

"""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


__UpperCAmelCase =				logging.get_logger(__name__)

__UpperCAmelCase =				{
    '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      __lowercase			(    __lowerCamelCase					):
  snake_case_				       =       """marian"""
  snake_case_				       =       ["""past_key_values"""]
  snake_case_				       =       {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}

  def __init__(  self		:			List[str]					,A		:			List[str]=58_101					,A		:			int=None					,A		:			Union[str, Any]=1_024					,A		:			Tuple=12					,A		:			Union[str, Any]=4_096					,A		:			int=16					,A		:			Union[str, Any]=12					,A		:			List[Any]=4_096					,A		:			Optional[Any]=16					,A		:			Union[str, Any]=0.0					,A		:			str=0.0					,A		:			Optional[Any]=True					,A		:			List[str]=True					,A		:			List[Any]="gelu"					,A		:			Union[str, Any]=1_024					,A		:			Any=0.1					,A		:			Optional[int]=0.0					,A		:			Optional[Any]=0.0					,A		:			List[str]=0.0_2					,A		:			Union[str, Any]=58_100					,A		:			List[Any]=False					,A		:			int=58_100					,A		:			Union[str, Any]=0					,A		:			Any=0					,A		:			List[str]=True					,**A		:			Any					,):






   '''simple docstring'''
   UpperCAmelCase__					:  Dict					   =					vocab_size
   UpperCAmelCase__					:  Any					   =					decoder_vocab_size or vocab_size
   UpperCAmelCase__					:  List[Any]					   =					max_position_embeddings
   UpperCAmelCase__					:  Dict					   =					d_model
   UpperCAmelCase__					:  List[str]					   =					encoder_ffn_dim
   UpperCAmelCase__					:  Union[str, Any]					   =					encoder_layers
   UpperCAmelCase__					:  Optional[int]					   =					encoder_attention_heads
   UpperCAmelCase__					:  str					   =					decoder_ffn_dim
   UpperCAmelCase__					:  int					   =					decoder_layers
   UpperCAmelCase__					:  str					   =					decoder_attention_heads
   UpperCAmelCase__					:  int					   =					dropout
   UpperCAmelCase__					:  int					   =					attention_dropout
   UpperCAmelCase__					:  Dict					   =					activation_dropout
   UpperCAmelCase__					:  Any					   =					activation_function
   UpperCAmelCase__					:  Optional[int]					   =					init_std
   UpperCAmelCase__					:  int					   =					encoder_layerdrop
   UpperCAmelCase__					:  Any					   =					decoder_layerdrop
   UpperCAmelCase__					:  List[str]					   =					use_cache
   UpperCAmelCase__					:  int					   =					encoder_layers
   UpperCAmelCase__					:  Union[str, Any]					   =					scale_embedding  # scale factor will be sqrt(d_model) if True
   UpperCAmelCase__					:  Tuple					   =					share_encoder_decoder_embeddings
   super().__init__(
       pad_token_id=A					,eos_token_id=A					,is_encoder_decoder=A					,decoder_start_token_id=A					,forced_eos_token_id=A					,**A					,)







class      __lowercase			(    __lowerCamelCase					):

  @property
  # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
  def    __lowercase       (  self		:			Optional[Any]    ):






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

    if self.use_past:
     UpperCAmelCase__					:  str					   =					{0: """batch"""}
     UpperCAmelCase__					:  Any					   =					{0: """batch""", 1: """past_decoder_sequence + sequence"""}
    else:
     UpperCAmelCase__					:  List[str]					   =					{0: """batch""", 1: """decoder_sequence"""}
     UpperCAmelCase__					:  int					   =					{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.
    UpperCAmelCase__					:  Optional[Any]					   =					OrderedDict(
        [
            ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
            ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
        ]    )
    if self.use_past:
     UpperCAmelCase__   ,      UpperCAmelCase__					:  List[str]					   =					self.num_layers
     for i in range(A    ):
      UpperCAmelCase__					:  List[str]					   =					{0: """batch""", 2: """past_sequence + sequence"""}
      UpperCAmelCase__					:  str					   =					{0: """batch""", 2: """past_sequence + sequence"""}
   else:
    UpperCAmelCase__					:  Union[str, Any]					   =					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    __lowercase       (  self		:			Optional[Any]    ):






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

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






   '''simple docstring'''
   UpperCAmelCase__					:  List[Any]					   =					self._generate_dummy_inputs_for_encoder_and_decoder(
       A					,A					,A					,A					,A    )

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

    UpperCAmelCase__					:  Optional[int]					   =					torch.cat(
        [common_inputs["""decoder_attention_mask"""], torch.ones(A					,A    )]					,dim=1    )

