





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


SCREAMING_SNAKE_CASE__       :			List[Any]			      =						logging.get_logger(__name__)







@add_end_docstrings(
    _UpperCamelCase		,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   __lowerCAmelCase			(					_UpperCamelCase  ):





   def 	_snake_case ( self					,			snake_case      )				->  np.ndarray:
      """simple docstring"""





      if self.framework == "tf":
         a__	:						Union[str, Any]      		=  tf.where(input_ids == self.tokenizer.mask_token_id      ).numpy()
      elif self.framework == "pt":
         a__	:						Optional[int]      		=  torch.nonzero(input_ids == self.tokenizer.mask_token_id					,			as_tuple=snake_case      )
      else:
         raise ValueError("Unsupported framework"      )
      return masked_index





   def 	_snake_case ( self					,			snake_case      )				->  np.ndarray:
      """simple docstring"""





      a__	:						Dict      		=  self.get_masked_index(snake_case      )
      a__	:						Tuple      		=  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 	_snake_case ( self					,			snake_case      )				->  int:
      """simple docstring"""





      if isinstance(snake_case					,			snake_case      ):
         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(snake_case      )





   def 	_snake_case ( self					,			snake_case					,			snake_case=None					,			**snake_case      )				->  Dict[str, GenericTensor]:
      """simple docstring"""





      if return_tensors is None:
         a__	:						List[str]      		=  self.framework
      a__	:						Tuple      		=  self.tokenizer(snake_case					,			return_tensors=snake_case      )
      self.ensure_exactly_one_mask_token(snake_case      )
      return model_inputs





   def 	_snake_case ( self					,			snake_case      )				->  List[str]:
      """simple docstring"""





      a__	:						List[str]      		=  self.model(**snake_case      )
      a__	:						Dict      		=  model_inputs["input_ids"]
      return model_outputs





   def 	_snake_case ( self					,			snake_case					,			snake_case=5					,			snake_case=None      )				->  List[Any]:
      """simple docstring"""





      if target_ids is not None and target_ids.shape[0] < top_k:
         a__	:						Any      		=  target_ids.shape[0]
      a__	:						Tuple      		=  model_outputs["input_ids"][0]
      a__	:						Tuple      		=  model_outputs["logits"]

      if self.framework == "tf":
         a__	:						Union[str, Any]      		=  tf.where(input_ids == self.tokenizer.mask_token_id      ).numpy()[:, 0]

         a__	:						Dict      		=  outputs.numpy()

         a__	:						str      		=  outputs[0, masked_index, :]
         a__	:						List[Any]      		=  stable_softmax(snake_case					,			axis=-1      )
         if target_ids is not None:
            a__	:						Union[str, Any]      		=  tf.gather_nd(tf.squeeze(snake_case					,			0      )					,			target_ids.reshape(-1					,			1      )      )
            a__	:						List[Any]      		=  tf.expand_dims(snake_case					,			0      )

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

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

         a__	:						Tuple      		=  probs.topk(snake_case      )

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

            a__	:						Dict      		=  p
            # Filter padding out:
            a__	:						Any      		=  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
            a__	:						Union[str, Any]      		=  self.tokenizer.decode(snake_case					,			skip_special_tokens=snake_case      )
            a__	:						List[Any]      		=  {"score": v, "token": p, "token_str": self.tokenizer.decode([p]      ), "sequence": sequence}
            row.append(snake_case      )
         result.append(snake_case      )
      if single_mask:
         return result[0]
      return result





   def 	_snake_case ( self					,			snake_case					,			snake_case=None      )				->  Dict:
      """simple docstring"""





      if isinstance(snake_case					,			snake_case      ):
         a__	:						Optional[int]      		=  [targets]
      try:
         a__	:						Dict      		=  self.tokenizer.get_vocab()
      except Exception:
         a__	:						Dict      		=  {}
      a__	:						List[Any]      		=  []
      for target in targets:
         a__	:						List[str]      		=  vocab.get(snake_case					,			snake_case      )
         if id_ is None:
            a__	:						List[Any]      		=  self.tokenizer(
                snake_case					,			add_special_tokens=snake_case					,			return_attention_mask=snake_case					,			return_token_type_ids=snake_case					,			max_length=1					,			truncation=snake_case					,			)["input_ids"]
            if len(snake_case      ) == 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
            a__	:						List[Any]      		=  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_      )
      a__	:						Tuple      		=  list(set(snake_case      )      )
      if len(snake_case      ) == 0:
         raise ValueError("At least one target must be provided when passed."      )
      a__	:						Dict      		=  np.array(snake_case      )
      return target_ids





   def 	_snake_case ( self					,			snake_case=None					,			snake_case=None      )				->  List[str]:
      """simple docstring"""





      a__	:						Optional[int]      		=  {}

      if targets is not None:
         a__	:						Tuple      		=  self.get_target_ids(snake_case					,			snake_case      )
         a__	:						Optional[int]      		=  target_ids

