:
							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 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   str:
							return FlaubertConfig(
							    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  ,)


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]  ,A_     :      List[Any]  ,A_     :      Tuple  ,A_     :      Optional[Any]  ,A_     :      Optional[int]  ,A_     :      Union[str, Any]  ,A_     :      Union[str, Any]  ,A_     :      Dict  ,A_     :      Union[str, Any]  ,A_     :      Optional[int]  ,)     ->   List[str]:
							A						   =   FlaubertModel(config=UpperCamelCase_      )
							model.to(UpperCamelCase_      )
							model.eval()
							A						   =   model(UpperCamelCase_  ,lengths=UpperCamelCase_  ,langs=UpperCamelCase_      )
							A						   =   model(UpperCamelCase_  ,langs=UpperCamelCase_      )
							A						   =   model(UpperCamelCase_      )
							self.parent.assertEqual(result.last_hidden_state.shape  ,(self.batch_size, self.seq_length, self.hidden_size)      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]  ,A_     :      str  ,A_     :      Any  ,A_     :      Any  ,A_     :      Union[str, Any]  ,A_     :      List[Any]  ,A_     :      Tuple  ,A_     :      List[Any]  ,A_     :      Any  ,A_     :      str  ,)     ->   Optional[int]:
							A						   =   FlaubertWithLMHeadModel(UpperCamelCase_      )
							model.to(UpperCamelCase_      )
							model.eval()

							A						   =   model(UpperCamelCase_  ,token_type_ids=UpperCamelCase_  ,labels=UpperCamelCase_      )
							self.parent.assertEqual(result.loss.shape  ,()      )
							self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.seq_length, self.vocab_size)      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]  ,A_     :      Optional[int]  ,A_     :      List[Any]  ,A_     :      Optional[int]  ,A_     :      List[str]  ,A_     :      Optional[Any]  ,A_     :      Tuple  ,A_     :      Tuple  ,A_     :      int  ,A_     :      int  ,)     ->   List[str]:
							A						   =   FlaubertForQuestionAnsweringSimple(UpperCamelCase_      )
							model.to(UpperCamelCase_      )
							model.eval()

							A						   =   model(UpperCamelCase_      )

							A						   =   model(UpperCamelCase_  ,start_positions=UpperCamelCase_  ,end_positions=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     :      List[Any]  ,A_     :      Any  ,A_     :      Union[str, Any]  ,A_     :      Tuple  ,A_     :      Dict  ,A_     :      Tuple  ,A_     :      Dict  ,A_     :      Dict  ,A_     :      Tuple  ,A_     :      Union[str, Any]  ,)     ->   Tuple:
							A						   =   FlaubertForQuestionAnswering(UpperCamelCase_      )
							model.to(UpperCamelCase_      )
							model.eval()

							A						   =   model(UpperCamelCase_      )

							A						   =   model(
							    UpperCamelCase_  ,start_positions=UpperCamelCase_  ,end_positions=UpperCamelCase_  ,cls_index=UpperCamelCase_  ,is_impossible=UpperCamelCase_  ,p_mask=UpperCamelCase_  ,)

							A						   =   model(
							    UpperCamelCase_  ,start_positions=UpperCamelCase_  ,end_positions=UpperCamelCase_  ,cls_index=UpperCamelCase_  ,is_impossible=UpperCamelCase_  ,)

							(A )						   =   result_with_labels.to_tuple()

							A						   =   model(UpperCamelCase_  ,start_positions=UpperCamelCase_  ,end_positions=UpperCamelCase_      )

							(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 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]  ,A_     :      Dict  ,A_     :      str  ,A_     :      int  ,A_     :      Union[str, Any]  ,A_     :      List[Any]  ,A_     :      Union[str, Any]  ,A_     :      List[str]  ,A_     :      Optional[int]  ,A_     :      Union[str, Any]  ,)     ->   Any:
							A						   =   FlaubertForSequenceClassification(UpperCamelCase_      )
							model.to(UpperCamelCase_      )
							model.eval()

