"""simple docstring"""





import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging


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


def    lowercase_   (   _lowerCamelCase:	List[str]						,   _lowerCamelCase:	Any						,   _lowerCamelCase:	Tuple						,   _lowerCamelCase:	str      ) ->      Dict:



       '''simple docstring'''

       __lowerCamelCase					:		Optional[Any]		   =     original_name.split("."      )[0]
       __lowerCamelCase					:		Any		   =     key.split("."      )
       __lowerCamelCase					:		str		   =     int(key_list[key_list.index(_UpperCamelCase      ) - 2]      )
       __lowerCamelCase					:		str		   =     int(key_list[key_list.index(_UpperCamelCase      ) - 1]      )
       __lowerCamelCase					:		int		   =     orig_block_num - offset

       __lowerCamelCase					:		Optional[Any]		   =     key.replace(F"""{orig_block_num}.{layer_num}.{original_name}"""						,   F"""block.{new_block_num}.{layer_num}.{new_name}"""      )
       return key


def    lowercase_   (   _lowerCamelCase:	int      ) ->      int:



       '''simple docstring'''

       __lowerCamelCase					:		int		   =     OrderedDict()
       __lowerCamelCase   ,  __lowerCamelCase					:		List[str]		   =     0, 0
       for key, value in state_dict.items():
              if key.startswith("network"      ):
                     __lowerCamelCase					:		Tuple		   =     key.replace("network"						,   "poolformer.encoder"      )
              if "proj" in key:
                     # Works for the first embedding as well as the internal embedding layers
                     if key.endswith("bias"      ) and "patch_embed" not in key:
                            patch_emb_offset += 1
                     __lowerCamelCase					:		int		   =     key[: key.find("proj"      )]
                     __lowerCamelCase					:		Optional[Any]		   =     key.replace(_UpperCamelCase						,   F"""patch_embeddings.{total_embed_found}."""      )
                     __lowerCamelCase					:		Tuple		   =     key.replace("proj"						,   "projection"      )
                     if key.endswith("bias"      ):
                            total_embed_found += 1
              if "patch_embeddings" in key:
                     __lowerCamelCase					:		Union[str, Any]		   =     "poolformer.encoder." + key
              if "mlp.fc1" in key:
                     __lowerCamelCase					:		Any		   =     replace_key_with_offset(_UpperCamelCase						,   _UpperCamelCase						,   "mlp.fc1"						,   "output.conv1"      )
              if "mlp.fc2" in key:
                     __lowerCamelCase					:		str		   =     replace_key_with_offset(_UpperCamelCase						,   _UpperCamelCase						,   "mlp.fc2"						,   "output.conv2"      )
              if "norm1" in key:
                     __lowerCamelCase					:		Any		   =     replace_key_with_offset(_UpperCamelCase						,   _UpperCamelCase						,   "norm1"						,   "before_norm"      )
              if "norm2" in key:
                     __lowerCamelCase					:		int		   =     replace_key_with_offset(_UpperCamelCase						,   _UpperCamelCase						,   "norm2"						,   "after_norm"      )
              if "layer_scale_1" in key:
                     __lowerCamelCase					:		Union[str, Any]		   =     replace_key_with_offset(_UpperCamelCase						,   _UpperCamelCase						,   "layer_scale_1"						,   "layer_scale_1"      )
              if "layer_scale_2" in key:
                     __lowerCamelCase					:		List[str]		   =     replace_key_with_offset(_UpperCamelCase						,   _UpperCamelCase						,   "layer_scale_2"						,   "layer_scale_2"      )
              if "head" in key:
                     __lowerCamelCase					:		int		   =     key.replace("head"						,   "classifier"      )
              __lowerCamelCase					:		Union[str, Any]		   =     value
       return new_state_dict


def    lowercase_   (   ) ->      Union[str, Any]:



       '''simple docstring'''

       __lowerCamelCase					:		Optional[int]		   =     "http://images.cocodataset.org/val2017/000000039769.jpg"
       __lowerCamelCase					:		int		   =     Image.open(requests.get(_UpperCamelCase						,   stream=_UpperCamelCase      ).raw      )

       return image


@torch.no_grad()
def    lowercase_   (   _lowerCamelCase:	Optional[Any]						,   _lowerCamelCase:	Optional[int]						,   _lowerCamelCase:	int      ) ->      Dict:



       '''simple docstring'''

       __lowerCamelCase					:		int		   =     PoolFormerConfig()

       # set attributes based on model_name
       __lowerCamelCase					:		Any		   =     "huggingface/label-files"
       __lowerCamelCase					:		Optional[int]		   =     model_name[-3:]
       __lowerCamelCase					:		List[str]		   =     1000
       __lowerCamelCase					:		List[str]		   =     "imagenet-1k-id2label.json"
       __lowerCamelCase					:		List[Any]		   =     (1, 1000)

