


'''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  (       A							,A							)  ->				str:
	lowercase						:		Optional[int]   					=		old_name

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

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

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

			lowercase						:		Dict   					=		"last_stage." + trimmed_name

	elif "network" in old_name and re.search(r".\d."							,A							):
		lowercase						:		Union[str, Any]   					=		old_name.replace("network"							,"intermediate_stages"							)

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

	return new_name




def 	_A  (       A							,A							)  ->				Optional[Any]:
	for key in checkpoint.copy().keys():
		lowercase						:		List[str]   					=		checkpoint.pop(A							)
		lowercase						:		int   					=		val

	return checkpoint




def 	_A  (       )  ->				Optional[int]:
	lowercase						:		str   					=		"http://images.cocodataset.org/val2017/000000039769.jpg"
	lowercase						:		Optional[Any]   					=		Image.open(requests.get(A							,stream=A							).raw							)

	return image




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

	lowercase						:		Optional[int]   					=		config.depths[-1] - config.num_metaad_blocks + 1
	lowercase						:		int   					=		convert_torch_checkpoint(A							,A							)

	model.load_state_dict(A							)
	model.eval()

	lowercase						:		List[Any]   					=		{
	    "bilinear": PILImageResampling.BILINEAR,
	    "bicubic": PILImageResampling.BICUBIC,
	    "nearest": PILImageResampling.NEAREST,
	}

	# prepare image
	lowercase						:		Tuple   					=		prepare_img()
	lowercase						:		Optional[int]   					=		2_5_6
	lowercase						:		str   					=		2_2_4
	lowercase						:		List[str]   					=		EfficientFormerImageProcessor(
	    size={"shortest_edge": image_size}							,crop_size={"height": crop_size, "width": crop_size}							,resample=pillow_resamplings["bicubic"]							,)
	lowercase						:		Union[str, Any]   					=		processor(images=A							,return_tensors="pt"							).pixel_values

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

	assert torch.allclose(A							,A							)

	lowercase						:		Union[str, Any]   					=		model(A							)
	lowercase						:		Any   					=		outputs.logits

	lowercase						:		List[str]   					=		(1, 1_0_0_0)

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


if __name__ == "__main__":
					lowerCAmelCase   : Tuple             =				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)

					lowerCAmelCase   : 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,
					)






'''simple docstring'''
import unittest

import numpy as np

from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
					import jax.numpy as jnp

					from transformers.models.distilbert.modeling_flax_distilbert import (
					    FlaxDistilBertForMaskedLM,
					    FlaxDistilBertForMultipleChoice,
					    FlaxDistilBertForQuestionAnswering,
					    FlaxDistilBertForSequenceClassification,
					    FlaxDistilBertForTokenClassification,
					    FlaxDistilBertModel,
					)




class        _UpperCamelCase	(    unittest.TestCase):


				'''simple docstring'''


				def __init__(     self							,      a_							,      a_=1_3							,      a_=7							,      a_=True							,      a_=True							,      a_=True							,      a_=True							,      a_=9_9							,      a_=3_2							,      a_=5							,      a_=4							,      a_=3_7							,      a_="gelu"							,      a_=0.1							,      a_=0.1							,      a_=5_1_2							,      a_=1_6							,      a_=2							,      a_=0.02							,      a_=4							,      )    ->  Tuple:
					lowercase						:		Optional[Any]   					=		parent
					lowercase						:		int   					=		batch_size
					lowercase						:		int   					=		seq_length
					lowercase						:		List[str]   					=		is_training
					lowercase						:		str   					=		use_attention_mask
					lowercase						:		List[str]   					=		use_token_type_ids
					lowercase						:		Optional[Any]   					=		use_labels
					lowercase						:		Dict   					=		vocab_size
					lowercase						:		Union[str, Any]   					=		hidden_size
					lowercase						:		int   					=		num_hidden_layers
					lowercase						:		Optional[Any]   					=		num_attention_heads
					lowercase						:		Optional[Any]   					=		intermediate_size
					lowercase						:		int   					=		hidden_act
					lowercase						:		Dict   					=		hidden_dropout_prob
					lowercase						:		Optional[Any]   					=		attention_probs_dropout_prob
					lowercase						:		Dict   					=		max_position_embeddings
					lowercase						:		Tuple   					=		type_vocab_size
					lowercase						:		int   					=		type_sequence_label_size
					lowercase						:		Optional[Any]   					=		initializer_range
					lowercase						:		Union[str, Any]   					=		num_choices

