






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


UpperCamelCase__						:      Any								=					logging.get_logger(__name__)

UpperCamelCase__						:      int								=					{
    """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 				lowerCamelCase_     (	a_     ):
							SCREAMING_SNAKE_CASE_						     =			'mbart'
							SCREAMING_SNAKE_CASE_						     =			['past_key_values']
							SCREAMING_SNAKE_CASE_						     =			{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
							def __init__(						self		:					Tuple							,__lowerCamelCase		:					str=5_02_65							,__lowerCamelCase		:					Optional[int]=10_24							,__lowerCamelCase		:					Tuple=12							,__lowerCamelCase		:					Tuple=40_96							,__lowerCamelCase		:					Optional[Any]=16							,__lowerCamelCase		:					Any=12							,__lowerCamelCase		:					Optional[int]=40_96							,__lowerCamelCase		:					str=16							,__lowerCamelCase		:					Tuple=0.0							,__lowerCamelCase		:					int=0.0							,__lowerCamelCase		:					Optional[Any]=True							,__lowerCamelCase		:					Optional[Any]=True							,__lowerCamelCase		:					List[str]="gelu"							,__lowerCamelCase		:					Tuple=10_24							,__lowerCamelCase		:					Union[str, Any]=0.1							,__lowerCamelCase		:					int=0.0							,__lowerCamelCase		:					int=0.0							,__lowerCamelCase		:					List[str]=0.02							,__lowerCamelCase		:					Any=0.0							,__lowerCamelCase		:					Dict=False							,__lowerCamelCase		:					Optional[int]=1							,__lowerCamelCase		:					str=0							,__lowerCamelCase		:					Union[str, Any]=2							,__lowerCamelCase		:					Optional[Any]=2							,**__lowerCamelCase		:					Optional[int]							,):





								'''simple docstring'''



								a		=			vocab_size
								a		=			max_position_embeddings
								a		=			d_model
								a		=			encoder_ffn_dim
								a		=			encoder_layers
								a		=			encoder_attention_heads
								a		=			decoder_ffn_dim
								a		=			decoder_layers
								a		=			decoder_attention_heads
								a		=			dropout
								a		=			attention_dropout
								a		=			activation_dropout
								a		=			activation_function
								a		=			init_std
								a		=			encoder_layerdrop
								a		=			decoder_layerdrop
								a		=			classifier_dropout
								a		=			use_cache
								a		=			encoder_layers
								a		=			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							,forced_eos_token_id=__lowerCamelCase							,**__lowerCamelCase							,)







class 				lowerCamelCase_     (	a_     ):
							@property
							def  SCREAMING_SNAKE_CASE_ (						self		:					Union[str, Any]					):





								'''simple docstring'''



								if self.task in ["default", "seq2seq-lm"]:
									a		=			OrderedDict(
									    [
									        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
									        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
									    ]					)

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

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





								'''simple docstring'''



								if self.task in ["default", "seq2seq-lm"]:
									a		=			super().outputs
								else:
									a		=			super(__lowerCamelCase							,self					).outputs
									if self.use_past:
										a	, a		=			self.num_layers
										for i in range(__lowerCamelCase					):
											a		=			{0: '''batch''', 2: '''past_sequence + sequence'''}
											a		=			{0: '''batch''', 2: '''past_sequence + sequence'''}
								return common_outputs
							def  SCREAMING_SNAKE_CASE_ (						self		:					Tuple							,__lowerCamelCase		:					PreTrainedTokenizer							,__lowerCamelCase		:					int = -1							,__lowerCamelCase		:					int = -1							,__lowerCamelCase		:					bool = False							,__lowerCamelCase		:					Optional[TensorType] = None							,):





								'''simple docstring'''



								a		=			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase					)

								# Generate decoder inputs
								a		=			seq_length if not self.use_past else 1
								a		=			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase					)
								a		=			{F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
								a		=			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
									a	, a		=			common_inputs['''input_ids'''].shape
									a		=			common_inputs['''decoder_input_ids'''].shape[1]
									a	, a		=			self.num_attention_heads
									a		=			(
									    batch,
									    num_encoder_attention_heads,
									    encoder_seq_length,
									    self._config.hidden_size // num_encoder_attention_heads,
									)
									a		=			decoder_seq_length + 3
									a		=			(
									    batch,
									    num_decoder_attention_heads,
									    decoder_past_length,
									    self._config.hidden_size // num_decoder_attention_heads,
									)

									a		=			torch.cat(
									    [common_inputs['''decoder_attention_mask'''], torch.ones(__lowerCamelCase							,__lowerCamelCase					)]							,dim=1					)

									a		=			[]
									# If the number of encoder and decoder layers are present in the model configuration, both are considered
									a	, a		=			self.num_layers
									a		=			min(__lowerCamelCase							,__lowerCamelCase					)
									a		=			max(__lowerCamelCase							,__lowerCamelCase					) - min_num_layers
									a		=			'''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.
									a		=			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
							def  SCREAMING_SNAKE_CASE_ (						self		:					List[Any]							,__lowerCamelCase		:					PreTrainedTokenizer							,__lowerCamelCase		:					int = -1							,__lowerCamelCase		:					int = -1							,__lowerCamelCase		:					bool = False							,__lowerCamelCase		:					Optional[TensorType] = None							,):





