'''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()
__SCREAMING_SNAKE_CASE       :     List[str]     =  logging.get_logger()

@dataclass
class 	lowerCamelCase_	:
      '''simple docstring'''

      __UpperCamelCase:  nn.Module
      __UpperCamelCase:  List[nn.Module]						=						field(default_factory=snake_case__    )
      __UpperCamelCase:  list						=						field(default_factory=snake_case__    )




      def      _A	(       self						:				List[str]						,					A						:				Optional[int]						,					A						:				Tensor						,					A						:				Tensor       ):
             _UpperCAmelCase     :  Union[str, 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						:				Any						,					A						:				Tensor       ):
             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[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 	lowerCamelCase_	:
      '''simple docstring'''

      __UpperCamelCase:  nn.Module
      __UpperCamelCase:  nn.Module
      __UpperCamelCase:  int						=						0
      __UpperCamelCase:  List						=						field(default_factory=snake_case__    )
      __UpperCamelCase:  List						=						field(default_factory=snake_case__    )




      def __call__(       self						:				Optional[Any]						,					A						:				Tensor       ):
             _UpperCAmelCase     :  Optional[Any]							      =    Tracker(self.dest       )(A       ).parametrized
             _UpperCAmelCase     :  List[Any]							      =    Tracker(self.src       )(A       ).parametrized

             _UpperCAmelCase     :  str							      =    list(filter(lambda A       : type(A       ) not in self.src_skip						,					A       )       )
             _UpperCAmelCase     :  int							      =    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 	UpperCamelCase_							(					_UpperCAmelCase  :      str			,	_UpperCAmelCase  :      ResNetConfig			,	_UpperCAmelCase  :      Path			,	_UpperCAmelCase  :      bool = True    )    ->    str:


       """simple docstring"""







       print(F"""Converting {name}..."""    )
       with torch.no_grad():
              _UpperCAmelCase     :  Any							      =    timm.create_model(_UpperCAmelCase			,	pretrained=_UpperCAmelCase    ).eval()
              _UpperCAmelCase     :  Union[str, Any]							      =    ResNetForImageClassification(_UpperCAmelCase    ).eval()
              _UpperCAmelCase     :  Optional[int]							      =    ModuleTransfer(src=_UpperCAmelCase			,	dest=_UpperCAmelCase    )
              _UpperCAmelCase     :  List[Any]							      =    torch.randn((1, 3, 224, 224)    )
              module_transfer(_UpperCAmelCase    )

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

       _UpperCAmelCase     :  Tuple							      =    F"""resnet{'-'.join(name.split('resnet'    )    )}"""
       print(_UpperCAmelCase    )

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

              # we can use the convnext one
              _UpperCAmelCase     :  Tuple							      =    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=_UpperCAmelCase			,	)

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

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Path			,	_UpperCAmelCase  :      str = None			,	_UpperCAmelCase  :      bool = True    )    ->    List[str]:


       """simple docstring"""







       _UpperCAmelCase     :  Dict							      =    "imagenet-1k-id2label.json"
       _UpperCAmelCase     :  Optional[int]							      =    1_000
       _UpperCAmelCase     :  Optional[int]							      =    (1, num_labels)

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

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

       _UpperCAmelCase     :  Union[str, Any]							      =    partial(_UpperCAmelCase			,	num_labels=_UpperCAmelCase			,	idalabel=_UpperCAmelCase			,	labelaid=_UpperCAmelCase    )

       _UpperCAmelCase     :  Optional[int]							      =    {
           "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, 1_024, 2_048]			,	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, 1_024, 2_048]			,	layer_type="bottleneck"    ),
           "resnet101": ImageNetPreTrainedConfig(
               depths=[3, 4, 23, 3]			,	hidden_sizes=[256, 512, 1_024, 2_048]			,	layer_type="bottleneck"    ),
           "resnet152": ImageNetPreTrainedConfig(
               depths=[3, 8, 36, 3]			,	hidden_sizes=[256, 512, 1_024, 2_048]			,	layer_type="bottleneck"    ),
       }

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


if __name__ == "__main__":
  __SCREAMING_SNAKE_CASE       :     Dict     =  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.""",
  )

  __SCREAMING_SNAKE_CASE       :     Tuple     =  parser.parse_args()
  __SCREAMING_SNAKE_CASE       :     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)


'''simple docstring'''




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_pegasus import PegasusTokenizer
else:
  __SCREAMING_SNAKE_CASE       :     Optional[Any]     =  None


__SCREAMING_SNAKE_CASE       :     Dict     =  logging.get_logger(__name__)


__SCREAMING_SNAKE_CASE       :     Union[str, Any]     =  """▁"""

__SCREAMING_SNAKE_CASE       :     str     =  {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

__SCREAMING_SNAKE_CASE       :     int     =  {
    """vocab_file""": {"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"""},
    """tokenizer_file""": {
        """google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"""
    },
}

