"""simple docstring"""





import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple

import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging


logging.set_verbosity_info()
__A  =				logging.get_logger()
@dataclass
class  _snake_case     :
						snake_case__			  =			42
						snake_case__			  =			field(default_factory=a__		)
						snake_case__			  =			field(default_factory=a__		)






						def       lowerCamelCase__     (       self      :			Union[str, Any]							,					UpperCAmelCase      :			Any							,					UpperCAmelCase      :			Tensor							,					UpperCAmelCase      :			Tensor    ):
													__lowerCamelCase					:		Tuple		   =     len(list(m.modules()    )    ) == 1 or isinstance(UpperCAmelCase							,					nn.Convad    ) or isinstance(UpperCAmelCase							,					nn.BatchNormad    )
													if has_not_submodules:
																				self.traced.append(UpperCAmelCase    )






						def __call__(       self      :			int							,					UpperCAmelCase      :			Tensor    ):
													for m in self.module.modules():
																				self.handles.append(m.register_forward_hook(self._forward_hook    )    )
													self.module(UpperCAmelCase    )
													[x.remove() for x in self.handles]
													return self







						@property
						def       lowerCamelCase__     (       self      :			List[Any]    ):
													# check the len of the state_dict keys to see if we have learnable params
													return list(filter(lambda UpperCAmelCase    : len(list(x.state_dict().keys()    )    ) > 0							,					self.traced    )    )

@dataclass
class  _snake_case     :
						snake_case__			  =			42
						snake_case__			  =			42
						snake_case__			  =			1
						snake_case__			  =			field(default_factory=a__		)
						snake_case__			  =			field(default_factory=a__		)
						snake_case__			  =			True






						def __call__(       self      :			Tuple							,					UpperCAmelCase      :			Tensor    ):
													__lowerCamelCase					:		int		   =     Tracker(self.dest    )(UpperCAmelCase    ).parametrized
													__lowerCamelCase					:		Union[str, Any]		   =     Tracker(self.src    )(UpperCAmelCase    ).parametrized

													__lowerCamelCase					:		Any		   =     list(filter(lambda UpperCAmelCase    : type(UpperCAmelCase    ) not in self.src_skip							,					UpperCAmelCase    )    )
													__lowerCamelCase					:		Tuple		   =     list(filter(lambda UpperCAmelCase    : type(UpperCAmelCase    ) not in self.dest_skip							,					UpperCAmelCase    )    )

													if len(UpperCAmelCase    ) != len(UpperCAmelCase    ) and self.raise_if_mismatch:
																				raise Exception(
																				    F"""Numbers of operations are different. Source module has {len(UpperCAmelCase    )} operations while"""
																				    F""" destination module has {len(UpperCAmelCase    )}."""    )

													for dest_m, src_m in zip(UpperCAmelCase							,					UpperCAmelCase    ):
																				dest_m.load_state_dict(src_m.state_dict()    )
																				if self.verbose == 1:
																											print(F"""Transfered from={src_m} to={dest_m}"""    )

class  _snake_case     (					nn.Module		):






						def __init__(       self      :			Dict							,					UpperCAmelCase      :			nn.Module    ):
													super().__init__()

													__lowerCamelCase					:		List[Tuple[str, nn.Module]]		   =     []
													# - get the stem
													feature_blocks.append(("conv1", model.stem)    )
													# - get all the feature blocks
													for k, v in model.trunk_output.named_children():
																				assert k.startswith("block"    ), F"""Unexpected layer name {k}"""
																				__lowerCamelCase					:		int		   =     len(UpperCAmelCase    ) + 1
																				feature_blocks.append((F"""res{block_index}""", v)    )

