




'''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

 lowerCAmelCase_      :  Dict				     =  True
except ImportError:
 lowerCAmelCase_      :  Optional[Any]				     =  False

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



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					Namespace				):




     """simple docstring"""


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







class      SCREAMING_SNAKE_CASE    (						lowercase_				):






      @staticmethod
      def 							lowercase_						( lowercase__   :  Any       ):


           '''simple docstring'''

           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=_lowercase						,						help="""Configuration file on which to run."""       )
           add_new_model_parser.add_argument(
               """--path"""						,						type=_lowercase						,						help="""Path to cookiecutter. Should only be used for testing purposes."""       )
           add_new_model_parser.set_defaults(func=_lowercase       )






      def __init__( self   :  int						,						lowercase__   :  Dict						,						lowercase__   :  str						,						lowercase__   :  str=None						,						*lowercase__   :  Optional[Any]       ):


           '''simple docstring'''

           a_     :							Any     						=  testing
           a_     :							Tuple     						=  testing_file
           a_     :							Any     						=  path






      def 							lowercase_						( self   :  Union[str, Any]       ):


           '''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
           a_     :							Tuple     						=  [directory for directory in os.listdir() if """cookiecutter-template-""" == directory[:22]]
           if len(_lowercase       ) > 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_     :							Union[str, Any]     						=  (
               Path(_lowercase       ).parent.parent.parent.parent if self._path is None else Path(self._path       ).parent.parent
           )
           a_     :							Union[str, Any]     						=  path_to_transformer_root / """templates""" / """adding_a_new_model"""

           # Execute cookiecutter
           if not self._testing:
                cookiecutter(str(_lowercase       )       )
           else:
                with open(self._testing_file						,						"""r"""       ) as configuration_file:
                     a_     :							List[str]     						=  json.load(_lowercase       )

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

           a_     :							str     						=  [directory for directory in os.listdir() if """cookiecutter-template-""" in directory[:22]][0]

           # Retrieve configuration
           with open(directory + """/configuration.json"""						,						"""r"""       ) as configuration_file:
                a_     :							Optional[Any]     						=  json.load(_lowercase       )

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

           a_     :							int     						=  """PyTorch""" in generate_tensorflow_pytorch_and_flax
           a_     :							Tuple     						=  """TensorFlow""" in generate_tensorflow_pytorch_and_flax
           a_     :							Any     						=  """Flax""" in generate_tensorflow_pytorch_and_flax

           a_     :							List[Any]     						=  F"{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}"
           os.makedirs(_lowercase						,						exist_ok=_lowercase       )
           os.makedirs(F"{path_to_transformer_root}/tests/models/{lowercase_model_name}"						,						exist_ok=_lowercase       )

           # 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(lowercase__   :  Dict       ):
                with open(_lowercase						,						"""r"""       ) as f:
                     a_     :							Any     						=  f.readlines()
                with open(_lowercase						,						"""w"""       ) as f:
                     for line in lines:
                          if "# Copied from transformers." not in line:
                               f.write(_lowercase       )

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

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

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

                               a_     :							Union[str, Any]     						=  []
                          elif "# Replace with" in line and "##" not in line:
                               a_     :							Optional[Any]     						=  []
                          elif "##" not in line:
                               lines_to_copy.append(_lowercase       )

                remove(_lowercase       )

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



'''simple docstring'''

import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE							:				Dict     	=			logging.get_logger()




def 							_UpperCamelCase				(   lowerCAmelCase__:					int		,lowerCAmelCase__:					str		,lowerCAmelCase__:					LevitConfig		,lowerCAmelCase__:					Path		,lowerCAmelCase__:					bool = True   )      -> Optional[int]:
							print(F"""Converting {name}..."""   )

							with torch.no_grad():
														if hidden_sizes == 128:
																					if name[-1] == "S":
																												SCREAMING_SNAKE_CASE_				=				timm.create_model('levit_128s'		,pretrained=lowerCAmelCase__   )
																					else:
																												SCREAMING_SNAKE_CASE_				=				timm.create_model('levit_128'		,pretrained=lowerCAmelCase__   )
														if hidden_sizes == 192:
																					SCREAMING_SNAKE_CASE_				=				timm.create_model('levit_192'		,pretrained=lowerCAmelCase__   )
														if hidden_sizes == 256:
																					SCREAMING_SNAKE_CASE_				=				timm.create_model('levit_256'		,pretrained=lowerCAmelCase__   )
														if hidden_sizes == 384:
																					SCREAMING_SNAKE_CASE_				=				timm.create_model('levit_384'		,pretrained=lowerCAmelCase__   )

														from_model.eval()
														SCREAMING_SNAKE_CASE_				=				LevitForImageClassificationWithTeacher(lowerCAmelCase__   ).eval()
														SCREAMING_SNAKE_CASE_				=				OrderedDict()

														SCREAMING_SNAKE_CASE_				=				from_model.state_dict()
														SCREAMING_SNAKE_CASE_				=				list(from_model.state_dict().keys()   )
														SCREAMING_SNAKE_CASE_				=				list(our_model.state_dict().keys()   )
														print(len(lowerCAmelCase__   )		,len(lowerCAmelCase__   )   )
														for i in range(len(lowerCAmelCase__   )   ):
																					SCREAMING_SNAKE_CASE_				=				weights[og_keys[i]]
														our_model.load_state_dict(lowerCAmelCase__   )

														SCREAMING_SNAKE_CASE_				=				torch.randn((2, 3, 224, 224)   )
														SCREAMING_SNAKE_CASE_				=				from_model(lowerCAmelCase__   )
														SCREAMING_SNAKE_CASE_				=				our_model(lowerCAmelCase__   ).logits

							assert torch.allclose(lowerCAmelCase__		,lowerCAmelCase__   ), "The model logits don't match the original one."

