


'''simple docstring'''
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_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		old_name

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

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

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

																					A_			    =		"last_stage." + trimmed_name

							elif "network" in old_name and re.search(R".\d."						,  UpperCamelCase_ ):
														A_			    =		old_name.replace("network"						,  "intermediate_stages" )

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

							return new_name







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							for key in checkpoint.copy().keys():
														A_			    =		checkpoint.pop(UpperCamelCase_ )
														A_			    =		val

							return checkpoint







def       a_				(	):
							A_			    =		"http://images.cocodataset.org/val2017/000000039769.jpg"
							A_			    =		Image.open(requests.get(UpperCamelCase_						,  stream=UpperCamelCase_ ).raw )

							return image







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

							A_			    =		config.depths[-1] - config.num_metaad_blocks + 1
							A_			    =		convert_torch_checkpoint(UpperCamelCase_						,  UpperCamelCase_ )

							model.load_state_dict(UpperCamelCase_ )
							model.eval()

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

							# prepare image
							A_			    =		prepare_img()
							A_			    =		2_5_6
							A_			    =		2_2_4
							A_			    =		EfficientFormerImageProcessor(
							    size={"shortest_edge": image_size}						,  crop_size={"height": crop_size, "width": crop_size}						,  resample=pillow_resamplings["bicubic"]						,  )
							A_			    =		processor(images=UpperCamelCase_						,  return_tensors="pt" ).pixel_values

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

							assert torch.allclose(UpperCamelCase_						,  UpperCamelCase_ )

							A_			    =		model(UpperCamelCase_ )
							A_			    =		outputs.logits

							A_			    =		(1, 1_0_0_0)

							if "l1" in model_name:
														A_			    =		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, :1_0]						,  UpperCamelCase_						,  atol=1e-3 )
														assert logits.shape == expected_shape
							elif "l3" in model_name:
														A_			    =		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, :1_0]						,  UpperCamelCase_						,  atol=1e-3 )
														assert logits.shape == expected_shape
							elif "l7" in model_name:
														A_			    =		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(UpperCamelCase_ ).mkdir(exist_ok=UpperCamelCase_ )
							model.save_pretrained(UpperCamelCase_ )
							print(f"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}" )
							processor.save_pretrained(UpperCamelCase_ )
							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=UpperCamelCase_						,  )
														processor.push_to_hub(
														    repo_id=f"Bearnardd/{pytorch_dump_path}"						,  commit_message="Add image processor"						,  use_temp_dir=UpperCamelCase_						,  )


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE					:				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)

							__SCREAMING_SNAKE_CASE					:				Tuple									=		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,
							)









'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


if is_sentencepiece_available():
							from ..ta.tokenization_ta import TaTokenizer
else:
							from ...utils.dummy_sentencepiece_objects import TaTokenizer

__SCREAMING_SNAKE_CASE					:				str									=		TaTokenizer

if is_tokenizers_available():
							from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
							from ...utils.dummy_tokenizers_objects import TaTokenizerFast

__SCREAMING_SNAKE_CASE					:				Any									=		TaTokenizerFast

__SCREAMING_SNAKE_CASE					:				Tuple									=		{'''configuration_mt5''': ['''MT5Config''', '''MT5OnnxConfig''']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__SCREAMING_SNAKE_CASE					:				List[Any]									=		[
							    '''MT5EncoderModel''',
							    '''MT5ForConditionalGeneration''',
							    '''MT5ForQuestionAnswering''',
							    '''MT5Model''',
							    '''MT5PreTrainedModel''',
							    '''MT5Stack''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__SCREAMING_SNAKE_CASE					:				str									=		['''TFMT5EncoderModel''', '''TFMT5ForConditionalGeneration''', '''TFMT5Model''']

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__SCREAMING_SNAKE_CASE					:				List[str]									=		['''FlaxMT5EncoderModel''', '''FlaxMT5ForConditionalGeneration''', '''FlaxMT5Model''']


if TYPE_CHECKING:
							from .configuration_mta import MTaConfig, MTaOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mta import (
														    MTaEncoderModel,
														    MTaForConditionalGeneration,
														    MTaForQuestionAnswering,
														    MTaModel,
														    MTaPreTrainedModel,
														    MTaStack,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
							import sys

							__SCREAMING_SNAKE_CASE					:				Union[str, Any]									=		_LazyModule(
							    __name__,
							    globals()['''__file__'''],
							    _import_structure,
							    extra_objects={'''MT5Tokenizer''': MTaTokenizer, '''MT5TokenizerFast''': MTaTokenizerFast},
							    module_spec=__spec__,
							)





