






"""simple docstring"""





def 							A(    snake_case_		,  snake_case_		,  snake_case_		,  snake_case_		,  snake_case_		,  snake_case_		,  ):
       """simple docstring"""





       lowercase__:							List[Any]         =     list(snake_case_						)
       lowercase__:							List[Any]         =     list(snake_case_						)
       lowercase__:							Union[str, Any]         =     len(snake_case_						)
       lowercase__:							Dict         =     len(snake_case_						)

       lowercase__:							Union[str, Any]         =     [
           [0 for _ in range(len_destination_seq + 1						)] for _ in range(len_source_seq + 1						)
       ]
       lowercase__:							List[str]         =     [
           ["0" for _ in range(len_destination_seq + 1						)] for _ in range(len_source_seq + 1						)
       ]

       for i in range(1		,  len_source_seq + 1						):
              lowercase__:							Dict         =     i * delete_cost
              lowercase__:							Optional[Any]         =     F"""D{source_seq[i - 1]:c}"""

       for i in range(1		,  len_destination_seq + 1						):
              lowercase__:							Any         =     i * insert_cost
              lowercase__:							Any         =     F"""I{destination_seq[i - 1]:c}"""

       for i in range(1		,  len_source_seq + 1						):
              for j in range(1		,  len_destination_seq + 1						):
                     if source_seq[i - 1] == destination_seq[j - 1]:
                            lowercase__:							List[Any]         =     costs[i - 1][j - 1] + copy_cost
                            lowercase__:							str         =     F"""C{source_seq[i - 1]:c}"""
                     else:
                            lowercase__:							Dict         =     costs[i - 1][j - 1] + replace_cost
                            lowercase__:							Optional[Any]         =     F"""R{source_seq[i - 1]:c}""" + str(destination_seq[j - 1]						)

                     if costs[i - 1][j] + delete_cost < costs[i][j]:
                            lowercase__:							List[Any]         =     costs[i - 1][j] + delete_cost
                            lowercase__:							List[str]         =     F"""D{source_seq[i - 1]:c}"""

                     if costs[i][j - 1] + insert_cost < costs[i][j]:
                            lowercase__:							Optional[int]         =     costs[i][j - 1] + insert_cost
                            lowercase__:							str         =     F"""I{destination_seq[j - 1]:c}"""

       return costs, ops






def 							A(    snake_case_		,  snake_case_		,  snake_case_						):
       """simple docstring"""





       if i == 0 and j == 0:
              return []
       else:
              if ops[i][j][0] == "C" or ops[i][j][0] == "R":
                     lowercase__:							Dict         =     assemble_transformation(snake_case_		,  i - 1		,  j - 1						)
                     seq.append(ops[i][j]						)
                     return seq
              elif ops[i][j][0] == "D":
                     lowercase__:							int         =     assemble_transformation(snake_case_		,  i - 1		,  snake_case_						)
                     seq.append(ops[i][j]						)
                     return seq
              else:
                     lowercase__:							Optional[Any]         =     assemble_transformation(snake_case_		,  snake_case_		,  j - 1						)
                     seq.append(ops[i][j]						)
                     return seq


if __name__ == "__main__":
  UpperCamelCase,     UpperCamelCase					=     compute_transform_tables("""Python""", """Algorithms""", -1, 1, 2, 2)

  UpperCamelCase					=     len(operations)
  UpperCamelCase					=     len(operations[0])
  UpperCamelCase					=     assemble_transformation(operations, m - 1, n - 1)

  UpperCamelCase					=     list("""Python""")
  UpperCamelCase					=     0
  UpperCamelCase					=     0

  with open("""min_cost.txt""", """w""") as file:
    for op in sequence:
      print("""""".join(string))

      if op[0] == "C":
        file.write("""%-16s""" % """Copy %c""" % op[1])
        file.write("""\t\t\t""" + """""".join(string))
        file.write("""\r\n""")

        cost -= 1
      elif op[0] == "R":
        UpperCamelCase					=     op[2]

        file.write("""%-16s""" % ("""Replace %c""" % op[1] + """ with """ + str(op[2])))
        file.write("""\t\t""" + """""".join(string))
        file.write("""\r\n""")

        cost += 1
      elif op[0] == "D":
        string.pop(i)

        file.write("""%-16s""" % """Delete %c""" % op[1])
        file.write("""\t\t\t""" + """""".join(string))
        file.write("""\r\n""")

        cost += 2
      else:
        string.insert(i, op[1])

        file.write("""%-16s""" % """Insert %c""" % op[1])
        file.write("""\t\t\t""" + """""".join(string))
        file.write("""\r\n""")

        cost += 2

      i += 1

    print("""""".join(string))
    print("""Cost: """, cost)

    file.write("""\r\nMinimum cost: """ + str(cost))







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


__lowerCAmelCase    :  Optional[int]										= logging.get_logger(__name__)

__lowerCAmelCase    :  Dict										= {
    '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 				lowerCamelCase			(							__snake_case					):
      __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  ,		__lowerCamelCase=9_61_03  ,		__lowerCamelCase=40_96  ,		__lowerCamelCase=16  ,		__lowerCamelCase=40_96  ,		__lowerCamelCase=16  ,		__lowerCamelCase=16  ,		__lowerCamelCase=40_96  ,		__lowerCamelCase=16  ,		__lowerCamelCase=0.0  ,		__lowerCamelCase=0.0  ,		__lowerCamelCase=True  ,		__lowerCamelCase=True  ,		__lowerCamelCase="gelu_new"  ,		__lowerCamelCase=10_24  ,		__lowerCamelCase=0.1  ,		__lowerCamelCase=0.0  ,		__lowerCamelCase=0.0  ,		__lowerCamelCase=0.02  ,		__lowerCamelCase=2  ,		__lowerCamelCase=0.0  ,		__lowerCamelCase=True  ,		__lowerCamelCase=0  ,		__lowerCamelCase=2  ,		__lowerCamelCase=1  ,		__lowerCamelCase="block_sparse"  ,		__lowerCamelCase=64  ,		__lowerCamelCase=3  ,		__lowerCamelCase=False  ,		**__lowerCamelCase  ,		)     ->				Dict:


          '''simple docstring'''







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

          # extra config
          snake_case:						Union[str, Any]         =   attention_type
          snake_case:						Optional[int]         =   block_size
          snake_case:						Optional[int]         =   num_random_blocks
          snake_case:						List[Any]         =   use_bias

          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 				lowerCamelCase			(							__snake_case					):






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


          '''simple docstring'''







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

              if self.use_past:
                  snake_case:						List[Any]         =   {0: """batch"""}
                  snake_case:						Optional[int]         =   {0: """batch""", 1: """past_decoder_sequence + sequence"""}
              else:
                  snake_case:						List[Any]         =   {0: """batch""", 1: """decoder_sequence"""}
                  snake_case:						Union[str, Any]         =   {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.
              snake_case:						List[str]         =   OrderedDict(
                  [
                      ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                      ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                  ]						)
              if self.use_past:
                  snake_case    , snake_case:						str         =   self.num_layers
                  for i in range(__lowerCamelCase						):
                      snake_case:						Any         =   {0: """batch""", 2: """past_sequence + sequence"""}
                      snake_case:						str         =   {0: """batch""", 2: """past_sequence + sequence"""}
          else:
              snake_case:						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
      def 			lowerCAmelCase_   (      self						)     ->				Mapping[str, Mapping[int, str]]:


          '''simple docstring'''







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






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


          '''simple docstring'''







          snake_case:						List[Any]         =   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              __lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase						)

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

              snake_case:						Tuple         =   torch.cat(
                  [common_inputs["""decoder_attention_mask"""], torch.ones(__lowerCamelCase  ,		__lowerCamelCase						)]  ,		dim=1						)

              snake_case:						Tuple         =   []
              # If the number of encoder and decoder layers are present in the model configuration, both are considered
              snake_case    , snake_case:						Union[str, Any]         =   self.num_layers
              snake_case:						List[str]         =   min(__lowerCamelCase  ,		__lowerCamelCase						)
              snake_case:						List[str]         =   max(__lowerCamelCase  ,		__lowerCamelCase						) - min_num_layers
              snake_case:						Union[str, Any]         =   """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.
              snake_case:						Dict         =   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 			lowerCAmelCase_   (      self  ,		__lowerCamelCase  ,		__lowerCamelCase = -1  ,		__lowerCamelCase = -1  ,		__lowerCamelCase = False  ,		__lowerCamelCase = None  ,		)     ->				Mapping[str, Any]:


          '''simple docstring'''







          snake_case:						Any         =   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
              snake_case    , snake_case:						Dict         =   common_inputs["""input_ids"""].shape
              # Not using the same length for past_key_values
              snake_case:						int         =   seqlen + 2
              snake_case    , snake_case:						Dict         =   self.num_layers
              snake_case    , snake_case:						Tuple         =   self.num_attention_heads
              snake_case:						Optional[int]         =   (
                  batch,
                  num_encoder_attention_heads,
                  past_key_values_length,
                  self._config.hidden_size // num_encoder_attention_heads,
              )

              snake_case:						int         =   common_inputs["""attention_mask"""].dtype
              snake_case:						Union[str, Any]         =   torch.cat(
                  [common_inputs["""attention_mask"""], torch.ones(__lowerCamelCase  ,		__lowerCamelCase  ,		dtype=__lowerCamelCase						)]  ,		dim=1						)
              snake_case:						Dict         =   [
                  (torch.zeros(__lowerCamelCase						), torch.zeros(__lowerCamelCase						)) for _ in range(__lowerCamelCase						)
              ]
          return common_inputs






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


          '''simple docstring'''







          snake_case:						str         =   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
          snake_case:						str         =   tokenizer.num_special_tokens_to_add(__lowerCamelCase						)
          snake_case:						int         =   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
          snake_case:						Dict         =   [""" """.join([tokenizer.unk_token]						) * seq_length] * batch_size
          snake_case:						Any         =   dict(tokenizer(__lowerCamelCase  ,		return_tensors=__lowerCamelCase						)						)
          return common_inputs






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


          '''simple docstring'''







          if self.task in ["default", "seq2seq-lm"]:
              snake_case:						Union[str, Any]         =   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":
              snake_case:						Union[str, Any]         =   self._generate_dummy_inputs_for_causal_lm(
                  __lowerCamelCase  ,		batch_size=__lowerCamelCase  ,		seq_length=__lowerCamelCase  ,		is_pair=__lowerCamelCase  ,		framework=__lowerCamelCase						)
          else:
              snake_case:						int         =   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 			lowerCAmelCase_   (      self  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase						)     ->				List[Any]:


          '''simple docstring'''







          if self.task in ["default", "seq2seq-lm"]:
              snake_case:						Optional[int]         =   super()._flatten_past_key_values_(__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase						)
          else:
              snake_case:						List[str]         =   super(__lowerCamelCase  ,		self						)._flatten_past_key_values_(
                  __lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase						)