													__lowerCamelCase					:		Optional[Any]		   =     nn.ModuleDict(UpperCAmelCase    )







						def       lowerCamelCase__     (       self      :			Union[str, Any]							,					UpperCAmelCase      :			Tensor    ):
													return get_trunk_forward_outputs(
													    UpperCAmelCase							,					out_feat_keys=UpperCAmelCase							,					feature_blocks=self._feature_blocks							,					)

class  _snake_case     (					a__		):






						def       lowerCamelCase__     (       self      :			Union[str, Any]							,					UpperCAmelCase      :			str    ):
													__lowerCamelCase					:		Union[str, Any]		   =     x.split("-"    )
													return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]    )







						def __getitem__(       self      :			Union[str, Any]							,					UpperCAmelCase      :			str    ):
													# default to timm!
													if x not in self:
																				__lowerCamelCase					:		List[str]		   =     self.convert_name_to_timm(UpperCAmelCase    )
																				__lowerCamelCase					:		Optional[int]		   =     partial(lambda: (timm.create_model(UpperCAmelCase							,					pretrained=UpperCAmelCase    ).eval(), None)    )

													else:
																				__lowerCamelCase					:		int		   =     super().__getitem__(UpperCAmelCase    )

													return val

class  _snake_case     (					a__		):






						def __getitem__(       self      :			Union[str, Any]							,					UpperCAmelCase      :			str    ):
													if "seer" in x and "in1k" not in x:
																				__lowerCamelCase					:		Union[str, Any]		   =     RegNetModel
													else:
																				__lowerCamelCase					:		Tuple		   =     RegNetForImageClassification
													return val


def    lowercase_   (   _lowerCamelCase:	Union[str, Any]						,   _lowerCamelCase:	str						,   _lowerCamelCase:	List[Tuple[str, str]]      ) ->      List[str]:



							'''simple docstring'''

							for from_key, to_key in keys:
														__lowerCamelCase					:		List[Any]		   =     from_state_dict[from_key].clone()
														print(F"""Copied key={from_key} to={to_key}"""      )
							return to_state_dict


def    lowercase_   (   _lowerCamelCase:	str						,   _lowerCamelCase:	Callable[[], nn.Module]						,   _lowerCamelCase:	Callable[[], nn.Module]						,   _lowerCamelCase:	RegNetConfig						,   _lowerCamelCase:	Path						,   _lowerCamelCase:	bool = True						,   ) ->      List[str]:



							'''simple docstring'''

							print(F"""Converting {name}..."""      )
							with torch.no_grad():
														__lowerCamelCase   ,  __lowerCamelCase					:		Dict		   =     from_model_func()
														__lowerCamelCase					:		int		   =     our_model_func(_lowerCamelCase      ).eval()
														__lowerCamelCase					:		Optional[Any]		   =     ModuleTransfer(src=_lowerCamelCase						,   dest=_lowerCamelCase						,   raise_if_mismatch=_lowerCamelCase      )
														__lowerCamelCase					:		List[str]		   =     torch.randn((1, 3, 224, 224)      )
														module_transfer(_lowerCamelCase      )

							if from_state_dict is not None:
														__lowerCamelCase					:		Optional[Any]		   =     []
														# for seer - in1k finetuned we have to manually copy the head
														if "seer" in name and "in1k" in name:
																					__lowerCamelCase					:		Optional[int]		   =     [("0.clf.0.weight", "classifier.1.weight"), ("0.clf.0.bias", "classifier.1.bias")]
														__lowerCamelCase					:		Union[str, Any]		   =     manually_copy_vissl_head(_lowerCamelCase						,   our_model.state_dict()						,   _lowerCamelCase      )
														our_model.load_state_dict(_lowerCamelCase      )

							__lowerCamelCase					:		Optional[Any]		   =     our_model(_lowerCamelCase						,   output_hidden_states=_lowerCamelCase      )
							__lowerCamelCase					:		Optional[Any]		   =     (
							    our_outputs.logits if isinstance(_lowerCamelCase						,   _lowerCamelCase      ) else our_outputs.last_hidden_state
							)

							__lowerCamelCase					:		Union[str, Any]		   =     from_model(_lowerCamelCase      )
							__lowerCamelCase					:		Any		   =     from_output[-1] if type(_lowerCamelCase      ) is list else from_output

							# now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
							if "seer" in name and "in1k" in name:
														__lowerCamelCase					:		Union[str, Any]		   =     our_outputs.hidden_states[-1]

