


# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


import warnings
from typing import List
from unittest.mock import Mock

import torch
from torch.utils.data import DataLoader, IterableDataset, TensorDataset

from accelerate.accelerator import Accelerator
from accelerate.utils.dataclasses import DistributedType



class 				_UpperCamelCase			(    _lowercase    ):

   '''simple docstring'''







   def __init__(	self	:			List[Any]							,	_lowerCamelCase	:			str				):






         '''simple docstring'''



         __lowerCamelCase  :   str							    =    data


   def __iter__(	self	:			List[Any]				):






         '''simple docstring'''



         for element in self.data:
               yield element





def 	_UpperCAmelCase							(  UpperCAmelCase   :      Dict=True   ):
      """simple docstring"""




      __lowerCamelCase  :   List[str]							    =    Accelerator(even_batches=__UpperCAmelCase   )
      assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
      return accelerator





def 	_UpperCAmelCase							(  UpperCAmelCase   :      Union[str, Any]    ,						UpperCAmelCase   :      Union[str, Any]    ,						UpperCAmelCase   :      int    ,						UpperCAmelCase   :      str = False   ):
      """simple docstring"""




      if iterable:
            __lowerCamelCase  :   Any							    =    DummyIterableDataset(torch.as_tensor(range(__UpperCAmelCase   )   )   )
      else:
            __lowerCamelCase  :   Any							    =    TensorDataset(torch.as_tensor(range(__UpperCAmelCase   )   )   )

      __lowerCamelCase  :   Optional[Any]							    =    DataLoader(__UpperCAmelCase    ,						batch_size=__UpperCAmelCase   )
      __lowerCamelCase  :   int							    =    accelerator.prepare(__UpperCAmelCase   )

      return dl





def 	_UpperCAmelCase							(  UpperCAmelCase   :      Union[str, Any]    ,						UpperCAmelCase   :      List[Any]    ,						UpperCAmelCase   :      List[Any]    ,						UpperCAmelCase   :      Any    ,						UpperCAmelCase   :      str    ,						):
      """simple docstring"""




      __lowerCamelCase  :   int							    =    create_dataloader(accelerator=__UpperCAmelCase    ,						dataset_size=__UpperCAmelCase    ,						batch_size=__UpperCAmelCase   )

      __lowerCamelCase  :   List[str]							    =    [len(batch[0]   ) for batch in dl]

      if accelerator.process_index == 0:
            assert batch_sizes == process_0_expected_batch_sizes
      elif accelerator.process_index == 1:
            assert batch_sizes == process_1_expected_batch_sizes





def 	_UpperCAmelCase							(  ):
      """simple docstring"""




      __lowerCamelCase  :   Any							    =    create_accelerator()

      # without padding, we would expect a different number of batches
      verify_dataloader_batch_sizes(
          __UpperCAmelCase    ,						dataset_size=3    ,						batch_size=1    ,						process_0_expected_batch_sizes=[1, 1]    ,						process_1_expected_batch_sizes=[1, 1]    ,						)

      # without padding, we would expect the same number of batches, but different sizes
      verify_dataloader_batch_sizes(
          __UpperCAmelCase    ,						dataset_size=7    ,						batch_size=2    ,						process_0_expected_batch_sizes=[2, 2]    ,						process_1_expected_batch_sizes=[2, 2]    ,						)





def 	_UpperCAmelCase							(  ):
      """simple docstring"""




      __lowerCamelCase  :   Optional[Any]							    =    create_accelerator(even_batches=__UpperCAmelCase   )

      verify_dataloader_batch_sizes(
          __UpperCAmelCase    ,						dataset_size=3    ,						batch_size=1    ,						process_0_expected_batch_sizes=[1, 1]    ,						process_1_expected_batch_sizes=[1]    ,						)

      verify_dataloader_batch_sizes(
          __UpperCAmelCase    ,						dataset_size=7    ,						batch_size=2    ,						process_0_expected_batch_sizes=[2, 2]    ,						process_1_expected_batch_sizes=[2, 1]    ,						)





def 	_UpperCAmelCase							(  ):
      """simple docstring"""




      __lowerCamelCase  :   str							    =    create_accelerator(even_batches=__UpperCAmelCase   )

      __lowerCamelCase  :   Any							    =    torch.nn.Linear(1    ,						1   )
      __lowerCamelCase  :   Tuple							    =    accelerator.prepare(__UpperCAmelCase   )

      __lowerCamelCase  :   Optional[Any]							    =    create_dataloader(__UpperCAmelCase    ,						dataset_size=3    ,						batch_size=1   )

