





'''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()
lowercase_					= logging.get_logger()


@dataclass
class 					a_     :

    '''simple docstring'''
    UpperCamelCase     = 42
    UpperCamelCase     = field(default_factory=snake_case_       )
    UpperCamelCase     = field(default_factory=snake_case_       )


    def 			snake_case_(		self ,		A ,		A ,		A       )		->   List[Any]:
        _SCREAMING_SNAKE_CASE									=			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 ,		A       )		->   int:
        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 			snake_case_(		self       )		->   List[Any]:
        # 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'''
    UpperCamelCase     = 42
    UpperCamelCase     = 42
    UpperCamelCase     = 1
    UpperCamelCase     = field(default_factory=snake_case_       )
    UpperCamelCase     = field(default_factory=snake_case_       )
    UpperCamelCase     = True


    def __call__(		self ,		A       )		->   int:
        _SCREAMING_SNAKE_CASE									=			Tracker(self.dest       )(A       ).parametrized
        _SCREAMING_SNAKE_CASE									=			Tracker(self.src       )(A       ).parametrized

        _SCREAMING_SNAKE_CASE									=			list(filter(lambda A       : type(A       ) not in self.src_skip ,		A       )       )
        _SCREAMING_SNAKE_CASE									=			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 ,		A       )		->   List[str]:
        super().__init__()

        _SCREAMING_SNAKE_CASE									=			[]
        # - 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									=			len(A       ) + 1
            feature_blocks.append((f'res{block_index}', v)       )

        _SCREAMING_SNAKE_CASE									=			nn.ModuleDict(A       )





    def 			snake_case_(		self ,		A       )		->   Tuple:
        return get_trunk_forward_outputs(
            A ,		out_feat_keys=A ,		feature_blocks=self._feature_blocks ,		)




class 					a_     (				snake_case_       ):

    '''simple docstring'''


    def 			snake_case_(		self ,		A       )		->   str:
        _SCREAMING_SNAKE_CASE									=			x.split("""-"""       )
        return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]       )





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

        else:
            _SCREAMING_SNAKE_CASE									=			super().__getitem__(A       )

        return val




class 					a_     (				snake_case_       ):

    '''simple docstring'''


    def __getitem__(		self ,		A       )		->   Callable[[], nn.Module]:
        if "seer" in x and "in1k" not in x:
            _SCREAMING_SNAKE_CASE									=			RegNetModel
        else:
            _SCREAMING_SNAKE_CASE									=			RegNetForImageClassification
        return val





def lowerCamelCase						(     __lowerCamelCase :    int					,       __lowerCamelCase :    int					,       __lowerCamelCase :    List[Tuple[str, str]]    )						->Tuple:
    for from_key, to_key in keys:
        _SCREAMING_SNAKE_CASE									=			from_state_dict[from_key].clone()
        print(F'Copied key={from_key} to={to_key}'    )
    return to_state_dict





def lowerCamelCase						(     __lowerCamelCase :    str					,       __lowerCamelCase :    Callable[[], nn.Module]					,       __lowerCamelCase :    Callable[[], nn.Module]					,       __lowerCamelCase :    RegNetConfig					,       __lowerCamelCase :    Path					,       __lowerCamelCase :    bool = True					,       )						->Dict:
    print(F'Converting {name}...'    )
    with torch.no_grad():
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			from_model_func()
        _SCREAMING_SNAKE_CASE									=			our_model_func(__lowerCamelCase    ).eval()
        _SCREAMING_SNAKE_CASE									=			ModuleTransfer(src=__lowerCamelCase					,       dest=__lowerCamelCase					,       raise_if_mismatch=__lowerCamelCase    )
        _SCREAMING_SNAKE_CASE									=			torch.randn((1, 3, 224, 224)    )
        module_transfer(__lowerCamelCase    )

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

    _SCREAMING_SNAKE_CASE									=			our_model(__lowerCamelCase					,       output_hidden_states=__lowerCamelCase    )
    _SCREAMING_SNAKE_CASE									=			(
        our_outputs.logits if isinstance(__lowerCamelCase					,       __lowerCamelCase    ) else our_outputs.last_hidden_state
    )

