






'''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


a_					=	logging.get_logger(__name__)

a_					=	{
    '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        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):
 snake_case_												=							"""marian"""
 snake_case_												=							["""past_key_values"""]
 snake_case_												=							{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}




 def __init__( self		:							Optional[Any]   , __lowercase		:							List[str]=5_81_01   , __lowercase		:							Optional[int]=None   , __lowercase		:							Tuple=10_24   , __lowercase		:							Union[str, Any]=12   , __lowercase		:							List[Any]=40_96   , __lowercase		:							Optional[int]=16   , __lowercase		:							List[str]=12   , __lowercase		:							str=40_96   , __lowercase		:							Optional[Any]=16   , __lowercase		:							List[str]=0.0   , __lowercase		:							List[str]=0.0   , __lowercase		:							Optional[Any]=True   , __lowercase		:							List[str]=True   , __lowercase		:							Optional[Any]="gelu"   , __lowercase		:							int=10_24   , __lowercase		:							Optional[Any]=0.1   , __lowercase		:							int=0.0   , __lowercase		:							List[str]=0.0   , __lowercase		:							int=0.02   , __lowercase		:							List[str]=5_81_00   , __lowercase		:							int=False   , __lowercase		:							str=5_81_00   , __lowercase		:							List[str]=0   , __lowercase		:							Union[str, Any]=0   , __lowercase		:							str=True   , **__lowercase		:							Union[str, Any]   , )		->  Optional[Any]:
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=vocab_size
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=decoder_vocab_size or vocab_size
       SCREAMING_SNAKE_CASE__       :			str													=max_position_embeddings
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=d_model
       SCREAMING_SNAKE_CASE__       :			List[str]													=encoder_ffn_dim
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=encoder_layers
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=encoder_attention_heads
       SCREAMING_SNAKE_CASE__       :			List[Any]													=decoder_ffn_dim
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=decoder_layers
       SCREAMING_SNAKE_CASE__       :			int													=decoder_attention_heads
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=dropout
       SCREAMING_SNAKE_CASE__       :			List[str]													=attention_dropout
       SCREAMING_SNAKE_CASE__       :			int													=activation_dropout
       SCREAMING_SNAKE_CASE__       :			List[Any]													=activation_function
       SCREAMING_SNAKE_CASE__       :			int													=init_std
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=encoder_layerdrop
       SCREAMING_SNAKE_CASE__       :			Tuple													=decoder_layerdrop
       SCREAMING_SNAKE_CASE__       :			Tuple													=use_cache
       SCREAMING_SNAKE_CASE__       :			str													=encoder_layers
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=scale_embedding  # scale factor will be sqrt(d_model) if True
       SCREAMING_SNAKE_CASE__       :			Dict													=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        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):




 @property
 # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
 def        __magic_name__		( self		:							int )		->  Mapping[str, Mapping[int, str]]:
       if self.task in ["default", "seq2seq-lm"]:
             SCREAMING_SNAKE_CASE__       :			List[Any]													=OrderedDict(
                 [
                     ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                     ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                 ] )

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




 def        __magic_name__		( self		:							Dict   , __lowercase		:							PreTrainedTokenizer   , __lowercase		:							int = -1   , __lowercase		:							int = -1   , __lowercase		:							bool = False   , __lowercase		:							Optional[TensorType] = None   , )		->  Mapping[str, Any]:
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=self._generate_dummy_inputs_for_encoder_and_decoder(
           __lowercase   , __lowercase   , __lowercase   , __lowercase   , __lowercase )

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

             SCREAMING_SNAKE_CASE__       :			Any													=torch.cat(
                 [common_inputs['''decoder_attention_mask'''], torch.ones(__lowercase   , __lowercase )]   , dim=1 )

             SCREAMING_SNAKE_CASE__       :			str													=[]
             # If the number of encoder and decoder layers are present in the model configuration, both are considered
             SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			Any													=self.num_layers
             SCREAMING_SNAKE_CASE__       :			Any													=min(__lowercase   , __lowercase )
             SCREAMING_SNAKE_CASE__       :			List[str]													=max(__lowercase   , __lowercase ) - min_num_layers
             SCREAMING_SNAKE_CASE__       :			Optional[int]													='''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.
             SCREAMING_SNAKE_CASE__       :			str													=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        __magic_name__		( self		:							Optional[int]   , __lowercase		:							PreTrainedTokenizer   , __lowercase		:							int = -1   , __lowercase		:							int = -1   , __lowercase		:							bool = False   , __lowercase		:							Optional[TensorType] = None   , )		->  Mapping[str, Any]:
       SCREAMING_SNAKE_CASE__       :			Dict													=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
             SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=common_inputs['''input_ids'''].shape
             # Not using the same length for past_key_values
             SCREAMING_SNAKE_CASE__       :			Optional[int]													=seqlen + 2
             SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=self.num_layers
             SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			List[Any]													=self.num_attention_heads
             SCREAMING_SNAKE_CASE__       :			Dict													=(
                 batch,
                 num_encoder_attention_heads,
                 past_key_values_length,
                 self._config.hidden_size // num_encoder_attention_heads,
             )

