






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase    							=      {
    'configuration_xlm_roberta': [
        'XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'XLMRobertaConfig',
        'XLMRobertaOnnxConfig',
    ],
}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase    							=      ['XLMRobertaTokenizer']

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase    							=      ['XLMRobertaTokenizerFast']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase    							=      [
           'XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
           'XLMRobertaForCausalLM',
           'XLMRobertaForMaskedLM',
           'XLMRobertaForMultipleChoice',
           'XLMRobertaForQuestionAnswering',
           'XLMRobertaForSequenceClassification',
           'XLMRobertaForTokenClassification',
           'XLMRobertaModel',
           'XLMRobertaPreTrainedModel',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase    							=      [
           'TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
           'TFXLMRobertaForCausalLM',
           'TFXLMRobertaForMaskedLM',
           'TFXLMRobertaForMultipleChoice',
           'TFXLMRobertaForQuestionAnswering',
           'TFXLMRobertaForSequenceClassification',
           'TFXLMRobertaForTokenClassification',
           'TFXLMRobertaModel',
           'TFXLMRobertaPreTrainedModel',
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase    							=      [
           'FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
           'FlaxXLMRobertaForMaskedLM',
           'FlaxXLMRobertaForCausalLM',
           'FlaxXLMRobertaForMultipleChoice',
           'FlaxXLMRobertaForQuestionAnswering',
           'FlaxXLMRobertaForSequenceClassification',
           'FlaxXLMRobertaForTokenClassification',
           'FlaxXLMRobertaModel',
           'FlaxXLMRobertaPreTrainedModel',
       ]

if TYPE_CHECKING:
       from .configuration_xlm_roberta import (
           XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
           XLMRobertaConfig,
           XLMRobertaOnnxConfig,
       )

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_xlm_roberta import XLMRobertaTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_xlm_roberta import (
                  XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  XLMRobertaForCausalLM,
                  XLMRobertaForMaskedLM,
                  XLMRobertaForMultipleChoice,
                  XLMRobertaForQuestionAnswering,
                  XLMRobertaForSequenceClassification,
                  XLMRobertaForTokenClassification,
                  XLMRobertaModel,
                  XLMRobertaPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_xlm_roberta import (
                  TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFXLMRobertaForCausalLM,
                  TFXLMRobertaForMaskedLM,
                  TFXLMRobertaForMultipleChoice,
                  TFXLMRobertaForQuestionAnswering,
                  TFXLMRobertaForSequenceClassification,
                  TFXLMRobertaForTokenClassification,
                  TFXLMRobertaModel,
                  TFXLMRobertaPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_xlm_roberta import (
                  FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  FlaxXLMRobertaForCausalLM,
                  FlaxXLMRobertaForMaskedLM,
                  FlaxXLMRobertaForMultipleChoice,
                  FlaxXLMRobertaForQuestionAnswering,
                  FlaxXLMRobertaForSequenceClassification,
                  FlaxXLMRobertaForTokenClassification,
                  FlaxXLMRobertaModel,
                  FlaxXLMRobertaPreTrainedModel,
              )

else:
       import sys

       UpperCamelCase    							=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)











from __future__ import annotations

from typing import Any







class 		_A						:

  def __init__(    self						:	List[Any]   ,				lowerCamelCase__						:	int   ,				lowerCamelCase__						:	int   ,				lowerCamelCase__						:	float = 0			):
     """simple docstring"""

     __UpperCamelCase	,     __UpperCamelCase	:			Tuple					=					row, column
     __UpperCamelCase	:			Tuple					=					[[default_value for c in range(lowerCamelCase__			)] for r in range(lowerCamelCase__			)]

  def __str__(    self						:	List[Any]			):
     """simple docstring"""

     __UpperCamelCase	:			Dict					=					f'Matrix consist of {self.row} rows and {self.column} columns\n'

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

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

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

  def __repr__(    self						:	Any			):
     """simple docstring"""

     return str(self			)

  def    a					(    self						:	str   ,				lowerCamelCase__						:	tuple[int, int]			):
     """simple docstring"""

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

  def __getitem__(    self						:	Union[str, Any]   ,				lowerCamelCase__						:	tuple[int, int]			):
     """simple docstring"""

     assert self.validate_indicies(lowerCamelCase__			)
     return self.array[loc[0]][loc[1]]

  def __setitem__(    self						:	Union[str, Any]   ,				lowerCamelCase__						:	tuple[int, int]   ,				lowerCamelCase__						:	float			):
     """simple docstring"""

     assert self.validate_indicies(lowerCamelCase__			)
     __UpperCamelCase	:			str					=					value

  def __add__(    self						:	int   ,				lowerCamelCase__						:	Matrix			):
     """simple docstring"""

     assert isinstance(lowerCamelCase__   ,				lowerCamelCase__			)
     assert self.row == another.row and self.column == another.column

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

  def __neg__(    self						:	Optional[int]			):
     """simple docstring"""

     __UpperCamelCase	:			str					=					Matrix(self.row   ,				self.column			)
     for r in range(self.row			):
        for c in range(self.column			):
           __UpperCamelCase	:			Any					=					-self[r, c]
     return result

  def __sub__(    self						:	Tuple   ,				lowerCamelCase__						:	Matrix			):
     """simple docstring"""

     return self + (-another)

  def __mul__(    self						:	Tuple   ,				lowerCamelCase__						:	int | float | Matrix			):
     """simple docstring"""

     if isinstance(lowerCamelCase__   ,				(int, float)			):  # Scalar multiplication
        __UpperCamelCase	:			List[str]					=					Matrix(self.row   ,				self.column			)
        for r in range(self.row			):
           for c in range(self.column			):
              __UpperCamelCase	:			Tuple					=					self[r, c] * another
        return result
     elif isinstance(lowerCamelCase__   ,				lowerCamelCase__			):  # Matrix multiplication
        assert self.column == another.row
        __UpperCamelCase	:			List[Any]					=					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	:			Any					=					f'Unsupported type given for another ({type(lowerCamelCase__			)})'
        raise TypeError(lowerCamelCase__			)

  def    a					(    self						:	Union[str, Any]			):
     """simple docstring"""

     __UpperCamelCase	:			Dict					=					Matrix(self.column   ,				self.row			)
     for r in range(self.row			):
        for c in range(self.column			):
           __UpperCamelCase	:			str					=					self[r, c]
     return result

  def    a					(    self						:	Any   ,				lowerCamelCase__						:	Matrix   ,				lowerCamelCase__						:	Matrix			):
     """simple docstring"""

     assert isinstance(lowerCamelCase__   ,				lowerCamelCase__			) and isinstance(lowerCamelCase__   ,				lowerCamelCase__			)
     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[int]					=					v.transpose()
     __UpperCamelCase	:			Tuple					=					(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     (							)     ->  None:
          # a^(-1)
          __UpperCamelCase	:			str					=					Matrix(3					,							3					,							0							)
          for i in range(3							):
             __UpperCamelCase	:			List[str]					=					1
          print(f'a^(-1) is {ainv}'							)
          # u, v
          __UpperCamelCase	:			Any					=					Matrix(3					,							1					,							0							)
          __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	:			Any					=					1, 2, -3
          __UpperCamelCase	:			int					=					Matrix(3					,							1					,							0							)
          __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	:			Dict					=					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(__lowerCAmelCase					,							__lowerCAmelCase							)}'							)


       def 				__lowerCamelCase     (							)     ->  None:
          import doctest

          doctest.testmod()



       testa()