    _SCREAMING_SNAKE_CASE									=			from_model(__lowerCamelCase    )
    _SCREAMING_SNAKE_CASE									=			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									=			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									=			224 if """seer""" not in name else 384
        # we can use the convnext one
        _SCREAMING_SNAKE_CASE									=			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 lowerCamelCase						(     __lowerCamelCase :    Path					,       __lowerCamelCase :    str = None					,       __lowerCamelCase :    bool = True    )						->List[Any]:
    _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(cached_download(hf_hub_url(__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									=			{
        """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, 1_1110, 2_8280]					,       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, 1_1110, 2_8280]					,       groups_width=1010    ),
    }

    _SCREAMING_SNAKE_CASE									=			NameToOurModelFuncMap()
    _SCREAMING_SNAKE_CASE									=			NameToFromModelFuncMap()
    # add seer weights logic

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

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

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

    _SCREAMING_SNAKE_CASE									=			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									=			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
    _SCREAMING_SNAKE_CASE									=			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									=			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									=			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									=			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__":
      lowercase_					= 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_					= parser.parse_args()

      lowercase_					= 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'''

import importlib.metadata
import operator
import re
import sys
from typing import Optional

from packaging import version


lowercase_					= {
    """<""": operator.lt,
    """<=""": operator.le,
    """==""": operator.eq,
    """!=""": operator.ne,
    """>=""": operator.ge,
    """>""": operator.gt,
}





def lowerCamelCase						(     __lowerCamelCase :    Tuple					,       __lowerCamelCase :    Optional[Any]					,       __lowerCamelCase :    Dict					,       __lowerCamelCase :    List[Any]					,       __lowerCamelCase :    Optional[int]					,       __lowerCamelCase :    Optional[Any]    )						->Tuple:
    if got_ver is None or want_ver is None:
        raise ValueError(
            F'Unable to compare versions for {requirement}: need={want_ver} found={got_ver}. This is unusual. Consider'
            F' reinstalling {pkg}.'    )
    if not ops[op](version.parse(__lowerCamelCase    )					,       version.parse(__lowerCamelCase    )    ):
        raise ImportError(
            F'{requirement} is required for a normal functioning of this module, but found {pkg}=={got_ver}.{hint}'    )





def lowerCamelCase						(     __lowerCamelCase :    str					,       __lowerCamelCase :    Optional[str] = None    )						->None:
    _SCREAMING_SNAKE_CASE									=			F'\n{hint}' if hint is not None else """"""

    # non-versioned check
    if re.match(R"""^[\w_\-\d]+$"""					,       __lowerCamelCase    ):
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			requirement, None, None
    else:
        _SCREAMING_SNAKE_CASE									=			re.findall(R"""^([^!=<>\s]+)([\s!=<>]{1,2}.+)"""					,       __lowerCamelCase    )
        if not match:
            raise ValueError(
                """requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23, but"""
                F' got {requirement}'    )
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			match[0]
        _SCREAMING_SNAKE_CASE									=			want_full.split(""","""    )  # there could be multiple requirements
        _SCREAMING_SNAKE_CASE									=			{}
        for w in want_range:
            _SCREAMING_SNAKE_CASE									=			re.findall(R"""^([\s!=<>]{1,2})(.+)"""					,       __lowerCamelCase    )
            if not match:
                raise ValueError(
                    """requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23,"""
                    F' but got {requirement}'    )
            _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			match[0]
            _SCREAMING_SNAKE_CASE									=			want_ver
            if op not in ops:
                raise ValueError(F'{requirement}: need one of {list(ops.keys()    )}, but got {op}'    )

    # special case
    if pkg == "python":
        _SCREAMING_SNAKE_CASE									=			""".""".join([str(__lowerCamelCase    ) for x in sys.version_info[:3]]    )
        for op, want_ver in wanted.items():
            _compare_versions(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase    )
        return

    # check if any version is installed
    try:
        _SCREAMING_SNAKE_CASE									=			importlib.metadata.version(__lowerCamelCase    )
    except importlib.metadata.PackageNotFoundError:
        raise importlib.metadata.PackageNotFoundError(
            F'The \'{requirement}\' distribution was not found and is required by this application. {hint}'    )

    # check that the right version is installed if version number or a range was provided
    if want_ver is not None:
        for op, want_ver in wanted.items():
            _compare_versions(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase    )






def lowerCamelCase						(     __lowerCamelCase :    Union[str, Any]    )						->str:
    _SCREAMING_SNAKE_CASE									=			"""Try: pip install transformers -U or pip install -e '.[dev]' if you're working with git main"""
    return require_version(__lowerCamelCase					,       __lowerCamelCase    )



