

import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple

import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging


logging.set_verbosity_info()
a_						     =    logging.get_logger()






@dataclass
class _UpperCamelCase							:
			'''simple docstring'''







			lowerCamelCase__           =42
			lowerCamelCase__           =field(default_factory=__A   )
			lowerCamelCase__           =field(default_factory=__A   )
			def 							__UpperCamelCase (			self							: str      ,      a							: Optional[int]      ,      a							: Tensor      ,      a							: Tensor	)							->       List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       len(list(m.modules()	)	) == 1 or isinstance(a      ,      nn.Convad	) or isinstance(a      ,      nn.BatchNormad	)
				if has_not_submodules:
					self.traced.append(a	)
			def __call__(			self							: List[Any]      ,      a							: Tensor	)							->       Optional[Any]:

				"""simple docstring"""

				for m in self.module.modules():
					self.handles.append(m.register_forward_hook(self._forward_hook	)	)
				self.module(a	)
				[x.remove() for x in self.handles]
				return self






			@property
			def 							__UpperCamelCase (			self							: Dict	)							->       Dict:

				"""simple docstring"""

				return list(filter(lambda a	: len(list(x.state_dict().keys()	)	) > 0      ,      self.traced	)	)






@dataclass
class _UpperCamelCase							:
			'''simple docstring'''







			lowerCamelCase__           =42
			lowerCamelCase__           =42
			lowerCamelCase__           =1
			lowerCamelCase__           =field(default_factory=__A   )
			lowerCamelCase__           =field(default_factory=__A   )
			lowerCamelCase__           =True
			def __call__(			self							: int      ,      a							: Tensor	)							->       Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Dict				     =       Tracker(self.dest	)(a	).parametrized
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       Tracker(self.src	)(a	).parametrized

				SCREAMING_SNAKE_CASE   :      List[str]				     =       list(filter(lambda a	: type(a	) not in self.src_skip      ,      a	)	)
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       list(filter(lambda a	: type(a	) not in self.dest_skip      ,      a	)	)

				if len(a	) != len(a	) and self.raise_if_mismatch:
					raise Exception(
					    F"Numbers of operations are different. Source module has {len(a	)} operations while"
					    F" destination module has {len(a	)}."	)

				for dest_m, src_m in zip(a      ,      a	):
					dest_m.load_state_dict(src_m.state_dict()	)
					if self.verbose == 1:
						print(F"Transfered from={src_m} to={dest_m}"	)






class _UpperCamelCase							(							nn.Module   ):
			'''simple docstring'''







			def __init__(			self							: Union[str, Any]      ,      a							: nn.Module	)							->       str:

				"""simple docstring"""

				super().__init__()

				SCREAMING_SNAKE_CASE   :      List[Tuple[str, nn.Module]]				     =       []
				# - get the stem
				feature_blocks.append(("conv1", model.stem)	)
				# - get all the feature blocks
				for k, v in model.trunk_output.named_children():
					assert k.startswith("block"	), F"Unexpected layer name {k}"
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       len(a	) + 1
					feature_blocks.append((F"res{block_index}", v)	)

				SCREAMING_SNAKE_CASE   :      List[str]				     =       nn.ModuleDict(a	)






			def 							__UpperCamelCase (			self							: Tuple      ,      a							: Tensor	)							->       Tuple:

				"""simple docstring"""

				return get_trunk_forward_outputs(
				    a      ,      out_feat_keys=a      ,      feature_blocks=self._feature_blocks      ,      )






class _UpperCamelCase							(							__A   ):
			'''simple docstring'''







			def 							__UpperCamelCase (			self							: str      ,      a							: str	)							->       str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Dict				     =       x.split("-"	)
				return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]	)






			def __getitem__(			self							: Union[str, Any]      ,      a							: str	)							->       Callable[[], Tuple[nn.Module, Dict]]:

				"""simple docstring"""

				if x not in self:
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       self.convert_name_to_timm(a	)
					SCREAMING_SNAKE_CASE   :      List[str]				     =       partial(lambda: (timm.create_model(a      ,      pretrained=a	).eval(), None)	)

				else:
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       super().__getitem__(a	)

				return val






class _UpperCamelCase							(							__A   ):
			'''simple docstring'''







			def __getitem__(			self							: Optional[int]      ,      a							: str	)							->       Callable[[], nn.Module]:

