





# 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    lowerCAmelCase							(					__UpperCamelCase			):
 def __init__(      self  :      Optional[Any]			,     UpperCAmelCase  :      int	)			->	int:
     lowerCamelCase__    :      Tuple					 =		data







 def __iter__(      self  :      Dict	)			->	List[str]:
     for element in self.data:
         yield element


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase=True )			->     int:
    lowerCamelCase__    :      Optional[int]					 =		Accelerator(even_batches=_UpperCAmelCase )
    assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
    return accelerator


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase = False )			->     List[str]:
    if iterable:
        lowerCamelCase__    :      Optional[Any]					 =		DummyIterableDataset(torch.as_tensor(range(_UpperCAmelCase ) ) )
    else:
        lowerCamelCase__    :      Tuple					 =		TensorDataset(torch.as_tensor(range(_UpperCAmelCase ) ) )

    lowerCamelCase__    :      str					 =		DataLoader(_UpperCAmelCase						,	batch_size=_UpperCAmelCase )
    lowerCamelCase__    :      str					 =		accelerator.prepare(_UpperCAmelCase )

    return dl


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	)			->     str:
    lowerCamelCase__    :      Any					 =		create_dataloader(accelerator=_UpperCAmelCase						,	dataset_size=_UpperCAmelCase						,	batch_size=_UpperCAmelCase )

    lowerCamelCase__    :      Any					 =		[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        SCREAMING_SNAKE_CASE      (       )			->     Optional[Any]:
    lowerCamelCase__    :      List[str]					 =		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        SCREAMING_SNAKE_CASE      (       )			->     Optional[Any]:
    lowerCamelCase__    :      Tuple					 =		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        SCREAMING_SNAKE_CASE      (       )			->     Union[str, Any]:
    lowerCamelCase__    :      Tuple					 =		create_accelerator(even_batches=_UpperCAmelCase )

    lowerCamelCase__    :      Any					 =		torch.nn.Linear(1						,	1 )
    lowerCamelCase__    :      List[Any]					 =		accelerator.prepare(_UpperCAmelCase )

    lowerCamelCase__    :      Union[str, Any]					 =		create_dataloader(_UpperCAmelCase						,	dataset_size=3						,	batch_size=1 )

    lowerCamelCase__    :      List[str]					 =		[]
    with accelerator.join_uneven_inputs([ddp_model] ):
        for batch_idx, batch in enumerate(_UpperCAmelCase ):
            lowerCamelCase__    :      Optional[int]					 =		ddp_model(batch[0].float() )
            lowerCamelCase__    :      int					 =		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        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     str:
    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        SCREAMING_SNAKE_CASE      (       )			->     Optional[Any]:
    lowerCamelCase__    :      List[Any]					 =		True
    lowerCamelCase__    :      Tuple					 =		False
    lowerCamelCase__    :      List[str]					 =		create_accelerator(even_batches=_UpperCAmelCase )
    lowerCamelCase__    :      Optional[Any]					 =		torch.nn.Linear(1						,	1 )
    lowerCamelCase__    :      List[Any]					 =		accelerator.prepare(_UpperCAmelCase )
    lowerCamelCase__    :      Union[str, Any]					 =		create_dataloader(_UpperCAmelCase						,	dataset_size=3						,	batch_size=1 )
    lowerCamelCase__    :      List[Any]					 =		create_dataloader(_UpperCAmelCase						,	dataset_size=3						,	batch_size=1 )

    with accelerator.join_uneven_inputs([ddp_model]						,	even_batches=_UpperCAmelCase ):
        lowerCamelCase__    :      Optional[int]					 =		train_dl.batch_sampler.even_batches
        lowerCamelCase__    :      Union[str, Any]					 =		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        SCREAMING_SNAKE_CASE      (       )			->     List[Any]:
    lowerCamelCase__    :      List[Any]					 =		True
    lowerCamelCase__    :      Optional[int]					 =		False
    lowerCamelCase__    :      Any					 =		create_accelerator(even_batches=_UpperCAmelCase )
    lowerCamelCase__    :      Tuple					 =		torch.nn.Linear(1						,	1 )
    lowerCamelCase__    :      Tuple					 =		accelerator.prepare(_UpperCAmelCase )
    create_dataloader(_UpperCAmelCase						,	dataset_size=3						,	batch_size=1						,	iterable=_UpperCAmelCase )
    lowerCamelCase__    :      int					 =		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__    :      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        SCREAMING_SNAKE_CASE      (       )			->     Dict:
    lowerCamelCase__    :      int					 =		create_accelerator()
    lowerCamelCase__    :      str					 =		torch.nn.Linear(1						,	1 )
    lowerCamelCase__    :      Any					 =		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        SCREAMING_SNAKE_CASE      (       )			->     List[Any]:
    lowerCamelCase__    :      str					 =		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__    :      Dict					 =		accelerator.state.distributed_type
    lowerCamelCase__    :      List[Any]					 =		DistributedType.FSDP
    test_join_raises_warning_for_non_ddp_distributed(_UpperCAmelCase )
    lowerCamelCase__    :      Dict					 =		original_state


if __name__ == "__main__":
    main()









'''simple docstring'''






import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List

from ..utils import logging
from . import BaseTransformersCLICommand


try:
  from cookiecutter.main import cookiecutter

  __A					:  List[Any]											=      True
except ImportError:
  __A					:  int											=      False

__A					:  str											=      logging.get_logger(__name__)  # pylint: disable=invalid-name







def 				UpperCamelCase_			(    A__					:   Namespace					):
  '''simple docstring'''





  return AddNewModelCommand(args.testing							,    args.testing_file							,    path=args.path					)






class    __snake_case       (   _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    @staticmethod
    def __lowercase   (   lowerCamelCase    :  ArgumentParser		)   ->  int:
      lowerCAmelCase_			:      Optional[int]			=   parser.add_parser("""add-new-model"""		)
      add_new_model_parser.add_argument("""--testing"""    ,						action="""store_true"""    ,						help="""If in testing mode."""		)
      add_new_model_parser.add_argument("""--testing_file"""    ,						type=lowerCamelCase    ,						help="""Configuration file on which to run."""		)
      add_new_model_parser.add_argument(
          """--path"""    ,						type=lowerCamelCase    ,						help="""Path to cookiecutter. Should only be used for testing purposes."""		)
      add_new_model_parser.set_defaults(func=lowerCamelCase		)

    def __init__(   self    :  List[str]    ,						lowerCamelCase    :  bool    ,						lowerCamelCase    :  str    ,						lowerCamelCase    :  Any=None    ,						*lowerCamelCase    :  List[str]		)   ->  Optional[Any]:
      lowerCAmelCase_			:      int			=   testing
      lowerCAmelCase_			:      Union[str, Any]			=   testing_file
      lowerCAmelCase_			:      Tuple			=   path





    def __lowercase   (   self    :  Tuple		)   ->  int:
      warnings.warn(
          """The command `transformers-cli add-new-model` is deprecated and will be removed in v5 of Transformers. """
          """It is not actively maintained anymore, so might give a result that won't pass all tests and quality """
          """checks, you should use `transformers-cli add-new-model-like` instead."""		)
      if not _has_cookiecutter:
        raise ImportError(
            """Model creation dependencies are required to use the `add_new_model` command. Install them by running """
            """the following at the root of your `transformers` clone:\n\n\t$ pip install -e .[modelcreation]\n"""		)
      # Ensure that there is no other `cookiecutter-template-xxx` directory in the current working directory
      lowerCAmelCase_			:      int			=   [directory for directory in os.listdir() if """cookiecutter-template-""" == directory[:22]]
      if len(lowerCamelCase		) > 0:
        raise ValueError(
            """Several directories starting with `cookiecutter-template-` in current working directory. """
            """Please clean your directory by removing all folders starting with `cookiecutter-template-` or """
            """change your working directory."""		)

      lowerCAmelCase_			:      List[Any]			=   (
          Path(lowerCamelCase		).parent.parent.parent.parent if self._path is None else Path(self._path		).parent.parent
      )
      lowerCAmelCase_			:      Dict			=   path_to_transformer_root / """templates""" / """adding_a_new_model"""