								'''simple docstring'''



								a		=			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase							,__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
									a	, a		=			common_inputs['''input_ids'''].shape
									# Not using the same length for past_key_values
									a		=			seqlen + 2
									a	, a		=			self.num_layers
									a	, a		=			self.num_attention_heads
									a		=			(
									    batch,
									    num_encoder_attention_heads,
									    past_key_values_length,
									    self._config.hidden_size // num_encoder_attention_heads,
									)

									a		=			common_inputs['''attention_mask'''].dtype
									a		=			torch.cat(
									    [common_inputs['''attention_mask'''], torch.ones(__lowerCamelCase							,__lowerCamelCase							,dtype=__lowerCamelCase					)]							,dim=1					)
									a		=			[
									    (torch.zeros(__lowerCamelCase					), torch.zeros(__lowerCamelCase					)) for _ in range(__lowerCamelCase					)
									]
								return common_inputs
							def  SCREAMING_SNAKE_CASE_ (						self		:					str							,__lowerCamelCase		:					PreTrainedTokenizer							,__lowerCamelCase		:					int = -1							,__lowerCamelCase		:					int = -1							,__lowerCamelCase		:					bool = False							,__lowerCamelCase		:					Optional[TensorType] = None							,):





								'''simple docstring'''



								a		=			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
								a		=			tokenizer.num_special_tokens_to_add(__lowerCamelCase					)
								a		=			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
								a		=			[''' '''.join([tokenizer.unk_token]					) * seq_length] * batch_size
								a		=			dict(tokenizer(__lowerCamelCase							,return_tensors=__lowerCamelCase					)					)
								return common_inputs
							def  SCREAMING_SNAKE_CASE_ (						self		:					str							,__lowerCamelCase		:					PreTrainedTokenizer							,__lowerCamelCase		:					int = -1							,__lowerCamelCase		:					int = -1							,__lowerCamelCase		:					bool = False							,__lowerCamelCase		:					Optional[TensorType] = None							,):





								'''simple docstring'''



								if self.task in ["default", "seq2seq-lm"]:
									a		=			self._generate_dummy_inputs_for_default_and_seqaseq_lm(
									    __lowerCamelCase							,batch_size=__lowerCamelCase							,seq_length=__lowerCamelCase							,is_pair=__lowerCamelCase							,framework=__lowerCamelCase					)

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

								return common_inputs




							def  SCREAMING_SNAKE_CASE_ (						self		:					str							,__lowerCamelCase		:					List[Any]							,__lowerCamelCase		:					str							,__lowerCamelCase		:					str							,__lowerCamelCase		:					Optional[int]					):





								'''simple docstring'''



								if self.task in ["default", "seq2seq-lm"]:
									a		=			super()._flatten_past_key_values_(__lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase					)
								else:
									a		=			super(__lowerCamelCase							,self					)._flatten_past_key_values_(
									    __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase					)













import numpy as np





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_="utf-8",  snake_case_="surrogatepass"  )   ->  List[str]:


	"""simple docstring"""



	a		=			bin(int.from_bytes(text.encode(snake_case_,  snake_case_  ),  '''big'''  )  )[2:]
	return bits.zfill(8 * ((len(snake_case_  ) + 7) // 8)  )





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_="utf-8",  snake_case_="surrogatepass"  )   ->  int:


	"""simple docstring"""



	a		=			int(snake_case_,  2  )
	return n.to_bytes((n.bit_length() + 7) // 8,  '''big'''  ).decode(snake_case_,  snake_case_  ) or "\0"





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_  )   ->  List[Any]:


	"""simple docstring"""



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

	a		=			[]
	a		=			[]
	a		=			[bin(snake_case_  )[2:] for x in range(1,  size_par + len(snake_case_  ) + 1  )]

	# sorted information data for the size of the output data
	a		=			[]
	# data position template + parity
	a		=			[]
	# parity bit counter
	a		=			0
	# counter position of data bits
	a		=			0

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

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

		qtd_bp += 1

	# Mount the message
	a		=			0  # parity bit counter
	for x in range(0,  size_par + len(snake_case_  )  ):
		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   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_  )   ->  str:


	"""simple docstring"""



	a		=			[]
	# Parity bit counter
	a		=			0
	# Counter p data bit reading
	a		=			0
	# list of parity received
	a		=			[]
	a		=			[]

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

	#  sorted information data for the size of the output data
	a		=			[]
	# Data position feedback + parity
	a		=			[]
	# Parity bit counter
	a		=			0
	# Counter p data bit reading
	a		=			0

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

    # Calculates parity
	a		=			0  # parity bit counter
	for bp in range(1,  size_par + 1  ):
		# Bit counter one for a certain parity
		a		=			0
		# Counter to control loop reading
		a		=			0
		for x in data_ord:
			if x is not None:
				try:
					a		=			(bin_pos[cont_loop])[-1 * (bp)]
				except IndexError:
					a		=			'''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
	a		=			0  # Parity bit counter
	for x in range(0,  size_par + len(snake_case_  )  ):
		if data_ord[x] is None:
			data_out.append(str(parity[cont_bp]  )  )
			cont_bp += 1
		else:
			data_out.append(data_ord[x]  )

	a		=			parity_received == parity
	return data_output, ack





