


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 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

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







class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   @staticmethod
   def _snake_case    (		__A ):






     """simple docstring"""





     lowerCamelCase :					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=__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 ):






     """simple docstring"""





     lowerCamelCase :					Optional[Any]    =				testing
     lowerCamelCase :					Optional[int]    =				testing_file
     lowerCamelCase :					List[Any]    =				path






   def _snake_case    (		self ):






     """simple docstring"""





     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 :					List[str]    =				[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 :					str    =				(
         Path(__A ).parent.parent.parent.parent if self._path is None else Path(self._path ).parent.parent
     )
     lowerCamelCase :					Optional[int]    =				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 :					str    =				json.load(__A )

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

     lowerCamelCase :					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:
       lowerCamelCase :					Any    =				json.load(__A )

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

     lowerCamelCase :					List[Any]    =				"PyTorch" in generate_tensorflow_pytorch_and_flax
     lowerCamelCase :					Dict    =				"TensorFlow" in generate_tensorflow_pytorch_and_flax
     lowerCamelCase :					Optional[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(__A ):
       with open(__A						,				"r" ) as f:
         lowerCamelCase :					str    =				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
       lowerCamelCase    ,	lowerCamelCase :					str    =				mkstemp()
       lowerCamelCase :					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:
               lowerCamelCase :					Union[str, 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:
         lowerCamelCase :					Any    =				[]
         lowerCamelCase :					Tuple    =				False
         lowerCamelCase :					str    =				False
         for line in datafile:
           if "# To replace in: " in line and "##" not in line:
             lowerCamelCase :					List[str]    =				line.split("\"" )[1]
             lowerCamelCase :					Any    =				skip_units(__A )
           elif "# Below: " in line and "##" not in line:
             lowerCamelCase :					Dict    =				line.split("\"" )[1]
             lowerCamelCase :					Dict    =				skip_units(__A )
           elif "# End." in line and "##" not in line:
             if not skip_file and not skip_snippet:
               replace(__A						,				__A						,				__A )

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







from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      Dict     		=		["image_processor", "tokenizer"]
   __A :      Dict     		=		"BridgeTowerImageProcessor"
   __A :      Optional[int]     		=		("RobertaTokenizer", "RobertaTokenizerFast")






   def __init__(		self						,				__A						,				__A ):






     """simple docstring"""





     super().__init__(__A						,				__A )






   def __call__(		self						,				__A						,				__A = None						,				__A = True						,				__A = False						,				__A = None						,				__A = None						,				__A = 0						,				__A = None						,				__A = None						,				__A = None						,				__A = False						,				__A = False						,				__A = False						,				__A = False						,				__A = True						,				__A = None						,				**__A						,				):






     """simple docstring"""





     lowerCamelCase :					str    =				self.tokenizer(
         text=__A						,				add_special_tokens=__A						,				padding=__A						,				truncation=__A						,				max_length=__A						,				stride=__A						,				pad_to_multiple_of=__A						,				return_token_type_ids=__A						,				return_attention_mask=__A						,				return_overflowing_tokens=__A						,				return_special_tokens_mask=__A						,				return_offsets_mapping=__A						,				return_length=__A						,				verbose=__A						,				return_tensors=__A						,				**__A						,				)
     # add pixel_values + pixel_mask
     lowerCamelCase :					int    =				self.image_processor(
         __A						,				return_tensors=__A						,				do_normalize=__A						,				do_center_crop=__A						,				**__A )
     encoding.update(__A )

     return encoding






   def _snake_case    (		self						,				*__A						,				**__A ):






     """simple docstring"""





     return self.tokenizer.batch_decode(*__A						,				**__A )






   def _snake_case    (		self						,				*__A						,				**__A ):






     """simple docstring"""





     return self.tokenizer.decode(*__A						,				**__A )






   @property
   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					List[Any]    =				self.tokenizer.model_input_names
     lowerCamelCase :					int    =				self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )



