






import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
	import torch

	from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin


class 					_snake_case						:






	def __init__(		self					,UpperCamelCase					,UpperCamelCase=None					,UpperCamelCase=None					,UpperCamelCase=None					,UpperCamelCase="resnet50"					,UpperCamelCase=3					,UpperCamelCase=32					,UpperCamelCase=3					,UpperCamelCase=True					,UpperCamelCase=True					,)						->    Optional[Any]:
					snake_case__      :List[str]         =   parent
					snake_case__      :Dict         =   out_indices if out_indices is not None else [4]
					snake_case__      :List[str]         =   stage_names
					snake_case__      :str         =   out_features
					snake_case__      :Union[str, Any]         =   backbone
					snake_case__      :int         =   batch_size
					snake_case__      :List[str]         =   image_size
					snake_case__      :Optional[Any]         =   num_channels
					snake_case__      :Any         =   use_pretrained_backbone
					snake_case__      :Tuple         =   is_training






	def    lowerCAmelCase_							(		self       )						->    List[Any]:
					snake_case__      :Union[str, Any]         =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )
					snake_case__      :List[str]         =   self.get_config()

					return config, pixel_values






	def    lowerCAmelCase_							(		self       )						->    str:
					return TimmBackboneConfig(
					    image_size=self.image_size					,num_channels=self.num_channels					,out_features=self.out_features					,out_indices=self.out_indices					,stage_names=self.stage_names					,use_pretrained_backbone=self.use_pretrained_backbone					,backbone=self.backbone					,)






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase       )						->    int:
					snake_case__      :Any         =   TimmBackbone(config=UpperCamelCase       )
					model.to(UpperCamelCase       )
					model.eval()
					with torch.no_grad():
									snake_case__      :List[str]         =   model(UpperCamelCase       )
					self.parent.assertEqual(
					    result.feature_map[-1].shape					,(self.batch_size, model.channels[-1], 14, 14)					,)






	def    lowerCAmelCase_							(		self       )						->    Dict:
					snake_case__      :List[Any]         =   self.prepare_config_and_inputs()
					snake_case__    ,						snake_case__      :List[Any]         =   config_and_inputs
					snake_case__      :str         =   {"pixel_values": pixel_values}
					return config, inputs_dict




@require_torch
@require_timm
class 					_snake_case						(			_A       ,    _A       ,    _A       ,    unittest.TestCase ):
	_A          =   (TimmBackbone,) if is_torch_available() else ()
	_A          =   {'feature-extraction': TimmBackbone} if is_torch_available() else {}
	_A          =   False
	_A          =   False
	_A          =   False
	_A          =   False






	def    lowerCAmelCase_							(		self       )						->    int:
					snake_case__      :List[Any]         =   TimmBackboneModelTester(self       )
					snake_case__      :Optional[Any]         =   ConfigTester(self					,config_class=UpperCamelCase					,has_text_modality=UpperCamelCase       )






	def    lowerCAmelCase_							(		self       )						->    Optional[Any]:
					self.config_tester.create_and_test_config_to_json_string()
					self.config_tester.create_and_test_config_to_json_file()
					self.config_tester.create_and_test_config_from_and_save_pretrained()
					self.config_tester.create_and_test_config_with_num_labels()
					self.config_tester.check_config_can_be_init_without_params()
					self.config_tester.check_config_arguments_init()






	def    lowerCAmelCase_							(		self       )						->    List[Any]:
					snake_case__      :Optional[Any]         =   "resnet18"
					snake_case__      :int         =   "microsoft/resnet-18"

					snake_case__      :List[Any]         =   AutoBackbone.from_pretrained(UpperCamelCase					,use_timm_backbone=UpperCamelCase       )
					snake_case__      :List[Any]         =   AutoBackbone.from_pretrained(UpperCamelCase       )

					self.assertEqual(len(timm_model.out_features       )					,len(transformers_model.out_features       )       )
					self.assertEqual(len(timm_model.stage_names       )					,len(transformers_model.stage_names       )       )
					self.assertEqual(timm_model.channels					,transformers_model.channels       )
					# Out indices are set to the last layer by default. For timm models, we don't know
					# the number of layers in advance, so we set it to (-1,), whereas for transformers
					# models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
					self.assertEqual(timm_model.out_indices					,(-1,)       )
					self.assertEqual(transformers_model.out_indices					,[len(timm_model.stage_names       ) - 1]       )

					snake_case__      :str         =   AutoBackbone.from_pretrained(UpperCamelCase					,use_timm_backbone=UpperCamelCase					,out_indices=[1, 2, 3]       )
					snake_case__      :str         =   AutoBackbone.from_pretrained(UpperCamelCase					,out_indices=[1, 2, 3]       )

					self.assertEqual(timm_model.out_indices					,transformers_model.out_indices       )
					self.assertEqual(len(timm_model.out_features       )					,len(transformers_model.out_features       )       )
					self.assertEqual(timm_model.channels					,transformers_model.channels       )