       # set config attributes
       __lowerCamelCase					:		Any		   =     json.load(open(hf_hub_download(_UpperCamelCase						,   _UpperCamelCase						,   repo_type="dataset"      )						,   "r"      )      )
       __lowerCamelCase					:		Tuple		   =     {int(_UpperCamelCase      ): v for k, v in idalabel.items()}
       __lowerCamelCase					:		List[str]		   =     idalabel
       __lowerCamelCase					:		Dict		   =     {v: k for k, v in idalabel.items()}
       if size == "s12":
              __lowerCamelCase					:		Tuple		   =     [2, 2, 6, 2]
              __lowerCamelCase					:		List[Any]		   =     [64, 128, 320, 512]
              __lowerCamelCase					:		Any		   =     4.0
              __lowerCamelCase					:		Optional[int]		   =     0.9
       elif size == "s24":
              __lowerCamelCase					:		str		   =     [4, 4, 12, 4]
              __lowerCamelCase					:		List[Any]		   =     [64, 128, 320, 512]
              __lowerCamelCase					:		Tuple		   =     4.0
              __lowerCamelCase					:		Optional[int]		   =     0.9
       elif size == "s36":
              __lowerCamelCase					:		List[str]		   =     [6, 6, 18, 6]
              __lowerCamelCase					:		int		   =     [64, 128, 320, 512]
              __lowerCamelCase					:		List[Any]		   =     4.0
              __lowerCamelCase					:		Any		   =     1E-6
              __lowerCamelCase					:		int		   =     0.9
       elif size == "m36":
              __lowerCamelCase					:		str		   =     [6, 6, 18, 6]
              __lowerCamelCase					:		str		   =     [96, 192, 384, 768]
              __lowerCamelCase					:		List[str]		   =     4.0
              __lowerCamelCase					:		Dict		   =     1E-6
              __lowerCamelCase					:		Any		   =     0.95
       elif size == "m48":
              __lowerCamelCase					:		Union[str, Any]		   =     [8, 8, 24, 8]
              __lowerCamelCase					:		List[str]		   =     [96, 192, 384, 768]
              __lowerCamelCase					:		Tuple		   =     4.0
              __lowerCamelCase					:		List[Any]		   =     1E-6
              __lowerCamelCase					:		str		   =     0.95
       else:
              raise ValueError(F"""Size {size} not supported"""      )

       # load image processor
       __lowerCamelCase					:		Optional[int]		   =     PoolFormerImageProcessor(crop_pct=_UpperCamelCase      )

       # Prepare image
       __lowerCamelCase					:		Union[str, Any]		   =     prepare_img()
       __lowerCamelCase					:		List[str]		   =     image_processor(images=_UpperCamelCase						,   return_tensors="pt"      ).pixel_values

       logger.info(F"""Converting model {model_name}..."""      )

       # load original state dict
       __lowerCamelCase					:		Optional[int]		   =     torch.load(_UpperCamelCase						,   map_location=torch.device("cpu"      )      )

       # rename keys
       __lowerCamelCase					:		Tuple		   =     rename_keys(_UpperCamelCase      )

       # create HuggingFace model and load state dict
       __lowerCamelCase					:		List[Any]		   =     PoolFormerForImageClassification(_UpperCamelCase      )
       model.load_state_dict(_UpperCamelCase      )
       model.eval()

       # Define image processor
       __lowerCamelCase					:		Union[str, Any]		   =     PoolFormerImageProcessor(crop_pct=_UpperCamelCase      )
       __lowerCamelCase					:		Optional[int]		   =     image_processor(images=prepare_img()						,   return_tensors="pt"      ).pixel_values

       # forward pass
       __lowerCamelCase					:		Optional[Any]		   =     model(_UpperCamelCase      )
       __lowerCamelCase					:		List[str]		   =     outputs.logits

       # define expected logit slices for different models
       if size == "s12":
              __lowerCamelCase					:		List[str]		   =     torch.tensor([-0.3045, -0.6758, -0.4869]      )
       elif size == "s24":
              __lowerCamelCase					:		List[str]		   =     torch.tensor([0.4402, -0.1374, -0.8045]      )
       elif size == "s36":
              __lowerCamelCase					:		int		   =     torch.tensor([-0.6080, -0.5133, -0.5898]      )
       elif size == "m36":
              __lowerCamelCase					:		Dict		   =     torch.tensor([0.3952, 0.2263, -1.2668]      )
       elif size == "m48":
              __lowerCamelCase					:		Optional[Any]		   =     torch.tensor([0.1167, -0.0656, -0.3423]      )
       else:
              raise ValueError(F"""Size {size} not supported"""      )

