

'''simple docstring'''


import unittest

from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM




@require_torch
class     a			:






						"""simple docstring"""




						def __init__(							self					:      List[Any]			,			snake_case_					:      List[Any]			,			snake_case_					:      Dict=9_9			,			snake_case_					:      Dict=1_3			,			snake_case_					:      Union[str, Any]=1_6			,			snake_case_					:      List[Any]=7			,			snake_case_					:      Union[str, Any]=True			,			snake_case_					:      Tuple=True			,			snake_case_					:      Any=True			,			snake_case_					:      Optional[int]=False			,			snake_case_					:      int=True			,			snake_case_					:      List[Any]=2			,			snake_case_					:      List[Any]=3_2			,			snake_case_					:      Optional[Any]=4			,			snake_case_					:      List[str]=4			,			snake_case_					:      int=3_0			,			snake_case_					:      Tuple=0			,			snake_case_					:      Optional[Any]=1			,			snake_case_					:      int=2			,			snake_case_					:      Tuple=None			,			):



								'''simple docstring'''



								snake_case__						:       Any       						=  parent
								snake_case__						:       str       						=  batch_size
								snake_case__						:       Any       						=  decoder_seq_length
								# For common tests
								snake_case__						:       Optional[int]       						=  self.decoder_seq_length
								snake_case__						:       List[Any]       						=  is_training
								snake_case__						:       Tuple       						=  use_attention_mask
								snake_case__						:       List[Any]       						=  use_labels

								snake_case__						:       Optional[int]       						=  vocab_size
								snake_case__						:       Optional[int]       						=  d_model
								snake_case__						:       Tuple       						=  d_model
								snake_case__						:       Any       						=  decoder_layers
								snake_case__						:       int       						=  decoder_layers
								snake_case__						:       Tuple       						=  decoder_ffn_dim
								snake_case__						:       Any       						=  decoder_attention_heads
								snake_case__						:       Dict       						=  decoder_attention_heads
								snake_case__						:       int       						=  eos_token_id
								snake_case__						:       Tuple       						=  bos_token_id
								snake_case__						:       int       						=  pad_token_id
								snake_case__						:       List[Any]       						=  decoder_start_token_id
								snake_case__						:       Dict       						=  use_cache
								snake_case__						:       Dict       						=  max_position_embeddings

								snake_case__						:       Tuple       						=  None
								snake_case__						:       int       						=  decoder_seq_length
								snake_case__						:       Tuple       						=  2
								snake_case__						:       Tuple       						=  1







						def       __magic_name__							(							self					:      Tuple  ):



								'''simple docstring'''



								snake_case__						:       Optional[int]       						=  ids_tensor([self.batch_size, self.decoder_seq_length]			,			self.vocab_size  )

								snake_case__						:       Optional[int]       						=  None
								if self.use_attention_mask:
										snake_case__						:       Optional[int]       						=  ids_tensor([self.batch_size, self.decoder_seq_length]			,			vocab_size=2  )

								snake_case__						:       List[Any]       						=  None
								if self.use_labels:
										snake_case__						:       Any       						=  ids_tensor([self.batch_size, self.decoder_seq_length]			,			self.vocab_size  )

								snake_case__						:       int       						=  TrOCRConfig(
								    vocab_size=self.vocab_size			,			d_model=self.d_model			,			decoder_layers=self.decoder_layers			,			decoder_ffn_dim=self.decoder_ffn_dim			,			decoder_attention_heads=self.decoder_attention_heads			,			eos_token_id=self.eos_token_id			,			bos_token_id=self.bos_token_id			,			use_cache=self.use_cache			,			pad_token_id=self.pad_token_id			,			decoder_start_token_id=self.decoder_start_token_id			,			max_position_embeddings=self.max_position_embeddings			,			)

								return (config, input_ids, attention_mask, lm_labels)







						def       __magic_name__							(							self					:      Dict			,			snake_case_					:      List[Any]			,			snake_case_					:      int			,			snake_case_					:      Union[str, Any]			,			snake_case_					:      str			,			):



