




from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


__SCREAMING_SNAKE_CASE			:      Optional[Any]      			=logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE			:      Optional[Any]      			={
    """facebook/mbart-large-cc25""": """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json""",
    # See all MBART models at https://huggingface.co/models?filter=mbart
}






class 	A	(						__lowercase						):
     """simple docstring"""

     _snake_case:		Optional[Any]   ='''mbart'''
     _snake_case:		Any   =['''past_key_values''']
     _snake_case:		Optional[Any]   ={'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}




     def __init__(			self     :      Any		,     A__     :      str=5_0_2_6_5		,     A__     :      Optional[int]=1_0_2_4		,     A__     :      str=1_2		,     A__     :      List[str]=4_0_9_6		,     A__     :      Union[str, Any]=1_6		,     A__     :      Union[str, Any]=1_2		,     A__     :      List[str]=4_0_9_6		,     A__     :      Optional[Any]=1_6		,     A__     :      List[str]=0.0		,     A__     :      Dict=0.0		,     A__     :      Optional[Any]=True		,     A__     :      List[Any]=True		,     A__     :      Optional[int]="gelu"		,     A__     :      Tuple=1_0_2_4		,     A__     :      Union[str, Any]=0.1		,     A__     :      Optional[Any]=0.0		,     A__     :      Union[str, Any]=0.0		,     A__     :      Dict=0.02		,     A__     :      str=0.0		,     A__     :      Optional[int]=False		,     A__     :      List[Any]=1		,     A__     :      List[Any]=0		,     A__     :      int=2		,     A__     :      Tuple=2		,     **A__     :      Optional[Any]		,     ) ->				List[Any]:
           lowerCamelCase__					     = vocab_size
           lowerCamelCase__					     = max_position_embeddings
           lowerCamelCase__					     = d_model
           lowerCamelCase__					     = encoder_ffn_dim
           lowerCamelCase__					     = encoder_layers
           lowerCamelCase__					     = encoder_attention_heads
           lowerCamelCase__					     = decoder_ffn_dim
           lowerCamelCase__					     = decoder_layers
           lowerCamelCase__					     = decoder_attention_heads
           lowerCamelCase__					     = dropout
           lowerCamelCase__					     = attention_dropout
           lowerCamelCase__					     = activation_dropout
           lowerCamelCase__					     = activation_function
           lowerCamelCase__					     = init_std
           lowerCamelCase__					     = encoder_layerdrop
           lowerCamelCase__					     = decoder_layerdrop
           lowerCamelCase__					     = classifier_dropout
           lowerCamelCase__					     = use_cache
           lowerCamelCase__					     = encoder_layers
           lowerCamelCase__					     = scale_embedding  # scale factor will be sqrt(d_model) if True
           super().__init__(
               pad_token_id=_UpperCamelCase		,     bos_token_id=_UpperCamelCase		,     eos_token_id=_UpperCamelCase		,     is_encoder_decoder=_UpperCamelCase		,     forced_eos_token_id=_UpperCamelCase		,     **_UpperCamelCase		,     )






class 	A	(						__lowercase						):
     """simple docstring"""

     @property
     def        __snake_case      (			self     :      Tuple	) ->				Optional[int]:
           if self.task in ["default", "seq2seq-lm"]:
                 lowerCamelCase__					     = OrderedDict(
                     [
                         ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                         ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                     ]	)

                 if self.use_past:
                       lowerCamelCase__					     = {0: 'batch'}
                       lowerCamelCase__					     = {0: 'batch', 1: 'past_decoder_sequence + sequence'}
                 else:
                       lowerCamelCase__					     = {0: 'batch', 1: 'decoder_sequence'}
                       lowerCamelCase__					     = {0: 'batch', 1: 'decoder_sequence'}

                 if self.use_past:
                       self.fill_with_past_key_values_(_UpperCamelCase		,     direction='inputs'	)
           elif self.task == "causal-lm":
                 # TODO: figure this case out.
                 lowerCamelCase__					     = OrderedDict(
                     [
                         ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                         ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                     ]	)
                 if self.use_past:
                       lowerCamelCase__     ,						lowerCamelCase__					     = self.num_layers
                       for i in range(_UpperCamelCase	):
                             lowerCamelCase__					     = {0: 'batch', 2: 'past_sequence + sequence'}
                             lowerCamelCase__					     = {0: 'batch', 2: 'past_sequence + sequence'}
           else:
                 lowerCamelCase__					     = OrderedDict(
                     [
                         ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                         ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                         ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
                         ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
                     ]	)