      __lowerCamelCase  :   Dict							    =    []
      with accelerator.join_uneven_inputs([ddp_model]   ):
            for batch_idx, batch in enumerate(__UpperCAmelCase   ):
                  __lowerCamelCase  :   List[str]							    =    ddp_model(batch[0].float()   )
                  __lowerCamelCase  :   Union[str, Any]							    =    output.sum()
                  loss.backward()
                  batch_idxs.append(__UpperCAmelCase   )

      accelerator.wait_for_everyone()

      if accelerator.process_index == 0:
            assert batch_idxs == [0, 1]
      elif accelerator.process_index == 1:
            assert batch_idxs == [0]





def 	_UpperCAmelCase							(  UpperCAmelCase   :      Any   ):
      """simple docstring"""




      with warnings.catch_warnings(record=__UpperCAmelCase   ) as w:
            with accelerator.join_uneven_inputs([Mock()]   ):
                  pass

            assert issubclass(w[-1].category    ,						__UpperCAmelCase   )
            assert "only supported for multi-GPU" in str(w[-1].message   )





def 	_UpperCAmelCase							(  ):
      """simple docstring"""




      __lowerCamelCase  :   Optional[int]							    =    True
      __lowerCamelCase  :   Any							    =    False
      __lowerCamelCase  :   Optional[int]							    =    create_accelerator(even_batches=__UpperCAmelCase   )
      __lowerCamelCase  :   int							    =    torch.nn.Linear(1    ,						1   )
      __lowerCamelCase  :   Tuple							    =    accelerator.prepare(__UpperCAmelCase   )
      __lowerCamelCase  :   List[str]							    =    create_dataloader(__UpperCAmelCase    ,						dataset_size=3    ,						batch_size=1   )
      __lowerCamelCase  :   Optional[int]							    =    create_dataloader(__UpperCAmelCase    ,						dataset_size=3    ,						batch_size=1   )

      with accelerator.join_uneven_inputs([ddp_model]    ,						even_batches=__UpperCAmelCase   ):
            __lowerCamelCase  :   Tuple							    =    train_dl.batch_sampler.even_batches
            __lowerCamelCase  :   Tuple							    =    valid_dl.batch_sampler.even_batches

      assert train_dl_overridden_value == overridden_even_batches
      assert valid_dl_overridden_value == overridden_even_batches
      assert train_dl.batch_sampler.even_batches == default_even_batches
      assert valid_dl.batch_sampler.even_batches == default_even_batches





def 	_UpperCAmelCase							(  ):
      """simple docstring"""




      __lowerCamelCase  :   Tuple							    =    True
      __lowerCamelCase  :   Dict							    =    False
      __lowerCamelCase  :   int							    =    create_accelerator(even_batches=__UpperCAmelCase   )
      __lowerCamelCase  :   Any							    =    torch.nn.Linear(1    ,						1   )
      __lowerCamelCase  :   int							    =    accelerator.prepare(__UpperCAmelCase   )
      create_dataloader(__UpperCAmelCase    ,						dataset_size=3    ,						batch_size=1    ,						iterable=__UpperCAmelCase   )
      __lowerCamelCase  :   Union[str, Any]							    =    create_dataloader(__UpperCAmelCase    ,						dataset_size=3    ,						batch_size=1   )

      with warnings.catch_warnings():
            warnings.filterwarnings("""ignore"""   )
            try:
                  with accelerator.join_uneven_inputs([ddp_model]    ,						even_batches=__UpperCAmelCase   ):
                        __lowerCamelCase  :   List[str]							    =    batch_dl.batch_sampler.even_batches
            except AttributeError:
                  # ensure attribute error is not raised when processing iterable dl
                  raise AssertionError

      assert batch_dl_overridden_value == overridden_even_batches
      assert batch_dl.batch_sampler.even_batches == default_even_batches





def 	_UpperCAmelCase							(  ):
      """simple docstring"""




      __lowerCamelCase  :   Tuple							    =    create_accelerator()
      __lowerCamelCase  :   List[str]							    =    torch.nn.Linear(1    ,						1   )
      __lowerCamelCase  :   Optional[int]							    =    accelerator.prepare(__UpperCAmelCase   )
      create_dataloader(__UpperCAmelCase    ,						dataset_size=3    ,						batch_size=1    ,						iterable=__UpperCAmelCase   )

      with warnings.catch_warnings(record=__UpperCAmelCase   ) as w:
            with accelerator.join_uneven_inputs([ddp_model]    ,						even_batches=__UpperCAmelCase   ):
                  pass

            assert issubclass(w[-1].category    ,						__UpperCAmelCase   )
            assert "only supported for map-style datasets" in str(w[-1].message   )





def 	_UpperCAmelCase							(  ):
      """simple docstring"""




