

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

__A :  Any			  =      logging.get_logger(__name__)  # pylint: disable=invalid-name
def 		SCREAMING_SNAKE_CASE__					(				_UpperCAmelCase					)       ->			List[Any]:



    '''simple docstring'''






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






class  __A					(				_UpperCamelCase			):






    @staticmethod
    def 						lowercase__			(						UpperCAmelCase_      :     ArgumentParser     ):
        lowerCAmelCase					:      Dict           =   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=__a     ,					help='Configuration file on which to run.'     )
        add_new_model_parser.add_argument(
            '--path'     ,					type=__a     ,					help='Path to cookiecutter. Should only be used for testing purposes.'     )
        add_new_model_parser.set_defaults(func=__a     )






    def __init__(						self      :     Tuple     ,					UpperCAmelCase_      :     bool     ,					UpperCAmelCase_      :     str     ,					UpperCAmelCase_      :     Optional[Any]=None     ,					*UpperCAmelCase_      :     List[Any]     ):
        lowerCAmelCase					:      List[str]           =   testing
        lowerCAmelCase					:      List[str]           =   testing_file
        lowerCAmelCase					:      Optional[Any]           =   path






    def 						lowercase__			(						self      :     Dict     ):
        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(__a     ) > 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					:      int           =   (
            Path(__a     ).parent.parent.parent.parent if self._path is None else Path(self._path     ).parent.parent
        )
        lowerCAmelCase					:      Optional[Any]           =   path_to_transformer_root / "templates" / "adding_a_new_model"

        # Execute cookiecutter
        if not self._testing:
            cookiecutter(str(__a     )     )
        else:
            with open(self._testing_file     ,					'r'     ) as configuration_file:
                lowerCAmelCase					:      List[Any]           =   json.load(__a     )

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

        lowerCAmelCase					:      List[Any]           =   [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					:      List[Any]           =   json.load(__a     )

        lowerCAmelCase					:      str           =   configuration["lowercase_modelname"]
        lowerCAmelCase					:      List[Any]           =   configuration["generate_tensorflow_pytorch_and_flax"]
        os.remove(f"{directory}/configuration.json"     )

        lowerCAmelCase					:      Tuple           =   "PyTorch" in generate_tensorflow_pytorch_and_flax
        lowerCAmelCase					:      Union[str, Any]           =   "TensorFlow" in generate_tensorflow_pytorch_and_flax
        lowerCAmelCase					:      List[Any]           =   "Flax" in generate_tensorflow_pytorch_and_flax

        lowerCAmelCase					:      int           =   f"{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}"
        os.makedirs(__a     ,					exist_ok=__a     )
        os.makedirs(f"{path_to_transformer_root}/tests/models/{lowercase_model_name}"     ,					exist_ok=__a     )

        # 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(UpperCAmelCase_      :     Union[str, Any]     ):
            with open(__a     ,					'r'     ) as f:
                lowerCAmelCase					:      int           =   f.readlines()
            with open(__a     ,					'w'     ) as f:
                for line in lines:
                    if "# Copied from transformers." not in line:
                        f.write(__a     )

        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(UpperCAmelCase_      :     str     ,					UpperCAmelCase_      :     str     ,					UpperCAmelCase_      :     List[str]     ):
            # Create temp file
            lowerCAmelCase					:      Optional[Any]           =   mkstemp()
            lowerCAmelCase					:      Tuple           =   False
            with fdopen(__a     ,					'w'     ) as new_file:
                with open(__a     ) as old_file:
                    for line in old_file:
                        new_file.write(__a     )
                        if line_to_copy_below in line:
                            lowerCAmelCase					:      int           =   True
                            for line_to_copy in lines_to_copy:
                                new_file.write(__a     )

            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(__a     ,					__a     )
            # Remove original file
            remove(__a     )
            # Move new file
            move(__a     ,					__a     )

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

                        lowerCAmelCase					:      Any           =   []
                    elif "# Replace with" in line and "##" not in line:
                        lowerCAmelCase					:      Optional[Any]           =   []
                    elif "##" not in line:
                        lines_to_copy.append(__a     )

            remove(__a     )

        replace_in_files(f"{directory}/to_replace_{lowercase_model_name}.py"     )
        os.rmdir(__a     )












