
"""simple docstring"""

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

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


_A            = logging.get_logger(__name__)

_A            = {
    'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json',
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}






class        _lowercase   (				__UpperCAmelCase		):
	lowercase_        =	'blenderbot'
	lowercase_        =	['past_key_values']
	lowercase_        =	{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}




	def __init__( self		, UpperCAmelCase_=8008		, UpperCAmelCase_=128		, UpperCAmelCase_=2		, UpperCAmelCase_=10240		, UpperCAmelCase_=32		, UpperCAmelCase_=24		, UpperCAmelCase_=10240		, UpperCAmelCase_=32		, UpperCAmelCase_=0.0		, UpperCAmelCase_=0.0		, UpperCAmelCase_=True		, UpperCAmelCase_=True		, UpperCAmelCase_="gelu"		, UpperCAmelCase_=2560		, UpperCAmelCase_=0.1		, UpperCAmelCase_=0.0		, UpperCAmelCase_=0.0		, UpperCAmelCase_=0.02		, UpperCAmelCase_=1		, UpperCAmelCase_=False		, UpperCAmelCase_=0		, UpperCAmelCase_=1		, UpperCAmelCase_=2		, UpperCAmelCase_=3		, UpperCAmelCase_=2		, **UpperCAmelCase_		, )		->							Optional[int]:
			lowerCamelCase     :			Tuple		=    vocab_size
			lowerCamelCase     :			List[str]		=    max_position_embeddings
			lowerCamelCase     :			List[Any]		=    d_model
			lowerCamelCase     :			Tuple		=    encoder_ffn_dim
			lowerCamelCase     :			Any		=    encoder_layers
			lowerCamelCase     :			Union[str, Any]		=    encoder_attention_heads
			lowerCamelCase     :			List[Any]		=    decoder_ffn_dim
			lowerCamelCase     :			Union[str, Any]		=    decoder_layers
			lowerCamelCase     :			List[str]		=    decoder_attention_heads
			lowerCamelCase     :			Any		=    dropout
			lowerCamelCase     :			str		=    attention_dropout
			lowerCamelCase     :			Any		=    activation_dropout
			lowerCamelCase     :			List[str]		=    activation_function
			lowerCamelCase     :			List[Any]		=    init_std
			lowerCamelCase     :			Union[str, Any]		=    encoder_layerdrop
			lowerCamelCase     :			Optional[int]		=    decoder_layerdrop
			lowerCamelCase     :			Union[str, Any]		=    use_cache
			lowerCamelCase     :			Union[str, Any]		=    encoder_layers
			lowerCamelCase     :			List[str]		=    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_		, decoder_start_token_id=UpperCAmelCase_		, encoder_no_repeat_ngram_size=UpperCAmelCase_		, forced_eos_token_id=UpperCAmelCase_		, **UpperCAmelCase_		, )






class        _lowercase   (				__UpperCAmelCase		):




	@property
	def 			_UpperCamelCase    ( self					)		->							Mapping[str, Mapping[int, str]]:
			if self.task in ["default", "seq2seq-lm"]:
					lowerCamelCase     :			int		=    OrderedDict(
					    [
					        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
					        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
					    ]					)
					if self.use_past:
							lowerCamelCase     :			Optional[Any]		=    {0: 'batch'}
							lowerCamelCase     :			List[Any]		=    {0: 'batch', 1: 'past_decoder_sequence + sequence'}
					else:
							lowerCamelCase     :			Union[str, Any]		=    {0: 'batch', 1: 'decoder_sequence'}
							lowerCamelCase     :			List[Any]		=    {0: 'batch', 1: 'decoder_sequence'}
					if self.use_past:
							self.fill_with_past_key_values_(UpperCAmelCase_		, direction='inputs'					)
			elif self.task == "causal-lm":
					lowerCamelCase     :			int		=    OrderedDict(
					    [
					        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
					        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
					    ]					)
					if self.use_past:
							lowerCamelCase	,    lowerCamelCase     :			Tuple		=    self.num_layers
							for i in range(UpperCAmelCase_					):
									lowerCamelCase     :			Union[str, Any]		=    {0: 'batch', 2: 'past_sequence + sequence'}
									lowerCamelCase     :			str		=    {0: 'batch', 2: 'past_sequence + sequence'}
			else:
					lowerCamelCase     :			List[str]		=    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
	# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
	def 			_UpperCamelCase    ( self					)		->							Mapping[str, Mapping[int, str]]:
			if self.task in ["default", "seq2seq-lm"]:
					lowerCamelCase     :			List[str]		=    super().outputs
			else:
					lowerCamelCase     :			Tuple		=    super(UpperCAmelCase_		, self					).outputs
					if self.use_past:
							lowerCamelCase	,    lowerCamelCase     :			Dict		=    self.num_layers
							for i in range(UpperCAmelCase_					):
									lowerCamelCase     :			Union[str, Any]		=    {0: 'batch', 2: 'past_sequence + sequence'}
									lowerCamelCase     :			Tuple		=    {0: 'batch', 2: 'past_sequence + sequence'}
			return common_outputs