__SCREAMING_SNAKE_CASE       :     str     =  {
    """google/pegasus-xsum""": 512,
}

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  Optional[int]						=						VOCAB_FILES_NAMES
      __UpperCamelCase:  Dict						=						PRETRAINED_VOCAB_FILES_MAP
      __UpperCamelCase:  List[Any]						=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __UpperCamelCase:  Optional[int]						=						PegasusTokenizer
      __UpperCamelCase:  Optional[Any]						=						["input_ids", "attention_mask"]




      def __init__(       self						:				Dict						,					A						:				List[str]=None						,					A						:				Union[str, Any]=None						,					A						:				Optional[int]="<pad>"						,					A						:				Tuple="</s>"						,					A						:				Union[str, Any]="<unk>"						,					A						:				Union[str, Any]="<mask_2>"						,					A						:				Dict="<mask_1>"						,					A						:				Union[str, Any]=None						,					A						:				int=103						,					**A						:				Optional[Any]						,					):
             _UpperCAmelCase     :  Dict							      =    offset

             if additional_special_tokens is not None:
                    if not isinstance(A						,					A       ):
                           raise TypeError(
                               F"""additional_special_tokens should be of type {type(A       )}, but is"""
                               F""" {type(A       )}"""       )

                    _UpperCAmelCase     :  Optional[int]							      =    (
                        ([mask_token_sent] + additional_special_tokens)
                        if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                        else additional_special_tokens
                    )
                    # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
                    additional_special_tokens_extended += [
                        F"""<unk_{i}>""" for i in range(len(A       )						,					self.offset - 1       )
                    ]

                    if len(set(A       )       ) != len(A       ):
                           raise ValueError(
                               "Please make sure that the provided additional_special_tokens do not contain an incorrectly"
                               F""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."""       )
                    _UpperCAmelCase     :  Any							      =    additional_special_tokens_extended
             else:
                    _UpperCAmelCase     :  Dict							      =    [mask_token_sent] if mask_token_sent is not None else []
                    additional_special_tokens += [F"""<unk_{i}>""" for i in range(2						,					self.offset       )]

             super().__init__(
                 A						,					tokenizer_file=A						,					pad_token=A						,					eos_token=A						,					unk_token=A						,					mask_token=A						,					mask_token_sent=A						,					offset=A						,					additional_special_tokens=A						,					**A						,					)
             _UpperCAmelCase     :  Optional[Any]							      =    vocab_file
             _UpperCAmelCase     :  Optional[Any]							      =    False if not self.vocab_file else True




      def      _A	(       self						:				List[str]						,					A						:				Optional[Any]       ):
             _UpperCAmelCase     :  Any							      =    set(self.all_special_ids       )  # call it once instead of inside list comp
             all_special_ids.remove(self.unk_token_id       )  # <unk> is only sometimes special

             if all_special_ids != set(range(len(self.additional_special_tokens       ) + 3       )       ):
                    raise ValueError(
                        "There should be 3 special tokens: mask_token, pad_token, and eos_token +"
                        F""" {len(self.additional_special_tokens       )} additional_special_tokens, but got {all_special_ids}"""       )

             return [1 if x in all_special_ids else 0 for x in seq]




      def      _A	(       self						:				str						,					A						:				List						,					A						:				Optional[List] = None						,					A						:				bool = False       ):
             if already_has_special_tokens:
                    return self._special_token_mask(A       )
             elif token_ids_a is None:
                    return self._special_token_mask(A       ) + [1]
             else:
                    return self._special_token_mask(token_ids_a + token_ids_a       ) + [1]




      def      _A	(       self						:				Optional[int]						,					A						:				Union[str, Any]						,					A						:				int=None       ):
             if token_ids_a is None:
                    return token_ids_a + [self.eos_token_id]
             # We don't expect to process pairs, but leave the pair logic for API consistency
             return token_ids_a + token_ids_a + [self.eos_token_id]





      def      _A	(       self						:				Union[str, Any]						,					A						:				str						,					A						:				Optional[str] = None       ):
             if not self.can_save_slow_tokenizer:
                    raise ValueError(
                        "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                        "tokenizer."       )

             if not os.path.isdir(A       ):
                    logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""       )
                    return
             _UpperCAmelCase     :  List[Any]							      =    os.path.join(
                 A						,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]       )

             if os.path.abspath(self.vocab_file       ) != os.path.abspath(A       ):
                    copyfile(self.vocab_file						,					A       )

             return (out_vocab_file,)

