






'''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


SCREAMING_SNAKE_CASE__     =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     =       {
    'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json',
    # See all Marian models at https://huggingface.co/models?filter=marian
}






class     a_						(      lowerCamelCase    ):
  lowercase							=				"""marian"""
  lowercase							=				["""past_key_values"""]
  lowercase							=				{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}

  def __init__(				self      ,		_SCREAMING_SNAKE_CASE=58101      ,		_SCREAMING_SNAKE_CASE=None      ,		_SCREAMING_SNAKE_CASE=1024      ,		_SCREAMING_SNAKE_CASE=12      ,		_SCREAMING_SNAKE_CASE=4096      ,		_SCREAMING_SNAKE_CASE=16      ,		_SCREAMING_SNAKE_CASE=12      ,		_SCREAMING_SNAKE_CASE=4096      ,		_SCREAMING_SNAKE_CASE=16      ,		_SCREAMING_SNAKE_CASE=0.0      ,		_SCREAMING_SNAKE_CASE=0.0      ,		_SCREAMING_SNAKE_CASE=True      ,		_SCREAMING_SNAKE_CASE=True      ,		_SCREAMING_SNAKE_CASE="gelu"      ,		_SCREAMING_SNAKE_CASE=1024      ,		_SCREAMING_SNAKE_CASE=0.1      ,		_SCREAMING_SNAKE_CASE=0.0      ,		_SCREAMING_SNAKE_CASE=0.0      ,		_SCREAMING_SNAKE_CASE=0.0_2      ,		_SCREAMING_SNAKE_CASE=58100      ,		_SCREAMING_SNAKE_CASE=False      ,		_SCREAMING_SNAKE_CASE=58100      ,		_SCREAMING_SNAKE_CASE=0      ,		_SCREAMING_SNAKE_CASE=0      ,		_SCREAMING_SNAKE_CASE=True      ,		**_SCREAMING_SNAKE_CASE      ,		)  ->				List[str]:



        """simple docstring"""






        UpperCamelCase      = vocab_size
        UpperCamelCase      = decoder_vocab_size or vocab_size
        UpperCamelCase      = max_position_embeddings
        UpperCamelCase      = d_model
        UpperCamelCase      = encoder_ffn_dim
        UpperCamelCase      = encoder_layers
        UpperCamelCase      = encoder_attention_heads
        UpperCamelCase      = decoder_ffn_dim
        UpperCamelCase      = decoder_layers
        UpperCamelCase      = decoder_attention_heads
        UpperCamelCase      = dropout
        UpperCamelCase      = attention_dropout
        UpperCamelCase      = activation_dropout
        UpperCamelCase      = activation_function
        UpperCamelCase      = init_std
        UpperCamelCase      = encoder_layerdrop
        UpperCamelCase      = decoder_layerdrop
        UpperCamelCase      = use_cache
        UpperCamelCase      = encoder_layers
        UpperCamelCase      = scale_embedding  # scale factor will be sqrt(d_model) if True
        UpperCamelCase      = share_encoder_decoder_embeddings
        super().__init__(
            pad_token_id=lowercase_      ,		eos_token_id=lowercase_      ,		is_encoder_decoder=lowercase_      ,		decoder_start_token_id=lowercase_      ,		forced_eos_token_id=lowercase_      ,		**lowercase_      ,		)






class     a_						(      lowerCamelCase    ):

  @property
  # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
  def   A__						(				self				)  ->				Mapping[str, Mapping[int, str]]:



        """simple docstring"""






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

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

              if self.use_past:
                    self.fill_with_past_key_values_(lowercase_      ,		direction="""inputs"""				)
        elif self.task == "causal-lm":
              # TODO: figure this case out.
              UpperCamelCase      = OrderedDict(
                  [
                      ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                      ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                  ]				)
              if self.use_past:
                    UpperCamelCase  ,UpperCamelCase      = self.num_layers
                    for i in range(lowercase_				):
                          UpperCamelCase      = {0: """batch""", 2: """past_sequence + sequence"""}
                          UpperCamelCase      = {0: """batch""", 2: """past_sequence + sequence"""}
        else:
              UpperCamelCase      = 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   A__						(				self				)  ->				Mapping[str, Mapping[int, str]]:



        """simple docstring"""






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

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



        """simple docstring"""






        UpperCamelCase      = self._generate_dummy_inputs_for_encoder_and_decoder(
            lowercase_      ,		lowercase_      ,		lowercase_      ,		lowercase_      ,		lowercase_				)