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

							if push_to_hub:
														our_model.push_to_hub(
														    repo_path_or_name=save_directory / name						,   commit_message="Add model"						,   use_temp_dir=_lowerCamelCase						,   )

														__lowerCamelCase					:		Union[str, Any]		   =     224 if "seer" not in name else 384
														# we can use the convnext one
														__lowerCamelCase					:		List[Any]		   =     AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"						,   size=_lowerCamelCase      )
														image_processor.push_to_hub(
														    repo_path_or_name=save_directory / name						,   commit_message="Add image processor"						,   use_temp_dir=_lowerCamelCase						,   )

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


def    lowercase_   (   _lowerCamelCase:	Path						,   _lowerCamelCase:	str = None						,   _lowerCamelCase:	bool = True      ) ->      str:



							'''simple docstring'''

							__lowerCamelCase					:		List[Any]		   =     "imagenet-1k-id2label.json"
							__lowerCamelCase					:		List[str]		   =     1000
							__lowerCamelCase					:		Optional[Any]		   =     (1, num_labels)

							__lowerCamelCase					:		str		   =     "huggingface/label-files"
							__lowerCamelCase					:		Optional[Any]		   =     num_labels
							__lowerCamelCase					:		Optional[Any]		   =     json.load(open(cached_download(hf_hub_url(_lowerCamelCase						,   _lowerCamelCase						,   repo_type="dataset"      )      )						,   "r"      )      )
							__lowerCamelCase					:		Union[str, Any]		   =     {int(_lowerCamelCase      ): v for k, v in idalabel.items()}

							__lowerCamelCase					:		Optional[int]		   =     idalabel
							__lowerCamelCase					:		Tuple		   =     {v: k for k, v in idalabel.items()}

							__lowerCamelCase					:		int		   =     partial(_lowerCamelCase						,   num_labels=_lowerCamelCase						,   idalabel=_lowerCamelCase						,   labelaid=_lowerCamelCase      )

