






'''simple docstring'''







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

import torch
import torch.nn as nn
from huggingface_hub import cached_download, hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, VanConfig, VanForImageClassification
from transformers.models.deprecated.van.modeling_van import VanLayerScaling
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCAmelCase					   =       logging.get_logger(__name__)




@dataclass
class      __SCREAMING_SNAKE_CASE					:


  """simple docstring"""


  _a     :   nn.Module
  _a     :   List[nn.Module]			 =    field(default_factory=__A					)
  _a     :   list			 =    field(default_factory=__A					)







  def       _a					( self      ,  UpperCamelCase__      ,  UpperCamelCase__      ,  UpperCamelCase__  ):






         """simple docstring"""


         a_	    = len(list(m.modules()  )  ) == 1 or isinstance(UpperCamelCase__      ,  nn.Convad  ) or isinstance(UpperCamelCase__      ,  nn.BatchNormad  )
         if has_not_submodules:
                if not isinstance(UpperCamelCase__      ,  UpperCamelCase__  ):
                       self.traced.append(UpperCamelCase__  )







  def __call__( self      ,  UpperCamelCase__  ):






         """simple docstring"""


         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       _a					( self  ):






         """simple docstring"""


         return list(filter(lambda UpperCamelCase__  : len(list(x.state_dict().keys()  )  ) > 0      ,  self.traced  )  )





@dataclass
class      __SCREAMING_SNAKE_CASE					:


  """simple docstring"""


  _a     :   nn.Module
  _a     :   nn.Module
  _a     :   int			 =    0
  _a     :   List			 =    field(default_factory=__A					)
  _a     :   List			 =    field(default_factory=__A					)







  def __call__( self      ,  UpperCamelCase__  ):






         """simple docstring"""


         a_	    = Tracker(self.dest  )(UpperCamelCase__  ).parametrized
         a_	    = Tracker(self.src  )(UpperCamelCase__  ).parametrized

         a_	    = list(filter(lambda UpperCamelCase__  : type(UpperCamelCase__  ) not in self.src_skip      ,  UpperCamelCase__  )  )
         a_	    = list(filter(lambda UpperCamelCase__  : type(UpperCamelCase__  ) not in self.dest_skip      ,  UpperCamelCase__  )  )

         if len(UpperCamelCase__  ) != len(UpperCamelCase__  ):
                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}'  )





def 			__UpperCamelCase				(			lowercase_					:     nn.Module      ,					lowercase_					:     nn.Module      ):
       """simple docstring"""




       a_	    = from_model.state_dict()
       a_	    = our_model.state_dict()
       a_	    = our_model.config
       a_	    = []
       for stage_idx in range(len(config.hidden_sizes      )      ):
              for block_id in range(config.depths[stage_idx]      ):
                     a_	    = F'block{stage_idx + 1}.{block_id}.layer_scale_1'
                     a_	    = F'van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight'

                     all_keys.append((from_key, to_key)      )
                     a_	    = F'block{stage_idx + 1}.{block_id}.layer_scale_2'
                     a_	    = F'van.encoder.stages.{stage_idx}.layers.{block_id}.mlp_scaling.weight'

                     all_keys.append((from_key, to_key)      )

       for from_key, to_key in all_keys:
              a_	    = from_state_dict.pop(lowercase_      )

       our_model.load_state_dict(lowercase_      )
       return our_model


def 			__UpperCamelCase				(			lowercase_					:     str      ,					lowercase_					:     VanConfig      ,					lowercase_					:     str      ,					lowercase_					:     nn.Module      ,					lowercase_					:     Path      ,					lowercase_					:     bool = True      ,					):
       """simple docstring"""




