





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()
__lowercase       :Union[str, Any]       =  logging.get_logger()



@dataclass
class    _a     :






							"""simple docstring"""

							snake_case_							 =						42
							snake_case_							 =						field(default_factory=lowercase__							)
							snake_case_							 =						field(default_factory=lowercase__							)

							def      A_  (      self				:     Tuple							,	a				:     Dict							,	a				:     Tensor							,	a				:     Tensor    )	->Optional[Any]:
														SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					len(list(m.modules()    )    ) == 1 or isinstance(a							,	nn.Convad    ) or isinstance(a							,	nn.BatchNormad    )
														if has_not_submodules:
																					self.traced.append(a    )

							def __call__(      self				:     List[str]							,	a				:     Tensor    )	->Any:
														for m in self.module.modules():
																					self.handles.append(m.register_forward_hook(self._forward_hook    )    )
														self.module(a    )
														[x.remove() for x in self.handles]
														return self






							@property
							def      A_  (      self				:     List[str]    )	->Optional[int]:
														# check the len of the state_dict keys to see if we have learnable params
														return list(filter(lambda a    : len(list(x.state_dict().keys()    )    ) > 0							,	self.traced    )    )







@dataclass
class    _a     :






							"""simple docstring"""

							snake_case_							 =						42
							snake_case_							 =						42
							snake_case_							 =						1
							snake_case_							 =						field(default_factory=lowercase__							)
							snake_case_							 =						field(default_factory=lowercase__							)
							snake_case_							 =						True

							def __call__(      self				:     int							,	a				:     Tensor    )	->Optional[int]:
														SCREAMING_SNAKE_CASE__     :				Optional[int]   =					Tracker(self.dest    )(a    ).parametrized
														SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					Tracker(self.src    )(a    ).parametrized

														SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					list(filter(lambda a    : type(a    ) not in self.src_skip							,	a    )    )
														SCREAMING_SNAKE_CASE__     :				Dict   =					list(filter(lambda a    : type(a    ) not in self.dest_skip							,	a    )    )

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

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







class    _a     (				nn.Module							):






							"""simple docstring"""

							def __init__(      self				:     Optional[int]							,	a				:     nn.Module    )	->int:
														super().__init__()

														SCREAMING_SNAKE_CASE__     :				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}"""
																					SCREAMING_SNAKE_CASE__     :				Tuple   =					len(a    ) + 1
																					feature_blocks.append((f"""res{block_index}""", v)    )

														SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					nn.ModuleDict(a    )






							def      A_  (      self				:     List[Any]							,	a				:     Tensor    )	->Optional[Any]:
														return get_trunk_forward_outputs(
														    a							,	out_feat_keys=a							,	feature_blocks=self._feature_blocks							,	)







class    _a     (				lowercase__							):






							"""simple docstring"""

							def      A_  (      self				:     Tuple							,	a				:     str    )	->str:
														SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					x.split("-"    )
														return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]    )






							def __getitem__(      self				:     List[str]							,	a				:     str    )	->Callable[[], Tuple[nn.Module, Dict]]:
														# default to timm!
														if x not in self:
																					SCREAMING_SNAKE_CASE__     :				Dict   =					self.convert_name_to_timm(a    )
																					SCREAMING_SNAKE_CASE__     :				List[Any]   =					partial(lambda: (timm.create_model(a							,	pretrained=a    ).eval(), None)    )

														else:
																					SCREAMING_SNAKE_CASE__     :				Dict   =					super().__getitem__(a    )

														return val







class    _a     (				lowercase__							):






							"""simple docstring"""

							def __getitem__(      self				:     Union[str, Any]							,	a				:     str    )	->Callable[[], nn.Module]:
														if "seer" in x and "in1k" not in x:
																					SCREAMING_SNAKE_CASE__     :				Tuple   =					RegNetModel
														else:
																					SCREAMING_SNAKE_CASE__     :				Tuple   =					RegNetForImageClassification
														return val







def       UpperCAmelCase							(     _lowerCamelCase      :  Union[str, Any]			,   _lowerCamelCase      :  int			,   _lowerCamelCase      :  List[Tuple[str, str]]     ):





							'''simple docstring'''


							for from_key, to_key in keys:
														SCREAMING_SNAKE_CASE__     :				Tuple   =					from_state_dict[from_key].clone()
														print(f"""Copied key={from_key} to={to_key}"""     )
							return to_state_dict







def       UpperCAmelCase							(     _lowerCamelCase      :  str			,   _lowerCamelCase      :  Callable[[], nn.Module]			,   _lowerCamelCase      :  Callable[[], nn.Module]			,   _lowerCamelCase      :  RegNetConfig			,   _lowerCamelCase      :  Path			,   _lowerCamelCase      :  bool = True			,   ):





							'''simple docstring'''