							__lowerCamelCase					:		Dict		   =     {
							    "regnet-x-002": ImageNetPreTrainedConfig(
							        depths=[1, 1, 4, 7]						,   hidden_sizes=[24, 56, 152, 368]						,   groups_width=8						,   layer_type="x"      ),
							    "regnet-x-004": ImageNetPreTrainedConfig(
							        depths=[1, 2, 7, 12]						,   hidden_sizes=[32, 64, 160, 384]						,   groups_width=16						,   layer_type="x"      ),
							    "regnet-x-006": ImageNetPreTrainedConfig(
							        depths=[1, 3, 5, 7]						,   hidden_sizes=[48, 96, 240, 528]						,   groups_width=24						,   layer_type="x"      ),
							    "regnet-x-008": ImageNetPreTrainedConfig(
							        depths=[1, 3, 7, 5]						,   hidden_sizes=[64, 128, 288, 672]						,   groups_width=16						,   layer_type="x"      ),
							    "regnet-x-016": ImageNetPreTrainedConfig(
							        depths=[2, 4, 10, 2]						,   hidden_sizes=[72, 168, 408, 912]						,   groups_width=24						,   layer_type="x"      ),
							    "regnet-x-032": ImageNetPreTrainedConfig(
							        depths=[2, 6, 15, 2]						,   hidden_sizes=[96, 192, 432, 1008]						,   groups_width=48						,   layer_type="x"      ),
							    "regnet-x-040": ImageNetPreTrainedConfig(
							        depths=[2, 5, 14, 2]						,   hidden_sizes=[80, 240, 560, 1360]						,   groups_width=40						,   layer_type="x"      ),
							    "regnet-x-064": ImageNetPreTrainedConfig(
							        depths=[2, 4, 10, 1]						,   hidden_sizes=[168, 392, 784, 1624]						,   groups_width=56						,   layer_type="x"      ),
							    "regnet-x-080": ImageNetPreTrainedConfig(
							        depths=[2, 5, 15, 1]						,   hidden_sizes=[80, 240, 720, 1920]						,   groups_width=120						,   layer_type="x"      ),
							    "regnet-x-120": ImageNetPreTrainedConfig(
							        depths=[2, 5, 11, 1]						,   hidden_sizes=[224, 448, 896, 2240]						,   groups_width=112						,   layer_type="x"      ),
							    "regnet-x-160": ImageNetPreTrainedConfig(
							        depths=[2, 6, 13, 1]						,   hidden_sizes=[256, 512, 896, 2048]						,   groups_width=128						,   layer_type="x"      ),
							    "regnet-x-320": ImageNetPreTrainedConfig(
							        depths=[2, 7, 13, 1]						,   hidden_sizes=[336, 672, 1344, 2520]						,   groups_width=168						,   layer_type="x"      ),
							    # y variant
							    "regnet-y-002": ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]						,   hidden_sizes=[24, 56, 152, 368]						,   groups_width=8      ),
							    "regnet-y-004": ImageNetPreTrainedConfig(
							        depths=[1, 3, 6, 6]						,   hidden_sizes=[48, 104, 208, 440]						,   groups_width=8      ),
							    "regnet-y-006": ImageNetPreTrainedConfig(
							        depths=[1, 3, 7, 4]						,   hidden_sizes=[48, 112, 256, 608]						,   groups_width=16      ),
							    "regnet-y-008": ImageNetPreTrainedConfig(
							        depths=[1, 3, 8, 2]						,   hidden_sizes=[64, 128, 320, 768]						,   groups_width=16      ),
							    "regnet-y-016": ImageNetPreTrainedConfig(
							        depths=[2, 6, 17, 2]						,   hidden_sizes=[48, 120, 336, 888]						,   groups_width=24      ),
							    "regnet-y-032": ImageNetPreTrainedConfig(
							        depths=[2, 5, 13, 1]						,   hidden_sizes=[72, 216, 576, 1512]						,   groups_width=24      ),
							    "regnet-y-040": ImageNetPreTrainedConfig(
							        depths=[2, 6, 12, 2]						,   hidden_sizes=[128, 192, 512, 1088]						,   groups_width=64      ),
							    "regnet-y-064": ImageNetPreTrainedConfig(
							        depths=[2, 7, 14, 2]						,   hidden_sizes=[144, 288, 576, 1296]						,   groups_width=72      ),
							    "regnet-y-080": ImageNetPreTrainedConfig(
							        depths=[2, 4, 10, 1]						,   hidden_sizes=[168, 448, 896, 2016]						,   groups_width=56      ),
							    "regnet-y-120": ImageNetPreTrainedConfig(
							        depths=[2, 5, 11, 1]						,   hidden_sizes=[224, 448, 896, 2240]						,   groups_width=112      ),
							    "regnet-y-160": ImageNetPreTrainedConfig(
							        depths=[2, 4, 11, 1]						,   hidden_sizes=[224, 448, 1232, 3024]						,   groups_width=112      ),
							    "regnet-y-320": ImageNetPreTrainedConfig(
							        depths=[2, 5, 12, 1]						,   hidden_sizes=[232, 696, 1392, 3712]						,   groups_width=232      ),
							    # models created by SEER -> https://arxiv.org/abs/2202.08360
							    "regnet-y-320-seer": RegNetConfig(depths=[2, 5, 12, 1]						,   hidden_sizes=[232, 696, 1392, 3712]						,   groups_width=232      ),
							    "regnet-y-640-seer": RegNetConfig(depths=[2, 5, 12, 1]						,   hidden_sizes=[328, 984, 1968, 4920]						,   groups_width=328      ),
							    "regnet-y-1280-seer": RegNetConfig(
							        depths=[2, 7, 17, 1]						,   hidden_sizes=[528, 1056, 2904, 7392]						,   groups_width=264      ),
							    "regnet-y-2560-seer": RegNetConfig(
							        depths=[3, 7, 16, 1]						,   hidden_sizes=[640, 1696, 2544, 5088]						,   groups_width=640      ),
							    "regnet-y-10b-seer": ImageNetPreTrainedConfig(
							        depths=[2, 7, 17, 1]						,   hidden_sizes=[2020, 4040, 11110, 28280]						,   groups_width=1010      ),
							    # finetuned on imagenet
							    "regnet-y-320-seer-in1k": ImageNetPreTrainedConfig(
							        depths=[2, 5, 12, 1]						,   hidden_sizes=[232, 696, 1392, 3712]						,   groups_width=232      ),
							    "regnet-y-640-seer-in1k": ImageNetPreTrainedConfig(
							        depths=[2, 5, 12, 1]						,   hidden_sizes=[328, 984, 1968, 4920]						,   groups_width=328      ),
							    "regnet-y-1280-seer-in1k": ImageNetPreTrainedConfig(
							        depths=[2, 7, 17, 1]						,   hidden_sizes=[528, 1056, 2904, 7392]						,   groups_width=264      ),
							    "regnet-y-2560-seer-in1k": ImageNetPreTrainedConfig(
							        depths=[3, 7, 16, 1]						,   hidden_sizes=[640, 1696, 2544, 5088]						,   groups_width=640      ),
							    "regnet-y-10b-seer-in1k": ImageNetPreTrainedConfig(
							        depths=[2, 7, 17, 1]						,   hidden_sizes=[2020, 4040, 11110, 28280]						,   groups_width=1010      ),
							}