       print(F'Downloading weights for {name}...'      )
       a_	    = cached_download(lowercase_      )
       print(F'Converting {name}...'      )
       a_	    = torch.load(lowercase_      )['state_dict']
       from_model.load_state_dict(lowercase_      )
       from_model.eval()
       with torch.no_grad():
              a_	    = VanForImageClassification(lowercase_      ).eval()
              a_	    = ModuleTransfer(src=lowercase_      ,					dest=lowercase_      )
              a_	    = torch.randn((1, 3, 224, 224)      )
              module_transfer(lowercase_      )
              a_	    = copy_parameters(lowercase_      ,					lowercase_      )

       if not torch.allclose(from_model(lowercase_      )      ,					our_model(lowercase_      ).logits      ):
              raise ValueError('The model logits don\'t match the original one.'      )

       a_	    = name
       print(lowercase_      )

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

              # we can use the convnext one
              a_	    = 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=lowercase_      ,					)

              print(F'Pushed {checkpoint_name}'      )


def 			__UpperCamelCase				(			lowercase_					:     Path      ,					lowercase_					:     str = None      ,					lowercase_					:     bool = True      ):
       """simple docstring"""




       a_	    = 'imagenet-1k-id2label.json'
       a_	    = 1_000

       a_	    = 'huggingface/label-files'
       a_	    = num_labels
       a_	    = json.load(open(hf_hub_download(lowercase_      ,					lowercase_      ,					repo_type='dataset'      )      ,					'r'      )      )
       a_	    = {int(lowercase_      ): v for k, v in idalabel.items()}

       a_	    = idalabel
       a_	    = {v: k for k, v in idalabel.items()}

       a_	    = partial(lowercase_      ,					num_labels=lowercase_      ,					idalabel=lowercase_      ,					labelaid=lowercase_      )

       a_	    = {
           'van-tiny': ImageNetPreTrainedConfig(
               hidden_sizes=[32, 64, 160, 256]      ,					depths=[3, 3, 5, 2]      ,					mlp_ratios=[8, 8, 4, 4]      ,					),
           'van-small': ImageNetPreTrainedConfig(
               hidden_sizes=[64, 128, 320, 512]      ,					depths=[2, 2, 4, 2]      ,					mlp_ratios=[8, 8, 4, 4]      ,					),
           'van-base': ImageNetPreTrainedConfig(
               hidden_sizes=[64, 128, 320, 512]      ,					depths=[3, 3, 12, 3]      ,					mlp_ratios=[8, 8, 4, 4]      ,					),
           'van-large': ImageNetPreTrainedConfig(
               hidden_sizes=[64, 128, 320, 512]      ,					depths=[3, 5, 27, 3]      ,					mlp_ratios=[8, 8, 4, 4]      ,					),
       }

       a_	    = {
           'van-tiny': van_tiny,
           'van-small': van_small,
           'van-base': van_base,
           'van-large': van_large,
       }

       a_	    = {
           'van-tiny': (
               'https://huggingface.co/Visual-Attention-Network/VAN-Tiny-original/resolve/main/van_tiny_754.pth.tar'
           ),
           'van-small': (
               'https://huggingface.co/Visual-Attention-Network/VAN-Small-original/resolve/main/van_small_811.pth.tar'
           ),
           'van-base': (
               'https://huggingface.co/Visual-Attention-Network/VAN-Base-original/resolve/main/van_base_828.pth.tar'
           ),
           'van-large': (
               'https://huggingface.co/Visual-Attention-Network/VAN-Large-original/resolve/main/van_large_839.pth.tar'
           ),
       }

       if model_name:
              convert_weight_and_push(
                  lowercase_      ,					names_to_config[model_name]      ,					checkpoint=names_to_original_checkpoints[model_name]      ,					from_model=names_to_original_models[model_name]()      ,					save_directory=lowercase_      ,					push_to_hub=lowercase_      ,					)
       else:
              for model_name, config in names_to_config.items():
                     convert_weight_and_push(
                         lowercase_      ,					lowercase_      ,					checkpoint=names_to_original_checkpoints[model_name]      ,					from_model=names_to_original_models[model_name]()      ,					save_directory=lowercase_      ,					push_to_hub=lowercase_      ,					)


if __name__ == "__main__":
  __lowerCAmelCase					   =       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: van-tiny/small/base/large. 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(
      "--van_dir",
      required=True,
      type=Path,
      help=(
          "A path to VAN's original implementation directory. You can download from here:"
          " https://github.com/Visual-Attention-Network/VAN-Classification"
      ),
  )
  parser.add_argument(
      "--push_to_hub",
      default=True,
      type=bool,
      required=False,
      help="If True, push model and image processor to the hub.",
  )