	@unittest.skip("TimmBackbone doesn't support feed forward chunking"       )
	def    lowerCAmelCase_							(		self       )						->    Tuple:
					pass






	@unittest.skip("TimmBackbone doesn't have num_hidden_layers attribute"       )
	def    lowerCAmelCase_							(		self       )						->    Optional[Any]:
					pass






	@unittest.skip("TimmBackbone initialization is managed on the timm side"       )
	def    lowerCAmelCase_							(		self       )						->    Optional[int]:
					pass






	@unittest.skip("TimmBackbone models doesn't have inputs_embeds"       )
	def    lowerCAmelCase_							(		self       )						->    str:
					pass






	@unittest.skip("TimmBackbone models doesn't have inputs_embeds"       )
	def    lowerCAmelCase_							(		self       )						->    List[Any]:
					pass






	@unittest.skip("TimmBackbone model cannot be created without specifying a backbone checkpoint"       )
	def    lowerCAmelCase_							(		self       )						->    Dict:
					pass






	@unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone"       )
	def    lowerCAmelCase_							(		self       )						->    Tuple:
					pass






	@unittest.skip("model weights aren't tied in TimmBackbone."       )
	def    lowerCAmelCase_							(		self       )						->    List[Any]:
					pass






	@unittest.skip("model weights aren't tied in TimmBackbone."       )
	def    lowerCAmelCase_							(		self       )						->    int:
					pass






	@unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone"       )
	def    lowerCAmelCase_							(		self       )						->    Dict:
					pass






	@unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone"       )
	def    lowerCAmelCase_							(		self       )						->    Union[str, Any]:
					pass






	@unittest.skip("TimmBackbone doesn't have hidden size info in its configuration."       )
	def    lowerCAmelCase_							(		self       )						->    Tuple:
					pass






	@unittest.skip("TimmBackbone doesn't support output_attentions."       )
	def    lowerCAmelCase_							(		self       )						->    Tuple:
					pass






	@unittest.skip("Safetensors is not supported by timm."       )
	def    lowerCAmelCase_							(		self       )						->    str:
					pass






	@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."       )
	def    lowerCAmelCase_							(		self       )						->    Optional[Any]:
					pass






	def    lowerCAmelCase_							(		self       )						->    Dict:
					snake_case__    ,						snake_case__      :Optional[int]         =   self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									snake_case__      :Tuple         =   model_class(UpperCamelCase       )
									snake_case__      :List[str]         =   inspect.signature(model.forward       )
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									snake_case__      :List[str]         =   [*signature.parameters.keys()]

									snake_case__      :Optional[Any]         =   ["pixel_values"]
									self.assertListEqual(arg_names[:1]					,UpperCamelCase       )






	def    lowerCAmelCase_							(		self       )						->    List[Any]:
					snake_case__    ,						snake_case__      :Optional[Any]         =   self.model_tester.prepare_config_and_inputs_for_common()
					snake_case__      :Any         =   True
					snake_case__      :str         =   self.has_attentions

					# no need to test all models as different heads yield the same functionality
					snake_case__      :Tuple         =   self.all_model_classes[0]
					snake_case__      :str         =   model_class(UpperCamelCase       )
					model.to(UpperCamelCase       )

					snake_case__      :Optional[int]         =   self._prepare_for_class(UpperCamelCase					,UpperCamelCase       )
					snake_case__      :Tuple         =   model(**UpperCamelCase       )
					snake_case__      :Union[str, Any]         =   outputs[0][-1]

					# Encoder-/Decoder-only models
					snake_case__      :Dict         =   outputs.hidden_states[0]
					hidden_states.retain_grad()

					if self.has_attentions:
									snake_case__      :Optional[Any]         =   outputs.attentions[0]
									attentions.retain_grad()

					output.flatten()[0].backward(retain_graph=UpperCamelCase       )

					self.assertIsNotNone(hidden_states.grad       )

					if self.has_attentions:
									self.assertIsNotNone(attentions.grad       )






	def    lowerCAmelCase_							(		self       )						->    int:
					snake_case__    ,						snake_case__      :Tuple         =   self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									snake_case__      :List[str]         =   model_class(UpperCamelCase       )
									model.to(UpperCamelCase       )
									model.eval()
									snake_case__      :Tuple         =   model(**UpperCamelCase       )

									self.assertEqual(len(result.feature_maps       )					,len(config.out_indices       )       )
									self.assertEqual(len(model.channels       )					,len(config.out_indices       )       )