							__lowerCamelCase					:		Tuple		   =     NameToOurModelFuncMap()
							__lowerCamelCase					:		Dict		   =     NameToFromModelFuncMap()
							# add seer weights logic

							def load_using_classy_vision(_lowerCamelCase:	str						,   _lowerCamelCase:	Callable[[], nn.Module]      ) -> Tuple[nn.Module, Dict]:
														__lowerCamelCase					:		List[str]		   =     torch.hub.load_state_dict_from_url(_lowerCamelCase						,   model_dir=str(_lowerCamelCase      )						,   map_location="cpu"      )
														__lowerCamelCase					:		List[Any]		   =     model_func()
														# check if we have a head, if yes add it
														__lowerCamelCase					:		Optional[Any]		   =     files["classy_state_dict"]["base_model"]["model"]
														__lowerCamelCase					:		Any		   =     model_state_dict["trunk"]
														model.load_state_dict(_lowerCamelCase      )
														return model.eval(), model_state_dict["heads"]

							# pretrained
							__lowerCamelCase					:		List[str]		   =     partial(
							    _lowerCamelCase						,   "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch"						,   lambda: FakeRegNetVisslWrapper(RegNetYaagf()      )						,   )

							__lowerCamelCase					:		int		   =     partial(
							    _lowerCamelCase						,   "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch"						,   lambda: FakeRegNetVisslWrapper(RegNetYaagf()      )						,   )

							__lowerCamelCase					:		List[Any]		   =     partial(
							    _lowerCamelCase						,   "https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch"						,   lambda: FakeRegNetVisslWrapper(RegNetYaaagf()      )						,   )

							__lowerCamelCase					:		Optional[Any]		   =     partial(
							    _lowerCamelCase						,   "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"						,   lambda: FakeRegNetVisslWrapper(
							        RegNet(RegNetParams(depth=27						,   group_width=1010						,   w_a=1744						,   w_a=620.83						,   w_m=2.52      )      )      )						,   )

							# IN1K finetuned
							__lowerCamelCase					:		Any		   =     partial(
							    _lowerCamelCase						,   "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch"						,   lambda: FakeRegNetVisslWrapper(RegNetYaagf()      )						,   )

							__lowerCamelCase					:		Dict		   =     partial(
							    _lowerCamelCase						,   "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch"						,   lambda: FakeRegNetVisslWrapper(RegNetYaagf()      )						,   )

							__lowerCamelCase					:		List[Any]		   =     partial(
							    _lowerCamelCase						,   "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch"						,   lambda: FakeRegNetVisslWrapper(RegNetYaaagf()      )						,   )