  __lowerCAmelCase					   =       parser.parse_args()
  __lowerCAmelCase					   =       args.pytorch_dump_folder_path
  pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
  __lowerCAmelCase					   =       args.van_dir
  # append the path to the parents to maskformer dir
  sys.path.append(str(van_dir.parent))
  from van.models.van import van_base, van_large, van_small, van_tiny

  convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)












'''simple docstring'''







import itertools
import os
import subprocess
from os.path import dirname

from parameterized import parameterized

from tests.trainer.test_trainer import TrainerIntegrationCommon  # noqa
from transformers import is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_tests_dir,
    require_deepspeed,
    require_torch_gpu,
    slow,
)
from transformers.trainer_utils import set_seed


if is_torch_available():
  from tests.trainer.test_trainer import (  # noqa
      RegressionModelConfig,
      RegressionPreTrainedModel,
      get_regression_trainer,
  )


set_seed(42)

__lowerCAmelCase					   =       get_tests_dir("fixtures")
__lowerCAmelCase					   =       os.path.join(dirname(get_tests_dir()))
__lowerCAmelCase					   =       dirname(os.path.abspath(__file__))

# default torch.distributed port
__lowerCAmelCase					   =       "10999"

__lowerCAmelCase					   =       "t5-small"

# *** Working Models ***
__lowerCAmelCase					   =       "hf-internal-testing/tiny-albert"
__lowerCAmelCase					   =       "sshleifer/bart-tiny-random"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-bert"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-bigbird_pegasus"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-big_bird"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-blenderbot"
__lowerCAmelCase					   =       "bigscience/bigscience-small-testing"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-deberta"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-deberta-v2"
__lowerCAmelCase					   =       "sshleifer/tiny-distilbert-base-cased"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-electra"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-flaubert"
__lowerCAmelCase					   =       "stas/tiny-wmt19-en-de"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-funnel"
__lowerCAmelCase					   =       "sshleifer/tiny-gpt2"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-gptj"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-gpt_neo"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-layoutlm"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-led"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-longformer"
__lowerCAmelCase					   =       "stas/tiny-m2m_100"  # hf tiny model is unsuitable
__lowerCAmelCase					   =       "sshleifer/tiny-marian-en-de"
__lowerCAmelCase					   =       "sshleifer/tiny-mbart"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-mobilebert"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-mpnet"
__lowerCAmelCase					   =       "stas/pegasus-cnn_dailymail-tiny-random"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-prophetnet"
__lowerCAmelCase					   =       "sshleifer/tiny-distilroberta-base"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-squeezebert"
__lowerCAmelCase					   =       "patrickvonplaten/t5-tiny-random"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-t5-v1.1"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-vit"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-xlm-roberta"
__lowerCAmelCase					   =       "sshleifer/tiny-xlnet-base-cased"


# *** To Fix ***


# *** tiny model issues ***
# missing model files:
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-mt5"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-camembert"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-openai-gpt"

# missing tokenizer files
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-convbert"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-layoutlmv2"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-hubert"

# issues with tokenizer
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-ctrl"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-transfo-xl"  # same as ctrl

# other issues with tiny models
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-ibert"  # multiple issues with either mlm/qa/clas
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-reformer"  # multiple issues with either mlm/qa/clas