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_ = -1		, UpperCAmelCase_ = -1		, UpperCAmelCase_ = False		, UpperCAmelCase_ = None		, )		->							Mapping[str, Any]:
			lowerCamelCase     :			Union[str, Any]		=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
			    UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_					)
			# Generate decoder inputs
			lowerCamelCase     :			Optional[Any]		=    seq_length if not self.use_past else 1
			lowerCamelCase     :			List[Any]		=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
			    UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_					)
			lowerCamelCase     :			Any		=    {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
			lowerCamelCase     :			int		=    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     :			Union[str, Any]		=    common_inputs['input_ids'].shape
					lowerCamelCase     :			List[str]		=    common_inputs['decoder_input_ids'].shape[1]
					lowerCamelCase	,    lowerCamelCase     :			List[Any]		=    self.num_attention_heads
					lowerCamelCase     :			List[str]		=    (
					    batch,
					    num_encoder_attention_heads,
					    encoder_seq_length,
					    self._config.hidden_size // num_encoder_attention_heads,
					)
					lowerCamelCase     :			Optional[int]		=    decoder_seq_length
					lowerCamelCase     :			Tuple		=    (
					    batch,
					    num_decoder_attention_heads,
					    decoder_past_length,
					    self._config.hidden_size // num_decoder_attention_heads,
					)
					lowerCamelCase     :			str		=    torch.cat(
					    [common_inputs['decoder_attention_mask'], torch.ones(UpperCAmelCase_		, UpperCAmelCase_					)]		, dim=1					)
					lowerCamelCase     :			Optional[int]		=    []
					lowerCamelCase	,    lowerCamelCase     :			Any		=    self.num_layers

					for _ in range(UpperCAmelCase_					):
							common_inputs["past_key_values"].append(
							    (
							        torch.zeros(UpperCAmelCase_					),
							        torch.zeros(UpperCAmelCase_					),
							        torch.zeros(UpperCAmelCase_					),
							        torch.zeros(UpperCAmelCase_					),
							    )					)
			return common_inputs




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_ = -1		, UpperCAmelCase_ = -1		, UpperCAmelCase_ = False		, UpperCAmelCase_ = None		, )		->							Mapping[str, Any]:
			lowerCamelCase     :			Optional[int]		=    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     :			int		=    common_inputs['input_ids'].shape
					lowerCamelCase     :			Optional[Any]		=    seqlen
					lowerCamelCase	,    lowerCamelCase     :			List[Any]		=    self.num_layers
					lowerCamelCase	,    lowerCamelCase     :			Any		=    self.num_attention_heads
					lowerCamelCase     :			int		=    (
					    batch,
					    num_encoder_attention_heads,
					    past_key_values_length,
					    self._config.hidden_size // num_encoder_attention_heads,
					)
					lowerCamelCase     :			str		=    common_inputs['attention_mask'].dtype
					lowerCamelCase     :			Optional[int]		=    torch.cat(
					    [common_inputs['attention_mask'], torch.ones(UpperCAmelCase_		, UpperCAmelCase_		, dtype=UpperCAmelCase_					)]		, dim=1					)
					lowerCamelCase     :			Optional[Any]		=    [
					    (torch.zeros(UpperCAmelCase_					), torch.zeros(UpperCAmelCase_					)) for _ in range(UpperCAmelCase_					)
					]
			return common_inputs




