
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      lowerCamelCase__       (       __lowerCAmelCase						:						Optional[int]					,							__lowerCAmelCase						:						Tuple       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			old_name

			if "patch_embed" in old_name:
						lowerCAmelCase_			,						lowerCAmelCase_			,						lowerCAmelCase_      			=			old_name.split("."       )

						if layer == "0":
									lowerCAmelCase_      			=			old_name.replace("0"					,							"convolution1"       )
						elif layer == "1":
									lowerCAmelCase_      			=			old_name.replace("1"					,							"batchnorm_before"       )
						elif layer == "3":
									lowerCAmelCase_      			=			old_name.replace("3"					,							"convolution2"       )
						else:
									lowerCAmelCase_      			=			old_name.replace("4"					,							"batchnorm_after"       )

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

									lowerCAmelCase_      			=			"last_stage." + trimmed_name

			elif "network" in old_name and re.search(r".\d."					,							__lowerCAmelCase       ):
						lowerCAmelCase_      			=			old_name.replace("network"					,							"intermediate_stages"       )

			if "fc" in new_name:
						lowerCAmelCase_      			=			new_name.replace("fc"					,							"convolution"       )
			elif ("norm1" in new_name) and ("layernorm1" not in new_name):
						lowerCAmelCase_      			=			new_name.replace("norm1"					,							"batchnorm_before"       )
			elif ("norm2" in new_name) and ("layernorm2" not in new_name):
						lowerCAmelCase_      			=			new_name.replace("norm2"					,							"batchnorm_after"       )
			if "proj" in new_name:
						lowerCAmelCase_      			=			new_name.replace("proj"					,							"projection"       )
			if "dist_head" in new_name:
						lowerCAmelCase_      			=			new_name.replace("dist_head"					,							"distillation_classifier"       )
			elif "head" in new_name:
						lowerCAmelCase_      			=			new_name.replace("head"					,							"classifier"       )
			elif "patch_embed" in new_name:
						lowerCAmelCase_      			=			"efficientformer." + new_name
			elif new_name == "norm.weight" or new_name == "norm.bias":
						lowerCAmelCase_      			=			new_name.replace("norm"					,							"layernorm"       )
						lowerCAmelCase_      			=			"efficientformer." + new_name
			else:
						lowerCAmelCase_      			=			"efficientformer.encoder." + new_name

			return new_name






def      lowerCamelCase__       (       __lowerCAmelCase						:						Optional[int]					,							__lowerCAmelCase						:						Optional[int]       ):




			"""simple docstring"""


			for key in checkpoint.copy().keys():
						lowerCAmelCase_      			=			checkpoint.pop(__lowerCAmelCase       )
						lowerCAmelCase_      			=			val

			return checkpoint






def      lowerCamelCase__       (       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			"http://images.cocodataset.org/val2017/000000039769.jpg"
			lowerCAmelCase_      			=			Image.open(requests.get(__lowerCAmelCase					,							stream=__lowerCAmelCase       ).raw       )

			return image






def      lowerCamelCase__       (       __lowerCAmelCase						:						Path					,							__lowerCAmelCase						:						Path					,							__lowerCAmelCase						:						Path					,							__lowerCAmelCase						:						bool       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			torch.load(__lowerCAmelCase					,							map_location="cpu"       )["model"]
			lowerCAmelCase_      			=			EfficientFormerConfig.from_json_file(__lowerCAmelCase       )
			lowerCAmelCase_      			=			EfficientFormerForImageClassificationWithTeacher(__lowerCAmelCase       )
			lowerCAmelCase_      			=			"_".join(checkpoint_path.split("/"       )[-1].split("."       )[0].split("_"       )[:-1]       )

			lowerCAmelCase_      			=			config.depths[-1] - config.num_metaad_blocks + 1
			lowerCAmelCase_      			=			convert_torch_checkpoint(__lowerCAmelCase					,							__lowerCAmelCase       )

			model.load_state_dict(__lowerCAmelCase       )
			model.eval()

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

			# prepare image
			lowerCAmelCase_      			=			prepare_img()
			lowerCAmelCase_      			=			256
			lowerCAmelCase_      			=			224
			lowerCAmelCase_      			=			EfficientFormerImageProcessor(
			    size={"shortest_edge": image_size}					,							crop_size={"height": crop_size, "width": crop_size}					,							resample=pillow_resamplings["bicubic"]					,							)
			lowerCAmelCase_      			=			processor(images=__lowerCAmelCase					,							return_tensors="pt"       ).pixel_values