           return common_inputs




     @property
     def        __snake_case      (			self     :      List[Any]	) ->				Dict:
           if self.task in ["default", "seq2seq-lm"]:
                 lowerCamelCase__					     = super().outputs
           else:
                 lowerCamelCase__					     = super(_UpperCamelCase		,     self	).outputs
                 if self.use_past:
                       lowerCamelCase__     ,						lowerCamelCase__					     = self.num_layers
                       for i in range(_UpperCamelCase	):
                             lowerCamelCase__					     = {0: 'batch', 2: 'past_sequence + sequence'}
                             lowerCamelCase__					     = {0: 'batch', 2: 'past_sequence + sequence'}
           return common_outputs




     def        __snake_case      (			self     :      Tuple		,     A__     :      Any		,     A__     :      List[str] = -1		,     A__     :      int = -1		,     A__     :      Optional[Any] = False		,     A__     :      Tuple = None		,     ) ->				Optional[int]:
           lowerCamelCase__					     = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               _UpperCamelCase		,     _UpperCamelCase		,     _UpperCamelCase		,     _UpperCamelCase		,     _UpperCamelCase	)

           # Generate decoder inputs
           lowerCamelCase__					     = seq_length if not self.use_past else 1
           lowerCamelCase__					     = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               _UpperCamelCase		,     _UpperCamelCase		,     _UpperCamelCase		,     _UpperCamelCase		,     _UpperCamelCase	)
           lowerCamelCase__					     = {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
           lowerCamelCase__					     = dict(**_UpperCamelCase		,     **_UpperCamelCase	)

           if self.use_past:
                 if not is_torch_available():
                       raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'	)
                 else:
                       import torch
                 lowerCamelCase__     ,						lowerCamelCase__					     = common_inputs['input_ids'].shape
                 lowerCamelCase__					     = common_inputs['decoder_input_ids'].shape[1]
                 lowerCamelCase__     ,						lowerCamelCase__					     = self.num_attention_heads
                 lowerCamelCase__					     = (
                     batch,
                     num_encoder_attention_heads,
                     encoder_seq_length,
                     self._config.hidden_size // num_encoder_attention_heads,
                 )
                 lowerCamelCase__					     = decoder_seq_length + 3
                 lowerCamelCase__					     = (
                     batch,
                     num_decoder_attention_heads,
                     decoder_past_length,
                     self._config.hidden_size // num_decoder_attention_heads,
                 )

                 lowerCamelCase__					     = torch.cat(
                     [common_inputs['decoder_attention_mask'], torch.ones(_UpperCamelCase		,     _UpperCamelCase	)]		,     dim=1	)

                 lowerCamelCase__					     = []
                 # If the number of encoder and decoder layers are present in the model configuration, both are considered
                 lowerCamelCase__     ,						lowerCamelCase__					     = self.num_layers
                 lowerCamelCase__					     = min(_UpperCamelCase		,     _UpperCamelCase	)
                 lowerCamelCase__					     = max(_UpperCamelCase		,     _UpperCamelCase	) - min_num_layers
                 lowerCamelCase__					     = 'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

                 for _ in range(_UpperCamelCase	):
                       common_inputs["past_key_values"].append(
                           (
                               torch.zeros(_UpperCamelCase	),
                               torch.zeros(_UpperCamelCase	),
                               torch.zeros(_UpperCamelCase	),
                               torch.zeros(_UpperCamelCase	),
                           )	)
                 # TODO: test this.
                 lowerCamelCase__					     = encoder_shape if remaining_side_name == 'encoder' else decoder_shape
                 for _ in range(_UpperCamelCase		,     _UpperCamelCase	):
                       common_inputs["past_key_values"].append((torch.zeros(_UpperCamelCase	), torch.zeros(_UpperCamelCase	))	)
           return common_inputs




