





'''simple docstring'''







from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


_lowercase       						=			logging.get_logger(__name__)
class 			_lowercase						(       __a					):







     def __init__( self    ,   A__    ,   A__    ,   A__    ,   **A__    ) ->	Union[str, Any]:
      snake_case				=			feature_size
      snake_case				=			sampling_rate
      snake_case				=			padding_value

      snake_case				=			kwargs.pop('''padding_side'''    ,   '''right'''    )
      snake_case				=			kwargs.pop('''return_attention_mask'''    ,   A__    )

      super().__init__(**A__    )







     def     UpperCamelCase			( self    ,   A__    ,   A__ = True    ,   A__ = None    ,   A__ = False    ,   A__ = None    ,   A__ = None    ,   A__ = None    ,   ) ->	BatchFeature:
      # If we have a list of dicts, let's convert it in a dict of lists
      # We do this to allow using this method as a collate_fn function in PyTorch Dataloader
      if isinstance(A__    ,   (list, tuple)    ) and isinstance(processed_features[0]    ,   (dict, BatchFeature)    ):
       snake_case				=			{
           key: [example[key] for example in processed_features] for key in processed_features[0].keys()
       }

      # The model's main input name, usually `input_values`, has be passed for padding
      if self.model_input_names[0] not in processed_features:
       raise ValueError(
           '''You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`'''
           F""" to this method that includes {self.model_input_names[0]}, but you provided"""
           F""" {list(processed_features.keys()    )}"""    )

      snake_case				=			processed_features[self.model_input_names[0]]
      snake_case				=			(
          return_attention_mask if return_attention_mask is not None else self.return_attention_mask
      )

      if len(A__    ) == 0:
       if return_attention_mask:
        snake_case				=			[]
       return processed_features

      # If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
      # and rebuild them afterwards if no return_tensors is specified
      # Note that we lose the specific device the tensor may be on for PyTorch

      snake_case				=			required_input[0]
      if isinstance(A__    ,   (list, tuple)    ):
       # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
       snake_case				=			0
       while len(required_input[index]    ) == 0:
        index += 1
       if index < len(A__    ):
        snake_case				=			required_input[index][0]

      if return_tensors is None:
       if is_tf_tensor(A__    ):
        snake_case				=			'''tf'''
       elif is_torch_tensor(A__    ):
        snake_case				=			'''pt'''
       elif isinstance(A__    ,   (int, float, list, tuple, np.ndarray)    ):
        snake_case				=			'''np'''
       else:
        raise ValueError(
            F"""type of {first_element} unknown: {type(A__    )}. """
            '''Should be one of a python, numpy, pytorch or tensorflow object.'''    )

      for key, value in processed_features.items():
       if isinstance(value[0]    ,   (int, float)    ):
        snake_case				=			to_numpy(A__    )
       else:
        snake_case				=			[to_numpy(A__    ) for v in value]

        # Convert padding_strategy in PaddingStrategy
      snake_case				=			self._get_padding_strategies(padding=A__    ,   max_length=A__    )

      snake_case				=			processed_features[self.model_input_names[0]]

      snake_case				=			len(A__    )
      if not all(len(A__    ) == batch_size for v in processed_features.values()    ):
       raise ValueError('''Some items in the output dictionary have a different batch size than others.'''    )

      snake_case				=			[]
      for i in range(A__    ):
       snake_case				=			{k: v[i] for k, v in processed_features.items()}
       # truncation
       snake_case				=			self._truncate(
           A__    ,   max_length=A__    ,   pad_to_multiple_of=A__    ,   truncation=A__    ,   )
       truncated_inputs.append(A__    )

      if padding_strategy == PaddingStrategy.LONGEST:
       # make sure that `max_length` cannot be longer than the longest truncated length
       snake_case				=			max(len(input_slice[self.model_input_names[0]]    ) for input_slice in truncated_inputs    )
       snake_case				=			PaddingStrategy.MAX_LENGTH

      snake_case				=			{}
      for i in range(A__    ):
       # padding
       snake_case				=			self._pad(
           truncated_inputs[i]    ,   max_length=A__    ,   padding_strategy=A__    ,   pad_to_multiple_of=A__    ,   return_attention_mask=A__    ,   )

       for key, value in outputs.items():
        if key not in batch_outputs:
         snake_case				=			[]
        if value.dtype is np.dtype(np.floataa    ):
         snake_case				=			value.astype(np.floataa    )
        batch_outputs[key].append(A__    )

      return BatchFeature(A__    ,   tensor_type=A__    )