      # Execute cookiecutter
      if not self._testing:
        cookiecutter(str(lowerCamelCase		)		)
      else:
        with open(self._testing_file    ,						"""r"""		) as configuration_file:
          lowerCAmelCase_			:      Tuple			=   json.load(lowerCamelCase		)

        cookiecutter(
            str(path_to_cookiecutter if self._path is None else self._path		)    ,						no_input=lowerCamelCase    ,						extra_context=lowerCamelCase    ,						)

      lowerCAmelCase_			:      List[str]			=   [directory for directory in os.listdir() if """cookiecutter-template-""" in directory[:22]][0]

      # Retrieve configuration
      with open(directory + """/configuration.json"""    ,						"""r"""		) as configuration_file:
        lowerCAmelCase_			:      Tuple			=   json.load(lowerCamelCase		)

      lowerCAmelCase_			:      str			=   configuration["""lowercase_modelname"""]
      lowerCAmelCase_			:      List[str]			=   configuration["""generate_tensorflow_pytorch_and_flax"""]
      os.remove(F'{directory}/configuration.json'		)

      lowerCAmelCase_			:      Dict			=   """PyTorch""" in generate_tensorflow_pytorch_and_flax
      lowerCAmelCase_			:      Optional[int]			=   """TensorFlow""" in generate_tensorflow_pytorch_and_flax
      lowerCAmelCase_			:      List[str]			=   """Flax""" in generate_tensorflow_pytorch_and_flax

      lowerCAmelCase_			:      Union[str, Any]			=   F'{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}'
      os.makedirs(lowerCamelCase    ,						exist_ok=lowerCamelCase		)
      os.makedirs(F'{path_to_transformer_root}/tests/models/{lowercase_model_name}'    ,						exist_ok=lowerCamelCase		)

      # Tests require submodules as they have parent imports
      with open(F'{path_to_transformer_root}/tests/models/{lowercase_model_name}/__init__.py'    ,						"""w"""		):
        pass

      shutil.move(
          F'{directory}/__init__.py'    ,						F'{model_dir}/__init__.py'    ,						)
      shutil.move(
          F'{directory}/configuration_{lowercase_model_name}.py'    ,						F'{model_dir}/configuration_{lowercase_model_name}.py'    ,						)

      def remove_copy_lines(lowerCamelCase    :  Any		):
        with open(lowerCamelCase    ,						"""r"""		) as f:
          lowerCAmelCase_			:      List[str]			=   f.readlines()
        with open(lowerCamelCase    ,						"""w"""		) as f:
          for line in lines:
            if "# Copied from transformers." not in line:
              f.write(lowerCamelCase		)

      if output_pytorch:
        if not self._testing:
          remove_copy_lines(F'{directory}/modeling_{lowercase_model_name}.py'		)

        shutil.move(
            F'{directory}/modeling_{lowercase_model_name}.py'    ,						F'{model_dir}/modeling_{lowercase_model_name}.py'    ,						)

        shutil.move(
            F'{directory}/test_modeling_{lowercase_model_name}.py'    ,						F'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_{lowercase_model_name}.py'    ,						)
      else:
        os.remove(F'{directory}/modeling_{lowercase_model_name}.py'		)
        os.remove(F'{directory}/test_modeling_{lowercase_model_name}.py'		)

      if output_tensorflow:
        if not self._testing:
          remove_copy_lines(F'{directory}/modeling_tf_{lowercase_model_name}.py'		)

        shutil.move(
            F'{directory}/modeling_tf_{lowercase_model_name}.py'    ,						F'{model_dir}/modeling_tf_{lowercase_model_name}.py'    ,						)

        shutil.move(
            F'{directory}/test_modeling_tf_{lowercase_model_name}.py'    ,						F'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_tf_{lowercase_model_name}.py'    ,						)
      else:
        os.remove(F'{directory}/modeling_tf_{lowercase_model_name}.py'		)
        os.remove(F'{directory}/test_modeling_tf_{lowercase_model_name}.py'		)

