"""simple docstring"""







import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger

class 		UpperCamelCase      (   lowercase						):



				def __init__(self    :				List[str]     ,  _A    :				Tuple     ,  _A    :				Any)   ->     Optional[int]:
					__snake_case      :							str						=			params

					__snake_case      :							Union[str, Any]						=			np.array(_A)
					__snake_case      :							List[str]						=			np.array([len(_A) for t in data])

					self.check()
					self.remove_long_sequences()
					self.remove_empty_sequences()
					self.remove_unknown_sequences()
					self.check()
					self.print_statistics()



				def __getitem__(self    :				Optional[Any]     ,  _A    :				Union[str, Any])   ->     str:
					return (self.token_ids[index], self.lengths[index])



				def __len__(self    :				List[str])   ->     Dict:
					return len(self.lengths)



				def  _lowercase					(self    :				Optional[Any])   ->     Optional[Any]:
					assert len(self.token_ids) == len(self.lengths)
					assert all(self.lengths[i] == len(self.token_ids[i]) for i in range(len(self.lengths)))



				def  _lowercase					(self    :				Dict)   ->     Union[str, Any]:
					__snake_case      :							Tuple						=			self.params.max_model_input_size
					__snake_case      :							Union[str, Any]						=			self.lengths > max_len
					logger.info(f"Splitting {sum(_A)} too long sequences.")

					def divide_chunks(_A    :				str     ,  _A    :				List[Any]):
						return [l[i : i + n] for i in range(0     ,  len(_A)     ,  _A)]

					__snake_case      :							Optional[int]						=			[]
					__snake_case      :							int						=			[]
					if self.params.mlm:
						__snake_case		,    __snake_case      :							int						=			self.params.special_tok_ids['cls_token'], self.params.special_tok_ids['sep_token']
					else:
						__snake_case		,    __snake_case      :							Optional[Any]						=			self.params.special_tok_ids['bos_token'], self.params.special_tok_ids['eos_token']

					for seq_, len_ in zip(self.token_ids     ,  self.lengths):
						assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
						if len_ <= max_len:
							new_tok_ids.append(seq_)
							new_lengths.append(len_)
						else:
							__snake_case      :							Optional[Any]						=			[]
							for sub_s in divide_chunks(seq_     ,  max_len - 2):
								if sub_s[0] != cls_id:
									__snake_case      :							Optional[Any]						=			np.insert(_A     ,  0     ,  _A)
								if sub_s[-1] != sep_id:
									__snake_case      :							List[Any]						=			np.insert(_A     ,  len(_A)     ,  _A)
								assert len(_A) <= max_len
								assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
								sub_seqs.append(_A)

							new_tok_ids.extend(_A)
							new_lengths.extend([len(_A) for l in sub_seqs])

					__snake_case      :							Union[str, Any]						=			np.array(_A)
					__snake_case      :							Union[str, Any]						=			np.array(_A)



				def  _lowercase					(self    :				Union[str, Any])   ->     Optional[Any]:
					__snake_case      :							List[str]						=			len(self)
					__snake_case      :							Dict						=			self.lengths > 11
					__snake_case      :							Union[str, Any]						=			self.token_ids[indices]
					__snake_case      :							str						=			self.lengths[indices]
					__snake_case      :							Any						=			len(self)
					logger.info(f"Remove {init_size - new_size} too short (<=11 tokens) sequences.")



				def  _lowercase					(self    :				int)   ->     List[str]:
					if "unk_token" not in self.params.special_tok_ids:
						return
					else:
						__snake_case      :							Optional[Any]						=			self.params.special_tok_ids['unk_token']
					__snake_case      :							Optional[Any]						=			len(self)
					__snake_case      :							Tuple						=			np.array([np.count_nonzero(a == unk_token_id) for a in self.token_ids])
					__snake_case      :							Any						=			(unk_occs / self.lengths) < 0.5
					__snake_case      :							Any						=			self.token_ids[indices]
					__snake_case      :							str						=			self.lengths[indices]
					__snake_case      :							Optional[Any]						=			len(self)
					logger.info(f"Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).")



				def  _lowercase					(self    :				Any)   ->     List[str]:
					if not self.params.is_master:
						return
					logger.info(f"{len(self)} sequences")
					# data_len = sum(self.lengths)
					# nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
					# logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

					# unk_idx = self.params.special_tok_ids['unk_token']
					# nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
					# logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')






				def  _lowercase					(self    :				Dict     ,  _A    :				Optional[Any])   ->     Dict:
					__snake_case      :							int						=			[t[0] for t in batch]
					__snake_case      :							Any						=			[t[1] for t in batch]
					assert len(_A) == len(_A)