      if top_k is not None:
         a__	:						Dict      		=  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					,			snake_case					,			*snake_case					,			**snake_case      )				->  Optional[Any]:
      """simple docstring"""





      a__	:						List[Any]      		=  super().__call__(snake_case					,			**snake_case      )
      if isinstance(snake_case					,			snake_case      ) and len(snake_case      ) == 1:
         return outputs[0]
      return outputs








import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   a__	:						Dict      		=  old_name

   if "patch_embed" in old_name:
      a__		,     a__		,     a__	:						Union[str, Any]      		=  old_name.split("."   )

      if layer == "0":
         a__	:						Union[str, Any]      		=  old_name.replace("0"							, "convolution1"   )
      elif layer == "1":
         a__	:						Dict      		=  old_name.replace("1"							, "batchnorm_before"   )
      elif layer == "3":
         a__	:						List[str]      		=  old_name.replace("3"							, "convolution2"   )
      else:
         a__	:						Optional[Any]      		=  old_name.replace("4"							, "batchnorm_after"   )

   if "network" in old_name and re.search(r"\d\.\d"							, lowerCamelCase   ):
      a__	:						List[str]      		=  r"\b\d{2}\b"
      if bool(re.search(lowerCamelCase							, lowerCamelCase   )   ):
         a__	:						Optional[int]      		=  re.search(r"\d\.\d\d."							, lowerCamelCase   ).group()
      else:
         a__	:						Any      		=  re.search(r"\d\.\d."							, lowerCamelCase   ).group()
      if int(match[0]   ) < 6:
         a__	:						List[Any]      		=  old_name.replace(lowerCamelCase							, ""   )
         a__	:						int      		=  trimmed_name.replace("network"							, match[0] + ".meta4D_layers.blocks." + match[2:-1]   )
         a__	:						List[Any]      		=  "intermediate_stages." + trimmed_name
      else:
         a__	:						Union[str, Any]      		=  old_name.replace(lowerCamelCase							, ""   )
         if int(match[2]   ) < num_meta4D_last_stage:
            a__	:						Optional[Any]      		=  trimmed_name.replace("network"							, "meta4D_layers.blocks." + match[2]   )
         else:
            a__	:						Union[str, Any]      		=  str(int(match[2]   ) - num_meta4D_last_stage   )
            a__	:						str      		=  trimmed_name.replace("network"							, "meta3D_layers.blocks." + layer_index   )
            if "norm1" in old_name:
               a__	:						List[str]      		=  trimmed_name.replace("norm1"							, "layernorm1"   )
            elif "norm2" in old_name:
               a__	:						Optional[int]      		=  trimmed_name.replace("norm2"							, "layernorm2"   )
            elif "fc1" in old_name:
               a__	:						List[str]      		=  trimmed_name.replace("fc1"							, "linear_in"   )
            elif "fc2" in old_name:
               a__	:						Any      		=  trimmed_name.replace("fc2"							, "linear_out"   )

         a__	:						Any      		=  "last_stage." + trimmed_name

   elif "network" in old_name and re.search(r".\d."							, lowerCamelCase   ):
      a__	:						List[str]      		=  old_name.replace("network"							, "intermediate_stages"   )

   if "fc" in new_name:
      a__	:						str      		=  new_name.replace("fc"							, "convolution"   )
   elif ("norm1" in new_name) and ("layernorm1" not in new_name):
      a__	:						str      		=  new_name.replace("norm1"							, "batchnorm_before"   )
   elif ("norm2" in new_name) and ("layernorm2" not in new_name):
      a__	:						Any      		=  new_name.replace("norm2"							, "batchnorm_after"   )
   if "proj" in new_name:
      a__	:						Optional[int]      		=  new_name.replace("proj"							, "projection"   )
   if "dist_head" in new_name:
      a__	:						Tuple      		=  new_name.replace("dist_head"							, "distillation_classifier"   )
   elif "head" in new_name:
      a__	:						Optional[int]      		=  new_name.replace("head"							, "classifier"   )
   elif "patch_embed" in new_name:
      a__	:						Tuple      		=  "efficientformer." + new_name
   elif new_name == "norm.weight" or new_name == "norm.bias":
      a__	:						Union[str, Any]      		=  new_name.replace("norm"							, "layernorm"   )
      a__	:						Optional[int]      		=  "efficientformer." + new_name
   else:
      a__	:						List[Any]      		=  "efficientformer.encoder." + new_name

   return new_name






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   for key in checkpoint.copy().keys():
      a__	:						Optional[Any]      		=  checkpoint.pop(lowerCamelCase   )
      a__	:						Dict      		=  val

   return checkpoint






def       _A  (      ):
   a__	:						Tuple      		=  "http://images.cocodataset.org/val2017/000000039769.jpg"
   a__	:						List[Any]      		=  Image.open(requests.get(lowerCamelCase							, stream=lowerCamelCase   ).raw   )