							SCREAMING_SNAKE_CASE_				=				name
							print(lowerCAmelCase__   )

							if push_to_hub:
														our_model.save_pretrained(save_directory / checkpoint_name   )
														SCREAMING_SNAKE_CASE_				=				LevitImageProcessor()
														image_processor.save_pretrained(save_directory / checkpoint_name   )

														print(F"""Pushed {checkpoint_name}"""   )




def 							_UpperCamelCase				(   lowerCAmelCase__:					Path		,lowerCAmelCase__:					str = None		,lowerCAmelCase__:					bool = True   )      -> Optional[int]:
							SCREAMING_SNAKE_CASE_				=				'imagenet-1k-id2label.json'
							SCREAMING_SNAKE_CASE_				=				1000
							SCREAMING_SNAKE_CASE_				=				(1, num_labels)

							SCREAMING_SNAKE_CASE_				=				'huggingface/label-files'
							SCREAMING_SNAKE_CASE_				=				num_labels
							SCREAMING_SNAKE_CASE_				=				json.load(open(hf_hub_download(lowerCAmelCase__		,lowerCAmelCase__		,repo_type='dataset'   )		,'r'   )   )
							SCREAMING_SNAKE_CASE_				=				{int(lowerCAmelCase__   ): v for k, v in idalabel.items()}

							SCREAMING_SNAKE_CASE_				=				idalabel
							SCREAMING_SNAKE_CASE_				=				{v: k for k, v in idalabel.items()}

							SCREAMING_SNAKE_CASE_				=				partial(lowerCAmelCase__		,num_labels=lowerCAmelCase__		,idalabel=lowerCAmelCase__		,labelaid=lowerCAmelCase__   )

							SCREAMING_SNAKE_CASE_				=				{
							    'levit-128S': 128,
							    'levit-128': 128,
							    'levit-192': 192,
							    'levit-256': 256,
							    'levit-384': 384,
							}

							SCREAMING_SNAKE_CASE_				=				{
							    'levit-128S': ImageNetPreTrainedConfig(
							        hidden_sizes=[128, 256, 384]		,num_attention_heads=[4, 6, 8]		,depths=[2, 3, 4]		,key_dim=[16, 16, 16]		,drop_path_rate=0		,),
							    'levit-128': ImageNetPreTrainedConfig(
							        hidden_sizes=[128, 256, 384]		,num_attention_heads=[4, 8, 12]		,depths=[4, 4, 4]		,key_dim=[16, 16, 16]		,drop_path_rate=0		,),
							    'levit-192': ImageNetPreTrainedConfig(
							        hidden_sizes=[192, 288, 384]		,num_attention_heads=[3, 5, 6]		,depths=[4, 4, 4]		,key_dim=[32, 32, 32]		,drop_path_rate=0		,),
							    'levit-256': ImageNetPreTrainedConfig(
							        hidden_sizes=[256, 384, 512]		,num_attention_heads=[4, 6, 8]		,depths=[4, 4, 4]		,key_dim=[32, 32, 32]		,drop_path_rate=0		,),
							    'levit-384': ImageNetPreTrainedConfig(
							        hidden_sizes=[384, 512, 768]		,num_attention_heads=[6, 9, 12]		,depths=[4, 4, 4]		,key_dim=[32, 32, 32]		,drop_path_rate=0.1		,),
							}

							if model_name:
														convert_weight_and_push(
														    names_to_hidden_sizes[model_name]		,lowerCAmelCase__		,names_to_config[model_name]		,lowerCAmelCase__		,lowerCAmelCase__   )
							else:
														for model_name, config in names_to_config.items():
																					convert_weight_and_push(names_to_hidden_sizes[model_name]		,lowerCAmelCase__		,lowerCAmelCase__		,lowerCAmelCase__		,lowerCAmelCase__   )
							return config, expected_shape


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE							:				str     	=			argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    "--model_name",
							    default=None,
							    type=str,
							    help="The name of the model you wish to convert, it must be one of the supported Levit* architecture,",
							)
							parser.add_argument(
							    "--pytorch_dump_folder_path",
							    default="levit-dump-folder/",
							    type=Path,
							    required=False,
							    help="Path to the output PyTorch model directory.",
							)
							parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")
							parser.add_argument(
							    "--no-push_to_hub",
							    dest="push_to_hub",
							    action="store_false",
							    help="Do not push model and image processor to the hub",
							)

							SCREAMING_SNAKE_CASE							:				Optional[Any]     	=			parser.parse_args()
							SCREAMING_SNAKE_CASE							:				Path     	=			args.pytorch_dump_folder_path
							pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
							convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)