					# Max for paddings
					__snake_case      :							Any						=			max(_A)

					# Pad token ids
					if self.params.mlm:
						__snake_case      :							Optional[int]						=			self.params.special_tok_ids['pad_token']
					else:
						__snake_case      :							Tuple						=			self.params.special_tok_ids['unk_token']
					__snake_case      :							List[str]						=			[list(t.astype(_A)) + [pad_idx] * (max_seq_len_ - len(_A)) for t in token_ids]
					assert len(tk_) == len(_A)
					assert all(len(_A) == max_seq_len_ for t in tk_)

					__snake_case      :							Tuple						=			torch.tensor(tk_)  # (bs, max_seq_len_)
					__snake_case      :							Any						=			torch.tensor(_A)  # (bs)
					return tk_t, lg_t






"""simple docstring"""







import numpy

# List of input, output pairs
_a					:					Optional[int]=			(
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
_a					:					int=			(((515, 22, 13), 555), ((61, 35, 49), 150))
_a					:					List[Any]=			[2, 4, 1, 5]
_a					:					Optional[int]=			len(train_data)
_a					:					str=			0.0_0_9
def   __UpperCAmelCase    (   UpperCAmelCase_   :    List[Any]      ,							UpperCAmelCase_   :    List[str]="train"  )							->      int:

	'''simple docstring'''





	return calculate_hypothesis_value(UpperCAmelCase_      ,							UpperCAmelCase_  ) - output(
	    UpperCAmelCase_      ,							UpperCAmelCase_  )
def   __UpperCAmelCase    (   UpperCAmelCase_   :    Tuple  )							->      Dict:

	'''simple docstring'''





	__snake_case      :							int						=			0
	for i in range(len(UpperCAmelCase_  ) - 1  ):
		hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
	hyp_val += parameter_vector[0]
	return hyp_val
def   __UpperCAmelCase    (   UpperCAmelCase_   :    str      ,							UpperCAmelCase_   :    List[Any]  )							->      List[Any]:

	'''simple docstring'''





	if data_set == "train":
		return train_data[example_no][1]
	elif data_set == "test":
		return test_data[example_no][1]
	return None
def   __UpperCAmelCase    (   UpperCAmelCase_   :    Optional[Any]      ,							UpperCAmelCase_   :    Optional[int]  )							->      Any:

	'''simple docstring'''





	if data_set == "train":
		return _hypothesis_value(train_data[example_no][0]  )
	elif data_set == "test":
		return _hypothesis_value(test_data[example_no][0]  )
	return None
def   __UpperCAmelCase    (   UpperCAmelCase_   :    Union[str, Any]      ,							UpperCAmelCase_   :    Any=m  )							->      Union[str, Any]:

	'''simple docstring'''





	__snake_case      :							Union[str, Any]						=			0
	for i in range(UpperCAmelCase_  ):
		if index == -1:
			summation_value += _error(UpperCAmelCase_  )
		else:
			summation_value += _error(UpperCAmelCase_  ) * train_data[i][0][index]
	return summation_value
def   __UpperCAmelCase    (   UpperCAmelCase_   :    int  )							->      str:

	'''simple docstring'''





	__snake_case      :							Dict						=			summation_of_cost_derivative(UpperCAmelCase_      ,							UpperCAmelCase_  ) / m
	return cost_derivative_value
def   __UpperCAmelCase    (   )							->      List[str]:

	'''simple docstring'''





	global parameter_vector
	# Tune these values to set a tolerance value for predicted output
	__snake_case      :							Dict						=			0.000_002
	__snake_case      :							Optional[int]						=			0
	__snake_case      :							str						=			0
	while True:
		j += 1
		__snake_case      :							Optional[int]						=			[0, 0, 0, 0]
		for i in range(0      ,							len(UpperCAmelCase_  )  ):
			__snake_case      :							Union[str, Any]						=			get_cost_derivative(i - 1  )
			__snake_case      :							Any						=			(
			    parameter_vector[i] - LEARNING_RATE * cost_derivative
			)
		if numpy.allclose(
		    UpperCAmelCase_      ,							UpperCAmelCase_      ,							atol=UpperCAmelCase_      ,							rtol=UpperCAmelCase_      ,							):
			break
		__snake_case      :							Optional[Any]						=			temp_parameter_vector
	print(('Number of iterations:', j)  )
def   __UpperCAmelCase    (   )							->      int:

	'''simple docstring'''





	for i in range(len(UpperCAmelCase_  )  ):
		print(('Actual output value:', output(UpperCAmelCase_      ,							'test'  ))  )
		print(('Hypothesis output:', calculate_hypothesis_value(UpperCAmelCase_      ,							'test'  ))  )


if __name__ == "__main__":
	run_gradient_descent()
	print("\nTesting gradient descent for a linear hypothesis function.\n")
	test_gradient_descent()