   return image






def       _A  (      lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, lowerCamelCase   ):
   a__	:						List[str]      		=  torch.load(lowerCamelCase							, map_location="cpu"   )["model"]
   a__	:						str      		=  EfficientFormerConfig.from_json_file(lowerCamelCase   )
   a__	:						int      		=  EfficientFormerForImageClassificationWithTeacher(lowerCamelCase   )
   a__	:						Optional[Any]      		=  "_".join(checkpoint_path.split("/"   )[-1].split("."   )[0].split("_"   )[:-1]   )

   a__	:						Tuple      		=  config.depths[-1] - config.num_metaad_blocks + 1
   a__	:						Union[str, Any]      		=  convert_torch_checkpoint(lowerCamelCase							, lowerCamelCase   )

   model.load_state_dict(lowerCamelCase   )
   model.eval()

   a__	:						Dict      		=  {
       "bilinear": PILImageResampling.BILINEAR,
       "bicubic": PILImageResampling.BICUBIC,
       "nearest": PILImageResampling.NEAREST,
   }

   # prepare image
   a__	:						str      		=  prepare_img()
   a__	:						Dict      		=  256
   a__	:						Union[str, Any]      		=  224
   a__	:						List[str]      		=  EfficientFormerImageProcessor(
       size={"shortest_edge": image_size}							, crop_size={"height": crop_size, "width": crop_size}							, resample=pillow_resamplings["bicubic"]							, )
   a__	:						List[str]      		=  processor(images=lowerCamelCase							, return_tensors="pt"   ).pixel_values

   # original processing pipeline
   a__	:						List[str]      		=  Compose(
       [
           Resize(lowerCamelCase							, interpolation=pillow_resamplings["bicubic"]   ),
           CenterCrop(lowerCamelCase   ),
           ToTensor(),
           Normalize(lowerCamelCase							, lowerCamelCase   ),
       ]   )
   a__	:						List[Any]      		=  image_transforms(lowerCamelCase   ).unsqueeze(0   )

   assert torch.allclose(lowerCamelCase							, lowerCamelCase   )

   a__	:						Optional[int]      		=  model(lowerCamelCase   )
   a__	:						Any      		=  outputs.logits

   a__	:						Optional[Any]      		=  (1, 1000)

   if "l1" in model_name:
      a__	:						Tuple      		=  torch.Tensor(
          [-0.1312, 0.4353, -1.0499, -0.5124, 0.4183, -0.6793, -1.3777, -0.0893, -0.7358, -2.4328]   )
      assert torch.allclose(logits[0, :10]							, lowerCamelCase							, atol=1E-3   )
      assert logits.shape == expected_shape
   elif "l3" in model_name:
      a__	:						int      		=  torch.Tensor(
          [-1.3150, -1.5456, -1.2556, -0.8496, -0.7127, -0.7897, -0.9728, -0.3052, 0.3751, -0.3127]   )
      assert torch.allclose(logits[0, :10]							, lowerCamelCase							, atol=1E-3   )
      assert logits.shape == expected_shape
   elif "l7" in model_name:
      a__	:						Optional[Any]      		=  torch.Tensor(
          [-1.0283, -1.4131, -0.5644, -1.3115, -0.5785, -1.2049, -0.7528, 0.1992, -0.3822, -0.0878]   )
      assert logits.shape == expected_shape
   else:
      raise ValueError(
          F"""Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7"""   )

   # Save Checkpoints
   Path(lowerCamelCase   ).mkdir(exist_ok=lowerCamelCase   )
   model.save_pretrained(lowerCamelCase   )
   print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""   )
   processor.save_pretrained(lowerCamelCase   )
   print(F"""Processor successfuly saved at {pytorch_dump_path}"""   )

   if push_to_hub:
      print("Pushing model to the hub..."   )

      model.push_to_hub(
          repo_id=F"""Bearnardd/{pytorch_dump_path}"""							, commit_message="Add model"							, use_temp_dir=lowerCamelCase							, )
      processor.push_to_hub(
          repo_id=F"""Bearnardd/{pytorch_dump_path}"""							, commit_message="Add image processor"							, use_temp_dir=lowerCamelCase							, )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE__       :			str			      =						argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--pytorch_model_path""",
      default=None,
      type=str,
      required=True,
      help="""Path to EfficientFormer pytorch checkpoint.""",
  )
  parser.add_argument(
      """--config_file""",
      default=None,
      type=str,
      required=True,
      help="""The json file for EfficientFormer model config.""",
  )
  parser.add_argument(
      """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )

  parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image processor to the hub""")
  parser.add_argument(
      """--no-push_to_hub""",
      dest="""push_to_hub""",
      action="""store_false""",
      help="""Do not push model and image processor to the hub""",
  )
  parser.set_defaults(push_to_hub=True)

  SCREAMING_SNAKE_CASE__       :			Optional[int]			      =						parser.parse_args()
  convert_efficientformer_checkpoint(
      checkpoint_path=args.pytorch_model_path,
      efficientformer_config_file=args.config_file,
      pytorch_dump_path=args.pytorch_dump_path,
      push_to_hub=args.push_to_hub,
  )

