



'''simple docstring'''



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer


lowerCamelCase	 =       logging.get_logger(__name__)

lowerCamelCase	 =       {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

lowerCamelCase	 =       {
    """vocab_file""": {
        """google/realm-cc-news-pretrained-embedder""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt"""
        ),
        """google/realm-cc-news-pretrained-encoder""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt"""
        ),
        """google/realm-cc-news-pretrained-scorer""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt"""
        ),
        """google/realm-cc-news-pretrained-openqa""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt"""
        ),
        """google/realm-orqa-nq-openqa""": """https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/vocab.txt""",
        """google/realm-orqa-nq-reader""": """https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/vocab.txt""",
        """google/realm-orqa-wq-openqa""": """https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/vocab.txt""",
        """google/realm-orqa-wq-reader""": """https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/vocab.txt""",
    },
    """tokenizer_file""": {
        """google/realm-cc-news-pretrained-embedder""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/tokenizer.jsont"""
        ),
        """google/realm-cc-news-pretrained-encoder""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/tokenizer.json"""
        ),
        """google/realm-cc-news-pretrained-scorer""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/tokenizer.json"""
        ),
        """google/realm-cc-news-pretrained-openqa""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/tokenizer.json"""
        ),
        """google/realm-orqa-nq-openqa""": (
            """https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/tokenizer.json"""
        ),
        """google/realm-orqa-nq-reader""": (
            """https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/tokenizer.json"""
        ),
        """google/realm-orqa-wq-openqa""": (
            """https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/tokenizer.json"""
        ),
        """google/realm-orqa-wq-reader""": (
            """https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/tokenizer.json"""
        ),
    },
}

lowerCamelCase	 =       {
    """google/realm-cc-news-pretrained-embedder""": 512,
    """google/realm-cc-news-pretrained-encoder""": 512,
    """google/realm-cc-news-pretrained-scorer""": 512,
    """google/realm-cc-news-pretrained-openqa""": 512,
    """google/realm-orqa-nq-openqa""": 512,
    """google/realm-orqa-nq-reader""": 512,
    """google/realm-orqa-wq-openqa""": 512,
    """google/realm-orqa-wq-reader""": 512,
}

lowerCamelCase	 =       {
    """google/realm-cc-news-pretrained-embedder""": {"""do_lower_case""": True},
    """google/realm-cc-news-pretrained-encoder""": {"""do_lower_case""": True},
    """google/realm-cc-news-pretrained-scorer""": {"""do_lower_case""": True},
    """google/realm-cc-news-pretrained-openqa""": {"""do_lower_case""": True},
    """google/realm-orqa-nq-openqa""": {"""do_lower_case""": True},
    """google/realm-orqa-nq-reader""": {"""do_lower_case""": True},
    """google/realm-orqa-wq-openqa""": {"""do_lower_case""": True},
    """google/realm-orqa-wq-reader""": {"""do_lower_case""": True},
}





class _UpperCamelCase		(     A		):





				'''simple docstring'''





				lowerCAmelCase__					    =    VOCAB_FILES_NAMES
				lowerCAmelCase__					    =    PRETRAINED_VOCAB_FILES_MAP
				lowerCAmelCase__					    =    PRETRAINED_INIT_CONFIGURATION
				lowerCAmelCase__					    =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowerCAmelCase__					    =    RealmTokenizer

				def __init__(							self  :  List[str]  ,       _lowerCAmelCase  :  Optional[int]=None  ,       _lowerCAmelCase  :  Optional[Any]=None  ,       _lowerCAmelCase  :  Union[str, Any]=True  ,       _lowerCAmelCase  :  List[Any]="[UNK]"  ,       _lowerCAmelCase  :  List[str]="[SEP]"  ,       _lowerCAmelCase  :  Union[str, Any]="[PAD]"  ,       _lowerCAmelCase  :  str="[CLS]"  ,       _lowerCAmelCase  :  Dict="[MASK]"  ,       _lowerCAmelCase  :  Any=True  ,       _lowerCAmelCase  :  Any=None  ,       **_lowerCAmelCase  :  Dict  ,       ):







