

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

       snake_case_        =			True
except ImportError:
       snake_case_        =			False

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

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Namespace      ):



     '''simple docstring'''





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


class 				SCREAMING_SNAKE_CASE__ (__snake_case					):





    @staticmethod
    def 				snake_case_    (				a):
         lowercase__   :       Union[str, Any]				=	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						,       a						,       a						,       a=None						,       *a):
         lowercase__   :       Any				=	testing
         lowercase__   :       str				=	testing_file
         lowercase__   :       Optional[Any]				=	path





    def 				snake_case_    (				self):
         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
         lowercase__   :       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.')

         lowercase__   :       Tuple				=	(
             Path(a).parent.parent.parent.parent if self._path is None else Path(self._path).parent.parent
         )
         lowercase__   :       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:
                   lowercase__   :       str				=	json.load(a)

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

         lowercase__   :       Dict				=	[directory for directory in os.listdir() if 'cookiecutter-template-' in directory[:22]][0]

         # Retrieve configuration
         with open(directory + '/configuration.json'						,       'r') as configuration_file:
              lowercase__   :       Dict				=	json.load(a)

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

         lowercase__   :       List[Any]				=	'PyTorch' in generate_tensorflow_pytorch_and_flax
         lowercase__   :       Any				=	'TensorFlow' in generate_tensorflow_pytorch_and_flax
         lowercase__   :       List[str]				=	'Flax' in generate_tensorflow_pytorch_and_flax

         lowercase__   :       Optional[Any]				=	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(a):
              with open(a						,       'r') as f:
                   lowercase__   :       Optional[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(a						,       a						,       a):
              # Create temp file
              lowercase__				,			lowercase__   :       Union[str, Any]				=	mkstemp()
              lowercase__   :       str				=	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:
                                  lowercase__   :       List[Any]				=	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(a):
              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(a):
              with open(a) as datafile:
                   lowercase__   :       int				=	[]
                   lowercase__   :       Any				=	False
                   lowercase__   :       Dict				=	False
                   for line in datafile:
                        if "# To replace in: " in line and "##" not in line:
                             lowercase__   :       Optional[Any]				=	line.split('"')[1]
                             lowercase__   :       List[str]				=	skip_units(a)
                        elif "# Below: " in line and "##" not in line:
                             lowercase__   :       Union[str, Any]				=	line.split('"')[1]
                             lowercase__   :       int				=	skip_units(a)
                        elif "# End." in line and "##" not in line:
                             if not skip_file and not skip_snippet:
                                  replace(a						,       a						,       a)

                             lowercase__   :       int				=	[]
                        elif "# Replace with" in line and "##" not in line:
                             lowercase__   :       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)




import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


snake_case_        =			logging.get_logger(__name__)

snake_case_        =			{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case_        =			{
    '''vocab_file''': {
        '''junnyu/roformer_chinese_small''': '''https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt''',
        '''junnyu/roformer_chinese_base''': '''https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt''',
        '''junnyu/roformer_chinese_char_small''': (
            '''https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt'''
        ),
        '''junnyu/roformer_chinese_char_base''': (
            '''https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt'''
        ),
        '''junnyu/roformer_small_discriminator''': (
            '''https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt'''
        ),
        '''junnyu/roformer_small_generator''': (
            '''https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt'''
        ),
    }
}

snake_case_        =			{
    '''junnyu/roformer_chinese_small''': 1_536,
    '''junnyu/roformer_chinese_base''': 1_536,
    '''junnyu/roformer_chinese_char_small''': 512,
    '''junnyu/roformer_chinese_char_base''': 512,
    '''junnyu/roformer_small_discriminator''': 128,
    '''junnyu/roformer_small_generator''': 128,
}


snake_case_        =			{
    '''junnyu/roformer_chinese_small''': {'''do_lower_case''': True},
    '''junnyu/roformer_chinese_base''': {'''do_lower_case''': True},
    '''junnyu/roformer_chinese_char_small''': {'''do_lower_case''': True},
    '''junnyu/roformer_chinese_char_base''': {'''do_lower_case''': True},
    '''junnyu/roformer_small_discriminator''': {'''do_lower_case''': True},
    '''junnyu/roformer_small_generator''': {'''do_lower_case''': True},
}


class 				SCREAMING_SNAKE_CASE__ (__snake_case					):
    __lowerCamelCase						:	Union[str, Any]														=					VOCAB_FILES_NAMES
    __lowerCamelCase						:	int														=					PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase						:	Any														=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase						:	int														=					PRETRAINED_INIT_CONFIGURATION
    __lowerCamelCase						:	Tuple														=					RoFormerTokenizer





    def __init__(				self						,       a=None						,       a=None						,       a=True						,       a="[UNK]"						,       a="[SEP]"						,       a="[PAD]"						,       a="[CLS]"						,       a="[MASK]"						,       a=True						,       a=None						,       **a						,       ):
         super().__init__(
             a						,       tokenizer_file=a						,       do_lower_case=a						,       unk_token=a						,       sep_token=a						,       pad_token=a						,       cls_token=a						,       mask_token=a						,       tokenize_chinese_chars=a						,       strip_accents=a						,       **a						,       )

         lowercase__   :       Optional[Any]				=	json.loads(self.backend_tokenizer.normalizer.__getstate__())
         if (
             pre_tok_state.get('lowercase'						,       a) != do_lower_case
             or pre_tok_state.get('strip_accents'						,       a) != strip_accents
         ):
              lowercase__   :       Optional[int]				=	getattr(a						,       pre_tok_state.pop('type'))
              lowercase__   :       str				=	do_lower_case
              lowercase__   :       Union[str, Any]				=	strip_accents
              lowercase__   :       int				=	pre_tok_class(**a)

         lowercase__   :       Optional[int]				=	do_lower_case





    def __getstate__(				self):
         lowercase__   :       str				=	self.__dict__.copy()
         lowercase__   :       Any				=	BertPreTokenizer()
         return state





    def __setstate__(				self						,       a):
         lowercase__   :       Union[str, Any]				=	d
         lowercase__   :       int				=	self.__dict__['_tokenizer'].get_vocab()
         lowercase__   :       List[str]				=	PreTokenizer.custom(JiebaPreTokenizer(a))





    def 				snake_case_    (				self						,       a						,       a=None):
         lowercase__   :       Union[str, Any]				=	[self.cls_token_id] + token_ids_a + [self.sep_token_id]

         if token_ids_a:
              output += token_ids_a + [self.sep_token_id]

         return output





    def 				snake_case_    (				self						,       a						,       a = None):
         lowercase__   :       Tuple				=	[self.sep_token_id]
         lowercase__   :       Optional[int]				=	[self.cls_token_id]
         if token_ids_a is None:
              return len(cls + token_ids_a + sep) * [0]
         return len(cls + token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1]





    def 				snake_case_    (				self						,       a						,       a = None):
         lowercase__   :       Optional[Any]				=	self._tokenizer.model.save(a						,       name=a)
         return tuple(a)





    def 				snake_case_    (				self						,       a						,       a=None						,       a=None						,       a=False						,       **a						,       ):
         lowercase__   :       List[str]				=	BertPreTokenizer()
         return super().save_pretrained(a						,       a						,       a						,       a						,       **a)