      __lowerCamelCase  :   List[Any]							    =    create_accelerator()

      accelerator.print("""Test that even_batches variable ensures uniform batches across processes"""   )
      test_default_ensures_even_batch_sizes()

      accelerator.print("""Run tests with even_batches disabled"""   )
      test_can_disable_even_batches()

      accelerator.print("""Test joining uneven inputs"""   )
      test_can_join_uneven_inputs()

      accelerator.print("""Test overriding even_batches when joining uneven inputs"""   )
      test_join_can_override_even_batches()

      accelerator.print("""Test overriding even_batches for mixed dataloader types"""   )
      test_join_can_override_for_mixed_type_dataloaders()

      accelerator.print("""Test overriding even_batches raises a warning for iterable dataloaders"""   )
      test_join_raises_warning_for_iterable_when_overriding_even_batches()

      accelerator.print("""Test join with non DDP distributed raises warning"""   )
      __lowerCamelCase  :   Tuple							    =    accelerator.state.distributed_type
      __lowerCamelCase  :   Optional[Any]							    =    DistributedType.FSDP
      test_join_raises_warning_for_non_ddp_distributed(__UpperCAmelCase   )
      __lowerCamelCase  :   Optional[Any]							    =    original_state


if __name__ == "__main__":
    main()








from __future__ import annotations

from typing import Any
class    __UpperCamelCase			:

 """simple docstring"""



 def __init__(	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE = 0      ) ->			None:
  a__							,			a__												= row, column
  a__												= [[default_value for c in range(SCREAMING_SNAKE_CASE      )] for r in range(SCREAMING_SNAKE_CASE      )]

 def __str__(	self      ) ->			str:
  a__												= f"Matrix consist of {self.row} rows and {self.column} columns\n"

  # Make string identifier
  a__												= 0
  for row_vector in self.array:
   for obj in row_vector:
    a__												= max(SCREAMING_SNAKE_CASE		,				len(str(SCREAMING_SNAKE_CASE      )      )      )
  a__												= f"%{max_element_length}s"

  # Make string and return
  def single_line(SCREAMING_SNAKE_CASE      ) -> str:
   nonlocal string_format_identifier
   a__												= '''['''
   line += ", ".join(string_format_identifier % (obj,) for obj in row_vector      )
   line += "]"
   return line

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

 def __repr__(	self      ) ->			str:
  return str(self      )

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

 def __getitem__(	self		,				SCREAMING_SNAKE_CASE      ) ->			Any:
  assert self.validate_indicies(SCREAMING_SNAKE_CASE      )
  return self.array[loc[0]][loc[1]]

 def __setitem__(	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ) ->			None:
  assert self.validate_indicies(SCREAMING_SNAKE_CASE      )
  a__												= value

 def __add__(	self		,				SCREAMING_SNAKE_CASE      ) ->			Matrix:
  assert isinstance(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )
  assert self.row == another.row and self.column == another.column

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

 def __neg__(	self      ) ->			Matrix:
  a__												= Matrix(self.row		,				self.column      )
  for r in range(self.row      ):
   for c in range(self.column      ):
    a__												= -self[r, c]
  return result

 def __sub__(	self		,				SCREAMING_SNAKE_CASE      ) ->			Matrix:
  return self + (-another)

 def __mul__(	self		,				SCREAMING_SNAKE_CASE      ) ->			Matrix:

  if isinstance(SCREAMING_SNAKE_CASE		,				(int, float)      ):  # Scalar multiplication
   a__												= Matrix(self.row		,				self.column      )
   for r in range(self.row      ):
    for c in range(self.column      ):
     a__												= self[r, c] * another
   return result
  elif isinstance(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ):  # Matrix multiplication
   assert self.column == another.row
   a__												= 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:
   a__												= f"Unsupported type given for another ({type(SCREAMING_SNAKE_CASE      )})"
   raise TypeError(SCREAMING_SNAKE_CASE      )

 def 						_UpperCAmelCase       (	self      ) ->			Matrix:
  a__												= Matrix(self.column		,				self.row      )
  for r in range(self.row      ):
   for c in range(self.column      ):
    a__												= self[r, c]
  return result



 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ) ->			Any:
  assert isinstance(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ) and isinstance(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )
  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
  a__												= v.transpose()
  a__												= (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     __a					(		):
      # a^(-1)
      a__												= Matrix(3							,  3							,  0       )
      for i in range(3       ):
       a__												= 1
      print(f"a^(-1) is {ainv}"       )
      # u, v
      a__												= Matrix(3							,  1							,  0       )
      a__							,			a__							,			a__												= 1, 2, -3
      a__												= Matrix(3							,  1							,  0       )
      a__							,			a__							,			a__												= 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     __a					(		):
      import doctest

      doctest.testmod()

     testa()





