
"""simple docstring"""


import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_lowercase							   =				logging.get_logger(__name__)

_lowercase							   =				{
    '''microsoft/conditional-detr-resnet-50''': (
        '''https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'''
    ),
}







class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						_lowerCamelCase:    List[Any]      =    '''conditional_detr'''
						_lowerCamelCase:    List[str]      =    ['''past_key_values''']
						_lowerCamelCase:    Optional[Any]      =    {
						    '''hidden_size''': '''d_model''',
						    '''num_attention_heads''': '''encoder_attention_heads''',
						}


						def __init__( self     :      Optional[int]  ,A_     :      List[Any]=True  ,A_     :      Optional[Any]=None  ,A_     :      List[str]=3  ,A_     :      str=300  ,A_     :      List[Any]=6  ,A_     :      List[str]=2048  ,A_     :      int=8  ,A_     :      str=6  ,A_     :      str=2048  ,A_     :      Union[str, Any]=8  ,A_     :      str=0.0  ,A_     :      List[str]=0.0  ,A_     :      Tuple=True  ,A_     :      int="relu"  ,A_     :      int=256  ,A_     :      Any=0.1  ,A_     :      List[str]=0.0  ,A_     :      Optional[int]=0.0  ,A_     :      int=0.02  ,A_     :      str=1.0  ,A_     :      List[Any]=False  ,A_     :      List[Any]="sine"  ,A_     :      Optional[Any]="resnet50"  ,A_     :      Any=True  ,A_     :      Union[str, Any]=False  ,A_     :      List[str]=2  ,A_     :      Tuple=5  ,A_     :      Tuple=2  ,A_     :      int=1  ,A_     :      Optional[int]=1  ,A_     :      Optional[Any]=2  ,A_     :      Union[str, Any]=5  ,A_     :      Any=2  ,A_     :      Any=0.25  ,**A_     :      Any  ,)     ->   Optional[Any]:
							if backbone_config is not None and use_timm_backbone:
								raise ValueError('You can\'t specify both `backbone_config` and `use_timm_backbone`.'      )

							if not use_timm_backbone:
								if backbone_config is None:
									logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'      )
									A						   =   CONFIG_MAPPING['resnet'](out_features=['stage4']      )
								elif isinstance(A_  ,A_      ):
									A						   =   backbone_config.get('model_type'      )
									A						   =   CONFIG_MAPPING[backbone_model_type]
									A						   =   config_class.from_dict(A_      )

							A						   =   use_timm_backbone
							A						   =   backbone_config
							A						   =   num_channels
							A						   =   num_queries
							A						   =   d_model
							A						   =   encoder_ffn_dim
							A						   =   encoder_layers
							A						   =   encoder_attention_heads
							A						   =   decoder_ffn_dim
							A						   =   decoder_layers
							A						   =   decoder_attention_heads
							A						   =   dropout
							A						   =   attention_dropout
							A						   =   activation_dropout
							A						   =   activation_function
							A						   =   init_std
							A						   =   init_xavier_std
							A						   =   encoder_layerdrop
							A						   =   decoder_layerdrop
							A						   =   encoder_layers
							A						   =   auxiliary_loss
							A						   =   position_embedding_type
							A						   =   backbone
							A						   =   use_pretrained_backbone
							A						   =   dilation
							# Hungarian matcher
							A						   =   class_cost
							A						   =   bbox_cost
							A						   =   giou_cost
							# Loss coefficients
							A						   =   mask_loss_coefficient
							A						   =   dice_loss_coefficient
							A						   =   cls_loss_coefficient
							A						   =   bbox_loss_coefficient
							A						   =   giou_loss_coefficient
							A						   =   focal_alpha
							super().__init__(is_encoder_decoder=A_  ,**A_      )


						@property
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple      )     ->   int:
							return self.encoder_attention_heads


						@property
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple      )     ->   int:
							return self.d_model




						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   Optional[Any]:
							A						   =   copy.deepcopy(self.__dict__      )
							if self.backbone_config is not None:
								A						   =   self.backbone_config.to_dict()
							A						   =   self.__class__.model_type
							return output







class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						_lowerCamelCase:    int      =    version.parse('''1.11'''    )


						@property
						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   Mapping[str, Mapping[int, str]]:
							return OrderedDict(
							    [
							        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
							        ('pixel_mask', {0: 'batch'}),
							    ]      )


						@property
						def 						_SCREAMING_SNAKE_CASE      ( self     :      int      )     ->   float:
							return 1e-5




						@property
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]      )     ->   int:
							return 12

"""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

		_lowercase							   =				True
except ImportError:
		_lowercase							   =				False

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






def       _snake_case				(		snake_case__   :     Namespace     ):
	return AddNewModelCommand(args.testing	,      args.testing_file	,      path=args.path     )







class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						@staticmethod
						def 						_SCREAMING_SNAKE_CASE      ( A_     :      ArgumentParser      )     ->   Any:
							A						   =   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     :      Tuple  ,A_     :      bool  ,A_     :      str  ,A_     :      Tuple=None  ,*A_     :      List[str]      )     ->   Union[str, Any]:
							A						   =   testing
							A						   =   testing_file
							A						   =   path




						def 						_SCREAMING_SNAKE_CASE      ( self     :      int      )     ->   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						   =   [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.'      )

							A						   =   (
							    Path(A_      ).parent.parent.parent.parent if self._path is None else Path(self._path      ).parent.parent
							)
							A						   =   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:
									A						   =   json.load(A_      )

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

							A						   =   [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						   =   json.load(A_      )

							A						   =   configuration['lowercase_modelname']
							A						   =   configuration['generate_tensorflow_pytorch_and_flax']
							os.remove(F'{directory}/configuration.json'      )

							A						   =   'PyTorch' in generate_tensorflow_pytorch_and_flax
							A						   =   'TensorFlow' in generate_tensorflow_pytorch_and_flax
							A						   =   'Flax' in generate_tensorflow_pytorch_and_flax

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

											A						   =   []
										elif "# Replace with" in line and "##" not in line:
											A						   =   []
										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_      )