"""simple docstring"""


import numpy as np




def        lowercase__     (       lowercase_		,lowercase_		,lowercase_ = 1e-12		,lowercase_ = 100		,)       ->		tuple[float, np.ndarray]:




      """simple docstring"""

      assert np.shape(lowercase_      )[0] == np.shape(lowercase_      )[1]
      # Ensure proper dimensionality.
      assert np.shape(lowercase_      )[0] == np.shape(lowercase_      )[0]
      # Ensure inputs are either both complex or both real
      assert np.iscomplexobj(lowercase_      ) == np.iscomplexobj(lowercase_      )
      _UpperCamelCase   :						Dict    =							np.iscomplexobj(lowercase_      )
      if is_complex:
            # Ensure complex input_matrix is Hermitian
            assert np.array_equal(lowercase_		,input_matrix.conj().T      )

      # Set convergence to False. Will define convergence when we exceed max_iterations
      # or when we have small changes from one iteration to next.

      _UpperCamelCase   :						List[Any]    =							False
      _UpperCamelCase   :						Optional[int]    =							0
      _UpperCamelCase   :						int    =							0
      _UpperCamelCase   :						Optional[int]    =							1e12

      while not convergence:
            # Multiple matrix by the vector.
            _UpperCamelCase   :						Optional[int]    =							np.dot(lowercase_		,lowercase_      )
            # Normalize the resulting output vector.
            _UpperCamelCase   :						List[Any]    =							w / np.linalg.norm(lowercase_      )
            # Find rayleigh quotient
            # (faster than usual b/c we know vector is normalized already)
            _UpperCamelCase   :						Optional[Any]    =							vector.conj().T if is_complex else vector.T
            _UpperCamelCase   :						Dict    =							np.dot(lowercase_		,np.dot(lowercase_		,lowercase_      )      )

            # Check convergence.
            _UpperCamelCase   :						Optional[int]    =							np.abs(lambda_ - lambda_previous      ) / lambda_
            iterations += 1

            if error <= error_tol or iterations >= max_iterations:
                  _UpperCamelCase   :						List[Any]    =							True

            _UpperCamelCase   :						Tuple    =							lambda_

      if is_complex:
            _UpperCamelCase   :						Tuple    =							np.real(lambda_      )

      return lambda_, vector




def        lowercase__     (       )       ->		None:




      """simple docstring"""

      _UpperCamelCase   :						Dict    =							np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]      )
      _UpperCamelCase   :						Optional[int]    =							np.array([41, 4, 20]      )
      _UpperCamelCase   :						Union[str, Any]    =							real_input_matrix.astype(np.complexaaa      )
      _UpperCamelCase   :						int    =							np.triu(1J * complex_input_matrix		,1      )
      complex_input_matrix += imag_matrix
      complex_input_matrix += -1 * imag_matrix.T
      _UpperCamelCase   :						Union[str, Any]    =							np.array([41, 4, 20]      ).astype(np.complexaaa      )

      for problem_type in ["real", "complex"]:
            if problem_type == "real":
                  _UpperCamelCase   :						Dict    =							real_input_matrix
                  _UpperCamelCase   :						Any    =							real_vector
            elif problem_type == "complex":
                  _UpperCamelCase   :						int    =							complex_input_matrix
                  _UpperCamelCase   :						Union[str, Any]    =							complex_vector

            # Our implementation.
            _UpperCamelCase,	_UpperCamelCase   :						int    =							power_iteration(lowercase_		,lowercase_      )

            # Numpy implementation.

            # Get eigenvalues and eigenvectors using built-in numpy
            # eigh (eigh used for symmetric or hermetian matrices).
            _UpperCamelCase,	_UpperCamelCase   :						str    =							np.linalg.eigh(lowercase_      )
            # Last eigenvalue is the maximum one.
            _UpperCamelCase   :						Any    =							eigen_values[-1]
            # Last column in this matrix is eigenvector corresponding to largest eigenvalue.
            _UpperCamelCase   :						Tuple    =							eigen_vectors[:, -1]

            # Check our implementation and numpy gives close answers.
            assert np.abs(eigen_value - eigen_value_max      ) <= 1e-6
            # Take absolute values element wise of each eigenvector.
            # as they are only unique to a minus sign.
            assert np.linalg.norm(np.abs(lowercase_      ) - np.abs(lowercase_      )      ) <= 1e-6


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  test_power_iteration()