       # verify logits
       assert logits.shape == expected_shape
       assert torch.allclose(logits[0, :3]						,   _UpperCamelCase						,   atol=1E-2      )

       # finally, save model and image processor
       logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""      )
       Path(_UpperCamelCase      ).mkdir(exist_ok=_UpperCamelCase      )
       model.save_pretrained(_UpperCamelCase      )
       print(F"""Saving image processor to {pytorch_dump_folder_path}"""      )
       image_processor.save_pretrained(_UpperCamelCase      )


if __name__ == "__main__":
    __A  =				argparse.ArgumentParser()

    parser.add_argument(
        '''--model_name''',
        default='''poolformer_s12''',
        type=str,
        help='''Name of the model you\'d like to convert.''',
    )
    parser.add_argument(
        '''--checkpoint_path''', default=None, type=str, help='''Path to the original PyTorch checkpoint (.pth file).'''
    )
    parser.add_argument(
        '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
    )
    __A  =				parser.parse_args()
    convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)
"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__A  =				{
    '''configuration_bert''': ['''BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BertConfig''', '''BertOnnxConfig'''],
    '''tokenization_bert''': ['''BasicTokenizer''', '''BertTokenizer''', '''WordpieceTokenizer'''],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A  =				['''BertTokenizerFast''']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A  =				[
				    '''BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''BertForMaskedLM''',
				    '''BertForMultipleChoice''',
				    '''BertForNextSentencePrediction''',
				    '''BertForPreTraining''',
				    '''BertForQuestionAnswering''',
				    '''BertForSequenceClassification''',
				    '''BertForTokenClassification''',
				    '''BertLayer''',
				    '''BertLMHeadModel''',
				    '''BertModel''',
				    '''BertPreTrainedModel''',
				    '''load_tf_weights_in_bert''',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A  =				[
				    '''TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''TFBertEmbeddings''',
				    '''TFBertForMaskedLM''',
				    '''TFBertForMultipleChoice''',
				    '''TFBertForNextSentencePrediction''',
				    '''TFBertForPreTraining''',
				    '''TFBertForQuestionAnswering''',
				    '''TFBertForSequenceClassification''',
				    '''TFBertForTokenClassification''',
				    '''TFBertLMHeadModel''',
				    '''TFBertMainLayer''',
				    '''TFBertModel''',
				    '''TFBertPreTrainedModel''',
				]
try:
				if not is_tensorflow_text_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A  =				['''TFBertTokenizer''']

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A  =				[
				    '''FlaxBertForCausalLM''',
				    '''FlaxBertForMaskedLM''',
				    '''FlaxBertForMultipleChoice''',
				    '''FlaxBertForNextSentencePrediction''',
				    '''FlaxBertForPreTraining''',
				    '''FlaxBertForQuestionAnswering''',
				    '''FlaxBertForSequenceClassification''',
				    '''FlaxBertForTokenClassification''',
				    '''FlaxBertModel''',
				    '''FlaxBertPreTrainedModel''',
				]

if TYPE_CHECKING:
				from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
				from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_bert_fast import BertTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_bert import (
								    BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    BertForMaskedLM,
								    BertForMultipleChoice,
								    BertForNextSentencePrediction,
								    BertForPreTraining,
								    BertForQuestionAnswering,
								    BertForSequenceClassification,
								    BertForTokenClassification,
								    BertLayer,
								    BertLMHeadModel,
								    BertModel,
								    BertPreTrainedModel,
								    load_tf_weights_in_bert,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_bert import (
								    TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFBertEmbeddings,
								    TFBertForMaskedLM,
								    TFBertForMultipleChoice,
								    TFBertForNextSentencePrediction,
								    TFBertForPreTraining,
								    TFBertForQuestionAnswering,
								    TFBertForSequenceClassification,
								    TFBertForTokenClassification,
								    TFBertLMHeadModel,
								    TFBertMainLayer,
								    TFBertModel,
								    TFBertPreTrainedModel,
								)

				try:
								if not is_tensorflow_text_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_bert_tf import TFBertTokenizer

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_bert import (
								    FlaxBertForCausalLM,
								    FlaxBertForMaskedLM,
								    FlaxBertForMultipleChoice,
								    FlaxBertForNextSentencePrediction,
								    FlaxBertForPreTraining,
								    FlaxBertForQuestionAnswering,
								    FlaxBertForSequenceClassification,
								    FlaxBertForTokenClassification,
								    FlaxBertModel,
								    FlaxBertPreTrainedModel,
								)

else:
				import sys

				__A  =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)