							A						   =   model(UpperCamelCase_      )
							A						   =   model(UpperCamelCase_  ,labels=UpperCamelCase_      )

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


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]  ,A_     :      str  ,A_     :      Any  ,A_     :      Optional[Any]  ,A_     :      List[str]  ,A_     :      List[str]  ,A_     :      Optional[int]  ,A_     :      Optional[int]  ,A_     :      List[str]  ,A_     :      Dict  ,)     ->   List[str]:
							A						   =   self.num_labels
							A						   =   FlaubertForTokenClassification(UpperCamelCase_      )
							model.to(UpperCamelCase_      )
							model.eval()

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


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]  ,A_     :      List[str]  ,A_     :      Dict  ,A_     :      Dict  ,A_     :      Optional[Any]  ,A_     :      Dict  ,A_     :      Optional[Any]  ,A_     :      Dict  ,A_     :      Optional[Any]  ,A_     :      List[Any]  ,)     ->   int:
							A						   =   self.num_choices
							A						   =   FlaubertForMultipleChoice(config=UpperCamelCase_      )
							model.to(UpperCamelCase_      )
							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(
							    UpperCamelCase_  ,attention_mask=UpperCamelCase_  ,token_type_ids=UpperCamelCase_  ,labels=UpperCamelCase_  ,)
							self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.num_choices)      )




						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   List[Any]:
							A						   =   self.prepare_config_and_inputs()
							(
							    A 
							)						   =   config_and_inputs
							A						   =   {
							    "input_ids": input_ids,
							    "token_type_ids": token_type_ids,
							    "lengths": input_lengths,
							    "attention_mask": input_mask,
							}
							return config, inputs_dict







@require_torch
class 				lowerCAmelCase_				(      _lowercase		,					_lowercase		,					unittest.TestCase    ):



						'''simple docstring'''


						_lowerCamelCase:    Tuple      =    (
						    (
						        FlaubertModel,
						        FlaubertWithLMHeadModel,
						        FlaubertForQuestionAnswering,
						        FlaubertForQuestionAnsweringSimple,
						        FlaubertForSequenceClassification,
						        FlaubertForTokenClassification,
						        FlaubertForMultipleChoice,
						    )
						    if is_torch_available()
						    else ()
						)
						_lowerCamelCase:    Optional[Any]      =    (
						    {
						        "feature-extraction": FlaubertModel,
						        "fill-mask": FlaubertWithLMHeadModel,
						        "question-answering": FlaubertForQuestionAnsweringSimple,
						        "text-classification": FlaubertForSequenceClassification,
						        "token-classification": FlaubertForTokenClassification,
						        "zero-shot": FlaubertForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]  ,A_     :      Union[str, Any]  ,A_     :      str  ,A_     :      Optional[int]  ,A_     :      Any  ,A_     :      Dict      )     ->   List[Any]:
							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     :      Dict  ,A_     :      int  ,A_     :      int  ,A_     :      Union[str, Any]=False      )     ->   Dict:
							A						   =   super()._prepare_for_class(UpperCamelCase_  ,UpperCamelCase_  ,return_labels=UpperCamelCase_      )

							if return_labels:
								if model_class.__name__ == "FlaubertForQuestionAnswering":
									A						   =   torch.zeros(
									    self.model_tester.batch_size  ,dtype=torch.long  ,device=UpperCamelCase_      )
									A						   =   torch.zeros(
									    self.model_tester.batch_size  ,dtype=torch.long  ,device=UpperCamelCase_      )

							return inputs_dict


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   Optional[Any]:
							A						   =   FlaubertModelTester(self      )
							A						   =   ConfigTester(self  ,config_class=UpperCamelCase_  ,emb_dim=37      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   Union[str, Any]:
							self.config_tester.run_common_tests()