    UpperCAmelCase__					:  Optional[int]					   =					[]
    # If the number of encoder and decoder layers are present in the model configuration, both are considered
    UpperCAmelCase__   ,      UpperCAmelCase__					:  Optional[Any]					   =					self.num_layers
    UpperCAmelCase__					:  Any					   =					min(A					,A    )
    UpperCAmelCase__					:  Union[str, Any]					   =					max(A					,A    ) - min_num_layers
    UpperCAmelCase__					:  Optional[int]					   =					"""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.
    UpperCAmelCase__					:  Optional[Any]					   =					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    __lowercase       (  self		:			Any					,A		:			PreTrainedTokenizer					,A		:			int = -1					,A		:			int = -1					,A		:			bool = False					,A		:			Optional[TensorType] = None					,):






   '''simple docstring'''
   UpperCAmelCase__					:  List[str]					   =					self._generate_dummy_inputs_for_encoder_and_decoder(
       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
    UpperCAmelCase__   ,      UpperCAmelCase__					:  str					   =					common_inputs["""input_ids"""].shape
    # Not using the same length for past_key_values
    UpperCAmelCase__					:  Optional[int]					   =					seqlen + 2
    UpperCAmelCase__   ,      UpperCAmelCase__					:  Union[str, Any]					   =					self.num_layers
    UpperCAmelCase__   ,      UpperCAmelCase__					:  int					   =					self.num_attention_heads
    UpperCAmelCase__					:  int					   =					(
        batch,
        num_encoder_attention_heads,
        past_key_values_length,
        self._config.hidden_size // num_encoder_attention_heads,
    )

    UpperCAmelCase__					:  List[str]					   =					common_inputs["""attention_mask"""].dtype
    UpperCAmelCase__					:  int					   =					torch.cat(
        [common_inputs["""attention_mask"""], torch.ones(A					,A					,dtype=A    )]					,dim=1    )
    UpperCAmelCase__					:  str					   =					[
        (torch.zeros(A    ), torch.zeros(A    )) for _ in range(A    )
    ]
   return common_inputs

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






   '''simple docstring'''
   # 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
   UpperCAmelCase__					:  Any					   =					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
   UpperCAmelCase__					:  Optional[Any]					   =					tokenizer.num_special_tokens_to_add(A    )
   UpperCAmelCase__					:  str					   =					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
   UpperCAmelCase__					:  str					   =					[""" """.join([tokenizer.unk_token]    ) * seq_length] * batch_size
   UpperCAmelCase__					:  int					   =					dict(tokenizer(A					,return_tensors=A    )    )
   return common_inputs

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






   '''simple docstring'''
   if self.task in ["default", "seq2seq-lm"]:
    UpperCAmelCase__					:  List[str]					   =					self._generate_dummy_inputs_for_default_and_seqaseq_lm(
        A					,batch_size=A					,seq_length=A					,is_pair=A					,framework=A    )

   else:
    UpperCAmelCase__					:  Optional[int]					   =					self._generate_dummy_inputs_for_causal_lm(
        A					,batch_size=A					,seq_length=A					,is_pair=A					,framework=A    )

   return common_inputs

  def    __lowercase       (  self		:			Dict					,A		:			Union[str, Any]					,A		:			Optional[Any]					,A		:			Any					,A		:			Dict    ):






   '''simple docstring'''
   if self.task in ["default", "seq2seq-lm"]:
    UpperCAmelCase__					:  int					   =					super()._flatten_past_key_values_(A					,A					,A					,A    )
   else:
    UpperCAmelCase__					:  List[Any]					   =					super(A					,self    )._flatten_past_key_values_(
        A					,A					,A					,A    )




  @property
  def    __lowercase       (  self		:			Optional[Any]    ):






   '''simple docstring'''
   return 1e-4








"""simple docstring"""




from __future__ import annotations

from typing import Any







class      __lowercase			:

  def __init__(  self		:			Optional[int]					,A		:			int					,A		:			int					,A		:			float = 0    ):






   '''simple docstring'''
   UpperCAmelCase__   ,      UpperCAmelCase__					:  str					   =					row, column
   UpperCAmelCase__					:  int					   =					[[default_value for c in range(A    )] for r in range(A    )]

  def __str__(  self		:			Union[str, Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Union[str, Any]					   =					f"Matrix consist of {self.row} rows and {self.column} columns\n"

   # Make string identifier
   UpperCAmelCase__					:  Optional[Any]					   =					0
   for row_vector in self.array:
    for obj in row_vector:
     UpperCAmelCase__					:  Optional[int]					   =					max(A					,len(str(A    )    )    )
   UpperCAmelCase__					:  int					   =					f"%{max_element_length}s"

   # Make string and return
   def single_line(A		:			list[float]    ) -> str:
    nonlocal string_format_identifier
    UpperCAmelCase__					:  List[str]					   =					"""["""
    line += ", ".join(string_format_identifier % (obj,) for obj in row_vector    )
    line += "]"
    return line

   s += "\n".join(single_line(A    ) for row_vector in self.array    )
   return s

  def __repr__(  self		:			Dict    ):