      if output_flax:
        if not self._testing:
          remove_copy_lines(F'{directory}/modeling_flax_{lowercase_model_name}.py'		)

        shutil.move(
            F'{directory}/modeling_flax_{lowercase_model_name}.py'    ,						F'{model_dir}/modeling_flax_{lowercase_model_name}.py'    ,						)

        shutil.move(
            F'{directory}/test_modeling_flax_{lowercase_model_name}.py'    ,						F'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_flax_{lowercase_model_name}.py'    ,						)
      else:
        os.remove(F'{directory}/modeling_flax_{lowercase_model_name}.py'		)
        os.remove(F'{directory}/test_modeling_flax_{lowercase_model_name}.py'		)

      shutil.move(
          F'{directory}/{lowercase_model_name}.md'    ,						F'{path_to_transformer_root}/docs/source/en/model_doc/{lowercase_model_name}.md'    ,						)

      shutil.move(
          F'{directory}/tokenization_{lowercase_model_name}.py'    ,						F'{model_dir}/tokenization_{lowercase_model_name}.py'    ,						)

      shutil.move(
          F'{directory}/tokenization_fast_{lowercase_model_name}.py'    ,						F'{model_dir}/tokenization_{lowercase_model_name}_fast.py'    ,						)

      from os import fdopen, remove
      from shutil import copymode, move
      from tempfile import mkstemp

      def replace(lowerCamelCase    :  str    ,						lowerCamelCase    :  str    ,						lowerCamelCase    :  List[str]		):
        # Create temp file
        lowerCAmelCase_,							lowerCAmelCase_			:      int			=   mkstemp()
        lowerCAmelCase_			:      List[Any]			=   False
        with fdopen(lowerCamelCase    ,						"""w"""		) as new_file:
          with open(lowerCamelCase		) as old_file:
            for line in old_file:
              new_file.write(lowerCamelCase		)
              if line_to_copy_below in line:
                lowerCAmelCase_			:      List[str]			=   True
                for line_to_copy in lines_to_copy:
                  new_file.write(lowerCamelCase		)

        if not line_found:
          raise ValueError(F'Line {line_to_copy_below} was not found in file.'		)

        # Copy the file permissions from the old file to the new file
        copymode(lowerCamelCase    ,						lowerCamelCase		)
        # Remove original file
        remove(lowerCamelCase		)
        # Move new file
        move(lowerCamelCase    ,						lowerCamelCase		)

      def skip_units(lowerCamelCase    :  Optional[int]		):
        return (
            ("generating PyTorch" in line and not output_pytorch)
            or ("generating TensorFlow" in line and not output_tensorflow)
            or ("generating Flax" in line and not output_flax)
        )

      def replace_in_files(lowerCamelCase    :  Any		):
        with open(lowerCamelCase		) as datafile:
          lowerCAmelCase_			:      Dict			=   []
          lowerCAmelCase_			:      List[str]			=   False
          lowerCAmelCase_			:      str			=   False
          for line in datafile:
            if "# To replace in: " in line and "##" not in line:
              lowerCAmelCase_			:      Dict			=   line.split("""\""""		)[1]
              lowerCAmelCase_			:      int			=   skip_units(lowerCamelCase		)
            elif "# Below: " in line and "##" not in line:
              lowerCAmelCase_			:      Any			=   line.split("""\""""		)[1]
              lowerCAmelCase_			:      Tuple			=   skip_units(lowerCamelCase		)
            elif "# End." in line and "##" not in line:
              if not skip_file and not skip_snippet:
                replace(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase		)

              lowerCAmelCase_			:      Dict			=   []
            elif "# Replace with" in line and "##" not in line:
              lowerCAmelCase_			:      int			=   []
            elif "##" not in line:
              lines_to_copy.append(lowerCamelCase		)

        remove(lowerCamelCase		)

      replace_in_files(F'{directory}/to_replace_{lowercase_model_name}.py'		)
      os.rmdir(lowerCamelCase		)



