





import numpy as np


def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		Union[str, Any]     ,							__lowercase:		List[str]="utf-8"     ,							__lowercase:		Tuple="surrogatepass"):



  '''simple docstring'''






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


def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		Tuple     ,							__lowercase:		List[Any]="utf-8"     ,							__lowercase:		int="surrogatepass"):



  '''simple docstring'''






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


def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		List[Any]     ,							__lowercase:		List[str]):



  '''simple docstring'''






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

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

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

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

    # Calculates parity
  __lowerCAmelCase			=				0  # parity bit counter
  for bp in range(1     ,							size_par + 1):
    # Bit counter one for a given parity
    __lowerCAmelCase			=				0
    # counter to control the loop reading
    __lowerCAmelCase			=				0
    for x in data_ord:
      if x is not None:
        try:
          __lowerCAmelCase			=				(bin_pos[cont_loop])[-1 * (bp)]
        except IndexError:
          __lowerCAmelCase			=				'''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			=				0  # parity bit counter
  for x in range(0     ,							size_par + len(__lowercase)):
    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  (							__lowercase:		Union[str, Any]     ,							__lowercase:		List[Any]):



  '''simple docstring'''






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

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

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

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

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

  __lowerCAmelCase			=				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, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


__lowercase													=			logging.get_logger(__name__)

__lowercase													=			{
    """google/bigbird-pegasus-large-arxiv""": (
        """https://huggingface.co/google/bigbird-pegasus-large-arxiv/resolve/main/config.json"""
    ),
    """google/bigbird-pegasus-large-pubmed""": (
        """https://huggingface.co/google/bigbird-pegasus-large-pubmed/resolve/main/config.json"""
    ),
    """google/bigbird-pegasus-large-bigpatent""": (
        """https://huggingface.co/google/bigbird-pegasus-large-bigpatent/resolve/main/config.json"""
    ),
    # See all BigBirdPegasus models at https://huggingface.co/models?filter=bigbird_pegasus
}

class   __snake_case				(			_a   ):






    '''simple docstring'''
    _lowerCAmelCase     =	"""bigbird_pegasus"""
    _lowerCAmelCase     =	["""past_key_values"""]
    _lowerCAmelCase     =	{
        """num_attention_heads""": """encoder_attention_heads""",
        """hidden_size""": """d_model""",
        """attention_probs_dropout_prob""": """attention_dropout""",
    }





    def __init__(   self						:		Any			,						snake_case						:		Optional[int]=96_103			,						snake_case						:		Tuple=4_096			,						snake_case						:		str=16			,						snake_case						:		Any=4_096			,						snake_case						:		Any=16			,						snake_case						:		Dict=16			,						snake_case						:		str=4_096			,						snake_case						:		Tuple=16			,						snake_case						:		Dict=0.0			,						snake_case						:		str=0.0			,						snake_case						:		Union[str, Any]=True			,						snake_case						:		List[Any]=True			,						snake_case						:		Optional[Any]="gelu_new"			,						snake_case						:		Union[str, Any]=1_024			,						snake_case						:		Union[str, Any]=0.1			,						snake_case						:		int=0.0			,						snake_case						:		Optional[Any]=0.0			,						snake_case						:		str=0.02			,						snake_case						:		int=2			,						snake_case						:		Any=0.0			,						snake_case						:		str=True			,						snake_case						:		Tuple=0			,						snake_case						:		Any=2			,						snake_case						:		int=1			,						snake_case						:		Any="block_sparse"			,						snake_case						:		List[Any]=64			,						snake_case						:		Dict=3			,						snake_case						:		Optional[Any]=False			,						**snake_case						:		Dict			,						):

      """simple docstring"""





      __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

      # extra config
      __lowerCAmelCase			=				attention_type
      __lowerCAmelCase			=				block_size
      __lowerCAmelCase			=				num_random_blocks
      __lowerCAmelCase			=				use_bias

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


class   __snake_case				(			_a   ):






    '''simple docstring'''





    @property
    def 	A		(   self						:		List[str]							):

      """simple docstring"""





      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_(snake_case			,						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(snake_case							):
            __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 	A		(   self						:		Optional[int]							):

      """simple docstring"""





      if self.task in ["default", "seq2seq-lm"]:
        __lowerCAmelCase			=				super().outputs
      else:
        __lowerCAmelCase			=				super(snake_case			,						self							).outputs
        if self.use_past:
          __lowerCAmelCase	,						__lowerCAmelCase			=				self.num_layers
          for i in range(snake_case							):
            __lowerCAmelCase			=				{0: '''batch''', 2: '''past_sequence + sequence'''}
            __lowerCAmelCase			=				{0: '''batch''', 2: '''past_sequence + sequence'''}
      return common_outputs





    def 	A		(   self						:		List[Any]			,						snake_case						:		PreTrainedTokenizer			,						snake_case						:		int = -1			,						snake_case						:		int = -1			,						snake_case						:		bool = False			,						snake_case						:		Optional[TensorType] = None			,						):

      """simple docstring"""





      __lowerCAmelCase			=				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          snake_case			,						snake_case			,						snake_case			,						snake_case			,						snake_case							)

      # Generate decoder inputs
      __lowerCAmelCase			=				seq_length if not self.use_past else 1
      __lowerCAmelCase			=				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          snake_case			,						snake_case			,						snake_case			,						snake_case			,						snake_case							)
      __lowerCAmelCase			=				{f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
      __lowerCAmelCase			=				dict(**snake_case			,						**snake_case							)

      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(snake_case			,						snake_case							)]			,						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(snake_case			,						snake_case							)
        __lowerCAmelCase			=				max(snake_case			,						snake_case							) - min_num_layers
        __lowerCAmelCase			=				'''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

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





    def 	A		(   self						:		Tuple			,						snake_case						:		PreTrainedTokenizer			,						snake_case						:		int = -1			,						snake_case						:		int = -1			,						snake_case						:		bool = False			,						snake_case						:		Optional[TensorType] = None			,						):

      """simple docstring"""





      __lowerCAmelCase			=				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          snake_case			,						snake_case			,						snake_case			,						snake_case			,						snake_case							)

      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(snake_case			,						snake_case			,						dtype=snake_case							)]			,						dim=1							)
        __lowerCAmelCase			=				[
            (torch.zeros(snake_case							), torch.zeros(snake_case							)) for _ in range(snake_case							)
        ]
      return common_inputs





    def 	A		(   self						:		Optional[Any]			,						snake_case						:		PreTrainedTokenizer			,						snake_case						:		int = -1			,						snake_case						:		int = -1			,						snake_case						:		bool = False			,						snake_case						:		Optional[TensorType] = None			,						):

      """simple docstring"""





      __lowerCAmelCase			=				compute_effective_axis_dimension(
          snake_case			,						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(snake_case							)
      __lowerCAmelCase			=				compute_effective_axis_dimension(
          snake_case			,						fixed_dimension=OnnxConfig.default_fixed_sequence			,						num_token_to_add=snake_case							)

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





    def 	A		(   self						:		List[str]			,						snake_case						:		PreTrainedTokenizer			,						snake_case						:		int = -1			,						snake_case						:		int = -1			,						snake_case						:		bool = False			,						snake_case						:		Optional[TensorType] = None			,						):

      """simple docstring"""





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

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

      return common_inputs





    def 	A		(   self						:		int			,						snake_case						:		Optional[int]			,						snake_case						:		List[Any]			,						snake_case						:		str			,						snake_case						:		Union[str, Any]							):

      """simple docstring"""





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