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_ = -1		, UpperCAmelCase_ = -1		, UpperCAmelCase_ = False		, UpperCAmelCase_ = None		, )		->							Mapping[str, Any]:
			# Copied from OnnxConfig.generate_dummy_inputs
			# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
			# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
			lowerCamelCase     :			Union[str, Any]		=    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     :			Any		=    tokenizer.num_special_tokens_to_add(UpperCAmelCase_					)
			lowerCamelCase     :			Any		=    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     :			Union[str, Any]		=    [' '.join([tokenizer.unk_token]					) * seq_length] * batch_size
			lowerCamelCase     :			Any		=    dict(tokenizer(UpperCAmelCase_		, return_tensors=UpperCAmelCase_					)					)
			return common_inputs




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_ = -1		, UpperCAmelCase_ = -1		, UpperCAmelCase_ = False		, UpperCAmelCase_ = None		, )		->							Mapping[str, Any]:
			if self.task in ["default", "seq2seq-lm"]:
					lowerCamelCase     :			Any		=    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     :			str		=    self._generate_dummy_inputs_for_causal_lm(
					    UpperCAmelCase_		, batch_size=UpperCAmelCase_		, seq_length=UpperCAmelCase_		, is_pair=UpperCAmelCase_		, framework=UpperCAmelCase_					)
			else:
					lowerCamelCase     :			List[str]		=    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 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_					)		->							List[str]:
			if self.task in ["default", "seq2seq-lm"]:
					lowerCamelCase     :			Optional[int]		=    super()._flatten_past_key_values_(UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_					)
			else:
					lowerCamelCase     :			List[str]		=    super(UpperCAmelCase_		, self					)._flatten_past_key_values_(
					    UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_					)		->							Optional[int]:
			if direction not in ["inputs", "outputs"]:
					raise ValueError(F"""direction must either be \"inputs\" or \"outputs\", but {direction} was given"""					)

			lowerCamelCase     :			Dict		=    'past_key_values' if direction == 'inputs' else 'present'
			lowerCamelCase	,    lowerCamelCase     :			Any		=    self.num_layers

			lowerCamelCase     :			Any		=    'past_encoder_sequence'
			lowerCamelCase     :			Any		=    'past_decoder_sequence' if direction == 'inputs' else 'past_decoder_sequence + sequence'

			for i in range(UpperCAmelCase_					):
					lowerCamelCase     :			List[str]		=    {0: 'batch', 2: decoder_sequence}
					lowerCamelCase     :			Dict		=    {0: 'batch', 2: decoder_sequence}
					lowerCamelCase     :			Any		=    {0: 'batch', 2: encoder_sequence}
					lowerCamelCase     :			Tuple		=    {0: 'batch', 2: encoder_sequence}








"""simple docstring"""

import numpy as np
def 		UpperCAmelCase					(		a_,						a_="utf-8",						a_="surrogatepass"			):





		'''simple docstring'''


		lowerCamelCase     :			str		=    bin(int.from_bytes(text.encode(a_,						a_			),						'big'			)			)[2:]
		return bits.zfill(8 * ((len(a_			) + 7) // 8)			)
def 		UpperCAmelCase					(		a_,						a_="utf-8",						a_="surrogatepass"			):





		'''simple docstring'''


		lowerCamelCase     :			List[Any]		=    int(a_,						2			)
		return n.to_bytes((n.bit_length() + 7) // 8,						'big'			).decode(a_,						a_			) or "\0"
def 		UpperCAmelCase					(		a_,						a_			):





		'''simple docstring'''


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

		lowerCamelCase     :			List[str]		=    []
		lowerCamelCase     :			str		=    []
		lowerCamelCase     :			Any		=    [bin(a_			)[2:] for x in range(1,						size_par + len(a_			) + 1			)]

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

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

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

				qtd_bp += 1

		# Mount the message
		lowerCamelCase     :			Tuple		=    0  # parity bit counter
		for x in range(0,						size_par + len(a_			)			):
				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 		UpperCAmelCase					(		a_,						a_			):





		'''simple docstring'''


		lowerCamelCase     :			str		=    []
		# Parity bit counter
		lowerCamelCase     :			Dict		=    0
		# Counter p data bit reading
		lowerCamelCase     :			int		=    0
		# list of parity received
		lowerCamelCase     :			Optional[int]		=    []
		lowerCamelCase     :			Any		=    []

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

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

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

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

		lowerCamelCase     :			Union[str, Any]		=    parity_received == parity
		return data_output, ack