   '''simple docstring'''
   return str(self    )

  def    __lowercase       (  self		:			int					,A		:			tuple[int, int]    ):






   '''simple docstring'''
   if not (isinstance(A					,(list, tuple)    ) and len(A    ) == 2):
    return False
   elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
    return False
   else:
    return True

  def __getitem__(  self		:			Optional[int]					,A		:			tuple[int, int]    ):






   '''simple docstring'''
   assert self.validate_indicies(A    )
   return self.array[loc[0]][loc[1]]

  def __setitem__(  self		:			List[Any]					,A		:			tuple[int, int]					,A		:			float    ):






   '''simple docstring'''
   assert self.validate_indicies(A    )
   UpperCAmelCase__					:  str					   =					value

  def __add__(  self		:			Any					,A		:			Matrix    ):






   '''simple docstring'''
   assert isinstance(A					,A    )
   assert self.row == another.row and self.column == another.column

   # Add
   UpperCAmelCase__					:  Optional[int]					   =					Matrix(self.row					,self.column    )
   for r in range(self.row    ):
    for c in range(self.column    ):
     UpperCAmelCase__					:  Union[str, Any]					   =					self[r, c] + another[r, c]
   return result

  def __neg__(  self		:			Union[str, Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Tuple					   =					Matrix(self.row					,self.column    )
   for r in range(self.row    ):
    for c in range(self.column    ):
     UpperCAmelCase__					:  int					   =					-self[r, c]
   return result

  def __sub__(  self		:			str					,A		:			Matrix    ):






   '''simple docstring'''
   return self + (-another)

  def __mul__(  self		:			Union[str, Any]					,A		:			int | float | Matrix    ):






   '''simple docstring'''

   if isinstance(A					,(int, float)    ):  # Scalar multiplication
    UpperCAmelCase__					:  Dict					   =					Matrix(self.row					,self.column    )
    for r in range(self.row    ):
     for c in range(self.column    ):
      UpperCAmelCase__					:  Any					   =					self[r, c] * another
    return result
   elif isinstance(A					,A    ):  # Matrix multiplication
    assert self.column == another.row
    UpperCAmelCase__					:  Optional[int]					   =					Matrix(self.row					,another.column    )
    for r in range(self.row    ):
     for c in range(another.column    ):
      for i in range(self.column    ):
       result[r, c] += self[r, i] * another[i, c]
    return result
   else:
    UpperCAmelCase__					:  Union[str, Any]					   =					f"Unsupported type given for another ({type(A    )})"
    raise TypeError(A    )

  def    __lowercase       (  self		:			str    ):






   '''simple docstring'''
   UpperCAmelCase__					:  List[Any]					   =					Matrix(self.column					,self.row    )
   for r in range(self.row    ):
    for c in range(self.column    ):
     UpperCAmelCase__					:  Optional[int]					   =					self[r, c]
   return result




  def    __lowercase       (  self		:			Any					,A		:			Matrix					,A		:			Matrix    ):






   '''simple docstring'''
   assert isinstance(A					,A    ) and isinstance(A					,A    )
   assert self.row == self.column == u.row == v.row  # u, v should be column vector
   assert u.column == v.column == 1  # u, v should be column vector

   # Calculate
   UpperCAmelCase__					:  Optional[Any]					   =					v.transpose()
   UpperCAmelCase__					:  Union[str, Any]					   =					(v_t * self * u)[0, 0] + 1
   if numerator_factor == 0:
    return None  # It's not invertable
   return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":
      def   lowerCAmelCase						(					):
       '''simple docstring'''





       UpperCAmelCase__					:  List[str]					   =					Matrix(3							,    3							,    0      )
       for i in range(3      ):
        UpperCAmelCase__					:  List[str]					   =					1
       print(F"a^(-1) is {ainv}"      )
       # u, v
       UpperCAmelCase__					:  List[Any]					   =					Matrix(3							,    1							,    0      )
       UpperCAmelCase__   ,      UpperCAmelCase__   ,      UpperCAmelCase__					:  Optional[int]					   =					1, 2, -3
       UpperCAmelCase__					:  List[str]					   =					Matrix(3							,    1							,    0      )
       UpperCAmelCase__   ,      UpperCAmelCase__   ,      UpperCAmelCase__					:  Tuple					   =					4, -2, 5
       print(F"u is {u}"      )
       print(F"v is {v}"      )
       print(F"uv^T is {u * v.transpose()}"      )
       # Sherman Morrison
       print(F"(a + uv^T)^(-1) is {ainv.sherman_morrison(__UpperCamelCase							,    __UpperCamelCase      )}"      )
      def   lowerCAmelCase						(					):
       '''simple docstring'''





       import doctest

       doctest.testmod()




      testa()