				"""simple docstring"""

				if "seer" in x and "in1k" not in x:
					SCREAMING_SNAKE_CASE   :      str				     =       RegNetModel
				else:
					SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       RegNetForImageClassification
				return val

def  lowerCamelCase__ (					_a							,			_a							,			_a):
	for from_key, to_key in keys:
		SCREAMING_SNAKE_CASE   :      List[Any]				     =       from_state_dict[from_key].clone()
		print(f"Copied key={from_key} to={to_key}")
	return to_state_dict

def  lowerCamelCase__ (					_a							,			_a							,			_a							,			_a							,			_a							,			_a = True							,			):
	print(f"Converting {name}...")
	with torch.no_grad():
		SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      List[Any]				     =       from_model_func()
		SCREAMING_SNAKE_CASE   :      Tuple				     =       our_model_func(_a).eval()
		SCREAMING_SNAKE_CASE   :      Dict				     =       ModuleTransfer(src=_a							,			dest=_a							,			raise_if_mismatch=_a)
		SCREAMING_SNAKE_CASE   :      Tuple				     =       torch.randn((1, 3, 224, 224))
		module_transfer(_a)

	if from_state_dict is not None:
		SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       []
		# for seer - in1k finetuned we have to manually copy the head
		if "seer" in name and "in1k" in name:
			SCREAMING_SNAKE_CASE   :      Tuple				     =       [("0.clf.0.weight", "classifier.1.weight"), ("0.clf.0.bias", "classifier.1.bias")]
		SCREAMING_SNAKE_CASE   :      List[str]				     =       manually_copy_vissl_head(_a							,			our_model.state_dict()							,			_a)
		our_model.load_state_dict(_a)

	SCREAMING_SNAKE_CASE   :      int				     =       our_model(_a							,			output_hidden_states=_a)
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       (
	    our_outputs.logits if isinstance(_a							,			_a) else our_outputs.last_hidden_state
	)

	SCREAMING_SNAKE_CASE   :      List[Any]				     =       from_model(_a)
	SCREAMING_SNAKE_CASE   :      Any				     =       from_output[-1] if type(_a) is list else from_output

	# now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
	if "seer" in name and "in1k" in name:
		SCREAMING_SNAKE_CASE   :      List[Any]				     =       our_outputs.hidden_states[-1]

	assert torch.allclose(_a							,			_a), "The model logits don't match the original one."

	if push_to_hub:
		our_model.push_to_hub(
		    repo_path_or_name=save_directory / name							,			commit_message="Add model"							,			use_temp_dir=_a							,			)

		SCREAMING_SNAKE_CASE   :      List[str]				     =       224 if "seer" not in name else 384
		# we can use the convnext one
		SCREAMING_SNAKE_CASE   :      Dict				     =       AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"							,			size=_a)
		image_processor.push_to_hub(
		    repo_path_or_name=save_directory / name							,			commit_message="Add image processor"							,			use_temp_dir=_a							,			)

		print(f"Pushed {name}")

def  lowerCamelCase__ (					_a							,			_a = None							,			_a = True):
	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       "imagenet-1k-id2label.json"
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       1000
	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       (1, num_labels)

	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       "huggingface/label-files"
	SCREAMING_SNAKE_CASE   :      int				     =       num_labels
	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       json.load(open(cached_download(hf_hub_url(_a							,			_a							,			repo_type="dataset"))							,			"r"))
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       {int(_a): v for k, v in idalabel.items()}

	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       idalabel
	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       {v: k for k, v in idalabel.items()}

	SCREAMING_SNAKE_CASE   :      int				     =       partial(_a							,			num_labels=_a							,			idalabel=_a							,			labelaid=_a)