     def     UpperCamelCase			( self    ,   A__    ,   A__ = None    ,   A__ = PaddingStrategy.DO_NOT_PAD    ,   A__ = None    ,   A__ = None    ,   ) ->	dict:
      snake_case				=			processed_features[self.model_input_names[0]]

      if padding_strategy == PaddingStrategy.LONGEST:
       snake_case				=			len(A__    )

      if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
       snake_case				=			((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

      snake_case				=			padding_strategy != PaddingStrategy.DO_NOT_PAD and len(A__    ) < max_length

      if return_attention_mask and "attention_mask" not in processed_features:
       snake_case				=			np.ones(len(A__    )    ,   dtype=np.intaa    )

      if needs_to_be_padded:
       snake_case				=			max_length - len(A__    )
       if self.padding_side == "right":
        if return_attention_mask:
         snake_case				=			np.pad(
             processed_features['''attention_mask''']    ,   (0, difference)    )
        snake_case				=			((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
        snake_case				=			np.pad(
            A__    ,   A__    ,   '''constant'''    ,   constant_values=self.padding_value    )
       elif self.padding_side == "left":
        if return_attention_mask:
         snake_case				=			np.pad(
             processed_features['''attention_mask''']    ,   (difference, 0)    )
        snake_case				=			((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
        snake_case				=			np.pad(
            A__    ,   A__    ,   '''constant'''    ,   constant_values=self.padding_value    )
       else:
        raise ValueError('''Invalid padding strategy:''' + str(self.padding_side    )    )

      return processed_features







     def     UpperCamelCase			( self    ,   A__    ,   A__ = None    ,   A__ = None    ,   A__ = None    ,   ) ->	Union[str, Any]:
      if not truncation:
       return processed_features
      elif truncation and max_length is None:
       raise ValueError('''When setting ``truncation=True``, make sure that ``max_length`` is defined.'''    )

      snake_case				=			processed_features[self.model_input_names[0]]

      # find `max_length` that fits `pad_to_multiple_of`
      if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
       snake_case				=			((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

      snake_case				=			len(A__    ) > max_length

      if needs_to_be_truncated:
       snake_case				=			processed_features[self.model_input_names[0]][:max_length]
       if "attention_mask" in processed_features:
        snake_case				=			processed_features['''attention_mask'''][:max_length]

      return processed_features







     def     UpperCamelCase			( self    ,   A__=False    ,   A__=None    ) ->	Union[str, Any]:

      # Get padding strategy
      if padding is not False:
       if padding is True:
        snake_case				=			PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
       elif not isinstance(A__    ,   A__    ):
        snake_case				=			PaddingStrategy(A__    )
       elif isinstance(A__    ,   A__    ):
        snake_case				=			padding
      else:
       snake_case				=			PaddingStrategy.DO_NOT_PAD

      # Set max length if needed
      if max_length is None:
       if padding_strategy == PaddingStrategy.MAX_LENGTH:
        raise ValueError(
            F"""When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined"""    )

        # Test if we have a padding value
      if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
       raise ValueError(
           '''Asking to pad but the feature_extractor does not have a padding value. Please select a value to use'''
           ''' as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.'''    )

      return padding_strategy








'''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()
_lowercase       						=			logging.get_logger(__name__)

def    __UpperCamelCase	(	a    : Dict					,   a    : Optional[int]					,   a    : Dict					,   a    : Dict     ) ->Union[str, Any]:
 snake_case				=			original_name.split('''.'''     )[0]
 snake_case				=			key.split('''.'''     )
 snake_case				=			int(key_list[key_list.index(a     ) - 2]     )
 snake_case				=			int(key_list[key_list.index(a     ) - 1]     )
 snake_case				=			orig_block_num - offset

 snake_case				=			key.replace(f"""{orig_block_num}.{layer_num}.{original_name}"""					,   f"""block.{new_block_num}.{layer_num}.{new_name}"""     )
 return key