			# original processing pipeline
			lowerCAmelCase_      			=			Compose(
			    [
			        Resize(__lowerCAmelCase					,							interpolation=pillow_resamplings["bicubic"]       ),
			        CenterCrop(__lowerCAmelCase       ),
			        ToTensor(),
			        Normalize(__lowerCAmelCase					,							__lowerCAmelCase       ),
			    ]       )
			lowerCAmelCase_      			=			image_transforms(__lowerCAmelCase       ).unsqueeze(0       )

			assert torch.allclose(__lowerCAmelCase					,							__lowerCAmelCase       )

			lowerCAmelCase_      			=			model(__lowerCAmelCase       )
			lowerCAmelCase_      			=			outputs.logits

			lowerCAmelCase_      			=			(1, 1000)

			if "l1" in model_name:
						lowerCAmelCase_      			=			torch.Tensor(
						    [-0.1_312, 0.4_353, -1.0_499, -0.5_124, 0.4_183, -0.6_793, -1.3_777, -0.0_893, -0.7_358, -2.4_328]       )
						assert torch.allclose(logits[0, :10]					,							__lowerCAmelCase					,							atol=1e-3       )
						assert logits.shape == expected_shape
			elif "l3" in model_name:
						lowerCAmelCase_      			=			torch.Tensor(
						    [-1.3_150, -1.5_456, -1.2_556, -0.8_496, -0.7_127, -0.7_897, -0.9_728, -0.3_052, 0.3_751, -0.3_127]       )
						assert torch.allclose(logits[0, :10]					,							__lowerCAmelCase					,							atol=1e-3       )
						assert logits.shape == expected_shape
			elif "l7" in model_name:
						lowerCAmelCase_      			=			torch.Tensor(
						    [-1.0_283, -1.4_131, -0.5_644, -1.3_115, -0.5_785, -1.2_049, -0.7_528, 0.1_992, -0.3_822, -0.0_878]       )
						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__":
			_A     =  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)

			_A     =  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,
			)





import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_A     =  "▁"

_A     =  {"vocab_file": "spiece.model"}

_A     =  {
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"}
}

_A     =  {
    "google/pegasus-xsum": 5_12,
}


_A     =  logging.get_logger(__name__)





class 			_lowerCAmelCase      (			__a ):
				_lowercase    	=VOCAB_FILES_NAMES

				_lowercase    	=VOCAB_FILES_NAMES
				_lowercase    	=PRETRAINED_VOCAB_FILES_MAP
				_lowercase    	=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_lowercase    	=['''input_ids''', '''attention_mask''']




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase="<pad>"       ,       _UpperCamelCase="</s>"       ,       _UpperCamelCase="<unk>"       ,       _UpperCamelCase="<mask_2>"       ,       _UpperCamelCase="<mask_1>"       ,       _UpperCamelCase=None       ,       _UpperCamelCase=103       ,       _UpperCamelCase = None       ,       **_UpperCamelCase       ,       )       ->   None:
							lowerCAmelCase_      			=			offset
							if additional_special_tokens is not None:
										if not isinstance(_UpperCamelCase       ,       _UpperCamelCase  ):
													raise TypeError(
													    f"""additional_special_tokens should be of type {type(_UpperCamelCase  )}, but is"""
													    f""" {type(_UpperCamelCase  )}"""  )

										lowerCAmelCase_      			=			(
										    ([mask_token_sent] + additional_special_tokens)
										    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
										    else additional_special_tokens
										)
										# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
										additional_special_tokens_extended += [
										    f"""<unk_{i}>""" for i in range(len(_UpperCamelCase  )       ,       self.offset - 1  )
										]

										if len(set(_UpperCamelCase  )  ) != len(_UpperCamelCase  ):
													raise ValueError(
													    "Please make sure that the provided additional_special_tokens do not contain an incorrectly"
													    f""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."""  )
										lowerCAmelCase_      			=			additional_special_tokens_extended
							else:
										lowerCAmelCase_      			=			[mask_token_sent] if mask_token_sent is not None else []
										additional_special_tokens += [f"""<unk_{i}>""" for i in range(2       ,       self.offset  )]

							lowerCAmelCase_      			=			{} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    eos_token=_UpperCamelCase       ,       unk_token=_UpperCamelCase       ,       mask_token=_UpperCamelCase       ,       pad_token=_UpperCamelCase       ,       mask_token_sent=_UpperCamelCase       ,       offset=_UpperCamelCase       ,       additional_special_tokens=_UpperCamelCase       ,       sp_model_kwargs=self.sp_model_kwargs       ,       **_UpperCamelCase       ,       )
							lowerCAmelCase_      			=			mask_token_sent
							lowerCAmelCase_      			=			vocab_file
							lowerCAmelCase_      			=			spm.SentencePieceProcessor(**self.sp_model_kwargs  )
							self.sp_model.Load(_UpperCamelCase  )