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

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

							SCREAMING_SNAKE_CASE__     :				Any   =					our_model(_lowerCamelCase			,   output_hidden_states=_lowerCamelCase     )
							SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					(
							    our_outputs.logits if isinstance(_lowerCamelCase			,   _lowerCamelCase     ) else our_outputs.last_hidden_state
							)

							SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					from_model(_lowerCamelCase     )
							SCREAMING_SNAKE_CASE__     :				Union[str, 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:
														SCREAMING_SNAKE_CASE__     :				Optional[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			,   )

														SCREAMING_SNAKE_CASE__     :				Tuple   =					224 if "seer" not in name else 384
														# we can use the convnext one
														SCREAMING_SNAKE_CASE__     :				Dict   =					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       UpperCAmelCase							(     _lowerCamelCase      :  Path			,   _lowerCamelCase      :  str = None			,   _lowerCamelCase      :  bool = True     ):





							'''simple docstring'''


							SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					"imagenet-1k-id2label.json"
							SCREAMING_SNAKE_CASE__     :				int   =					1_000
							SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					(1, num_labels)

							SCREAMING_SNAKE_CASE__     :				Dict   =					"huggingface/label-files"
							SCREAMING_SNAKE_CASE__     :				Tuple   =					num_labels
							SCREAMING_SNAKE_CASE__     :				Dict   =					json.load(open(cached_download(hf_hub_url(_lowerCamelCase			,   _lowerCamelCase			,   repo_type="dataset"     )     )			,   "r"     )     )
							SCREAMING_SNAKE_CASE__     :				Any   =					{int(_lowerCamelCase     ): v for k, v in idalabel.items()}

							SCREAMING_SNAKE_CASE__     :				str   =					idalabel
							SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					{v: k for k, v in idalabel.items()}

							SCREAMING_SNAKE_CASE__     :				Dict   =					partial(_lowerCamelCase			,   num_labels=_lowerCamelCase			,   idalabel=_lowerCamelCase			,   labelaid=_lowerCamelCase     )

							SCREAMING_SNAKE_CASE__     :				Tuple   =					{
							    "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, 1_008]			,   groups_width=48			,   layer_type="x"     ),
							    "regnet-x-040": ImageNetPreTrainedConfig(
							        depths=[2, 5, 14, 2]			,   hidden_sizes=[80, 240, 560, 1_360]			,   groups_width=40			,   layer_type="x"     ),
							    "regnet-x-064": ImageNetPreTrainedConfig(
							        depths=[2, 4, 10, 1]			,   hidden_sizes=[168, 392, 784, 1_624]			,   groups_width=56			,   layer_type="x"     ),
							    "regnet-x-080": ImageNetPreTrainedConfig(
							        depths=[2, 5, 15, 1]			,   hidden_sizes=[80, 240, 720, 1_920]			,   groups_width=120			,   layer_type="x"     ),
							    "regnet-x-120": ImageNetPreTrainedConfig(
							        depths=[2, 5, 11, 1]			,   hidden_sizes=[224, 448, 896, 2_240]			,   groups_width=112			,   layer_type="x"     ),
							    "regnet-x-160": ImageNetPreTrainedConfig(
							        depths=[2, 6, 13, 1]			,   hidden_sizes=[256, 512, 896, 2_048]			,   groups_width=128			,   layer_type="x"     ),
							    "regnet-x-320": ImageNetPreTrainedConfig(
							        depths=[2, 7, 13, 1]			,   hidden_sizes=[336, 672, 1_344, 2_520]			,   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, 1_512]			,   groups_width=24     ),
							    "regnet-y-040": ImageNetPreTrainedConfig(
							        depths=[2, 6, 12, 2]			,   hidden_sizes=[128, 192, 512, 1_088]			,   groups_width=64     ),
							    "regnet-y-064": ImageNetPreTrainedConfig(
							        depths=[2, 7, 14, 2]			,   hidden_sizes=[144, 288, 576, 1_296]			,   groups_width=72     ),
							    "regnet-y-080": ImageNetPreTrainedConfig(
							        depths=[2, 4, 10, 1]			,   hidden_sizes=[168, 448, 896, 2_016]			,   groups_width=56     ),
							    "regnet-y-120": ImageNetPreTrainedConfig(
							        depths=[2, 5, 11, 1]			,   hidden_sizes=[224, 448, 896, 2_240]			,   groups_width=112     ),
							    "regnet-y-160": ImageNetPreTrainedConfig(
							        depths=[2, 4, 11, 1]			,   hidden_sizes=[224, 448, 1_232, 3_024]			,   groups_width=112     ),
							    "regnet-y-320": ImageNetPreTrainedConfig(
							        depths=[2, 5, 12, 1]			,   hidden_sizes=[232, 696, 1_392, 3_712]			,   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, 1_392, 3_712]			,   groups_width=232     ),
							    "regnet-y-640-seer": RegNetConfig(depths=[2, 5, 12, 1]			,   hidden_sizes=[328, 984, 1_968, 4_920]			,   groups_width=328     ),
							    "regnet-y-1280-seer": RegNetConfig(
							        depths=[2, 7, 17, 1]			,   hidden_sizes=[528, 1_056, 2_904, 7_392]			,   groups_width=264     ),
							    "regnet-y-2560-seer": RegNetConfig(
							        depths=[3, 7, 16, 1]			,   hidden_sizes=[640, 1_696, 2_544, 5_088]			,   groups_width=640     ),
							    "regnet-y-10b-seer": ImageNetPreTrainedConfig(
							        depths=[2, 7, 17, 1]			,   hidden_sizes=[2_020, 4_040, 11_110, 28_280]			,   groups_width=1_010     ),
							    # finetuned on imagenet
							    "regnet-y-320-seer-in1k": ImageNetPreTrainedConfig(
							        depths=[2, 5, 12, 1]			,   hidden_sizes=[232, 696, 1_392, 3_712]			,   groups_width=232     ),
							    "regnet-y-640-seer-in1k": ImageNetPreTrainedConfig(
							        depths=[2, 5, 12, 1]			,   hidden_sizes=[328, 984, 1_968, 4_920]			,   groups_width=328     ),
							    "regnet-y-1280-seer-in1k": ImageNetPreTrainedConfig(
							        depths=[2, 7, 17, 1]			,   hidden_sizes=[528, 1_056, 2_904, 7_392]			,   groups_width=264     ),
							    "regnet-y-2560-seer-in1k": ImageNetPreTrainedConfig(
							        depths=[3, 7, 16, 1]			,   hidden_sizes=[640, 1_696, 2_544, 5_088]			,   groups_width=640     ),
							    "regnet-y-10b-seer-in1k": ImageNetPreTrainedConfig(
							        depths=[2, 7, 17, 1]			,   hidden_sizes=[2_020, 4_040, 11_110, 28_280]			,   groups_width=1_010     ),
							}

							SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					NameToOurModelFuncMap()
							SCREAMING_SNAKE_CASE__     :				Any   =					NameToFromModelFuncMap()
							# add seer weights logic

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

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

							SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					partial(
							    _lowerCamelCase			,   "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch"			,   lambda: FakeRegNetVisslWrapper(RegNetYaagf()     )			,   )

							SCREAMING_SNAKE_CASE__     :				Optional[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()     )			,   )

							SCREAMING_SNAKE_CASE__     :				Dict   =					partial(
							    _lowerCamelCase			,   "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"			,   lambda: FakeRegNetVisslWrapper(
							        RegNet(RegNetParams(depth=27			,   group_width=1_010			,   w_a=1_744			,   w_a=620.83			,   w_m=2.5_2     )     )     )			,   )

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

							SCREAMING_SNAKE_CASE__     :				List[Any]   =					partial(
							    _lowerCamelCase			,   "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch"			,   lambda: FakeRegNetVisslWrapper(RegNetYaagf()     )			,   )

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

							SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					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=1_010			,   w_a=1_744			,   w_a=620.83			,   w_m=2.5_2     )     )     )			,   )

							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__":
				__lowercase       :List[Any]       =  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.",
				)

				__lowercase       :Optional[int]       =  parser.parse_args()

				__lowercase       :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)






# 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.
__lowercase       :List[Any]       =  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       UpperCAmelCase							(     _lowerCamelCase      :  int     ):





						'''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       UpperCAmelCase							(     _lowerCamelCase      :  str     ):





						'''simple docstring'''


						from transformers.testing_utils import pytest_addoption_shared

						pytest_addoption_shared(_lowerCamelCase     )







def       UpperCAmelCase							(     _lowerCamelCase      :  Tuple     ):





						'''simple docstring'''


						from transformers.testing_utils import pytest_terminal_summary_main

						SCREAMING_SNAKE_CASE__     :				List[str]   =					terminalreporter.config.getoption("--make-reports"     )
						if make_reports:
												pytest_terminal_summary_main(_lowerCamelCase			,   id=_lowerCamelCase     )







def       UpperCAmelCase							(     _lowerCamelCase      :  List[Any]			,   _lowerCamelCase      :  Dict     ):





						'''simple docstring'''


						if exitstatus == 5:
												SCREAMING_SNAKE_CASE__     :				List[str]   =					0


# Doctest custom flag to ignore output.
__lowercase       :Optional[Any]       =  doctest.register_optionflag("IGNORE_RESULT")

__lowercase       :Dict       =  doctest.OutputChecker



class    _a     (				lowercase__							):






							"""simple docstring"""

							def      A_  (      self				:     Dict							,	a				:     List[str]							,	a				:     Dict							,	a				:     int    )	->Optional[Any]:
													if IGNORE_RESULT & optionflags:
																			return True
													return OutputChecker.check_output(self							,	a							,	a							,	a    )







__lowercase       :Any       =  CustomOutputChecker
__lowercase       :Any       =  HfDoctestModule



__lowercase       :int       =  HfDocTestParser