             SCREAMING_SNAKE_CASE__       :			str													=common_inputs['''attention_mask'''].dtype
             SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=torch.cat(
                 [common_inputs['''attention_mask'''], torch.ones(__lowercase   , __lowercase   , dtype=__lowercase )]   , dim=1 )
             SCREAMING_SNAKE_CASE__       :			int													=[
                 (torch.zeros(__lowercase ), torch.zeros(__lowercase )) for _ in range(__lowercase )
             ]
       return common_inputs




 def        __magic_name__		( self		:							Optional[Any]   , __lowercase		:							PreTrainedTokenizer   , __lowercase		:							int = -1   , __lowercase		:							int = -1   , __lowercase		:							bool = False   , __lowercase		:							Optional[TensorType] = 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
       SCREAMING_SNAKE_CASE__       :			int													=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
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=tokenizer.num_special_tokens_to_add(__lowercase )
       SCREAMING_SNAKE_CASE__       :			List[Any]													=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
       SCREAMING_SNAKE_CASE__       :			int													=[''' '''.join([tokenizer.unk_token] ) * seq_length] * batch_size
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=dict(tokenizer(__lowercase   , return_tensors=__lowercase ) )
       return common_inputs




 def        __magic_name__		( self		:							List[Any]   , __lowercase		:							PreTrainedTokenizer   , __lowercase		:							int = -1   , __lowercase		:							int = -1   , __lowercase		:							bool = False   , __lowercase		:							Optional[TensorType] = None   , )		->  Mapping[str, Any]:
       if self.task in ["default", "seq2seq-lm"]:
             SCREAMING_SNAKE_CASE__       :			Any													=self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                 __lowercase   , batch_size=__lowercase   , seq_length=__lowercase   , is_pair=__lowercase   , framework=__lowercase )

       else:
             SCREAMING_SNAKE_CASE__       :			List[Any]													=self._generate_dummy_inputs_for_causal_lm(
                 __lowercase   , batch_size=__lowercase   , seq_length=__lowercase   , is_pair=__lowercase   , framework=__lowercase )

       return common_inputs




 def        __magic_name__		( self		:							Any   , __lowercase		:							Any   , __lowercase		:							Optional[Any]   , __lowercase		:							str   , __lowercase		:							List[Any] )		->  Any:
       if self.task in ["default", "seq2seq-lm"]:
             SCREAMING_SNAKE_CASE__       :			str													=super()._flatten_past_key_values_(__lowercase   , __lowercase   , __lowercase   , __lowercase )
       else:
             SCREAMING_SNAKE_CASE__       :			str													=super(__lowercase   , self )._flatten_past_key_values_(
                 __lowercase   , __lowercase   , __lowercase   , __lowercase )




 @property
 def        __magic_name__		( self		:							Tuple )		->  float:
       return 1e-4







'''simple docstring'''
from __future__ import annotations





def 	_a(					UpperCamelCase__			:  List[str],		UpperCamelCase__			:  Union[str, Any],		UpperCamelCase__			:  int,		UpperCamelCase__			:  List[str]							):  # noqa: E741




      '''simple docstring'''






      while r - l > 1:
            SCREAMING_SNAKE_CASE__       :			List[str]													=(l + r) // 2
            if v[m] >= key:
                  SCREAMING_SNAKE_CASE__       :			Dict													=m
            else:
                  SCREAMING_SNAKE_CASE__       :			Any													=m  # noqa: E741
      return r





def 	_a(					UpperCamelCase__			:  list[int]							):




      '''simple docstring'''






      if len(UpperCamelCase__							) == 0:
            return 0

      SCREAMING_SNAKE_CASE__       :			List[Any]													=[0] * len(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__       :			Dict													=1

      SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=v[0]

      for i in range(1,		len(UpperCamelCase__							)							):
            if v[i] < tail[0]:
                  SCREAMING_SNAKE_CASE__       :			List[Any]													=v[i]
            elif v[i] > tail[length - 1]:
                  SCREAMING_SNAKE_CASE__       :			int													=v[i]
                  length += 1
            else:
                  SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=v[i]

      return length


if __name__ == "__main__":
  import doctest

  doctest.testmod()