


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

   UpperCAmelCase_				:			Union[str, Any]   =	True
except ImportError:
   UpperCAmelCase_				:			Tuple   =	False

UpperCAmelCase_				:			Optional[int]   =	logging.get_logger(__name__)  # pylint: disable=invalid-name







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Namespace    )     ->		Tuple:

       """simple docstring"""

       return AddNewModelCommand(args.testing  ,   args.testing_file  ,   path=args.path    )
class 			SCREAMING_SNAKE_CASE__  (    lowercase__      ):
      @staticmethod
      def 	SCREAMING_SNAKE_CASE							(	SCREAMING_SNAKE_CASE__    :	ArgumentParser					)		->      List[str]:
             a_ :		Tuple							=  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=SCREAMING_SNAKE_CASE__					,							help='Configuration file on which to run.'					)
             add_new_model_parser.add_argument(
                 '--path'					,							type=SCREAMING_SNAKE_CASE__					,							help='Path to cookiecutter. Should only be used for testing purposes.'					)
             add_new_model_parser.set_defaults(func=SCREAMING_SNAKE_CASE__					)
      def __init__(	self    :	Tuple					,							SCREAMING_SNAKE_CASE__    :	bool					,							SCREAMING_SNAKE_CASE__    :	str					,							SCREAMING_SNAKE_CASE__    :	int=None					,							*SCREAMING_SNAKE_CASE__    :	str					)		->      Tuple:
             a_ :		Dict							=  testing
             a_ :		Optional[Any]							=  testing_file
             a_ :		List[str]							=  path

      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					)		->      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
             a_ :		List[str]							=  [directory for directory in os.listdir() if 'cookiecutter-template-' == directory[:2_2]]
             if len(SCREAMING_SNAKE_CASE__					) > 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.'					)

             a_ :		List[str]							=  (
                 Path(SCREAMING_SNAKE_CASE__					).parent.parent.parent.parent if self._path is None else Path(self._path					).parent.parent
             )
             a_ :		Dict							=  path_to_transformer_root / 'templates' / 'adding_a_new_model'

             # Execute cookiecutter
             if not self._testing:
                    cookiecutter(str(SCREAMING_SNAKE_CASE__					)					)
             else:
                    with open(self._testing_file					,							'r'					) as configuration_file:
                           a_ :		int							=  json.load(SCREAMING_SNAKE_CASE__					)

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

             a_ :		str							=  [directory for directory in os.listdir() if 'cookiecutter-template-' in directory[:2_2]][0]

             # Retrieve configuration
             with open(directory + '/configuration.json'					,							'r'					) as configuration_file:
                    a_ :		List[Any]							=  json.load(SCREAMING_SNAKE_CASE__					)

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

             a_ :		int							=  'PyTorch' in generate_tensorflow_pytorch_and_flax
             a_ :		Optional[Any]							=  'TensorFlow' in generate_tensorflow_pytorch_and_flax
             a_ :		Tuple							=  'Flax' in generate_tensorflow_pytorch_and_flax

             a_ :		Dict							=  F"""{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}"""
             os.makedirs(SCREAMING_SNAKE_CASE__					,							exist_ok=SCREAMING_SNAKE_CASE__					)
             os.makedirs(F"""{path_to_transformer_root}/tests/models/{lowercase_model_name}"""					,							exist_ok=SCREAMING_SNAKE_CASE__					)

             # 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(SCREAMING_SNAKE_CASE__    :	str					):
                    with open(SCREAMING_SNAKE_CASE__					,							'r'					) as f:
                           a_ :		Optional[int]							=  f.readlines()
                    with open(SCREAMING_SNAKE_CASE__					,							'w'					) as f:
                           for line in lines:
                                  if "# Copied from transformers." not in line:
                                         f.write(SCREAMING_SNAKE_CASE__					)

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

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

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

                                         a_ :		Optional[int]							=  []
                                  elif "# Replace with" in line and "##" not in line:
                                         a_ :		Tuple							=  []
                                  elif "##" not in line:
                                         lines_to_copy.append(SCREAMING_SNAKE_CASE__					)

                    remove(SCREAMING_SNAKE_CASE__					)

             replace_in_files(F"""{directory}/to_replace_{lowercase_model_name}.py"""					)
             os.rmdir(SCREAMING_SNAKE_CASE__					)






# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class 			SCREAMING_SNAKE_CASE__  (    lowercase__						,   lowercase__      ):
      snake_case__    :					Dict					       =						1
      @register_to_config
      def __init__(	self    :	Optional[int]					,							SCREAMING_SNAKE_CASE__    :	Tuple=2_0_0_0					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]=0.1					,							SCREAMING_SNAKE_CASE__    :	Optional[int]=2_0					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]=1E-3					)		->      Optional[int]:
             a_ :		Tuple							=  None
             a_ :		int							=  None
             a_ :		Tuple							=  None
      def 	SCREAMING_SNAKE_CASE							(	self    :	str					,							SCREAMING_SNAKE_CASE__    :	Any					,							SCREAMING_SNAKE_CASE__    :	Union[str, torch.device] = None					)		->      List[str]:
             a_ :		Tuple							=  torch.linspace(1					,							self.config.sampling_eps					,							SCREAMING_SNAKE_CASE__					,							device=SCREAMING_SNAKE_CASE__					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Dict					,							SCREAMING_SNAKE_CASE__    :	List[Any]					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]					,							SCREAMING_SNAKE_CASE__    :	Any					,							SCREAMING_SNAKE_CASE__    :	Tuple=None					)		->      Tuple:
             if self.timesteps is None:
                    raise ValueError(
                        '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'					)

             # TODO(Patrick) better comments + non-PyTorch
             # postprocess model score
             a_ :		Tuple							=  (
                 -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
             )
             a_ :		int							=  torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff					)					)
             a_ :		Dict							=  std.flatten()
             while len(std.shape					) < len(score.shape					):
                    a_ :		str							=  std.unsqueeze(-1					)
             a_ :		List[str]							=  -score / std

             # compute
             a_ :		List[str]							=  -1.0 / len(self.timesteps					)

             a_ :		Optional[int]							=  self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
             a_ :		Optional[Any]							=  beta_t.flatten()
             while len(beta_t.shape					) < len(x.shape					):
                    a_ :		List[str]							=  beta_t.unsqueeze(-1					)
             a_ :		Optional[Any]							=  -0.5 * beta_t * x

             a_ :		Tuple							=  torch.sqrt(SCREAMING_SNAKE_CASE__					)
             a_ :		List[Any]							=  drift - diffusion**2 * score
             a_ :		List[str]							=  x + drift * dt

             # add noise
             a_ :		Optional[Any]							=  randn_tensor(x.shape					,							layout=x.layout					,							generator=SCREAMING_SNAKE_CASE__					,							device=x.device					,							dtype=x.dtype					)
             a_ :		Optional[Any]							=  x_mean + diffusion * math.sqrt(-dt					) * noise

             return x, x_mean

      def __len__(	self    :	int					)		->      Tuple:
             return self.config.num_train_timesteps


