


import math
import time
from typing import Dict, List, Optional

from torch.utils.data import Dataset

from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
 import torch_xla.core.xla_model as xm
 import torch_xla.debug.metrics as met






class _lowerCamelCase(		_a  ):
       def __init__(   self, *lowerCamelCase, lowerCamelCase=None, lowerCamelCase=None, **lowerCamelCase)			->  List[Any]:




           """simple docstring"""

           super().__init__(*lowerCamelCase, **lowerCamelCase)
           _lowercase			:				Any 						=						eval_examples
           _lowercase			:				List[Any] 						=						post_process_function
       def      UpperCamelCase	(   self, lowerCamelCase = None, lowerCamelCase=None, lowerCamelCase = None, lowerCamelCase = "eval", **lowerCamelCase, )			->  Dict[str, float]:




           """simple docstring"""

           _lowercase			:				Optional[Any] 						=						gen_kwargs.copy()
           _lowercase			:				List[Any] 						=						(
               gen_kwargs['max_length'] if gen_kwargs.get('max_length') is not None else self.args.generation_max_length
           )
           _lowercase			:				Any 						=						(
               gen_kwargs['num_beams'] if gen_kwargs.get('num_beams') is not None else self.args.generation_num_beams
           )
           _lowercase			:				Optional[Any] 						=						gen_kwargs

           _lowercase			:				Optional[int] 						=						self.eval_dataset if eval_dataset is None else eval_dataset
           _lowercase			:				Optional[int] 						=						self.get_eval_dataloader(lowerCamelCase)
           _lowercase			:				Optional[Any] 						=						self.eval_examples if eval_examples is None else eval_examples

           # Temporarily disable metric computation, we will do it in the loop here.
           _lowercase			:				List[Any] 						=						self.compute_metrics
           _lowercase			:				int 						=						None
           _lowercase			:				Optional[Any] 						=						time.time()
           _lowercase			:				Optional[Any] 						=						self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
           try:
               _lowercase			:				int 						=						eval_loop(
                   lowerCamelCase, description='Evaluation', prediction_loss_only=True if compute_metrics is None else None, ignore_keys=lowerCamelCase, metric_key_prefix=lowerCamelCase, )
           finally:
               _lowercase			:				Union[str, Any] 						=						compute_metrics
           _lowercase			:				Optional[int] 						=						self.args.eval_batch_size * self.args.world_size
           if F'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
               start_time += output.metrics[F'''{metric_key_prefix}_jit_compilation_time''']
           output.metrics.update(
               speed_metrics(
                   lowerCamelCase, lowerCamelCase, num_samples=output.num_samples, num_steps=math.ceil(output.num_samples / total_batch_size), ))

           if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
               # Only the main node write the results by default
               _lowercase			:				Dict 						=						self.post_process_function(lowerCamelCase, lowerCamelCase, lowerCamelCase)
               _lowercase			:				List[Any] 						=						self.compute_metrics(lowerCamelCase)

               # Prefix all keys with metric_key_prefix + '_'
               for key in list(metrics.keys()):
                   if not key.startswith(F'''{metric_key_prefix}_'''):
                       _lowercase			:				Optional[int] 						=						metrics.pop(lowerCamelCase)

               metrics.update(output.metrics)
           else:
               _lowercase			:				Dict 						=						output.metrics

           if self.args.should_log:
               # Only the main node log the results by default
               self.log(lowerCamelCase)

           if self.args.tpu_metrics_debug or self.args.debug:
               # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
               xm.master_print(met.metrics_report())

           _lowercase			:				List[str] 						=						self.callback_handler.on_evaluate(self.args, self.state, self.control, lowerCamelCase)
           return metrics




       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase=None, lowerCamelCase = "test", **lowerCamelCase)			->  List[str]:




           """simple docstring"""

           _lowercase			:				str 						=						gen_kwargs.copy()

           _lowercase			:				str 						=						self.get_test_dataloader(lowerCamelCase)

           # Temporarily disable metric computation, we will do it in the loop here.
           _lowercase			:				List[str] 						=						self.compute_metrics
           _lowercase			:				Any 						=						None
           _lowercase			:				str 						=						time.time()
           _lowercase			:				Optional[int] 						=						self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
           try:
               _lowercase			:				str 						=						eval_loop(
                   lowerCamelCase, description='Prediction', prediction_loss_only=True if compute_metrics is None else None, ignore_keys=lowerCamelCase, metric_key_prefix=lowerCamelCase, )
           finally:
               _lowercase			:				int 						=						compute_metrics

           _lowercase			:				int 						=						self.args.eval_batch_size * self.args.world_size
           if F'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
               start_time += output.metrics[F'''{metric_key_prefix}_jit_compilation_time''']
           output.metrics.update(
               speed_metrics(
                   lowerCamelCase, lowerCamelCase, num_samples=output.num_samples, num_steps=math.ceil(output.num_samples / total_batch_size), ))
           if self.post_process_function is None or self.compute_metrics is None:
               return output

