





'''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 lowerCamelCase						(     __lowerCamelCase :    str					,       __lowerCamelCase :    Any					,       __lowerCamelCase :    Optional[int]					,       __lowerCamelCase :    Optional[Any]    )						->List[Any]:
    _SCREAMING_SNAKE_CASE									=			original_name.split("""."""    )[0]
    _SCREAMING_SNAKE_CASE									=			key.split("""."""    )
    _SCREAMING_SNAKE_CASE									=			int(key_list[key_list.index(__lowerCamelCase    ) - 2]    )
    _SCREAMING_SNAKE_CASE									=			int(key_list[key_list.index(__lowerCamelCase    ) - 1]    )
    _SCREAMING_SNAKE_CASE									=			orig_block_num - offset

    _SCREAMING_SNAKE_CASE									=			key.replace(F'{orig_block_num}.{layer_num}.{original_name}'					,       F'block.{new_block_num}.{layer_num}.{new_name}'    )
    return key





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





def lowerCamelCase						(     )						->int:
    _SCREAMING_SNAKE_CASE									=			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
    _SCREAMING_SNAKE_CASE									=			Image.open(requests.get(__lowerCamelCase					,       stream=__lowerCamelCase    ).raw    )

    return image





@torch.no_grad()
def lowerCamelCase						(     __lowerCamelCase :    int					,       __lowerCamelCase :    Tuple					,       __lowerCamelCase :    int    )						->Optional[Any]:
    _SCREAMING_SNAKE_CASE									=			PoolFormerConfig()

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

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

    # load image processor
    _SCREAMING_SNAKE_CASE									=			PoolFormerImageProcessor(crop_pct=__lowerCamelCase    )

    # Prepare image
    _SCREAMING_SNAKE_CASE									=			prepare_img()
    _SCREAMING_SNAKE_CASE									=			image_processor(images=__lowerCamelCase					,       return_tensors="""pt"""    ).pixel_values

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

    # load original state dict
    _SCREAMING_SNAKE_CASE									=			torch.load(__lowerCamelCase					,       map_location=torch.device("""cpu"""    )    )

    # rename keys
    _SCREAMING_SNAKE_CASE									=			rename_keys(__lowerCamelCase    )

    # create HuggingFace model and load state dict
    _SCREAMING_SNAKE_CASE									=			PoolFormerForImageClassification(__lowerCamelCase    )
    model.load_state_dict(__lowerCamelCase    )
    model.eval()

    # Define image processor
    _SCREAMING_SNAKE_CASE									=			PoolFormerImageProcessor(crop_pct=__lowerCamelCase    )
    _SCREAMING_SNAKE_CASE									=			image_processor(images=prepare_img()					,       return_tensors="""pt"""    ).pixel_values

    # forward pass
    _SCREAMING_SNAKE_CASE									=			model(__lowerCamelCase    )
    _SCREAMING_SNAKE_CASE									=			outputs.logits

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

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


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)











from typing import Dict

import numpy as np

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException


if is_tf_available():
							import tensorflow as tf

							from ..tf_utils import stable_softmax


if is_torch_available():
							import torch


A__	:			Dict			   = logging.get_logger(__name__)
@add_end_docstrings(
    A__		,r"""
        top_k (`int`, defaults to 5):
            The number of predictions to return.
        targets (`str` or `List[str]`, *optional*):
            When passed, the model will limit the scores to the passed targets instead of looking up in the whole
            vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting
            token will be used (with a warning, and that might be slower).