									# Check output of last stage is taken if out_features=None, out_indices=None
									snake_case__      :Optional[Any]         =   copy.deepcopy(UpperCamelCase       )
									snake_case__      :List[str]         =   None
									snake_case__      :List[Any]         =   model_class(UpperCamelCase       )
									model.to(UpperCamelCase       )
									model.eval()
									snake_case__      :List[Any]         =   model(**UpperCamelCase       )

									self.assertEqual(len(result.feature_maps       )					,1       )
									self.assertEqual(len(model.channels       )					,1       )

									# Check backbone can be initialized with fresh weights
									snake_case__      :Tuple         =   copy.deepcopy(UpperCamelCase       )
									snake_case__      :Tuple         =   False
									snake_case__      :Tuple         =   model_class(UpperCamelCase       )
									model.to(UpperCamelCase       )
									model.eval()
									snake_case__      :Optional[int]         =   model(**UpperCamelCase       )







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

	__UpperCAmelCase							:   Dict							=  True
except ImportError:
	__UpperCAmelCase							:   List[Any]							=  False

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





def 					lowercase_ (    __snake_case     :							Namespace      )     -> Dict:





				'''simple docstring'''


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


class 					_snake_case						(			_A ):






	@staticmethod
	def    lowerCAmelCase_							(		UpperCamelCase       )						->    Any:
					snake_case__      :Dict         =   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=UpperCamelCase					,help="Configuration file on which to run."       )
					add_new_model_parser.add_argument(
					    "--path"					,type=UpperCamelCase					,help="Path to cookiecutter. Should only be used for testing purposes."       )
					add_new_model_parser.set_defaults(func=UpperCamelCase       )






	def __init__(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase=None					,*UpperCamelCase       )						->    Any:
					snake_case__      :Union[str, Any]         =   testing
					snake_case__      :Union[str, Any]         =   testing_file
					snake_case__      :List[str]         =   path






	def    lowerCAmelCase_							(		self       )						->    List[Any]:
					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
					snake_case__      :Tuple         =   [directory for directory in os.listdir() if "cookiecutter-template-" == directory[:22]]
					if len(UpperCamelCase       ) > 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."       )

					snake_case__      :str         =   (
					    Path(UpperCamelCase       ).parent.parent.parent.parent if self._path is None else Path(self._path       ).parent.parent
					)
					snake_case__      :Tuple         =   path_to_transformer_root / "templates" / "adding_a_new_model"

					# Execute cookiecutter
					if not self._testing:
									cookiecutter(str(UpperCamelCase       )       )
					else:
									with open(self._testing_file					,"r"       ) as configuration_file:
													snake_case__      :str         =   json.load(UpperCamelCase       )

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

					snake_case__      :List[Any]         =   [directory for directory in os.listdir() if "cookiecutter-template-" in directory[:22]][0]

					# Retrieve configuration
					with open(directory + "/configuration.json"					,"r"       ) as configuration_file:
									snake_case__      :Dict         =   json.load(UpperCamelCase       )

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

					snake_case__      :Any         =   "PyTorch" in generate_tensorflow_pytorch_and_flax
					snake_case__      :Any         =   "TensorFlow" in generate_tensorflow_pytorch_and_flax
					snake_case__      :Any         =   "Flax" in generate_tensorflow_pytorch_and_flax

					snake_case__      :Dict         =   f'{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}'
					os.makedirs(UpperCamelCase					,exist_ok=UpperCamelCase       )
					os.makedirs(f'{path_to_transformer_root}/tests/models/{lowercase_model_name}'					,exist_ok=UpperCamelCase       )

					# 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(UpperCamelCase       ):
									with open(UpperCamelCase					,"r"       ) as f:
													snake_case__      :List[str]         =   f.readlines()
									with open(UpperCamelCase					,"w"       ) as f:
													for line in lines:
																	if "# Copied from transformers." not in line:
																					f.write(UpperCamelCase       )

					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(UpperCamelCase					,UpperCamelCase					,UpperCamelCase       ):
									# Create temp file
									snake_case__    ,						snake_case__      :Optional[Any]         =   mkstemp()
									snake_case__      :Optional[Any]         =   False
									with fdopen(UpperCamelCase					,"w"       ) as new_file:
													with open(UpperCamelCase       ) as old_file:
																	for line in old_file:
																					new_file.write(UpperCamelCase       )
																					if line_to_copy_below in line:
																									snake_case__      :Optional[Any]         =   True
																									for line_to_copy in lines_to_copy:
																													new_file.write(UpperCamelCase       )

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

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

																					snake_case__      :Tuple         =   []
																	elif "# Replace with" in line and "##" not in line:
																					snake_case__      :Optional[Any]         =   []
																	elif "##" not in line:
																					lines_to_copy.append(UpperCamelCase       )

									remove(UpperCamelCase       )

					replace_in_files(f'{directory}/to_replace_{lowercase_model_name}.py'       )
					os.rmdir(UpperCamelCase       )