     def        __snake_case      (			self     :      List[str]		,     A__     :      Union[str, Any]		,     A__     :      Union[str, Any] = -1		,     A__     :      str = -1		,     A__     :      Union[str, Any] = False		,     A__     :      Tuple = None		,     ) ->				Tuple:
           lowerCamelCase__					     = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               _UpperCamelCase		,     _UpperCamelCase		,     _UpperCamelCase		,     _UpperCamelCase		,     _UpperCamelCase	)

           if self.use_past:
                 if not is_torch_available():
                       raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'	)
                 else:
                       import torch
                 lowerCamelCase__     ,						lowerCamelCase__					     = common_inputs['input_ids'].shape
                 # Not using the same length for past_key_values
                 lowerCamelCase__					     = seqlen + 2
                 lowerCamelCase__     ,						lowerCamelCase__					     = self.num_layers
                 lowerCamelCase__     ,						lowerCamelCase__					     = self.num_attention_heads
                 lowerCamelCase__					     = (
                     batch,
                     num_encoder_attention_heads,
                     past_key_values_length,
                     self._config.hidden_size // num_encoder_attention_heads,
                 )

                 lowerCamelCase__					     = common_inputs['attention_mask'].dtype
                 lowerCamelCase__					     = torch.cat(
                     [common_inputs['attention_mask'], torch.ones(_UpperCamelCase		,     _UpperCamelCase		,     dtype=_UpperCamelCase	)]		,     dim=1	)
                 lowerCamelCase__					     = [
                     (torch.zeros(_UpperCamelCase	), torch.zeros(_UpperCamelCase	)) for _ in range(_UpperCamelCase	)
                 ]
           return common_inputs




     def        __snake_case      (			self     :      str		,     A__     :      Optional[int]		,     A__     :      str = -1		,     A__     :      Optional[int] = -1		,     A__     :      Dict = False		,     A__     :      int = None		,     ) ->				List[str]:
           lowerCamelCase__					     = compute_effective_axis_dimension(
               _UpperCamelCase		,     fixed_dimension=OnnxConfig.default_fixed_batch		,     num_token_to_add=0	)

           # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
           lowerCamelCase__					     = tokenizer.num_special_tokens_to_add(_UpperCamelCase	)
           lowerCamelCase__					     = compute_effective_axis_dimension(
               _UpperCamelCase		,     fixed_dimension=OnnxConfig.default_fixed_sequence		,     num_token_to_add=_UpperCamelCase	)

           # Generate dummy inputs according to compute batch and sequence
           lowerCamelCase__					     = [' '.join([tokenizer.unk_token]	) * seq_length] * batch_size
           lowerCamelCase__					     = dict(tokenizer(_UpperCamelCase		,     return_tensors=_UpperCamelCase	)	)
           return common_inputs




     def        __snake_case      (			self     :      Tuple		,     A__     :      int		,     A__     :      str = -1		,     A__     :      Optional[int] = -1		,     A__     :      Dict = False		,     A__     :      int = None		,     ) ->				str:
           if self.task in ["default", "seq2seq-lm"]:
                 lowerCamelCase__					     = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                     _UpperCamelCase		,     batch_size=_UpperCamelCase		,     seq_length=_UpperCamelCase		,     is_pair=_UpperCamelCase		,     framework=_UpperCamelCase	)

           elif self.task == "causal-lm":
                 lowerCamelCase__					     = self._generate_dummy_inputs_for_causal_lm(
                     _UpperCamelCase		,     batch_size=_UpperCamelCase		,     seq_length=_UpperCamelCase		,     is_pair=_UpperCamelCase		,     framework=_UpperCamelCase	)
           else:
                 lowerCamelCase__					     = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                     _UpperCamelCase		,     batch_size=_UpperCamelCase		,     seq_length=_UpperCamelCase		,     is_pair=_UpperCamelCase		,     framework=_UpperCamelCase	)

           return common_inputs




     def        __snake_case      (			self     :      Optional[Any]		,     A__     :      Tuple		,     A__     :      str		,     A__     :      List[Any]		,     A__     :      Optional[int]	) ->				Union[str, Any]:
           if self.task in ["default", "seq2seq-lm"]:
                 lowerCamelCase__					     = super()._flatten_past_key_values_(_UpperCamelCase		,     _UpperCamelCase		,     _UpperCamelCase		,     _UpperCamelCase	)
           else:
                 lowerCamelCase__					     = super(_UpperCamelCase		,     self	)._flatten_past_key_values_(
                     _UpperCamelCase		,     _UpperCamelCase		,     _UpperCamelCase		,     _UpperCamelCase	)







import numpy as np
import requests







def 		_UpperCamelCase      (			)     ->      Dict:

						"""simple docstring"""



						lowerCAmelCase__			  =  requests.get(
						    'https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/'
						    'master/Week1/ADRvsRating.csv'						)
						lowerCAmelCase__			  =  response.text.splitlines()
						lowerCAmelCase__			  =  []
						for item in lines:
												lowerCAmelCase__			  =  item.split(','						)
												data.append(UpperCamelCase_						)
						data.pop(0						)  # This is for removing the labels from the list
						lowerCAmelCase__			  =  np.matrix(UpperCamelCase_						)
						return dataset







def 		_UpperCamelCase      (			UpperCamelCase_				:    Union[str, Any]			,				UpperCamelCase_				:    Optional[Any]			,				UpperCamelCase_				:    Dict			,				UpperCamelCase_				:    List[Any]			,				UpperCamelCase_				:    int						)     ->      int:

						"""simple docstring"""



						lowerCAmelCase__			  =  len_data

						lowerCAmelCase__			  =  np.dot(UpperCamelCase_			,				data_x.transpose()						)
						prod -= data_y.transpose()
						lowerCAmelCase__			  =  np.dot(UpperCamelCase_			,				UpperCamelCase_						)
						lowerCAmelCase__			  =  theta - (alpha / n) * sum_grad
						return theta







def 		_UpperCamelCase      (			UpperCamelCase_				:    str			,				UpperCamelCase_				:    Any			,				UpperCamelCase_				:    str			,				UpperCamelCase_				:    List[Any]						)     ->      Dict:

						"""simple docstring"""



						lowerCAmelCase__			  =  np.dot(UpperCamelCase_			,				data_x.transpose()						)
						prod -= data_y.transpose()
						lowerCAmelCase__			  =  np.sum(np.square(UpperCamelCase_						)						)
						lowerCAmelCase__			  =  sum_elem / (2 * len_data)
						return error







def 		_UpperCamelCase      (			UpperCamelCase_				:    Any			,				UpperCamelCase_				:    List[Any]						)     ->      str:

						"""simple docstring"""



						lowerCAmelCase__			  =  10_0000
						lowerCAmelCase__			  =  0.000_1550

						lowerCAmelCase__			  =  data_x.shape[1]
						lowerCAmelCase__			  =  data_x.shape[0] - 1

						lowerCAmelCase__			  =  np.zeros((1, no_features)						)

						for i in range(0			,				UpperCamelCase_						):
												lowerCAmelCase__			  =  run_steep_gradient_descent(UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_						)
												lowerCAmelCase__			  =  sum_of_square_error(UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_						)
												print(F"At Iteration {i + 1} - Error is {error:.5f}"						)

						return theta







def 		_UpperCamelCase      (			UpperCamelCase_				:    Dict			,				UpperCamelCase_				:    Any						)     ->      Union[str, Any]:

						"""simple docstring"""



						lowerCAmelCase__			  =  sum(abs(y - predicted_y[i]						) for i, y in enumerate(UpperCamelCase_						)						)
						return total / len(UpperCamelCase_						)







def 		_UpperCamelCase      (			)     ->      List[Any]:

						"""simple docstring"""



						lowerCAmelCase__			  =  collect_dataset()

						lowerCAmelCase__			  =  data.shape[0]
						lowerCAmelCase__			  =  np.c_[np.ones(UpperCamelCase_						), data[:, :-1]].astype(UpperCamelCase_						)
						lowerCAmelCase__			  =  data[:, -1].astype(UpperCamelCase_						)

						lowerCAmelCase__			  =  run_linear_regression(UpperCamelCase_			,				UpperCamelCase_						)
						lowerCAmelCase__			  =  theta.shape[1]
						print('Resultant Feature vector : '						)
						for i in range(0			,				UpperCamelCase_						):
												print(F"{theta[0, i]:.5f}"						)


if __name__ == "__main__":
					main()





