




import numpy as np







def 			lowerCAmelCase  (     UpperCAmelCase,     UpperCAmelCase="utf-8",     UpperCAmelCase="surrogatepass"	)  ->Tuple:






					"""simple docstring"""






					__magic_name__ :							Union[str, Any]		       =							bin(int.from_bytes(text.encode(UpperCAmelCase,     UpperCAmelCase	),     '''big'''	)	)[2:]
					return bits.zfill(8 * ((len(UpperCAmelCase	) + 7) // 8)	)







def 			lowerCAmelCase  (     UpperCAmelCase,     UpperCAmelCase="utf-8",     UpperCAmelCase="surrogatepass"	)  ->Optional[Any]:






					"""simple docstring"""






					__magic_name__ :							Dict		       =							int(UpperCAmelCase,     2	)
					return n.to_bytes((n.bit_length() + 7) // 8,     '''big'''	).decode(UpperCAmelCase,     UpperCAmelCase	) or "\0"







def 			lowerCAmelCase  (     UpperCAmelCase,     UpperCAmelCase	)  ->Dict:






					"""simple docstring"""






					if size_par + len(UpperCAmelCase	) <= 2**size_par - (len(UpperCAmelCase	) - 1):
										raise ValueError('''size of parity don\'t match with size of data'''	)

					__magic_name__ :							Any		       =							[]
					__magic_name__ :							Optional[int]		       =							[]
					__magic_name__ :							List[str]		       =							[bin(UpperCAmelCase	)[2:] for x in range(1,     size_par + len(UpperCAmelCase	) + 1	)]

					# sorted information data for the size of the output data
					__magic_name__ :							Optional[Any]		       =							[]
					# data position template + parity
					__magic_name__ :							Union[str, Any]		       =							[]
					# parity bit counter
					__magic_name__ :							int		       =							0
					# counter position of data bits
					__magic_name__ :							Dict		       =							0

					for x in range(1,     size_par + len(UpperCAmelCase	) + 1	):
										# Performs a template of bit positions - who should be given,
										# and who should be parity
										if qtd_bp < size_par:
															if (np.log(UpperCAmelCase	) / np.log(2	)).is_integer():
																				data_out_gab.append('''P'''	)
																				__magic_name__ :							Optional[Any]		       =							qtd_bp + 1
															else:
																				data_out_gab.append('''D'''	)
										else:
															data_out_gab.append('''D'''	)

										# Sorts the data to the new output size
										if data_out_gab[-1] == "D":
															data_ord.append(data[cont_data]	)
															cont_data += 1
										else:
															data_ord.append(UpperCAmelCase	)

    # Calculates parity
					__magic_name__ :							List[Any]		       =							0  # parity bit counter
					for bp in range(1,     size_par + 1	):
										# Bit counter one for a given parity
										__magic_name__ :							List[str]		       =							0
										# counter to control the loop reading
										__magic_name__ :							Any		       =							0
										for x in data_ord:
															if x is not None:
																				try:
																									__magic_name__ :							Any		       =							(bin_pos[cont_loop])[-1 * (bp)]
																				except IndexError:
																									__magic_name__ :							Dict		       =							'''0'''
																				if aux == "1" and x == "1":
																									cont_bo += 1
															cont_loop += 1
										parity.append(cont_bo % 2	)

										qtd_bp += 1

					# Mount the message
					__magic_name__ :							int		       =							0  # parity bit counter
					for x in range(0,     size_par + len(UpperCAmelCase	)	):
										if data_ord[x] is None:
															data_out.append(str(parity[cont_bp]	)	)
															cont_bp += 1
										else:
															data_out.append(data_ord[x]	)

					return data_out







def 			lowerCAmelCase  (     UpperCAmelCase,     UpperCAmelCase	)  ->List[Any]:






					"""simple docstring"""






					__magic_name__ :							str		       =							[]
					# Parity bit counter
					__magic_name__ :							str		       =							0
					# Counter p data bit reading
					__magic_name__ :							Tuple		       =							0
					# list of parity received
					__magic_name__ :							List[str]		       =							[]
					__magic_name__ :							int		       =							[]

					for x in range(1,     len(UpperCAmelCase	) + 1	):
										# Performs a template of bit positions - who should be given,
										#  and who should be parity
										if qtd_bp < size_par and (np.log(UpperCAmelCase	) / np.log(2	)).is_integer():
															data_out_gab.append('''P'''	)
															__magic_name__ :							Optional[Any]		       =							qtd_bp + 1
										else:
															data_out_gab.append('''D'''	)