    """		,)
class       _UpperCAmelCase					(       A__	):

				"""simple docstring"""




				def 				lowercase__   (     self						:    Optional[int],  lowerCamelCase						:    GenericTensor  ):

								'''simple docstring'''
								if self.framework == "tf":
												lowercase__    =			tf.where(input_ids == self.tokenizer.mask_token_id  ).numpy()
								elif self.framework == "pt":
												lowercase__    =			torch.nonzero(input_ids == self.tokenizer.mask_token_id,  as_tuple=lowerCamelCase  )
								else:
												raise ValueError('''Unsupported framework'''  )
								return masked_index




				def 				lowercase__   (     self						:    List[str],  lowerCamelCase						:    GenericTensor  ):

								'''simple docstring'''
								lowercase__    =			self.get_masked_index(lowerCamelCase  )
								lowercase__    =			np.prod(masked_index.shape  )
								if numel < 1:
												raise PipelineException(
												    '''fill-mask''',  self.model.base_model_prefix,  F"""No mask_token ({self.tokenizer.mask_token}) found on the input""",  )




				def 				lowercase__   (     self						:    Optional[Any],  lowerCamelCase						:    GenericTensor  ):

								'''simple docstring'''
								if isinstance(lowerCamelCase,  lowerCamelCase  ):
												for model_input in model_inputs:
																self._ensure_exactly_one_mask_token(model_input['''input_ids'''][0]  )
								else:
												for input_ids in model_inputs["input_ids"]:
																self._ensure_exactly_one_mask_token(lowerCamelCase  )




				def 				lowercase__   (     self						:    List[str],  lowerCamelCase						:    Union[str, Any],  lowerCamelCase						:    Optional[int]=None,  **lowerCamelCase						:    Dict  ):

								'''simple docstring'''
								if return_tensors is None:
												lowercase__    =			self.framework
								lowercase__    =			self.tokenizer(lowerCamelCase,  return_tensors=lowerCamelCase  )
								self.ensure_exactly_one_mask_token(lowerCamelCase  )
								return model_inputs




				def 				lowercase__   (     self						:    Optional[Any],  lowerCamelCase						:    int  ):

								'''simple docstring'''
								lowercase__    =			self.model(**lowerCamelCase  )
								lowercase__    =			model_inputs['''input_ids''']
								return model_outputs




				def 				lowercase__   (     self						:    Optional[Any],  lowerCamelCase						:    List[str],  lowerCamelCase						:    Tuple=5,  lowerCamelCase						:    List[Any]=None  ):

								'''simple docstring'''
								# Cap top_k if there are targets
								if target_ids is not None and target_ids.shape[0] < top_k:
												lowercase__    =			target_ids.shape[0]
								lowercase__    =			model_outputs['''input_ids'''][0]
								lowercase__    =			model_outputs['''logits''']

								if self.framework == "tf":
												lowercase__    =			tf.where(input_ids == self.tokenizer.mask_token_id  ).numpy()[:, 0]

												lowercase__    =			outputs.numpy()

												lowercase__    =			outputs[0, masked_index, :]
												lowercase__    =			stable_softmax(lowerCamelCase,  axis=-1  )
												if target_ids is not None:
																lowercase__    =			tf.gather_nd(tf.squeeze(lowerCamelCase,  0  ),  target_ids.reshape(-1,  1  )  )
																lowercase__    =			tf.expand_dims(lowerCamelCase,  0  )

												lowercase__    =			tf.math.top_k(lowerCamelCase,  k=lowerCamelCase  )
												lowercase__ ,						lowercase__    =			topk.values.numpy(), topk.indices.numpy()
								else:
												lowercase__    =			torch.nonzero(input_ids == self.tokenizer.mask_token_id,  as_tuple=lowerCamelCase  ).squeeze(-1  )
												# Fill mask pipeline supports only one ${mask_token} per sample

												lowercase__    =			outputs[0, masked_index, :]
												lowercase__    =			logits.softmax(dim=-1  )
												if target_ids is not None:
																lowercase__    =			probs[..., target_ids]

												lowercase__ ,						lowercase__    =			probs.topk(lowerCamelCase  )

