





'''simple docstring'''

# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import torch

from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce





def lowerCamelCase						(     __lowerCamelCase :    int    )						->str:
    return (torch.arange(state.num_processes    ) + 1.0 + (state.num_processes * state.process_index)).to(state.device    )





def lowerCamelCase						(     __lowerCamelCase :    int    )						->int:
    _SCREAMING_SNAKE_CASE									=			create_tensor(__lowerCamelCase    )
    _SCREAMING_SNAKE_CASE									=			gather(__lowerCamelCase    )
    assert gathered_tensor.tolist() == list(range(1					,       state.num_processes**2 + 1    )    )





def lowerCamelCase						(     __lowerCamelCase :    Dict    )						->Optional[int]:
    _SCREAMING_SNAKE_CASE									=			[state.process_index]
    _SCREAMING_SNAKE_CASE									=			gather_object(__lowerCamelCase    )
    assert len(__lowerCamelCase    ) == state.num_processes, F'{gathered_obj}, {len(__lowerCamelCase    )} != {state.num_processes}'
    assert gathered_obj == list(range(state.num_processes    )    ), F'{gathered_obj} != {list(range(state.num_processes    )    )}'





def lowerCamelCase						(     __lowerCamelCase :    List[str]    )						->Tuple:
    _SCREAMING_SNAKE_CASE									=			create_tensor(__lowerCamelCase    )
    _SCREAMING_SNAKE_CASE									=			broadcast(__lowerCamelCase    )
    assert broadcasted_tensor.shape == torch.Size([state.num_processes]    )
    assert broadcasted_tensor.tolist() == list(range(1					,       state.num_processes + 1    )    )





def lowerCamelCase						(     __lowerCamelCase :    List[Any]    )						->str:
    # We need to pad the tensor with one more element if we are the main process
    # to ensure that we can pad
    if state.is_main_process:
        _SCREAMING_SNAKE_CASE									=			torch.arange(state.num_processes + 1    ).to(state.device    )
    else:
        _SCREAMING_SNAKE_CASE									=			torch.arange(state.num_processes    ).to(state.device    )
    _SCREAMING_SNAKE_CASE									=			pad_across_processes(__lowerCamelCase    )
    assert padded_tensor.shape == torch.Size([state.num_processes + 1]    )
    if not state.is_main_process:
        assert padded_tensor.tolist() == list(range(0					,       state.num_processes    )    ) + [0]





def lowerCamelCase						(     __lowerCamelCase :    Union[str, Any]    )						->Optional[int]:
    # For now runs on only two processes
    if state.num_processes != 2:
        return
    _SCREAMING_SNAKE_CASE									=			create_tensor(__lowerCamelCase    )
    _SCREAMING_SNAKE_CASE									=			reduce(__lowerCamelCase					,       """sum"""    )
    _SCREAMING_SNAKE_CASE									=			torch.tensor([4.0, 6]    ).to(state.device    )
    assert torch.allclose(__lowerCamelCase					,       __lowerCamelCase    ), F'{reduced_tensor} != {truth_tensor}'





def lowerCamelCase						(     __lowerCamelCase :    Any    )						->Optional[Any]:
    # For now runs on only two processes
    if state.num_processes != 2:
        return
    _SCREAMING_SNAKE_CASE									=			create_tensor(__lowerCamelCase    )
    _SCREAMING_SNAKE_CASE									=			reduce(__lowerCamelCase					,       """mean"""    )
    _SCREAMING_SNAKE_CASE									=			torch.tensor([2.0, 3]    ).to(state.device    )
    assert torch.allclose(__lowerCamelCase					,       __lowerCamelCase    ), F'{reduced_tensor} != {truth_tensor}'





def lowerCamelCase						(     __lowerCamelCase :    int    )						->int:
    # For xla_spawn (TPUs)
    main()





def lowerCamelCase						(     )						->Dict:
    _SCREAMING_SNAKE_CASE									=			PartialState()
    state.print(F'State: {state}'    )
    state.print("""testing gather"""    )
    test_gather(__lowerCamelCase    )
    state.print("""testing gather_object"""    )
    test_gather_object(__lowerCamelCase    )
    state.print("""testing broadcast"""    )
    test_broadcast(__lowerCamelCase    )
    state.print("""testing pad_across_processes"""    )
    test_pad_across_processes(__lowerCamelCase    )
    state.print("""testing reduce_sum"""    )
    test_reduce_sum(__lowerCamelCase    )
    state.print("""testing reduce_mean"""    )
    test_reduce_mean(__lowerCamelCase    )


if __name__ == "__main__":
      main()