										# Sorts the data to the new output size
										if data_out_gab[-1] == "D":
															data_output.append(data[cont_data]	)
										else:
															parity_received.append(data[cont_data]	)
										cont_data += 1

					# -----------calculates the parity with the data
					__magic_name__ :							List[Any]		       =							[]
					__magic_name__ :							List[Any]		       =							[]
					__magic_name__ :							List[Any]		       =							[bin(UpperCAmelCase	)[2:] for x in range(1,     size_par + len(UpperCAmelCase	) + 1	)]

					#  sorted information data for the size of the output data
					__magic_name__ :							List[Any]		       =							[]
					# Data position feedback + parity
					__magic_name__ :							int		       =							[]
					# Parity bit counter
					__magic_name__ :							List[str]		       =							0
					# Counter p data bit reading
					__magic_name__ :							int		       =							0

					for x in range(1,     size_par + len(UpperCAmelCase	) + 1	):
										# Performs a template position of bits - who should be given,
										# and who should be parity
										if qtd_bp < size_par and (np.log(UpperCAmelCase	) / np.log(2	)).is_integer():
															data_out_gab.append('''P'''	)
															__magic_name__ :							Any		       =							qtd_bp + 1
										else:
															data_out_gab.append('''D'''	)

										# Sorts the data to the new output size
										if data_out_gab[-1] == "D":
															data_ord.append(data_output[cont_data]	)
															cont_data += 1
										else:
															data_ord.append(UpperCAmelCase	)

    # Calculates parity
					__magic_name__ :							List[Any]		       =							0  # parity bit counter
					for bp in range(1,     size_par + 1	):
										# Bit counter one for a certain parity
										__magic_name__ :							Dict		       =							0
										# Counter to control loop reading
										__magic_name__ :							Union[str, Any]		       =							0
										for x in data_ord:
															if x is not None:
																				try:
																									__magic_name__ :							int		       =							(bin_pos[cont_loop])[-1 * (bp)]
																				except IndexError:
																									__magic_name__ :							Dict		       =							'''0'''
																				if aux == "1" and x == "1":
																									cont_bo += 1
															cont_loop += 1
										parity.append(str(cont_bo % 2	)	)

										qtd_bp += 1

					# Mount the message
					__magic_name__ :							Tuple		       =							0  # Parity bit counter
					for x in range(0,     size_par + len(UpperCAmelCase	)	):
										if data_ord[x] is None:
															data_out.append(str(parity[cont_bp]	)	)
															cont_bp += 1
										else:
															data_out.append(data_ord[x]	)

					__magic_name__ :							List[str]		       =							parity_received == parity
					return data_output, ack









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

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


lowercase_		=							logging.get_logger(__name__)

lowercase_		=							{
    '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/config.json''',
    # See all M2M100 models at https://huggingface.co/models?filter=m2m_100
}





class 		A__    (       __SCREAMING_SNAKE_CASE			):
	lowerCamelCase__		:			Optional[int]       ="m2m_100"
	lowerCamelCase__		:			List[str]       =["past_key_values"]
	lowerCamelCase__		:			Union[str, Any]       ={"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}






	def __init__(     self    ,					lowerCamelCase=128112    ,					lowerCamelCase=1024    ,					lowerCamelCase=12    ,					lowerCamelCase=4096    ,					lowerCamelCase=16    ,					lowerCamelCase=12    ,					lowerCamelCase=4096    ,					lowerCamelCase=16    ,					lowerCamelCase=0.0_5    ,					lowerCamelCase=0.0_5    ,					lowerCamelCase=True    ,					lowerCamelCase=True    ,					lowerCamelCase="relu"    ,					lowerCamelCase=1024    ,					lowerCamelCase=0.1    ,					lowerCamelCase=0.1    ,					lowerCamelCase=0.0    ,					lowerCamelCase=0.0_2    ,					lowerCamelCase=2    ,					lowerCamelCase=True    ,					lowerCamelCase=1    ,					lowerCamelCase=0    ,					lowerCamelCase=2    ,					**lowerCamelCase    ,					)				->       List[str]:




						"""simple docstring"""