        # Generate decoder inputs
        UpperCamelCase      = seq_length if not self.use_past else 1
        UpperCamelCase      = self._generate_dummy_inputs_for_encoder_and_decoder(
            lowercase_      ,		lowercase_      ,		lowercase_      ,		lowercase_      ,		lowercase_				)
        UpperCamelCase      = {F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
        UpperCamelCase      = dict(**lowercase_      ,		**lowercase_				)

        if self.use_past:
              if not is_torch_available():
                    raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""				)
              else:
                    import torch
              UpperCamelCase  ,UpperCamelCase      = common_inputs["""input_ids"""].shape
              UpperCamelCase      = common_inputs["""decoder_input_ids"""].shape[1]
              UpperCamelCase  ,UpperCamelCase      = self.num_attention_heads
              UpperCamelCase      = (
                  batch,
                  num_encoder_attention_heads,
                  encoder_seq_length,
                  self._config.hidden_size // num_encoder_attention_heads,
              )
              UpperCamelCase      = decoder_seq_length + 3
              UpperCamelCase      = (
                  batch,
                  num_decoder_attention_heads,
                  decoder_past_length,
                  self._config.hidden_size // num_decoder_attention_heads,
              )

              UpperCamelCase      = torch.cat(
                  [common_inputs["""decoder_attention_mask"""], torch.ones(lowercase_      ,		lowercase_				)]      ,		dim=1				)

              UpperCamelCase      = []
              # If the number of encoder and decoder layers are present in the model configuration, both are considered
              UpperCamelCase  ,UpperCamelCase      = self.num_layers
              UpperCamelCase      = min(lowercase_      ,		lowercase_				)
              UpperCamelCase      = max(lowercase_      ,		lowercase_				) - min_num_layers
              UpperCamelCase      = """encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

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

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



        """simple docstring"""






        UpperCamelCase      = self._generate_dummy_inputs_for_encoder_and_decoder(
            lowercase_      ,		lowercase_      ,		lowercase_      ,		lowercase_      ,		lowercase_				)

        if self.use_past:
              if not is_torch_available():
                    raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""				)
              else:
                    import torch
              UpperCamelCase  ,UpperCamelCase      = common_inputs["""input_ids"""].shape
              # Not using the same length for past_key_values
              UpperCamelCase      = seqlen + 2
              UpperCamelCase  ,UpperCamelCase      = self.num_layers
              UpperCamelCase  ,UpperCamelCase      = self.num_attention_heads
              UpperCamelCase      = (
                  batch,
                  num_encoder_attention_heads,
                  past_key_values_length,
                  self._config.hidden_size // num_encoder_attention_heads,
              )

              UpperCamelCase      = common_inputs["""attention_mask"""].dtype
              UpperCamelCase      = torch.cat(
                  [common_inputs["""attention_mask"""], torch.ones(lowercase_      ,		lowercase_      ,		dtype=lowercase_				)]      ,		dim=1				)
              UpperCamelCase      = [
                  (torch.zeros(lowercase_				), torch.zeros(lowercase_				)) for _ in range(lowercase_				)
              ]
        return common_inputs

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



        """simple docstring"""






        UpperCamelCase      = compute_effective_axis_dimension(
            lowercase_      ,		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
        UpperCamelCase      = tokenizer.num_special_tokens_to_add(lowercase_				)
        UpperCamelCase      = compute_effective_axis_dimension(
            lowercase_      ,		fixed_dimension=OnnxConfig.default_fixed_sequence      ,		num_token_to_add=lowercase_				)

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

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



        """simple docstring"""






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

        else:
              UpperCamelCase      = self._generate_dummy_inputs_for_causal_lm(
                  lowercase_      ,		batch_size=lowercase_      ,		seq_length=lowercase_      ,		is_pair=lowercase_      ,		framework=lowercase_				)

        return common_inputs

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)  ->				Union[str, Any]:



        """simple docstring"""






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


  @property
  def   A__						(				self				)  ->				float:



        """simple docstring"""






        return 1e-4








'''simple docstring'''

from timeit import timeit







def    lowercase__			(				__UpperCamelCase   )->			int:
      if number < 0:
            raise ValueError("""the value of input must not be negative"""   )
      UpperCamelCase      = 0
      while number:
            number &= number - 1
            result += 1
      return result







def    lowercase__			(				__UpperCamelCase   )->			int:
      if number < 0:
            raise ValueError("""the value of input must not be negative"""   )
      UpperCamelCase      = 0
      while number:
            if number % 2 == 1:
                  result += 1
            number >>= 1
      return result







def    lowercase__			(				)->			None:

      def do_benchmark(__UpperCamelCase   ) -> None:
            UpperCamelCase      = """import __main__ as z"""
            print(F"Benchmark when {number = }:"   )
            print(F"{get_set_bits_count_using_modulo_operator(__UpperCamelCase   ) = }"   )
            UpperCamelCase      = timeit("""z.get_set_bits_count_using_modulo_operator(25)"""		,						setup=__UpperCamelCase   )
            print(F"timeit() runs in {timing} seconds"   )
            print(F"{get_set_bits_count_using_brian_kernighans_algorithm(__UpperCamelCase   ) = }"   )
            UpperCamelCase      = timeit(
                """z.get_set_bits_count_using_brian_kernighans_algorithm(25)"""		,						setup=__UpperCamelCase		,						)
            print(F"timeit() runs in {timing} seconds"   )

      for number in (25, 37, 58, 0):
            do_benchmark(__UpperCamelCase   )
            print()


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     benchmark()