# *** Lacking official examples to test with ***
# or not working with examples
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-dpr"
# - "dpr"  examples/research_projects/rag-end2end-retriever/
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-rag"
# - "rag" research_projects
__lowerCAmelCase					   =       ""
# - "luke" Entities classes - no plan to make such example
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-lxmert"
# - "lxmert" doesn't work with run_qa.py
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-clip"
# - "clip" nothing under pytorch examples - XXX: Suraj is working on adding some - check by end of Sep
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-speech_to_text"
# - "speech_to_text", nothing under pytorch examples


# *** Reactive mode ***
# models with low usage, unstable API, things about to change - do nothing about the following until someone runs into a problem
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-tapas"


def 			__UpperCamelCase				(			lowercase_					:     Optional[int]=False      ):
       """simple docstring"""




       a_	    = min(2      ,					get_gpu_count()      ) if distributed else 1
       a_	    = os.environ.get('DS_TEST_PORT'      ,					lowercase_      )
       return F'deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'.split()


def 			__UpperCamelCase				(			):
       """simple docstring"""




       a_	    = F'{FIXTURE_DIRECTORY}/tests_samples'
       a_	    = F'{data_dir_samples}/wmt_en_ro'
       a_	    = F'{data_dir_samples}/xsum'
       a_	    = '\n        --do_train\n        --max_train_samples 4\n        --per_device_train_batch_size 2\n        --num_train_epochs 1\n        --fp16\n        --report_to none\n        --overwrite_output_dir\n        '.split()

       # try to cover as many models as possible once (it's enough to run on one task per model)
       # but need a tiny model for each
       #
       # should have "{model_type.upper()}_TINY" corresponding vars defined, e.g., T5_TINY, etc.
       a_	    = {
           'trans': [
               'bart',
               'fsmt',
               'm2m_100',
               'marian',
               'mbart',
               't5',
               't5_v1',
               # "mt5", missing model files
           ],
           'sum': [
               'pegasus',
           ],
           'clm': [
               'big_bird',
               'bigbird_pegasus',
               'blenderbot',
               'bloom',
               'gpt2',
               'gpt_neo',
               'gptj',
               'xlm-roberta',
               'prophetnet',
               # "camembert", missing model files
           ],
           'mlm': [
               'albert',
               'deberta',
               'deberta-v2',
               'distilbert',
               'electra',
               'flaubert',
               'funnel',
               'layoutlm',
               # "reformer", # multiple issues with either mlm/qa/clas
           ],
           'qa': [
               'led',
               'longformer',
               'mobilebert',
               'mpnet',
               'roberta',
               'squeezebert',
               # "convbert", # missing tokenizer files
               # "layoutlmv2", missing model files
           ],
           'clas': [
               'bert',
               'xlnet',
               # "hubert", # missing tokenizer files
               # "ibert", # multiple issues with either mlm/qa/clas
               # "transfo-xl", # tokenizer issues as ctrl
               # "ctrl", # tokenizer issues
               # "openai-gpt", missing model files
               # "tapas", multiple issues
           ],
           'img_clas': [
               'vit',
           ],
       }

       a_	    = F'{ROOT_DIRECTORY}/examples/pytorch'

       a_	    = {
           'trans': F'\n        {scripts_dir}/translation/run_translation.py\n        --train_file {data_dir_wmt}/train.json\n        --source_lang en\n        --target_lang ro\n        ',
           'sum': F'\n        {scripts_dir}/summarization/run_summarization.py\n        --train_file {data_dir_xsum}/sample.json\n        --max_source_length 12\n        --max_target_length 12\n        --lang en\n        ',
           'clm': F'\n        {scripts_dir}/language-modeling/run_clm.py\n        --train_file {FIXTURE_DIRECTORY}/sample_text.txt\n        --block_size 8\n        ',
           'mlm': F'\n        {scripts_dir}/language-modeling/run_mlm.py\n        --train_file {FIXTURE_DIRECTORY}/sample_text.txt\n        ',
           'qa': F'\n        {scripts_dir}/question-answering/run_qa.py\n        --train_file {data_dir_samples}/SQUAD/sample.json\n        ',
           'clas': F'\n        {scripts_dir}/text-classification/run_glue.py\n        --train_file {data_dir_samples}/MRPC/train.csv\n        --max_seq_length 12\n        --task_name MRPC\n        ',
           'img_clas': F'\n        {scripts_dir}/image-classification/run_image_classification.py\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --remove_unused_columns False\n            --max_steps 10\n            --image_processor_name {DS_TESTS_DIRECTORY}/vit_feature_extractor.json\n        ',
       }