							__lowerCamelCase					:		int		   =     partial(
							    _lowerCamelCase						,   "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"						,   lambda: FakeRegNetVisslWrapper(
							        RegNet(RegNetParams(depth=27						,   group_width=1010						,   w_a=1744						,   w_a=620.83						,   w_m=2.52      )      )      )						,   )

							if model_name:
														convert_weight_and_push(
														    _lowerCamelCase						,   names_to_from_model_map[model_name]						,   names_to_ours_model_map[model_name]						,   names_to_config[model_name]						,   _lowerCamelCase						,   _lowerCamelCase						,   )
							else:
														for model_name, config in names_to_config.items():
																					convert_weight_and_push(
																					    _lowerCamelCase						,   names_to_from_model_map[model_name]						,   names_to_ours_model_map[model_name]						,   _lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   )
							return config, expected_shape


if __name__ == "__main__":
				__A  =				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 regnet* architecture,'''
				        ''' currently: regnetx-*, regnety-*. If `None`, all of them will the converted.'''
				    ),
				)
				parser.add_argument(
				    '''--pytorch_dump_folder_path''',
				    default=None,
				    type=Path,
				    required=True,
				    help='''Path to the output PyTorch model directory.''',
				)
				parser.add_argument(
				    '''--push_to_hub''',
				    default=True,
				    type=bool,
				    required=False,
				    help='''If True, push model and image processor to the hub.''',
				)

				__A  =				parser.parse_args()

				__A  =				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)
"""simple docstring"""





# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import doctest
import sys
import warnings
from os.path import abspath, dirname, join

import _pytest

from transformers.testing_utils import HfDoctestModule, HfDocTestParser


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
__A  =				abspath(join(dirname(__file__), '''src'''))
sys.path.insert(1, git_repo_path)

# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='''ignore''', category=FutureWarning)


def    lowercase_   (   _lowerCamelCase:	List[Any]      ) ->      Union[str, Any]:



							'''simple docstring'''

							config.addinivalue_line(
							    "markers"						,   "is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested"      )
							config.addinivalue_line(
							    "markers"						,   "is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested"      )
							config.addinivalue_line("markers"						,   "is_pipeline_test: mark test to run only when pipelines are tested"      )
							config.addinivalue_line("markers"						,   "is_staging_test: mark test to run only in the staging environment"      )
							config.addinivalue_line("markers"						,   "accelerate_tests: mark test that require accelerate"      )
							config.addinivalue_line("markers"						,   "tool_tests: mark the tool tests that are run on their specific schedule"      )


def    lowercase_   (   _lowerCamelCase:	Union[str, Any]      ) ->      Any:



							'''simple docstring'''

							from transformers.testing_utils import pytest_addoption_shared

							pytest_addoption_shared(_lowerCamelCase      )


def    lowercase_   (   _lowerCamelCase:	Optional[int]      ) ->      Tuple:



							'''simple docstring'''

							from transformers.testing_utils import pytest_terminal_summary_main

							__lowerCamelCase					:		Any		   =     terminalreporter.config.getoption("--make-reports"      )
							if make_reports:
														pytest_terminal_summary_main(_lowerCamelCase						,   id=_lowerCamelCase      )


def    lowercase_   (   _lowerCamelCase:	Union[str, Any]						,   _lowerCamelCase:	Tuple      ) ->      Dict:



							'''simple docstring'''

							if exitstatus == 5:
														__lowerCamelCase					:		Optional[int]		   =     0


# Doctest custom flag to ignore output.
__A  =				doctest.register_optionflag('''IGNORE_RESULT''')

__A  =				doctest.OutputChecker
class  _snake_case     (					a__		):






						def       lowerCamelCase__     (       self      :			List[str]							,					UpperCAmelCase      :			int							,					UpperCAmelCase      :			Any							,					UpperCAmelCase      :			int    ):
													if IGNORE_RESULT & optionflags:
																				return True
													return OutputChecker.check_output(self							,					UpperCAmelCase							,					UpperCAmelCase							,					UpperCAmelCase    )

__A  =				CustomOutputChecker
__A  =				HfDoctestModule







__A  =				HfDocTestParser