							# add special tokens to encoder dict
							lowerCAmelCase_      			=			{
							    0: self.pad_token,
							    1: self.eos_token,
							}

							if self.mask_token_sent is not None:
										self.encoder.update(
										    {
										        2: self.mask_token_sent,
										        3: self.mask_token,
										    }  )

							if self.offset > 0:
										# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
										# mask_token_sent is already added to list -> so start at 1
										self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1       ,       self.offset - 1  )}  )

							lowerCAmelCase_      			=			{v: k for k, v in self.encoder.items()}




				@property
				def   __a		(      self  )       ->   int:
							return len(self.sp_model  ) + self.offset




				def   __a		(      self  )       ->   Dict[str, int]:
							lowerCAmelCase_      			=			{self.convert_ids_to_tokens(_UpperCamelCase  ): i for i in range(self.vocab_size  )}
							vocab.update(self.added_tokens_encoder  )
							return vocab




				def __getstate__(      self  )       ->   List[Any]:
							lowerCAmelCase_      			=			self.__dict__.copy()
							lowerCAmelCase_      			=			None
							return state




				def __setstate__(      self       ,       _UpperCamelCase  )       ->   Optional[int]:
							lowerCAmelCase_      			=			d

							# for backward compatibility
							if not hasattr(self       ,       "sp_model_kwargs"  ):
										lowerCAmelCase_      			=			{}

							lowerCAmelCase_      			=			spm.SentencePieceProcessor(**self.sp_model_kwargs  )
							self.sp_model.Load(self.vocab_file  )




				def   __a		(      self       ,       _UpperCamelCase  )       ->   List[str]:
							return self.sp_model.encode(_UpperCamelCase       ,       out_type=_UpperCamelCase  )




				def   __a		(      self       ,       _UpperCamelCase  )       ->   int:
							if token in self.decoder:
										return self.decoder[token]
							elif token in self.added_tokens_decoder:
										return self.added_tokens_decoder[token]
							lowerCAmelCase_      			=			self.sp_model.piece_to_id(_UpperCamelCase  )
							return sp_id + self.offset




				def   __a		(      self       ,       _UpperCamelCase  )       ->   str:
							if index in self.encoder:
										return self.encoder[index]
							elif index in self.added_tokens_encoder:
										return self.added_tokens_encoder[index]
							else:
										lowerCAmelCase_      			=			self.sp_model.IdToPiece(index - self.offset  )
							return token




				def   __a		(      self       ,       _UpperCamelCase  )       ->   Optional[Any]:
							lowerCAmelCase_      			=			[]
							lowerCAmelCase_      			=			""
							for token in tokens:
										# make sure that special tokens are not decoded using sentencepiece model
										if token in self.all_special_tokens:
													out_string += self.sp_model.decode(_UpperCamelCase  ) + token
													lowerCAmelCase_      			=			[]
										else:
													current_sub_tokens.append(_UpperCamelCase  )
							out_string += self.sp_model.decode(_UpperCamelCase  )
							return out_string.strip()




				def   __a		(      self       ,       _UpperCamelCase=False  )       ->   Optional[int]:
							return 1




				def   __a		(      self       ,       _UpperCamelCase  )       ->   int:
							lowerCAmelCase_      			=			set(self.all_special_ids  )  # call it once instead of inside list comp
							all_special_ids.remove(self.unk_token_id  )  # <unk> is only sometimes special

							return [1 if x in all_special_ids else 0 for x in seq]




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase = None       ,       _UpperCamelCase = False  )       ->   List[int]:
							if already_has_special_tokens:
										return self._special_token_mask(_UpperCamelCase  )
							elif token_ids_a is None:
										return self._special_token_mask(_UpperCamelCase  ) + [1]
							else:
										return self._special_token_mask(token_ids_a + token_ids_a  ) + [1]




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase=None  )       ->   List[int]:
							if token_ids_a is None:
										return token_ids_a + [self.eos_token_id]
							# We don't expect to process pairs, but leave the pair logic for API consistency
							return token_ids_a + token_ids_a + [self.eos_token_id]




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase = None  )       ->   Tuple[str]:
							if not os.path.isdir(_UpperCamelCase  ):
										logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""  )
										return
							lowerCAmelCase_      			=			os.path.join(
							    _UpperCamelCase       ,       (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]  )

							if os.path.abspath(self.vocab_file  ) != os.path.abspath(_UpperCamelCase  ) and os.path.isfile(self.vocab_file  ):
										copyfile(self.vocab_file       ,       _UpperCamelCase  )
							elif not os.path.isfile(self.vocab_file  ):
										with open(_UpperCamelCase       ,       "wb"  ) as fi:
													lowerCAmelCase_      			=			self.sp_model.serialized_model_proto()
													fi.write(_UpperCamelCase  )

							return (out_vocab_file,)