								'''simple docstring'''



								snake_case__						:       Optional[int]       						=  True
								snake_case__						:       Union[str, Any]       						=  TrOCRDecoder(config=snake_case_  ).to(snake_case_  ).eval()
								snake_case__						:       str       						=  input_ids[:2]

								input_ids[input_ids == 0] += 1
								# first forward pass
								snake_case__						:       Optional[Any]       						=  model(snake_case_			,			use_cache=snake_case_  )
								snake_case__						:       Any       						=  model(snake_case_  )
								snake_case__						:       List[str]       						=  model(snake_case_			,			use_cache=snake_case_  )

								self.parent.assertTrue(len(snake_case_  ) == len(snake_case_  )  )
								self.parent.assertTrue(len(snake_case_  ) == len(snake_case_  ) + 1  )

								snake_case__						:       Optional[int]       						=  outputs['''past_key_values''']

								# create hypothetical next token and extent to next_input_ids
								snake_case__						:       Union[str, Any]       						=  ids_tensor((2, 1)			,			config.vocab_size - 1  ) + 1

								# append to next input_ids and
								snake_case__						:       str       						=  torch.cat([input_ids, next_tokens]			,			dim=-1  )

								snake_case__						:       str       						=  model(snake_case_  )['''last_hidden_state''']
								snake_case__						:       str       						=  model(snake_case_			,			past_key_values=snake_case_  )['''last_hidden_state''']

								# select random slice
								snake_case__						:       Dict       						=  ids_tensor((1,)			,			output_from_past.shape[-1]  ).item()
								snake_case__						:       int       						=  output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
								snake_case__						:       Optional[Any]       						=  output_from_past[:, 0, random_slice_idx].detach()

								# test that outputs are equal for slice
								assert torch.allclose(snake_case_			,			snake_case_			,			atol=1e-3  )







						def       __magic_name__							(							self					:      int  ):



								'''simple docstring'''



								snake_case__						:       str       						=  self.prepare_config_and_inputs()
								snake_case__						,       snake_case__						,       snake_case__						,       snake_case__						:       Tuple       						=  config_and_inputs

								snake_case__						:       Any       						=  {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
								return config, inputs_dict





@require_torch
class     a			(				SCREAMING_SNAKE_CASE			,			SCREAMING_SNAKE_CASE			,			SCREAMING_SNAKE_CASE			,			unittest.TestCase       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    (TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
						__UpperCAmelCase			     =    (TrOCRForCausalLM,) if is_torch_available() else ()
						__UpperCAmelCase			     =    {"""text-generation""": TrOCRForCausalLM} if is_torch_available() else {}
						__UpperCAmelCase			     =    True
						__UpperCAmelCase			     =    False







						def       __magic_name__							(							self					:      Any  ):



								'''simple docstring'''



								snake_case__						:       Union[str, Any]       						=  TrOCRStandaloneDecoderModelTester(self			,			is_training=snake_case_  )
								snake_case__						:       Optional[int]       						=  ConfigTester(self			,			config_class=snake_case_  )







						def       __magic_name__							(							self					:      Union[str, Any]  ):



								'''simple docstring'''



								pass







						def       __magic_name__							(							self					:      Optional[int]  ):



								'''simple docstring'''



								pass







						def       __magic_name__							(							self					:      Union[str, Any]  ):



								'''simple docstring'''



								pass







						def       __magic_name__							(							self					:      Optional[int]  ):



								'''simple docstring'''



								self.config_tester.run_common_tests()







						def       __magic_name__							(							self					:      List[Any]  ):



								'''simple docstring'''



								snake_case__						:       List[str]       						=  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_decoder_model_past(*snake_case_  )







						def       __magic_name__							(							self					:      Union[str, Any]  ):



								'''simple docstring'''



								return







						@unittest.skip('''The model doesn\'t support left padding'''  )  # and it's not used enough to be worth fixing :)
						def       __magic_name__							(							self					:      int  ):



								'''simple docstring'''



								pass






'''simple docstring'''


import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX




def 	_a       (				__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      list     ):

		"""simple docstring"""

		snake_case__						:       Union[str, Any]       						=  np.array([[1, item, train_mtch[i]] for i, item in enumerate(__lowerCAmelCase     )]     )
		snake_case__						:       Dict       						=  np.array(__lowerCAmelCase     )
		snake_case__						:       List[str]       						=  np.dot(np.dot(np.linalg.inv(np.dot(x.transpose()   ,							__lowerCAmelCase     )     )   ,							x.transpose()     )   ,							__lowerCAmelCase     )
		return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2]     )




def 	_a       (				__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      list     ):