           _lowercase			:				Dict 						=						self.post_process_function(lowerCamelCase, lowerCamelCase, lowerCamelCase, 'predict')
           _lowercase			:				Optional[Any] 						=						self.compute_metrics(lowerCamelCase)

           # Prefix all keys with metric_key_prefix + '_'
           for key in list(metrics.keys()):
               if not key.startswith(F'''{metric_key_prefix}_'''):
                   _lowercase			:				Optional[Any] 						=						metrics.pop(lowerCamelCase)
           metrics.update(output.metrics)
           return PredictionOutput(predictions=predictions.predictions, label_ids=predictions.label_ids, metrics=lowerCamelCase)






from __future__ import annotations

from typing import Any






class _lowerCamelCase:
       def __init__(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase = 0)			->  None:




           """simple docstring"""

           _lowercase						,							_lowercase			:				str 						=						row, column
           _lowercase			:				Any 						=						[[default_value for c in range(lowerCamelCase)] for r in range(lowerCamelCase)]
       def __str__(   self)			->  str:




           """simple docstring"""

           _lowercase			:				Tuple 						=						F'''Matrix consist of {self.row} rows and {self.column} columns\n'''

           # Make string identifier
           _lowercase			:				str 						=						0
           for row_vector in self.array:
               for obj in row_vector:
                   _lowercase			:				Optional[int] 						=						max(lowerCamelCase, len(str(lowerCamelCase)))
           _lowercase			:				List[str] 						=						F'''%{max_element_length}s'''

           # Make string and return
           def single_line(lowerCamelCase) -> str:
               nonlocal string_format_identifier
               _lowercase			:				Union[str, 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)			->  str:




           """simple docstring"""

           return str(self)
       def      UpperCamelCase	(   self, lowerCamelCase)			->  bool:




           """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, lowerCamelCase)			->  Any:




           """simple docstring"""

           assert self.validate_indicies(lowerCamelCase)
           return self.array[loc[0]][loc[1]]
       def __setitem__(   self, lowerCamelCase, lowerCamelCase)			->  None:




           """simple docstring"""

           assert self.validate_indicies(lowerCamelCase)
           _lowercase			:				Optional[Any] 						=						value
       def __add__(   self, lowerCamelCase)			->  Matrix:




           """simple docstring"""

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

           # Add
           _lowercase			:				Any 						=						Matrix(self.row, self.column)
           for r in range(self.row):
               for c in range(self.column):
                   _lowercase			:				int 						=						self[r, c] + another[r, c]
           return result
       def __neg__(   self)			->  Matrix:




           """simple docstring"""

           _lowercase			:				List[Any] 						=						Matrix(self.row, self.column)
           for r in range(self.row):
               for c in range(self.column):
                   _lowercase			:				List[str] 						=						-self[r, c]
           return result
       def __sub__(   self, lowerCamelCase)			->  Matrix:




           """simple docstring"""

           return self + (-another)
       def __mul__(   self, lowerCamelCase)			->  Matrix:




           """simple docstring"""

           if isinstance(lowerCamelCase, (int, float)):  # Scalar multiplication
               _lowercase			:				Dict 						=						Matrix(self.row, self.column)
               for r in range(self.row):
                   for c in range(self.column):
                       _lowercase			:				Union[str, Any] 						=						self[r, c] * another
               return result
           elif isinstance(lowerCamelCase, lowerCamelCase):  # Matrix multiplication
               assert self.column == another.row
               _lowercase			:				str 						=						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:
               _lowercase			:				Tuple 						=						F'''Unsupported type given for another ({type(lowerCamelCase)})'''
               raise TypeError(lowerCamelCase)
       def      UpperCamelCase	(   self)			->  Matrix:




           """simple docstring"""

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




       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase)			->  Any:




           """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
           _lowercase			:				Dict 						=						v.transpose()
           _lowercase			:				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 							UpperCamelCase_( )       ->		None:
     # a^(-1)
     _lowercase			:				Optional[int] 						=						Matrix(3				,					3				,					0					)
     for i in range(3					):
         _lowercase			:				int 						=						1
     print(F'''a^(-1) is {ainv}'''					)
     # u, v
     _lowercase			:				Dict 						=						Matrix(3				,					1				,					0					)
     _lowercase						,							_lowercase						,							_lowercase			:				Dict 						=						1, 2, -3
     _lowercase			:				List[Any] 						=						Matrix(3				,					1				,					0					)
     _lowercase						,							_lowercase						,							_lowercase			:				int 						=						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 							UpperCamelCase_( )       ->		None:
     import doctest

     doctest.testmod()

 testa()