				def 	a__		(     self							)    ->  Optional[int]:
					lowercase						:		List[Any]   					=		ids_tensor([self.batch_size, self.seq_length]							,      self.vocab_size							)

					lowercase						:		Any   					=		None
					if self.use_attention_mask:
						lowercase						:		str   					=		random_attention_mask([self.batch_size, self.seq_length]							)

					lowercase						:		Optional[Any]   					=		DistilBertConfig(
					    vocab_size=self.vocab_size							,      dim=self.hidden_size							,      n_layers=self.num_hidden_layers							,      n_heads=self.num_attention_heads							,      hidden_dim=self.intermediate_size							,      hidden_act=self.hidden_act							,      dropout=self.hidden_dropout_prob							,      attention_dropout=self.attention_probs_dropout_prob							,      max_position_embeddings=self.max_position_embeddings							,      initializer_range=self.initializer_range							,      tie_weights_=a_							,      )

					return config, input_ids, attention_mask


				def 	a__		(     self							)    ->  List[str]:
					lowercase						:		Dict   					=		self.prepare_config_and_inputs()
					lowercase					,		lowercase					,		lowercase						:		Dict   					=		config_and_inputs
					lowercase						:		Any   					=		{"input_ids": input_ids, "attention_mask": attention_mask}
					return config, inputs_dict




@require_flax
class        _UpperCamelCase	(    SCREAMING_SNAKE_CASE						,			unittest.TestCase):


				'''simple docstring'''


				_snake_case						       =				(
				    (
				        FlaxDistilBertModel,
				        FlaxDistilBertForMaskedLM,
				        FlaxDistilBertForMultipleChoice,
				        FlaxDistilBertForQuestionAnswering,
				        FlaxDistilBertForSequenceClassification,
				        FlaxDistilBertForTokenClassification,
				        FlaxDistilBertForQuestionAnswering,
				    )
				    if is_flax_available()
				    else ()
				)

				def 	a__		(     self							)    ->  Dict:
					lowercase						:		Optional[int]   					=		FlaxDistilBertModelTester(self							)


				@slow
				def 	a__		(     self							)    ->  Union[str, Any]:
					for model_class_name in self.all_model_classes:
						lowercase						:		int   					=		model_class_name.from_pretrained("distilbert-base-uncased"							)
						lowercase						:		Tuple   					=		model(np.ones((1, 1)							)							)
						self.assertIsNotNone(a_							)




@require_flax
class        _UpperCamelCase	(    unittest.TestCase):


				'''simple docstring'''


				@slow
				def 	a__		(     self							)    ->  Union[str, Any]:
					lowercase						:		str   					=		FlaxDistilBertModel.from_pretrained("distilbert-base-uncased"							)
					lowercase						:		int   					=		np.array([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]							)
					lowercase						:		int   					=		np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]							)
					lowercase						:		List[str]   					=		model(a_							,      attention_mask=a_							)[0]
					lowercase						:		int   					=		(1, 1_1, 7_6_8)
					self.assertEqual(output.shape							,      a_							)
					lowercase						:		List[str]   					=		np.array([[[-0.16_39, 0.32_99, 0.16_48], [-0.17_46, 0.32_89, 0.17_10], [-0.18_84, 0.33_57, 0.18_10]]]							)

					self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]							,      a_							,      atol=1e-4							)							)