	SCREAMING_SNAKE_CASE   :      Tuple				     =       {
	    "regnet-x-002": ImageNetPreTrainedConfig(
	        depths=[1, 1, 4, 7]							,			hidden_sizes=[24, 56, 152, 368]							,			groups_width=8							,			layer_type="x"),
	    "regnet-x-004": ImageNetPreTrainedConfig(
	        depths=[1, 2, 7, 12]							,			hidden_sizes=[32, 64, 160, 384]							,			groups_width=16							,			layer_type="x"),
	    "regnet-x-006": ImageNetPreTrainedConfig(
	        depths=[1, 3, 5, 7]							,			hidden_sizes=[48, 96, 240, 528]							,			groups_width=24							,			layer_type="x"),
	    "regnet-x-008": ImageNetPreTrainedConfig(
	        depths=[1, 3, 7, 5]							,			hidden_sizes=[64, 128, 288, 672]							,			groups_width=16							,			layer_type="x"),
	    "regnet-x-016": ImageNetPreTrainedConfig(
	        depths=[2, 4, 10, 2]							,			hidden_sizes=[72, 168, 408, 912]							,			groups_width=24							,			layer_type="x"),
	    "regnet-x-032": ImageNetPreTrainedConfig(
	        depths=[2, 6, 15, 2]							,			hidden_sizes=[96, 192, 432, 1008]							,			groups_width=48							,			layer_type="x"),
	    "regnet-x-040": ImageNetPreTrainedConfig(
	        depths=[2, 5, 14, 2]							,			hidden_sizes=[80, 240, 560, 1360]							,			groups_width=40							,			layer_type="x"),
	    "regnet-x-064": ImageNetPreTrainedConfig(
	        depths=[2, 4, 10, 1]							,			hidden_sizes=[168, 392, 784, 1624]							,			groups_width=56							,			layer_type="x"),
	    "regnet-x-080": ImageNetPreTrainedConfig(
	        depths=[2, 5, 15, 1]							,			hidden_sizes=[80, 240, 720, 1920]							,			groups_width=120							,			layer_type="x"),
	    "regnet-x-120": ImageNetPreTrainedConfig(
	        depths=[2, 5, 11, 1]							,			hidden_sizes=[224, 448, 896, 2240]							,			groups_width=112							,			layer_type="x"),
	    "regnet-x-160": ImageNetPreTrainedConfig(
	        depths=[2, 6, 13, 1]							,			hidden_sizes=[256, 512, 896, 2048]							,			groups_width=128							,			layer_type="x"),
	    "regnet-x-320": ImageNetPreTrainedConfig(
	        depths=[2, 7, 13, 1]							,			hidden_sizes=[336, 672, 1344, 2520]							,			groups_width=168							,			layer_type="x"),
	    # y variant
	    "regnet-y-002": ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]							,			hidden_sizes=[24, 56, 152, 368]							,			groups_width=8),
	    "regnet-y-004": ImageNetPreTrainedConfig(
	        depths=[1, 3, 6, 6]							,			hidden_sizes=[48, 104, 208, 440]							,			groups_width=8),
	    "regnet-y-006": ImageNetPreTrainedConfig(
	        depths=[1, 3, 7, 4]							,			hidden_sizes=[48, 112, 256, 608]							,			groups_width=16),
	    "regnet-y-008": ImageNetPreTrainedConfig(
	        depths=[1, 3, 8, 2]							,			hidden_sizes=[64, 128, 320, 768]							,			groups_width=16),
	    "regnet-y-016": ImageNetPreTrainedConfig(
	        depths=[2, 6, 17, 2]							,			hidden_sizes=[48, 120, 336, 888]							,			groups_width=24),
	    "regnet-y-032": ImageNetPreTrainedConfig(
	        depths=[2, 5, 13, 1]							,			hidden_sizes=[72, 216, 576, 1512]							,			groups_width=24),
	    "regnet-y-040": ImageNetPreTrainedConfig(
	        depths=[2, 6, 12, 2]							,			hidden_sizes=[128, 192, 512, 1088]							,			groups_width=64),
	    "regnet-y-064": ImageNetPreTrainedConfig(
	        depths=[2, 7, 14, 2]							,			hidden_sizes=[144, 288, 576, 1296]							,			groups_width=72),
	    "regnet-y-080": ImageNetPreTrainedConfig(
	        depths=[2, 4, 10, 1]							,			hidden_sizes=[168, 448, 896, 2016]							,			groups_width=56),
	    "regnet-y-120": ImageNetPreTrainedConfig(
	        depths=[2, 5, 11, 1]							,			hidden_sizes=[224, 448, 896, 2240]							,			groups_width=112),
	    "regnet-y-160": ImageNetPreTrainedConfig(
	        depths=[2, 4, 11, 1]							,			hidden_sizes=[224, 448, 1232, 3024]							,			groups_width=112),
	    "regnet-y-320": ImageNetPreTrainedConfig(
	        depths=[2, 5, 12, 1]							,			hidden_sizes=[232, 696, 1392, 3712]							,			groups_width=232),
	    # models created by SEER -> https://arxiv.org/abs/2202.08360
	    "regnet-y-320-seer": RegNetConfig(depths=[2, 5, 12, 1]							,			hidden_sizes=[232, 696, 1392, 3712]							,			groups_width=232),
	    "regnet-y-640-seer": RegNetConfig(depths=[2, 5, 12, 1]							,			hidden_sizes=[328, 984, 1968, 4920]							,			groups_width=328),
	    "regnet-y-1280-seer": RegNetConfig(
	        depths=[2, 7, 17, 1]							,			hidden_sizes=[528, 1056, 2904, 7392]							,			groups_width=264),
	    "regnet-y-2560-seer": RegNetConfig(
	        depths=[3, 7, 16, 1]							,			hidden_sizes=[640, 1696, 2544, 5088]							,			groups_width=640),
	    "regnet-y-10b-seer": ImageNetPreTrainedConfig(
	        depths=[2, 7, 17, 1]							,			hidden_sizes=[2020, 4040, 11110, 28280]							,			groups_width=1010),
	    # finetuned on imagenet
	    "regnet-y-320-seer-in1k": ImageNetPreTrainedConfig(
	        depths=[2, 5, 12, 1]							,			hidden_sizes=[232, 696, 1392, 3712]							,			groups_width=232),
	    "regnet-y-640-seer-in1k": ImageNetPreTrainedConfig(
	        depths=[2, 5, 12, 1]							,			hidden_sizes=[328, 984, 1968, 4920]							,			groups_width=328),
	    "regnet-y-1280-seer-in1k": ImageNetPreTrainedConfig(
	        depths=[2, 7, 17, 1]							,			hidden_sizes=[528, 1056, 2904, 7392]							,			groups_width=264),
	    "regnet-y-2560-seer-in1k": ImageNetPreTrainedConfig(
	        depths=[3, 7, 16, 1]							,			hidden_sizes=[640, 1696, 2544, 5088]							,			groups_width=640),
	    "regnet-y-10b-seer-in1k": ImageNetPreTrainedConfig(
	        depths=[2, 7, 17, 1]							,			hidden_sizes=[2020, 4040, 11110, 28280]							,			groups_width=1010),
	}

	SCREAMING_SNAKE_CASE   :      int				     =       NameToOurModelFuncMap()
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       NameToFromModelFuncMap()
	# add seer weights logic

	def load_using_classy_vision(_a							,			_a) -> Tuple[nn.Module, Dict]:
		SCREAMING_SNAKE_CASE   :      Dict				     =       torch.hub.load_state_dict_from_url(_a							,			model_dir=str(_a)							,			map_location="cpu")
		SCREAMING_SNAKE_CASE   :      Optional[int]				     =       model_func()
		# check if we have a head, if yes add it
		SCREAMING_SNAKE_CASE   :      Any				     =       files["classy_state_dict"]["base_model"]["model"]
		SCREAMING_SNAKE_CASE   :      int				     =       model_state_dict["trunk"]
		model.load_state_dict(_a)
		return model.eval(), model_state_dict["heads"]

	# pretrained
	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       partial(
	    _a							,			"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch"							,			lambda: FakeRegNetVisslWrapper(RegNetYaagf())							,			)

	SCREAMING_SNAKE_CASE   :      str				     =       partial(
	    _a							,			"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch"							,			lambda: FakeRegNetVisslWrapper(RegNetYaagf())							,			)

	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       partial(
	    _a							,			"https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch"							,			lambda: FakeRegNetVisslWrapper(RegNetYaaagf())							,			)

	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       partial(
	    _a							,			"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"							,			lambda: FakeRegNetVisslWrapper(
	        RegNet(RegNetParams(depth=27							,			group_width=1010							,			w_a=1744							,			w_a=620.83							,			w_m=2.52)))							,			)

	# IN1K finetuned
	SCREAMING_SNAKE_CASE   :      Dict				     =       partial(
	    _a							,			"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch"							,			lambda: FakeRegNetVisslWrapper(RegNetYaagf())							,			)

	SCREAMING_SNAKE_CASE   :      int				     =       partial(
	    _a							,			"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch"							,			lambda: FakeRegNetVisslWrapper(RegNetYaagf())							,			)

	SCREAMING_SNAKE_CASE   :      Any				     =       partial(
	    _a							,			"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch"							,			lambda: FakeRegNetVisslWrapper(RegNetYaaagf())							,			)

