



'''simple docstring'''





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 __A      (  A						):



 '''simple docstring'''







 __lowerCamelCase   :	Dict										=	'bigbird_pegasus'
 __lowerCamelCase   :	int										=	['past_key_values']
 __lowerCamelCase   :	List[str]										=	{
     'num_attention_heads': 'encoder_attention_heads',
     'hidden_size': 'd_model',
     'attention_probs_dropout_prob': 'attention_dropout',
 }



 def __init__(self			,       A=96_103			,       A=4_096			,       A=16			,       A=4_096			,       A=16			,       A=16			,       A=4_096			,       A=16			,       A=0.0			,       A=0.0			,       A=True			,       A=True			,       A="gelu_new"			,       A=1_024			,       A=0.1			,       A=0.0			,       A=0.0			,       A=0.02			,       A=2			,       A=0.0			,       A=True			,       A=0			,       A=2			,       A=1			,       A="block_sparse"			,       A=64			,       A=3			,       A=False			,       **A			,       )   ->					Tuple:


      """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

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

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






class __A      (  A						):



 '''simple docstring'''







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


      """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_(A			,       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(A	):
                     _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  a__  (self	)   ->					Mapping[str, Mapping[int, str]]:


      """simple docstring"""



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



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


      """simple docstring"""



      _a     =  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          A			,       A			,       A			,       A			,       A	)

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

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

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



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


      """simple docstring"""



      _a     =  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          A			,       A			,       A			,       A			,       A	)

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



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


      """simple docstring"""



      _a     =  compute_effective_axis_dimension(
          A			,       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(A	)
      _a     =  compute_effective_axis_dimension(
          A			,       fixed_dimension=OnnxConfig.default_fixed_sequence			,       num_token_to_add=A	)

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



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


      """simple docstring"""



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

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

      return common_inputs



 def  a__  (self			,       A			,       A			,       A			,       A	)   ->					Optional[int]:


      """simple docstring"""



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








'''simple docstring'''





import os
from string import ascii_letters

lowercase_			=  ascii_letters + " \t\n"




def lowerCAmelCase  ():



     """simple docstring"""



     _a     =  os.path.split(os.path.realpath(__A))
     _a     =  {}
     with open(path[0] + '''/dictionary.txt''') as dictionary_file:
          for word in dictionary_file.read().split('''\n'''):
               _a     =  None
     return english_words


lowercase_			=  load_dictionary()




def lowerCAmelCase  (__A):



     """simple docstring"""



     _a     =  message.upper()
     _a     =  remove_non_letters(__A)
     _a     =  message.split()
     _a     =  len([word for word in possible_words if word in ENGLISH_WORDS])
     return float(__A) / len(__A)




def lowerCAmelCase  (__A):



     """simple docstring"""



     return "".join(symbol for symbol in message if symbol in LETTERS_AND_SPACE)




def lowerCAmelCase  (__A  ,     __A = 20  ,     __A = 85):



     """simple docstring"""



     _a     =  get_english_count(__A) * 100 >= word_percentage
     _a     =  len(remove_non_letters(__A))
     _a     =  (float(__A) / len(__A)) * 100
     _a     =  message_letters_percentage >= letter_percentage
     return words_match and letters_match


if __name__ == "__main__":
  import doctest

  doctest.testmod()