       a_	    = get_launcher(distributed=lowercase_      )

       a_	    = {}
       for task, args in tasks.items():
              a_	    = args.split()
              for model in tasksamodels[task]:
                     a_	    = globals()[F'{model.upper().replace("-"      ,					"_"      )}_TINY']
                     a_	    = F'--model_name_or_path {model_name}'.split()
                     a_	    = launcher + args + args_model + args_main

                     # # generation special case
                     # if task == "gen":
                     #     launcher = f"deepspeed --num_nodes 1 --num_gpus 1".split()
                     #     args_model += f"--model_type {model}".split()
                     #     cmds[f"{task}_{model}"] = launcher + args + args_model
                     # else:

       return cmds


__lowerCAmelCase					   =       make_task_cmds()

__lowerCAmelCase					   =       "zero2"
__lowerCAmelCase					   =       "zero3"

__lowerCAmelCase					   =       [ZEROa, ZEROa]


def 			__UpperCamelCase				(			lowercase_					:     str      ,					lowercase_					:     Optional[int]      ,					lowercase_					:     Dict      ):
       """simple docstring"""




       a_	    = parameterized.to_safe_name('_'.join(str(lowercase_      ) for x in param.args      )      )
       return F'{func.__name__}_{param_based_name}'


# Cartesian-product of zero stages with models to test
__lowerCAmelCase					   =       list(itertools.product(stages, task_cmds.keys()))




@slow
@require_deepspeed
@require_torch_gpu
class      __SCREAMING_SNAKE_CASE					(__A					):


  """simple docstring"""


  def       _a					( self      ,  UpperCamelCase__      ,  UpperCamelCase__  ):






         """simple docstring"""


         if task not in task_cmds:
                raise ValueError(f'don\'t know of task {task}, have {task_cmds.keys()}'  )

         a_	    = task_cmds[task]
         a_	    = f'--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json'.split()

         a_	    = self.get_auto_remove_tmp_dir()
         a_	    = f'--output_dir {output_dir}'.split()

         cmd += args_ds + args_out

         return cmd, output_dir







  @parameterized.expand(UpperCamelCase__      ,  name_func=UpperCamelCase__  )
  def       _a					( self      ,  UpperCamelCase__      ,  UpperCamelCase__  ):






         """simple docstring"""


         a_    ,  a_	    = self.get_task_cmd(UpperCamelCase__      ,  UpperCamelCase__  )

         # 1. generate the checkpoint
         cmd += "--save_steps 1".split()
         # keep for quick debug
         # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] + cmd)); die
         execute_subprocess_async(UpperCamelCase__      ,  env=self.get_env()  )

         # 2. test that the fp32 weights get reconsolidated
         a_	    = f'{output_dir}/checkpoint-1'
         a_	    = f'{chkpt_dir}/out.bin'
         a_	    = f'{chkpt_dir}/zero_to_fp32.py {chkpt_dir} {recovered_model_path}'
         # keep for quick debug
         # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
         subprocess.check_call(UpperCamelCase__      ,  shell=UpperCamelCase__  )
         assert os.path.exists(UpperCamelCase__  ), f'{recovered_model_path} was not found'

         # possibly could also test that the resulting saved model is usable but given that we use
         # random models we won't know if it's any good




