
"""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     :			Tuple  						=			True
except ImportError:
   _A     :			Optional[int]  						=			False

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


def     __magic_name__      ( __snake_case							:			Namespace    )					->							List[str]:
       return AddNewModelCommand(args.testing	,					args.testing_file	,					path=args.path    )

class 						a__		( a_		):
   @staticmethod
   def      __magic_name__     (     _a							):
          lowercase       :							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=_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       :							str									=				testing
          lowercase       :							List[str]									=				testing_file
          lowercase       :							Dict									=				path

   def      __magic_name__     (     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       :							Dict									=				[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       :							List[Any]									=				(
              Path(_a							).parent.parent.parent.parent if self._path is None else Path(self._path							).parent.parent
          )
          lowercase       :							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       :							Tuple									=				json.load(_a							)

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

          lowercase       :							Tuple									=				[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       :							List[str]									=				configuration["lowercase_modelname"]
          lowercase       :							Optional[int]									=				configuration["generate_tensorflow_pytorch_and_flax"]
          os.remove(f"""{directory}/configuration.json"""							)

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

          lowercase       :							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(_a							):
                 with open(_a      ,       "r"							) as f:
                        lowercase       :							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       :							int									=				mkstemp()
                 lowercase       :							int									=				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       :							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(_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       :							Union[str, Any]									=				[]
                        lowercase       :							str									=				False
                        lowercase       :							Union[str, Any]									=				False
                        for line in datafile:
                               if "# To replace in: " in line and "##" not in line:
                                      lowercase       :							List[str]									=				line.split("\""							)[1]
                                      lowercase       :							Any									=				skip_units(_a							)
                               elif "# Below: " in line and "##" not in line:
                                      lowercase       :							List[Any]									=				line.split("\""							)[1]
                                      lowercase       :							Union[str, Any]									=				skip_units(_a							)
                               elif "# End." in line and "##" not in line:
                                      if not skip_file and not skip_snippet:
                                             replace(_a      ,       _a      ,       _a							)

                                      lowercase       :							Optional[int]									=				[]
                               elif "# Replace with" in line and "##" not in line:
                                      lowercase       :							str									=				[]
                               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 os
from typing import List, Optional, Union

from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging


_A     :			Tuple  						=			logging.get_logger(__name__)

_A     :			Dict  						=			{"""vocab_file""": """vocab.txt"""}

_A     :			Optional[Any]  						=			{
    """vocab_file""": {
        """facebook/esm2_t6_8M_UR50D""": """https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt""",
        """facebook/esm2_t12_35M_UR50D""": """https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt""",
    },
}

_A     :			Optional[Any]  						=			{
    """facebook/esm2_t6_8M_UR50D""": 10_24,
    """facebook/esm2_t12_35M_UR50D""": 10_24,
}


def     __magic_name__      ( __snake_case							:			Tuple    )					->							str:
       with open(__snake_case	,					"r"    ) as f:
              lowercase       :							Dict									=				f.read().splitlines()
              return [l.strip() for l in lines]

class 						a__		( a_		):
   __lowerCAmelCase				=							VOCAB_FILES_NAMES
   __lowerCAmelCase				=							PRETRAINED_VOCAB_FILES_MAP
   __lowerCAmelCase				=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCAmelCase				=							["""input_ids""", """attention_mask"""]
   def __init__(     self      ,       _a      ,       _a="<unk>"      ,       _a="<cls>"      ,       _a="<pad>"      ,       _a="<mask>"      ,       _a="<eos>"      ,       **_a      ,       ):
          super().__init__(**_a							)
          lowercase       :							Union[str, Any]									=				load_vocab_file(_a							)
          lowercase       :							Union[str, Any]									=				dict(enumerate(self.all_tokens							)							)
          lowercase       :							Any									=				{tok: ind for ind, tok in enumerate(self.all_tokens							)}
          lowercase       :							Dict									=				unk_token
          lowercase       :							Optional[int]									=				cls_token
          lowercase       :							Optional[int]									=				pad_token
          lowercase       :							Any									=				mask_token
          lowercase       :							List[str]									=				eos_token
          lowercase       :							Tuple									=				self.all_tokens
          self._create_trie(self.unique_no_split_tokens							)
   def      __magic_name__     (     self      ,       _a							):
          return self._id_to_token.get(_a      ,       self.unk_token							)
   def      __magic_name__     (     self      ,       _a							):
          return self._token_to_id.get(_a      ,       self._token_to_id.get(self.unk_token							)							)
   def      __magic_name__     (     self      ,       _a      ,       **_a							):
          return text.split()
   def      __magic_name__     (     self      ,       _a=False							):
          return len(self._id_to_token							)
   def      __magic_name__     (     self							):
          return {token: i for i, token in enumerate(self.all_tokens							)}
   def      __magic_name__     (     self      ,       _a							):
          return self._token_to_id.get(_a      ,       self._token_to_id.get(self.unk_token							)							)
   def      __magic_name__     (     self      ,       _a							):
          return self._id_to_token.get(_a      ,       self.unk_token							)
   def      __magic_name__     (     self      ,       _a      ,       _a = None							):
          lowercase       :							Optional[int]									=				[self.cls_token_id]
          lowercase       :							Dict									=				[self.eos_token_id]  # No sep token in ESM vocabulary
          if token_ids_a is None:
                 if self.eos_token_id is None:
                        return cls + token_ids_a
                 else:
                        return cls + token_ids_a + sep
          elif self.eos_token_id is None:
                 raise ValueError("Cannot tokenize multiple sequences when EOS token is not set!"							)
          return cls + token_ids_a + sep + token_ids_a + sep  # Multiple inputs always have an EOS token
   def      __magic_name__     (     self      ,       _a      ,       _a = None      ,       _a = False							):
          if already_has_special_tokens:
                 if token_ids_a is not None:
                        raise ValueError(
                            "You should not supply a second sequence if the provided sequence of "
                            "ids is already formatted with special tokens for the model."							)

                 return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
          lowercase       :							Union[str, Any]									=				[1] + ([0] * len(_a							)) + [1]
          if token_ids_a is not None:
                 mask += [0] * len(_a							) + [1]
          return mask
   def      __magic_name__     (     self      ,       _a      ,       _a							):
          lowercase       :							Optional[Any]									=				os.path.join(_a      ,       (filename_prefix + "-" if filename_prefix else "") + "vocab.txt"							)
          with open(_a      ,       "w"							) as f:
                 f.write("\n".join(self.all_tokens							)							)
          return (vocab_file,)
   @property
   def      __magic_name__     (     self							):
          return self.get_vocab_size(with_added_tokens=_a							)

   def      __magic_name__     (     self      ,       _a      ,       _a = False							):
          return super()._add_tokens(_a      ,       special_tokens=_a							)