											'''simple docstring'''

											super().__init__(
											    _lowerCAmelCase  ,       tokenizer_file=_lowerCAmelCase  ,       do_lower_case=_lowerCAmelCase  ,       unk_token=_lowerCAmelCase  ,       sep_token=_lowerCAmelCase  ,       pad_token=_lowerCAmelCase  ,       cls_token=_lowerCAmelCase  ,       mask_token=_lowerCAmelCase  ,       tokenize_chinese_chars=_lowerCAmelCase  ,       strip_accents=_lowerCAmelCase  ,       **_lowerCAmelCase  ,       )

											__lowercase       	=json.loads(self.backend_tokenizer.normalizer.__getstate__())
											if (
											    normalizer_state.get('lowercase'  ,       _lowerCAmelCase) != do_lower_case
											    or normalizer_state.get('strip_accents'  ,       _lowerCAmelCase) != strip_accents
											    or normalizer_state.get('handle_chinese_chars'  ,       _lowerCAmelCase) != tokenize_chinese_chars
											):
																		__lowercase       	=getattr(_lowerCAmelCase  ,       normalizer_state.pop('type'))
																		__lowercase       	=do_lower_case
																		__lowercase       	=strip_accents
																		__lowercase       	=tokenize_chinese_chars
																		__lowercase       	=normalizer_class(**_lowerCAmelCase)

											__lowercase       	=do_lower_case

				def 				__lowerCamelCase     (							self  :  List[str]  ,       _lowerCAmelCase  :  Dict  ,       **_lowerCAmelCase  :  Tuple):







											'''simple docstring'''

											__lowercase       	=PaddingStrategy.MAX_LENGTH

											__lowercase       	=text
											__lowercase       	=kwargs.pop('text_pair'  ,       _lowerCAmelCase)
											__lowercase       	=kwargs.pop('return_tensors'  ,       _lowerCAmelCase)

											__lowercase       	={
											    'input_ids': [],
											    'attention_mask': [],
											    'token_type_ids': [],
											}

											for idx, candidate_text in enumerate(_lowerCAmelCase):
																		if batch_text_pair is not None:
																									__lowercase       	=batch_text_pair[idx]
																		else:
																									__lowercase       	=None

																		__lowercase       	=super().__call__(_lowerCAmelCase  ,       _lowerCAmelCase  ,       return_tensors=_lowerCAmelCase  ,       **_lowerCAmelCase)

																		__lowercase       	=encoded_candidates.get('input_ids')
																		__lowercase       	=encoded_candidates.get('attention_mask')
																		__lowercase       	=encoded_candidates.get('token_type_ids')

																		if encoded_input_ids is not None:
																									output_data["input_ids"].append(_lowerCAmelCase)
																		if encoded_attention_mask is not None:
																									output_data["attention_mask"].append(_lowerCAmelCase)
																		if encoded_token_type_ids is not None:
																									output_data["token_type_ids"].append(_lowerCAmelCase)

											__lowercase       	={key: item for key, item in output_data.items() if len(_lowerCAmelCase) != 0}

											return BatchEncoding(_lowerCAmelCase  ,       tensor_type=_lowerCAmelCase)

				def 				__lowerCamelCase     (							self  :  Tuple  ,       _lowerCAmelCase  :  Any  ,       _lowerCAmelCase  :  Optional[int]=None):







											'''simple docstring'''

											__lowercase       	=[self.cls_token_id] + token_ids_a + [self.sep_token_id]

											if token_ids_a:
																		output += token_ids_a + [self.sep_token_id]

											return output

				def 				__lowerCamelCase     (							self  :  List[Any]  ,       _lowerCAmelCase  :  List[int]  ,       _lowerCAmelCase  :  Optional[List[int]] = None):







											'''simple docstring'''