	SCREAMING_SNAKE_CASE   :      List[Any]				     =       partial(
	    _a							,			"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"							,			lambda: FakeRegNetVisslWrapper(
	        RegNet(RegNetParams(depth=27							,			group_width=1010							,			w_a=1744							,			w_a=620.83							,			w_m=2.52)))							,			)

	if model_name:
		convert_weight_and_push(
		    _a							,			names_to_from_model_map[model_name]							,			names_to_ours_model_map[model_name]							,			names_to_config[model_name]							,			_a							,			_a							,			)
	else:
		for model_name, config in names_to_config.items():
			convert_weight_and_push(
			    _a							,			names_to_from_model_map[model_name]							,			names_to_ours_model_map[model_name]							,			_a							,			_a							,			_a							,			)
	return config, expected_shape


if __name__ == "__main__":
			a_						     =    argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '--model_name',
			    default=None,
			    type=str,
			    help=(
			        'The name of the model you wish to convert, it must be one of the supported regnet* architecture,'
			        ' currently: regnetx-*, regnety-*. If `None`, all of them will the converted.'
			    ),
			)
			parser.add_argument(
			    '--pytorch_dump_folder_path',
			    default=None,
			    type=Path,
			    required=True,
			    help='Path to the output PyTorch model directory.',
			)
			parser.add_argument(
			    '--push_to_hub',
			    default=True,
			    type=bool,
			    required=False,
			    help='If True, push model and image processor to the hub.',
			)

			a_						     =    parser.parse_args()

			a_						     =    args.pytorch_dump_folder_path
			pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
			convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)


import inspect
import unittest
from typing import List

import numpy as np

from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
			import tensorflow as tf

			from transformers import (
			    TFEfficientFormerForImageClassification,
			    TFEfficientFormerForImageClassificationWithTeacher,
			    TFEfficientFormerModel,
			)
			from transformers.models.efficientformer.modeling_tf_efficientformer import (
			    TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
			)


if is_vision_available():
			from PIL import Image

			from transformers import EfficientFormerImageProcessor






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(			self							: int      ,      a							: Optional[Any]      ,      a							: int = 13      ,      a							: int = 64      ,      a							: int = 2      ,      a							: int = 3      ,      a							: int = 3      ,      a							: bool = True      ,      a							: bool = True      ,      a							: int = 128      ,      a							: Any=[16, 32, 64, 128]      ,      a							: int = 7      ,      a							: int = 4      ,      a							: int = 37      ,      a							: str = "gelu"      ,      a							: float = 0.1      ,      a							: float = 0.1      ,      a							: int = 10      ,      a							: float = 0.02      ,      a							: int = 2      ,      a							: int = 1      ,      a							: int = 128      ,      a							: List[int] = [2, 2, 2, 2]      ,      a							: int = 2      ,      a							: int = 2      ,      )							->       Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       parent
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       batch_size
				SCREAMING_SNAKE_CASE   :      str				     =       image_size
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       patch_size
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       num_channels
				SCREAMING_SNAKE_CASE   :      int				     =       is_training
				SCREAMING_SNAKE_CASE   :      str				     =       use_labels
				SCREAMING_SNAKE_CASE   :      Tuple				     =       hidden_size
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       num_hidden_layers
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       num_attention_heads
				SCREAMING_SNAKE_CASE   :      Tuple				     =       intermediate_size
				SCREAMING_SNAKE_CASE   :      List[str]				     =       hidden_act
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       hidden_dropout_prob
				SCREAMING_SNAKE_CASE   :      int				     =       attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE   :      int				     =       type_sequence_label_size
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       initializer_range
				SCREAMING_SNAKE_CASE   :      str				     =       encoder_stride
				SCREAMING_SNAKE_CASE   :      Dict				     =       num_attention_outputs
				SCREAMING_SNAKE_CASE   :      Any				     =       embed_dim
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       embed_dim + 1
				SCREAMING_SNAKE_CASE   :      Dict				     =       resolution
				SCREAMING_SNAKE_CASE   :      str				     =       depths
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       hidden_sizes
				SCREAMING_SNAKE_CASE   :      str				     =       dim
				SCREAMING_SNAKE_CASE   :      List[str]				     =       mlp_expansion_ratio
			def 							__UpperCamelCase (			self							: Optional[Any]	)							->       str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      str				     =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