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]      )     ->   Optional[int]:
							A						   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_flaubert_model(*UpperCamelCase_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   int:
							A						   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_flaubert_lm_head(*UpperCamelCase_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict      )     ->   int:
							A						   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_flaubert_simple_qa(*UpperCamelCase_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   Optional[Any]:
							A						   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_flaubert_qa(*UpperCamelCase_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      int      )     ->   Optional[Any]:
							A						   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_flaubert_sequence_classif(*UpperCamelCase_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      int      )     ->   Optional[Any]:
							A						   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_flaubert_token_classif(*UpperCamelCase_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple      )     ->   Union[str, Any]:
							A						   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_flaubert_multiple_choice(*UpperCamelCase_      )


						@slow
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   Optional[int]:
							for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								A						   =   FlaubertModel.from_pretrained(UpperCamelCase_      )
								self.assertIsNotNone(UpperCamelCase_      )




						@slow
						@require_torch_gpu
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple      )     ->   Tuple:
							A						   =   self.model_tester.prepare_config_and_inputs_for_common()
							for model_class in self.all_model_classes:
								# FlauBertForMultipleChoice behaves incorrectly in JIT environments.
								if model_class == FlaubertForMultipleChoice:
									return

								A						   =   True
								A						   =   model_class(config=UpperCamelCase_      )

								A						   =   self._prepare_for_class(UpperCamelCase_  ,UpperCamelCase_      )
								A						   =   torch.jit.trace(
								    UpperCamelCase_  ,(inputs_dict['input_ids'].to('cpu'      ), inputs_dict['attention_mask'].to('cpu'      ))      )

								with tempfile.TemporaryDirectory() as tmp:
									torch.jit.save(UpperCamelCase_  ,os.path.join(UpperCamelCase_  ,'traced_model.pt'      )      )
									A						   =   torch.jit.load(os.path.join(UpperCamelCase_  ,'traced_model.pt'      )  ,map_location=UpperCamelCase_      )
									loaded(inputs_dict['input_ids'].to(UpperCamelCase_      )  ,inputs_dict['attention_mask'].to(UpperCamelCase_      )      )







@require_torch
class 				lowerCAmelCase_				(      unittest.TestCase    ):



						'''simple docstring'''


						@slow
						def 						_SCREAMING_SNAKE_CASE      ( self     :      int      )     ->   Dict:
							A						   =   FlaubertModel.from_pretrained('flaubert/flaubert_base_cased'      )
							A						   =   torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]      )
							with torch.no_grad():
								A						   =   model(UpperCamelCase_      )[0]
							A						   =   torch.Size((1, 11, 768)      )
							self.assertEqual(output.shape  ,UpperCamelCase_      )
							A						   =   torch.tensor(
							    [[[-2.62_51, -1.42_98, -0.02_27], [-2.85_10, -1.63_87, 0.22_58], [-2.81_14, -1.18_32, -0.30_66]]]      )

							self.assertTrue(torch.allclose(output[:, :3, :3]  ,UpperCamelCase_  ,atol=1e-4      )      )

"""simple docstring"""


from math import pi, sqrt






def       _snake_case				(		snake_case__   :     float     ):
	if num <= 0:
		raise ValueError('math domain error'     )
	if num > 171.5:
		raise OverflowError('math range error'     )
	elif num - int(snake_case__     ) not in (0, 0.5):
		raise NotImplementedError('num must be an integer or a half-integer'     )
	elif num == 0.5:
		return sqrt(snake_case__     )
	else:
		return 1.0 if num == 1 else (num - 1) * gamma(num - 1     )






def       _snake_case				(		):
	assert gamma(0.5     ) == sqrt(snake_case__     )
	assert gamma(1     ) == 1.0
	assert gamma(2     ) == 1.0


if __name__ == "__main__":
		from doctest import testmod

		testmod()
		_lowercase							   =				1.0
		while num:
				_lowercase							   =				float(input('''Gamma of: '''))
				print(F"""gamma({num}) = {gamma(num)}""")
				print('''\nEnter 0 to exit...''')