											__lowercase       	=[self.sep_token_id]
											__lowercase       	=[self.cls_token_id]
											if token_ids_a is None:
																		return len(cls + token_ids_a + sep) * [0]
											return len(cls + token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1]

				def 				__lowerCamelCase     (							self  :  Any  ,       _lowerCAmelCase  :  str  ,       _lowerCAmelCase  :  Optional[str] = None):







											'''simple docstring'''

											__lowercase       	=self._tokenizer.model.save(_lowerCAmelCase  ,       name=_lowerCAmelCase)
											return tuple(_lowerCAmelCase)








'''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()
lowerCamelCase	 =       logging.get_logger()





@dataclass
class _UpperCamelCase		:





				'''simple docstring'''





				lowerCAmelCase__					    =    42
				lowerCAmelCase__					    =    field(default_factory=A		)
				lowerCAmelCase__					    =    field(default_factory=A		)

				def 				__lowerCamelCase     (							self  :  int  ,       _lowerCAmelCase  :  List[Any]  ,       _lowerCAmelCase  :  Tensor  ,       _lowerCAmelCase  :  Tensor):







											'''simple docstring'''

											__lowercase       	=len(list(m.modules())) == 1 or isinstance(_lowerCAmelCase  ,       nn.Convad) or isinstance(_lowerCAmelCase  ,       nn.BatchNormad)
											if has_not_submodules:
																		self.traced.append(_lowerCAmelCase)

				def __call__(							self  :  Dict  ,       _lowerCAmelCase  :  Tensor):







											'''simple docstring'''

											for m in self.module.modules():
																		self.handles.append(m.register_forward_hook(self._forward_hook))
											self.module(_lowerCAmelCase)
											[x.remove() for x in self.handles]
											return self

				@property
				def 				__lowerCamelCase     (							self  :  Any):







											'''simple docstring'''

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





@dataclass
class _UpperCamelCase		:





				'''simple docstring'''





				lowerCAmelCase__					    =    42
				lowerCAmelCase__					    =    42
				lowerCAmelCase__					    =    0
				lowerCAmelCase__					    =    field(default_factory=A		)
				lowerCAmelCase__					    =    field(default_factory=A		)

				def __call__(							self  :  Any  ,       _lowerCAmelCase  :  Tensor):







											'''simple docstring'''

											__lowercase       	=Tracker(self.dest)(_lowerCAmelCase).parametrized
											__lowercase       	=Tracker(self.src)(_lowerCAmelCase).parametrized

											__lowercase       	=list(filter(lambda _lowerCAmelCase: type(_lowerCAmelCase) not in self.src_skip  ,       _lowerCAmelCase))
											__lowercase       	=list(filter(lambda _lowerCAmelCase: type(_lowerCAmelCase) not in self.dest_skip  ,       _lowerCAmelCase))

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

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



def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase = True					):



							"""simple docstring"""



							print(f"""Converting {name}..."""					)
							with torch.no_grad():
														__lowercase       	=timm.create_model(_lowerCAmelCase  ,   pretrained=_lowerCAmelCase					).eval()
														__lowercase       	=ResNetForImageClassification(_lowerCAmelCase					).eval()
														__lowercase       	=ModuleTransfer(src=_lowerCAmelCase  ,   dest=_lowerCAmelCase					)
														__lowercase       	=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."

							__lowercase       	=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
														__lowercase       	=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   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase = None  ,   _lowerCAmelCase = True					):



							"""simple docstring"""



							__lowercase       	='imagenet-1k-id2label.json'
							__lowercase       	=1_000
							__lowercase       	=(1, num_labels)

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

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

							__lowercase       	=partial(_lowerCAmelCase  ,   num_labels=_lowerCAmelCase  ,   idalabel=_lowerCAmelCase  ,   labelaid=_lowerCAmelCase					)

							__lowercase       	={
							    '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(_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__":
		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: 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.""",
		)

		lowerCamelCase	 =       parser.parse_args()
		lowerCamelCase	 =       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)