				SCREAMING_SNAKE_CASE   :      int				     =       None
				if self.use_labels:
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       ids_tensor([self.batch_size]      ,      self.type_sequence_label_size	)

				SCREAMING_SNAKE_CASE   :      Any				     =       self.get_config()

				return config, pixel_values, labels
			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       Any:

				"""simple docstring"""

				return EfficientFormerConfig(
				    image_size=self.image_size      ,      patch_size=self.patch_size      ,      num_channels=self.num_channels      ,      hidden_size=self.hidden_size      ,      num_hidden_layers=self.num_hidden_layers      ,      num_attention_heads=self.num_attention_heads      ,      intermediate_size=self.intermediate_size      ,      hidden_act=self.hidden_act      ,      hidden_dropout_prob=self.hidden_dropout_prob      ,      attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,      is_decoder=a      ,      initializer_range=self.initializer_range      ,      encoder_stride=self.encoder_stride      ,      resolution=self.resolution      ,      depths=self.depths      ,      hidden_sizes=self.hidden_sizes      ,      dim=self.dim      ,      mlp_expansion_ratio=self.mlp_expansion_ratio      ,      )
			def 							__UpperCamelCase (			self							: Any      ,      a							: Union[str, Any]      ,      a							: Tuple      ,      a							: Union[str, Any]	)							->       List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Tuple				     =       TFEfficientFormerModel(config=a	)
				SCREAMING_SNAKE_CASE   :      int				     =       model(a      ,      training=a	)
				self.parent.assertEqual(result.last_hidden_state.shape      ,      (self.batch_size, self.seq_length, self.hidden_size)	)
			def 							__UpperCamelCase (			self							: Optional[Any]      ,      a							: str      ,      a							: Union[str, Any]      ,      a							: Dict	)							->       Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[str]				     =       self.type_sequence_label_size
				SCREAMING_SNAKE_CASE   :      Tuple				     =       TFEfficientFormerForImageClassification(a	)
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       model(a      ,      labels=a      ,      training=a	)
				self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.type_sequence_label_size)	)

				# test greyscale images
				SCREAMING_SNAKE_CASE   :      Dict				     =       1
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       TFEfficientFormerForImageClassification(a	)

				SCREAMING_SNAKE_CASE   :      str				     =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
				SCREAMING_SNAKE_CASE   :      List[str]				     =       model(a      ,      labels=a	)
				self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.type_sequence_label_size)	)






			def 							__UpperCamelCase (			self							: List[str]	)							->       Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       self.prepare_config_and_inputs()
				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       config_and_inputs
				SCREAMING_SNAKE_CASE   :      Tuple				     =       {"pixel_values": pixel_values}
				return config, inputs_dict






@require_tf
class _UpperCamelCase							(							__A							,	__A							,	unittest.TestCase   ):
			'''simple docstring'''







			lowerCamelCase__           =(
			    (
			        TFEfficientFormerModel,
			        TFEfficientFormerForImageClassificationWithTeacher,
			        TFEfficientFormerForImageClassification,
			    )
			    if is_tf_available()
			    else ()
			)
			lowerCamelCase__           =(
			    {
			        'feature-extraction': TFEfficientFormerModel,
			        'image-classification': (
			            TFEfficientFormerForImageClassification,
			            TFEfficientFormerForImageClassificationWithTeacher,
			        ),
			    }
			    if is_tf_available()
			    else {}
			)

			lowerCamelCase__           =False

			lowerCamelCase__           =False
			lowerCamelCase__           =False
			lowerCamelCase__           =False
			lowerCamelCase__           =False
			def 							__UpperCamelCase (			self							: List[Any]	)							->       Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Dict				     =       TFEfficientFormerModelTester(self	)
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       ConfigTester(
				    self      ,      config_class=a      ,      has_text_modality=a      ,      hidden_size=37	)
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       Optional[Any]:

				"""simple docstring"""

				self.config_tester.run_common_tests()
			@unittest.skip(reason="EfficientFormer does not use inputs_embeds"	)
			def 							__UpperCamelCase (			self							: List[str]	)							->       str:

				"""simple docstring"""

				pass
			@unittest.skip(reason="EfficientFormer does not support input and output embeddings"	)
			def 							__UpperCamelCase (			self							: Any	)							->       Dict:

				"""simple docstring"""

				pass
			def 							__UpperCamelCase (			self							: Tuple	)							->       Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Optional[int]				     =       self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       model_class(a	)
					SCREAMING_SNAKE_CASE   :      Any				     =       inspect.signature(model.call	)
					# signature.parameters is an OrderedDict => so arg_names order is deterministic
					SCREAMING_SNAKE_CASE   :      Dict				     =       [*signature.parameters.keys()]

					SCREAMING_SNAKE_CASE   :      Tuple				     =       ["pixel_values"]
					self.assertListEqual(arg_names[:1]      ,      a	)
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       Optional[Any]:

				"""simple docstring"""

				def check_hidden_states_output(a							: Optional[int]      ,      a							: Any      ,      a							: List[Any]	):
					SCREAMING_SNAKE_CASE   :      Dict				     =       model_class(a	)

					SCREAMING_SNAKE_CASE   :      str				     =       model(**self._prepare_for_class(a      ,      a	)      ,      training=a	)
					SCREAMING_SNAKE_CASE   :      Any				     =       outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       getattr(
					    self.model_tester      ,      "expected_num_hidden_layers"      ,      self.model_tester.num_hidden_layers + 1	)
					self.assertEqual(len(a	)      ,      a	)

					if hasattr(self.model_tester      ,      "encoder_seq_length"	):
						SCREAMING_SNAKE_CASE   :      Any				     =       self.model_tester.encoder_seq_length
						if hasattr(self.model_tester      ,      "chunk_length"	) and self.model_tester.chunk_length > 1:
							SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       seq_length * self.model_tester.chunk_length
					else:
						SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       self.model_tester.seq_length

					self.assertListEqual(
					    list(hidden_states[-1].shape[-2:]	)      ,      [seq_length, self.model_tester.hidden_size]      ,      )

					if config.is_encoder_decoder:
						SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       outputs.decoder_hidden_states

						self.asseretIsInstance(a      ,      (list, tuple)	)
						self.assertEqual(len(a	)      ,      a	)
						SCREAMING_SNAKE_CASE   :      Tuple				     =       getattr(self.model_tester      ,      "seq_length"      ,      a	)
						SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       getattr(self.model_tester      ,      "decoder_seq_length"      ,      a	)

						self.assertListEqual(
						    list(hidden_states[-1].shape[-2:]	)      ,      [decoder_seq_length, self.model_tester.hidden_size]      ,      )

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Any				     =       self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					SCREAMING_SNAKE_CASE   :      int				     =       True
					check_hidden_states_output(a      ,      a      ,      a	)

					# check that output_hidden_states also work using config
					del inputs_dict["output_hidden_states"]
					SCREAMING_SNAKE_CASE   :      str				     =       True

					check_hidden_states_output(a      ,      a      ,      a	)
			def 							__UpperCamelCase (			self							: Dict      ,      a							: Optional[int]      ,      a							: List[str]      ,      a							: Optional[Any]=False	)							->       Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Dict				     =       super()._prepare_for_class(a      ,      a      ,      return_labels=a	)

				if return_labels:
					if model_class.__name__ == "TFEfficientFormerForImageClassificationWithTeacher":
						del inputs_dict["labels"]

				return inputs_dict
			def 							__UpperCamelCase (			self							: Tuple	)							->       List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Tuple				     =       self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*a	)
			@unittest.skip(reason="EfficientFormer does not implement masked image modeling yet"	)
			def 							__UpperCamelCase (			self							: Any	)							->       Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Dict				     =       self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_masked_image_modeling(*a	)
			def 							__UpperCamelCase (			self							: Dict	)							->       Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*a	)
			@slow
			def 							__UpperCamelCase (			self							: int	)							->       Any:

				"""simple docstring"""

				for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       TFEfficientFormerModel.from_pretrained(a	)
					self.assertIsNotNone(a	)
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Tuple				     =       self.model_tester.prepare_config_and_inputs_for_common()
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       True

				SCREAMING_SNAKE_CASE   :      str				     =       getattr(self.model_tester      ,      "seq_length"      ,      a	)
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       getattr(self.model_tester      ,      "encoder_seq_length"      ,      a	)
				SCREAMING_SNAKE_CASE   :      List[str]				     =       getattr(self.model_tester      ,      "key_length"      ,      a	)
				SCREAMING_SNAKE_CASE   :      int				     =       getattr(self.model_tester      ,      "chunk_length"      ,      a	)

				if chunk_length is not None and hasattr(self.model_tester      ,      "num_hashes"	):
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       encoder_seq_length * self.model_tester.num_hashes

				for model_class in self.all_model_classes:
					SCREAMING_SNAKE_CASE   :      Any				     =       True
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       False
					SCREAMING_SNAKE_CASE   :      Tuple				     =       True
					SCREAMING_SNAKE_CASE   :      Any				     =       model_class(a	)