		"""simple docstring"""

		snake_case__						:       List[str]       						=  (1, 2, 1)
		snake_case__						:       Union[str, Any]       						=  (1, 1, 0, 7)
		snake_case__						:       List[str]       						=  SARIMAX(
		    __lowerCAmelCase   ,							exog=__lowerCAmelCase   ,							order=__lowerCAmelCase   ,							seasonal_order=__lowerCAmelCase     )
		snake_case__						:       str       						=  model.fit(disp=__lowerCAmelCase   ,							maxiter=6_00   ,							method='''nm'''     )
		snake_case__						:       List[str]       						=  model_fit.predict(1   ,							len(__lowerCAmelCase     )   ,							exog=[test_match]     )
		return result[0]




def 	_a       (				__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      list     ):

		"""simple docstring"""

		snake_case__						:       List[Any]       						=  SVR(kernel='''rbf'''   ,							C=1   ,							gamma=0.1   ,							epsilon=0.1     )
		regressor.fit(__lowerCAmelCase   ,							__lowerCAmelCase     )
		snake_case__						:       Optional[Any]       						=  regressor.predict(__lowerCAmelCase     )
		return y_pred[0]




def 	_a       (				__lowerCAmelCase		:      list     ):

		"""simple docstring"""

		train_user.sort()
		snake_case__						:       int       						=  np.percentile(__lowerCAmelCase   ,							25     )
		snake_case__						:       List[str]       						=  np.percentile(__lowerCAmelCase   ,							75     )
		snake_case__						:       Dict       						=  qa - qa
		snake_case__						:       Optional[int]       						=  qa - (iqr * 0.1)
		return low_lim




def 	_a       (				__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      float     ):

		"""simple docstring"""

		snake_case__						:       int       						=  0
		snake_case__						:       List[Any]       						=  0
		for i in list_vote:
				if i > actual_result:
						snake_case__						:       Optional[Any]       						=  not_safe + 1
				else:
						if abs(abs(__lowerCAmelCase     ) - abs(__lowerCAmelCase     )     ) <= 0.1:
								safe += 1
						else:
								not_safe += 1
		return safe > not_safe


if __name__ == "__main__":
					# data_input_df = pd.read_csv("ex_data.csv", header=None)
					lowerCAmelCase__						:       Optional[int]										=						[[18231, 0.0, 1], [22621, 1.0, 2], [15675, 0.0, 3], [23583, 1.0, 4]]
					lowerCAmelCase__						:       List[Any]										=						pd.DataFrame(
					    data_input, columns=["""total_user""", """total_even""", """days"""]
					)
					lowerCAmelCase__						:       Union[str, Any]										=						Normalizer().fit_transform(data_input_df.values)
					# split data
					lowerCAmelCase__						:       Union[str, Any]										=						normalize_df[:, 2].tolist()
					lowerCAmelCase__						:       List[str]										=						normalize_df[:, 0].tolist()
					lowerCAmelCase__						:       List[str]										=						normalize_df[:, 1].tolist()

					# for svr (input variable = total date and total match)
					lowerCAmelCase__						:       Dict										=						normalize_df[:, [1, 2]].tolist()
					lowerCAmelCase__						:       Union[str, Any]										=						x[: len(x) - 1]
					lowerCAmelCase__						:       List[str]										=						x[len(x) - 1 :]

					# for linear regression & sarimax
					lowerCAmelCase__						:       Union[str, Any]										=						total_date[: len(total_date) - 1]
					lowerCAmelCase__						:       Union[str, Any]										=						total_user[: len(total_user) - 1]
					lowerCAmelCase__						:       List[Any]										=						total_match[: len(total_match) - 1]

					lowerCAmelCase__						:       Optional[int]										=						total_date[len(total_date) - 1 :]
					lowerCAmelCase__						:       Union[str, Any]										=						total_user[len(total_user) - 1 :]
					lowerCAmelCase__						:       List[Any]										=						total_match[len(total_match) - 1 :]

					# voting system with forecasting
					lowerCAmelCase__						:       Optional[Any]										=						[
					    linear_regression_prediction(
					        trn_date, trn_user, trn_match, tst_date, tst_match
					    ),
					    sarimax_predictor(trn_user, trn_match, tst_match),
					    support_vector_regressor(x_train, x_test, trn_user),
					]

					# check the safety of today's data
					lowerCAmelCase__						:       Optional[int]										=						"""""" if data_safety_checker(res_vote, tst_user) else """not """
					print("""Today's data is {not_str}safe.""")