						__magic_name__ :							List[Any]		       =							vocab_size
						__magic_name__ :							str		       =							max_position_embeddings
						__magic_name__ :							Union[str, Any]		       =							d_model
						__magic_name__ :							Dict		       =							encoder_ffn_dim
						__magic_name__ :							List[Any]		       =							encoder_layers
						__magic_name__ :							Optional[int]		       =							encoder_attention_heads
						__magic_name__ :							Dict		       =							decoder_ffn_dim
						__magic_name__ :							List[str]		       =							decoder_layers
						__magic_name__ :							Optional[Any]		       =							decoder_attention_heads
						__magic_name__ :							List[Any]		       =							dropout
						__magic_name__ :							List[str]		       =							attention_dropout
						__magic_name__ :							Optional[int]		       =							activation_dropout
						__magic_name__ :							Dict		       =							activation_function
						__magic_name__ :							Tuple		       =							init_std
						__magic_name__ :							Optional[int]		       =							encoder_layerdrop
						__magic_name__ :							int		       =							decoder_layerdrop
						__magic_name__ :							Union[str, Any]		       =							use_cache
						__magic_name__ :							Optional[int]		       =							encoder_layers
						__magic_name__ :							Dict		       =							scale_embedding  # scale factor will be sqrt(d_model) if True

						super().__init__(
						    pad_token_id=lowerCamelCase    ,					bos_token_id=lowerCamelCase    ,					eos_token_id=lowerCamelCase    ,					is_encoder_decoder=lowerCamelCase    ,					decoder_start_token_id=lowerCamelCase    ,					**lowerCamelCase    ,					)





class 		A__    (       __SCREAMING_SNAKE_CASE			):






	@property
	def 			lowercase				(     self						)				->       Mapping[str, Mapping[int, str]]:




						"""simple docstring"""






						__magic_name__ :							Dict		       =							OrderedDict(
						    [
						        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
						        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
						    ]						)

						if self.use_past:
											__magic_name__ :							Optional[Any]		       =							{0: '''batch'''}
											__magic_name__ :							Any		       =							{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
						else:
											__magic_name__ :							Union[str, Any]		       =							{0: '''batch''', 1: '''decoder_sequence'''}
											__magic_name__ :							Optional[Any]		       =							{0: '''batch''', 1: '''decoder_sequence'''}

						if self.use_past:
											self.fill_with_past_key_values_(lowerCamelCase    ,					direction='''inputs'''						)
						return common_inputs






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase = -1    ,					lowerCamelCase = -1    ,					lowerCamelCase = False    ,					lowerCamelCase = None    ,					)				->       Mapping[str, Any]:




						"""simple docstring"""






						__magic_name__ :							int		       =							compute_effective_axis_dimension(
						    lowerCamelCase    ,					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
						__magic_name__ :							str		       =							tokenizer.num_special_tokens_to_add(lowerCamelCase						)
						__magic_name__ :							Optional[Any]		       =							compute_effective_axis_dimension(
						    lowerCamelCase    ,					fixed_dimension=OnnxConfig.default_fixed_sequence    ,					num_token_to_add=lowerCamelCase						)

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






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase = -1    ,					lowerCamelCase = -1    ,					lowerCamelCase = False    ,					lowerCamelCase = None    ,					)				->       Mapping[str, Any]:




						"""simple docstring"""






						__magic_name__ :							Any		       =							self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase						)

						# Generate decoder inputs
						__magic_name__ :							int		       =							seq_length if not self.use_past else 1
						__magic_name__ :							Optional[Any]		       =							self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase						)
						__magic_name__ :							List[str]		       =							{F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
						__magic_name__ :							Optional[Any]		       =							dict(**lowerCamelCase    ,					**lowerCamelCase						)

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

											__magic_name__ :							int		       =							torch.cat(
											    [common_inputs['''decoder_attention_mask'''], torch.ones(lowerCamelCase    ,					lowerCamelCase						)]    ,					dim=1						)

											__magic_name__ :							List[str]		       =							[]
											# If the number of encoder and decoder layers are present in the model configuration, both are considered
											__magic_name__       ,			__magic_name__ :							int		       =							self.num_layers
											__magic_name__ :							List[Any]		       =							min(lowerCamelCase    ,					lowerCamelCase						)
											__magic_name__ :							int		       =							max(lowerCamelCase    ,					lowerCamelCase						) - min_num_layers
											__magic_name__ :							Optional[int]		       =							'''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

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





	lowerCamelCase__		:			List[str]       =_generate_dummy_inputs_for_default_and_seqaseq_lm