					SCREAMING_SNAKE_CASE   :      Tuple				     =       model(**self._prepare_for_class(a      ,      a	)      ,      training=a	)
					SCREAMING_SNAKE_CASE   :      Dict				     =       outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
					self.assertEqual(len(a	)      ,      self.model_tester.num_attention_outputs	)

					# check that output_attentions also work using config
					del inputs_dict["output_attentions"]
					SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       True
					SCREAMING_SNAKE_CASE   :      Dict				     =       model_class(a	)
					SCREAMING_SNAKE_CASE   :      Any				     =       model(**self._prepare_for_class(a      ,      a	)      ,      training=a	)

					SCREAMING_SNAKE_CASE   :      List[Any]				     =       outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
					self.assertEqual(len(a	)      ,      self.model_tester.num_attention_outputs	)

					if chunk_length is not None:
						self.assertListEqual(
						    list(attentions[0].shape[-4:]	)      ,      [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length]      ,      )
					else:
						self.assertListEqual(
						    list(attentions[0].shape[-3:]	)      ,      [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]      ,      )






			def 							__UpperCamelCase (			self							: Optional[int]	)							->       List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Tuple				     =       self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					# Prepare our model
					SCREAMING_SNAKE_CASE   :      str				     =       model_class(a	)
					# These are maximally general inputs for the model, with multiple None dimensions
					# Hopefully this will catch any conditionals that fail for flexible shapes
					SCREAMING_SNAKE_CASE   :      Any				     =       {
					    key: tf.keras.Input(shape=val.shape[1:]      ,      dtype=val.dtype      ,      name=a	)
					    for key, val in model.input_signature.items()
					    if key in model.dummy_inputs
					}
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       model(a	)
					self.assertTrue(outputs_dict is not None	)

def  lowerCamelCase__ (					):
	SCREAMING_SNAKE_CASE   :      Tuple				     =       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
	return image






@require_tf
@require_vision
class _UpperCamelCase							(							unittest.TestCase   ):
			'''simple docstring'''







			@cached_property
			def 							__UpperCamelCase (			self							: Dict	)							->       Dict:

				"""simple docstring"""

				return (
				    EfficientFormerImageProcessor.from_pretrained("snap-research/efficientformer-l1-300"	)
				    if is_vision_available()
				    else None
				)
			@slow
			def 							__UpperCamelCase (			self							: Tuple	)							->       List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[str]				     =       TFEfficientFormerForImageClassification.from_pretrained("snap-research/efficientformer-l1-300"	)
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.default_image_processor
				SCREAMING_SNAKE_CASE   :      Any				     =       prepare_img()
				SCREAMING_SNAKE_CASE   :      Tuple				     =       image_processor(images=a      ,      return_tensors="tf"	)
				# forward pass
				SCREAMING_SNAKE_CASE   :      Tuple				     =       model(**a      ,      training=a	)
				# verify the logits
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       tf.TensorShape((1, 1000)	)
				self.assertEqual(outputs.logits.shape      ,      a	)
				SCREAMING_SNAKE_CASE   :      str				     =       tf.constant([-0.0555, 0.4825, -0.0852]	)
				self.assertTrue(np.allclose(outputs.logits[0, :3]      ,      a      ,      atol=1e-4	)	)






			@slow
			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Any				     =       TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
				    "snap-research/efficientformer-l1-300"	)
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       self.default_image_processor
				SCREAMING_SNAKE_CASE   :      Dict				     =       prepare_img()
				SCREAMING_SNAKE_CASE   :      str				     =       image_processor(images=a      ,      return_tensors="tf"	)
				# forward pass
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       model(**a      ,      training=a	)
				# verify the logits
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       tf.TensorShape((1, 1000)	)
				self.assertEqual(outputs.logits.shape      ,      a	)
				SCREAMING_SNAKE_CASE   :      Tuple				     =       tf.constant([-0.1312, 0.4353, -1.0499]	)
				self.assertTrue(np.allclose(outputs.logits[0, :3]      ,      a      ,      atol=1e-4	)	)