





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        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase )			->     Optional[Any]:
    lowerCamelCase__    :      int					 =		old_name

    if "patch_embed" in old_name:
        lowerCamelCase__    :      Any					 =		old_name.split('.' )

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

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

            lowerCamelCase__    :      List[str]					 =		'''last_stage.''' + trimmed_name

    elif "network" in old_name and re.search(r'.\d.'						,	lowercase__ ):
        lowerCamelCase__    :      List[Any]					 =		old_name.replace('network'						,	'intermediate_stages' )

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

    return new_name


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase )			->     Tuple:
    for key in checkpoint.copy().keys():
        lowerCamelCase__    :      Tuple					 =		checkpoint.pop(lowercase__ )
        lowerCamelCase__    :      Tuple					 =		val

    return checkpoint


def        SCREAMING_SNAKE_CASE      (       )			->     str:
    lowerCamelCase__    :      Optional[int]					 =		'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    lowerCamelCase__    :      Dict					 =		Image.open(requests.get(lowercase__						,	stream=lowercase__ ).raw )

    return image


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase )			->     Optional[Any]:
    lowerCamelCase__    :      Tuple					 =		torch.load(lowercase__						,	map_location='cpu' )['''model''']
    lowerCamelCase__    :      Optional[Any]					 =		EfficientFormerConfig.from_json_file(lowercase__ )
    lowerCamelCase__    :      Tuple					 =		EfficientFormerForImageClassificationWithTeacher(lowercase__ )
    lowerCamelCase__    :      List[Any]					 =		'''_'''.join(checkpoint_path.split('/' )[-1].split('.' )[0].split('_' )[:-1] )

    lowerCamelCase__    :      List[str]					 =		config.depths[-1] - config.num_metaad_blocks + 1
    lowerCamelCase__    :      int					 =		convert_torch_checkpoint(lowercase__						,	lowercase__ )

    model.load_state_dict(lowercase__ )
    model.eval()

    lowerCamelCase__    :      Optional[Any]					 =		{
        '''bilinear''': PILImageResampling.BILINEAR,
        '''bicubic''': PILImageResampling.BICUBIC,
        '''nearest''': PILImageResampling.NEAREST,
    }

    # prepare image
    lowerCamelCase__    :      Optional[Any]					 =		prepare_img()
    lowerCamelCase__    :      Any					 =		256
    lowerCamelCase__    :      int					 =		224
    lowerCamelCase__    :      List[str]					 =		EfficientFormerImageProcessor(
        size={'shortest_edge': image_size}						,	crop_size={'height': crop_size, 'width': crop_size}						,	resample=pillow_resamplings['bicubic']						,	)
    lowerCamelCase__    :      int					 =		processor(images=lowercase__						,	return_tensors='pt' ).pixel_values

    # original processing pipeline
    lowerCamelCase__    :      int					 =		Compose(
        [
            Resize(lowercase__						,	interpolation=pillow_resamplings['bicubic'] ),
            CenterCrop(lowercase__ ),
            ToTensor(),
            Normalize(lowercase__						,	lowercase__ ),
        ] )
    lowerCamelCase__    :      List[str]					 =		image_transforms(lowercase__ ).unsqueeze(0 )

    assert torch.allclose(lowercase__						,	lowercase__ )

    lowerCamelCase__    :      Optional[Any]					 =		model(lowercase__ )
    lowerCamelCase__    :      Tuple					 =		outputs.logits

    lowerCamelCase__    :      Union[str, Any]					 =		(1, 1000)

    if "l1" in model_name:
        lowerCamelCase__    :      Optional[int]					 =		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]						,	lowercase__						,	atol=1e-3 )
        assert logits.shape == expected_shape
    elif "l3" in model_name:
        lowerCamelCase__    :      List[Any]					 =		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]						,	lowercase__						,	atol=1e-3 )
        assert logits.shape == expected_shape
    elif "l7" in model_name:
        lowerCamelCase__    :      str					 =		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(lowercase__ ).mkdir(exist_ok=lowercase__ )
    model.save_pretrained(lowercase__ )
    print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}""" )
    processor.save_pretrained(lowercase__ )
    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=lowercase__						,	)
        processor.push_to_hub(
            repo_id=F"""Bearnardd/{pytorch_dump_path}"""						,	commit_message='Add image processor'						,	use_temp_dir=lowercase__						,	)


if __name__ == "__main__":
    _UpperCAmelCase				:       Optional[Any]	   =				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)

    _UpperCAmelCase				:       str	   =				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 json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
						from transformers.pipelines.conversational import Conversation


__lowerCAmelCase	: Optional[int]  				=logging.get_logger(__name__)

__lowerCAmelCase	: Optional[Any]  				={'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

__lowerCAmelCase	: List[str]  				={
    'tokenizer_file': {
        'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json',
    },
}

__lowerCAmelCase	: Optional[int]  				={
    'gpt-neox-20b': 2_0_4_8,
}
class        _lowercase      (			A__       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			str						=		VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE__				:			Dict						=		PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE__				:			Any						=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE__				:			Optional[Any]						=		['''input_ids''', '''attention_mask''']


				def __init__(       self			:int			,			lowerCAmelCase__			:Any=None			,			lowerCAmelCase__			:Optional[Any]=None			,			lowerCAmelCase__			:List[Any]=None			,			lowerCAmelCase__			:str="<|endoftext|>"			,			lowerCAmelCase__			:str="<|endoftext|>"			,			lowerCAmelCase__			:Dict="<|endoftext|>"			,			lowerCAmelCase__			:Union[str, Any]=False			,			**lowerCAmelCase__			:List[str]			,			)		->			Any:
										super().__init__(
										    lowerCAmelCase__			,			lowerCAmelCase__			,			tokenizer_file=lowerCAmelCase__			,			unk_token=lowerCAmelCase__			,			bos_token=lowerCAmelCase__			,			eos_token=lowerCAmelCase__			,			add_prefix_space=lowerCAmelCase__			,			**lowerCAmelCase__			,			)

										__SCREAMING_SNAKE_CASE						:     List[Any]					=		json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
										if pre_tok_state.get('''add_prefix_space'''			,			lowerCAmelCase__	) != add_prefix_space:
																__SCREAMING_SNAKE_CASE						:     List[str]					=		getattr(lowerCAmelCase__			,			pre_tok_state.pop('''type'''	)	)
																__SCREAMING_SNAKE_CASE						:     str					=		add_prefix_space
																__SCREAMING_SNAKE_CASE						:     Any					=		pre_tok_class(**lowerCAmelCase__	)

										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		add_prefix_space


				def 		__magic_name__(       self			:Union[str, Any]			,			lowerCAmelCase__			:str			,			lowerCAmelCase__			:Optional[str] = None	)		->			Tuple[str]:
										__SCREAMING_SNAKE_CASE						:     List[str]					=		self._tokenizer.model.save(lowerCAmelCase__			,			name=lowerCAmelCase__	)
										return tuple(lowerCAmelCase__	)



				def 		__magic_name__(       self			:Optional[Any]			,			lowerCAmelCase__			:"Conversation"	)		->			List[int]:
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		[]
										for is_user, text in conversation.iter_texts():
																input_ids.extend(self.encode(lowerCAmelCase__			,			add_special_tokens=lowerCAmelCase__	) + [self.eos_token_id]	)

										if len(lowerCAmelCase__	) > self.model_max_length:
																__SCREAMING_SNAKE_CASE						:     List[str]					=		input_ids[-self.model_max_length :]
										return input_ids