'''simple docstring'''

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

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
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       )		->   Optional[int]:
        _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       )		->   str:
        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       )		->   str:
        # 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     = 0
    UpperCamelCase     = field(default_factory=snake_case_       )
    UpperCamelCase     = field(default_factory=snake_case_       )


    def __call__(		self ,		A       )		->   List[str]:
        _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       ):
            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}'       )





def lowerCamelCase						(     __lowerCamelCase :    str					,       __lowerCamelCase :    ResNetConfig					,       __lowerCamelCase :    Path					,       __lowerCamelCase :    bool = True    )						->int:
    print(F'Converting {name}...'    )
    with torch.no_grad():
        _SCREAMING_SNAKE_CASE									=			timm.create_model(__lowerCamelCase					,       pretrained=__lowerCamelCase    ).eval()
        _SCREAMING_SNAKE_CASE									=			ResNetForImageClassification(__lowerCamelCase    ).eval()
        _SCREAMING_SNAKE_CASE									=			ModuleTransfer(src=__lowerCamelCase					,       dest=__lowerCamelCase    )
        _SCREAMING_SNAKE_CASE									=			torch.randn((1, 3, 224, 224)    )
        module_transfer(__lowerCamelCase    )

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

    _SCREAMING_SNAKE_CASE									=			F'resnet{"-".join(name.split("resnet"    )    )}'
    print(__lowerCamelCase    )

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

        # we can use the convnext one
        _SCREAMING_SNAKE_CASE									=			AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""    )
        image_processor.push_to_hub(
            repo_path_or_name=save_directory / checkpoint_name					,       commit_message="""Add image processor"""					,       use_temp_dir=__lowerCamelCase					,       )

        print(F'Pushed {checkpoint_name}'    )





def lowerCamelCase						(     __lowerCamelCase :    Path					,       __lowerCamelCase :    str = None					,       __lowerCamelCase :    bool = True    )						->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(hf_hub_download(__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									=			{
        """resnet18""": ImageNetPreTrainedConfig(
            depths=[2, 2, 2, 2]					,       hidden_sizes=[64, 128, 256, 512]					,       layer_type="""basic"""    ),
        """resnet26""": ImageNetPreTrainedConfig(
            depths=[2, 2, 2, 2]					,       hidden_sizes=[256, 512, 1024, 2048]					,       layer_type="""bottleneck"""    ),
        """resnet34""": ImageNetPreTrainedConfig(
            depths=[3, 4, 6, 3]					,       hidden_sizes=[64, 128, 256, 512]					,       layer_type="""basic"""    ),
        """resnet50""": ImageNetPreTrainedConfig(
            depths=[3, 4, 6, 3]					,       hidden_sizes=[256, 512, 1024, 2048]					,       layer_type="""bottleneck"""    ),
        """resnet101""": ImageNetPreTrainedConfig(
            depths=[3, 4, 23, 3]					,       hidden_sizes=[256, 512, 1024, 2048]					,       layer_type="""bottleneck"""    ),
        """resnet152""": ImageNetPreTrainedConfig(
            depths=[3, 8, 36, 3]					,       hidden_sizes=[256, 512, 1024, 2048]					,       layer_type="""bottleneck"""    ),
    }

    if model_name:
        convert_weight_and_push(__lowerCamelCase					,       names_to_config[model_name]					,       __lowerCamelCase					,       __lowerCamelCase    )
    else:
        for model_name, config in names_to_config.items():
            convert_weight_and_push(__lowerCamelCase					,       __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 resnet* architecture,"""
              """ currently: resnet18,26,34,50,101,152. 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)