def    __UpperCamelCase	(	a    : Tuple     ) ->Dict:
 snake_case				=			OrderedDict()
 snake_case				,		snake_case				=			0, 0
 for key, value in state_dict.items():
  if key.startswith('''network'''     ):
   snake_case				=			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
   snake_case				=			key[: key.find('''proj'''     )]
   snake_case				=			key.replace(a					,   f"""patch_embeddings.{total_embed_found}."""     )
   snake_case				=			key.replace('''proj'''					,   '''projection'''     )
   if key.endswith('''bias'''     ):
    total_embed_found += 1
  if "patch_embeddings" in key:
   snake_case				=			'''poolformer.encoder.''' + key
  if "mlp.fc1" in key:
   snake_case				=			replace_key_with_offset(a					,   a					,   '''mlp.fc1'''					,   '''output.conv1'''     )
  if "mlp.fc2" in key:
   snake_case				=			replace_key_with_offset(a					,   a					,   '''mlp.fc2'''					,   '''output.conv2'''     )
  if "norm1" in key:
   snake_case				=			replace_key_with_offset(a					,   a					,   '''norm1'''					,   '''before_norm'''     )
  if "norm2" in key:
   snake_case				=			replace_key_with_offset(a					,   a					,   '''norm2'''					,   '''after_norm'''     )
  if "layer_scale_1" in key:
   snake_case				=			replace_key_with_offset(a					,   a					,   '''layer_scale_1'''					,   '''layer_scale_1'''     )
  if "layer_scale_2" in key:
   snake_case				=			replace_key_with_offset(a					,   a					,   '''layer_scale_2'''					,   '''layer_scale_2'''     )
  if "head" in key:
   snake_case				=			key.replace('''head'''					,   '''classifier'''     )
  snake_case				=			value
 return new_state_dict

def    __UpperCamelCase	(	) ->Optional[int]:
 snake_case				=			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
 snake_case				=			Image.open(requests.get(a					,   stream=a     ).raw     )

 return image

@torch.no_grad()
def    __UpperCamelCase	(	a    : Dict					,   a    : Optional[Any]					,   a    : Tuple     ) ->List[str]:
 snake_case				=			PoolFormerConfig()

 # set attributes based on model_name
 snake_case				=			'''huggingface/label-files'''
 snake_case				=			model_name[-3:]
 snake_case				=			1000
 snake_case				=			'''imagenet-1k-id2label.json'''
 snake_case				=			(1, 1000)

 # set config attributes
 snake_case				=			json.load(open(hf_hub_download(a					,   a					,   repo_type='''dataset'''     )					,   '''r'''     )     )
 snake_case				=			{int(a     ): v for k, v in idalabel.items()}
 snake_case				=			idalabel
 snake_case				=			{v: k for k, v in idalabel.items()}
 if size == "s12":
  snake_case				=			[2, 2, 6, 2]
  snake_case				=			[64, 128, 320, 512]
  snake_case				=			4.0
  snake_case				=			0.9
 elif size == "s24":
  snake_case				=			[4, 4, 12, 4]
  snake_case				=			[64, 128, 320, 512]
  snake_case				=			4.0
  snake_case				=			0.9
 elif size == "s36":
  snake_case				=			[6, 6, 18, 6]
  snake_case				=			[64, 128, 320, 512]
  snake_case				=			4.0
  snake_case				=			1e-6
  snake_case				=			0.9
 elif size == "m36":
  snake_case				=			[6, 6, 18, 6]
  snake_case				=			[96, 192, 384, 768]
  snake_case				=			4.0
  snake_case				=			1e-6
  snake_case				=			0.95
 elif size == "m48":
  snake_case				=			[8, 8, 24, 8]
  snake_case				=			[96, 192, 384, 768]
  snake_case				=			4.0
  snake_case				=			1e-6
  snake_case				=			0.95
 else:
  raise ValueError(f"""Size {size} not supported"""     )

 # load image processor
 snake_case				=			PoolFormerImageProcessor(crop_pct=a     )

 # Prepare image
 snake_case				=			prepare_img()
 snake_case				=			image_processor(images=a					,   return_tensors='''pt'''     ).pixel_values

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

 # load original state dict
 snake_case				=			torch.load(a					,   map_location=torch.device('''cpu'''     )     )

 # rename keys
 snake_case				=			rename_keys(a     )

 # create HuggingFace model and load state dict
 snake_case				=			PoolFormerForImageClassification(a     )
 model.load_state_dict(a     )
 model.eval()

 # Define image processor
 snake_case				=			PoolFormerImageProcessor(crop_pct=a     )
 snake_case				=			image_processor(images=prepare_img()					,   return_tensors='''pt'''     ).pixel_values

 # forward pass
 snake_case				=			model(a     )
 snake_case				=			outputs.logits

 # define expected logit slices for different models
 if size == "s12":
  snake_case				=			torch.tensor([-0.3045, -0.6758, -0.4869]     )
 elif size == "s24":
  snake_case				=			torch.tensor([0.4402, -0.1374, -0.8045]     )
 elif size == "s36":
  snake_case				=			torch.tensor([-0.6080, -0.5133, -0.5898]     )
 elif size == "m36":
  snake_case				=			torch.tensor([0.3952, 0.2263, -1.2668]     )
 elif size == "m48":
  snake_case				=			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]					,   a					,   atol=1e-2     )

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


if __name__ == "__main__":
   _lowercase       						=			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.'
   )
   _lowercase       						=			parser.parse_args()
   convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)