								lowercase__    =			[]
								lowercase__    =			values.shape[0] == 1
								for i, (_values, _predictions) in enumerate(zip(values.tolist(),  predictions.tolist()  )  ):
												lowercase__    =			[]
												for v, p in zip(_values,  _predictions  ):
																# Copy is important since we're going to modify this array in place
																lowercase__    =			input_ids.numpy().copy()
																if target_ids is not None:
																				lowercase__    =			target_ids[p].tolist()

																lowercase__    =			p
																# Filter padding out:
																lowercase__    =			tokens[np.where(tokens != self.tokenizer.pad_token_id  )]
																# Originally we skip special tokens to give readable output.
																# For multi masks though, the other [MASK] would be removed otherwise
																# making the output look odd, so we add them back
																lowercase__    =			self.tokenizer.decode(lowerCamelCase,  skip_special_tokens=lowerCamelCase  )
																lowercase__    =			{'''score''': v, '''token''': p, '''token_str''': self.tokenizer.decode([p]  ), '''sequence''': sequence}
																row.append(lowerCamelCase  )
												result.append(lowerCamelCase  )
								if single_mask:
												return result[0]
								return result




				def 				lowercase__   (     self						:    int,  lowerCamelCase						:    Optional[int],  lowerCamelCase						:    Dict=None  ):

								'''simple docstring'''
								if isinstance(lowerCamelCase,  lowerCamelCase  ):
												lowercase__    =			[targets]
								try:
												lowercase__    =			self.tokenizer.get_vocab()
								except Exception:
												lowercase__    =			{}
								lowercase__    =			[]
								for target in targets:
												lowercase__    =			vocab.get(lowerCamelCase,  lowerCamelCase  )
												if id_ is None:
																lowercase__    =			self.tokenizer(
																    lowerCamelCase,  add_special_tokens=lowerCamelCase,  return_attention_mask=lowerCamelCase,  return_token_type_ids=lowerCamelCase,  max_length=1,  truncation=lowerCamelCase,  )['''input_ids''']
																if len(lowerCamelCase  ) == 0:
																				logger.warning(
																				    F"""The specified target token `{target}` does not exist in the model vocabulary. """
																				    '''We cannot replace it with anything meaningful, ignoring it'''  )
																				continue
																lowercase__    =			input_ids[0]
																# XXX: If users encounter this pass
																# it becomes pretty slow, so let's make sure
																# The warning enables them to fix the input to
																# get faster performance.
																logger.warning(
																    F"""The specified target token `{target}` does not exist in the model vocabulary. """
																    F"""Replacing with `{self.tokenizer.convert_ids_to_tokens(id_  )}`."""  )
												target_ids.append(id_  )
								lowercase__    =			list(set(lowerCamelCase  )  )
								if len(lowerCamelCase  ) == 0:
												raise ValueError('''At least one target must be provided when passed.'''  )
								lowercase__    =			np.array(lowerCamelCase  )
								return target_ids




				def 				lowercase__   (     self						:    List[str],  lowerCamelCase						:    int=None,  lowerCamelCase						:    Any=None  ):

								'''simple docstring'''
								lowercase__    =			{}

								if targets is not None:
												lowercase__    =			self.get_target_ids(lowerCamelCase,  lowerCamelCase  )
												lowercase__    =			target_ids

								if top_k is not None:
												lowercase__    =			top_k

								if self.tokenizer.mask_token_id is None:
												raise PipelineException(
												    '''fill-mask''',  self.model.base_model_prefix,  '''The tokenizer does not define a `mask_token`.'''  )
								return {}, {}, postprocess_params




				def __call__(     self						:    List[Any],  lowerCamelCase						:    Optional[Any],  *lowerCamelCase						:    Optional[Any],  **lowerCamelCase						:    Optional[Any]  ):

								'''simple docstring'''
								lowercase__    =			super().__call__(lowerCamelCase,  **lowerCamelCase  )
								if isinstance(lowerCamelCase,  lowerCamelCase  ) and len(lowerCamelCase  ) == 1:
												return outputs[0]
								return outputs




