


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:


					def __init__(    self						:		Optional[int]  ,     SCREAMING_SNAKE_CASE						:		int  ,     SCREAMING_SNAKE_CASE						:		int = 1_3  ,     SCREAMING_SNAKE_CASE						:		int = 6_4  ,     SCREAMING_SNAKE_CASE						:		int = 2  ,     SCREAMING_SNAKE_CASE						:		int = 3  ,     SCREAMING_SNAKE_CASE						:		int = 3  ,     SCREAMING_SNAKE_CASE						:		bool = True  ,     SCREAMING_SNAKE_CASE						:		bool = True  ,     SCREAMING_SNAKE_CASE						:		int = 1_2_8  ,     SCREAMING_SNAKE_CASE						:		List[Any]=[1_6, 3_2, 6_4, 1_2_8]  ,     SCREAMING_SNAKE_CASE						:		int = 7  ,     SCREAMING_SNAKE_CASE						:		int = 4  ,     SCREAMING_SNAKE_CASE						:		int = 3_7  ,     SCREAMING_SNAKE_CASE						:		str = "gelu"  ,     SCREAMING_SNAKE_CASE						:		float = 0.1  ,     SCREAMING_SNAKE_CASE						:		float = 0.1  ,     SCREAMING_SNAKE_CASE						:		int = 1_0  ,     SCREAMING_SNAKE_CASE						:		float = 0.02  ,     SCREAMING_SNAKE_CASE						:		int = 2  ,     SCREAMING_SNAKE_CASE						:		int = 1  ,     SCREAMING_SNAKE_CASE						:		int = 1_2_8  ,     SCREAMING_SNAKE_CASE						:		List[int] = [2, 2, 2, 2]  ,     SCREAMING_SNAKE_CASE						:		int = 2  ,     SCREAMING_SNAKE_CASE						:		int = 2  ,     )      ->						str:




											'''simple docstring'''

											__snake_case				=		parent
											__snake_case				=		batch_size
											__snake_case				=		image_size
											__snake_case				=		patch_size
											__snake_case				=		num_channels
											__snake_case				=		is_training
											__snake_case				=		use_labels
											__snake_case				=		hidden_size
											__snake_case				=		num_hidden_layers
											__snake_case				=		num_attention_heads
											__snake_case				=		intermediate_size
											__snake_case				=		hidden_act
											__snake_case				=		hidden_dropout_prob
											__snake_case				=		attention_probs_dropout_prob
											__snake_case				=		type_sequence_label_size
											__snake_case				=		initializer_range
											__snake_case				=		encoder_stride
											__snake_case				=		num_attention_outputs
											__snake_case				=		embed_dim
											__snake_case				=		embed_dim + 1
											__snake_case				=		resolution
											__snake_case				=		depths
											__snake_case				=		hidden_sizes
											__snake_case				=		dim
											__snake_case				=		mlp_expansion_ratio


					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[Any]    )      ->						Tuple:




											'''simple docstring'''

											__snake_case				=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

											__snake_case				=		None
											if self.use_labels:
																	__snake_case				=		ids_tensor([self.batch_size]  ,     self.type_sequence_label_size    )

											__snake_case				=		self.get_config()

											return config, pixel_values, labels


					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[Any]    )      ->						Tuple:




											'''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=SCREAMING_SNAKE_CASE  ,     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 				SCREAMING_SNAKE_CASE_	(    self						:		str  ,     SCREAMING_SNAKE_CASE						:		str  ,     SCREAMING_SNAKE_CASE						:		Any  ,     SCREAMING_SNAKE_CASE						:		Tuple    )      ->						int:




											'''simple docstring'''

											__snake_case				=		TFEfficientFormerModel(config=SCREAMING_SNAKE_CASE    )
											__snake_case				=		model(SCREAMING_SNAKE_CASE  ,     training=SCREAMING_SNAKE_CASE    )
											self.parent.assertEqual(result.last_hidden_state.shape  ,     (self.batch_size, self.seq_length, self.hidden_size)    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[str]  ,     SCREAMING_SNAKE_CASE						:		Optional[Any]  ,     SCREAMING_SNAKE_CASE						:		int  ,     SCREAMING_SNAKE_CASE						:		List[Any]    )      ->						List[Any]:




											'''simple docstring'''

											__snake_case				=		self.type_sequence_label_size
											__snake_case				=		TFEfficientFormerForImageClassification(SCREAMING_SNAKE_CASE    )
											__snake_case				=		model(SCREAMING_SNAKE_CASE  ,     labels=SCREAMING_SNAKE_CASE  ,     training=SCREAMING_SNAKE_CASE    )
											self.parent.assertEqual(result.logits.shape  ,     (self.batch_size, self.type_sequence_label_size)    )

											# test greyscale images
											__snake_case				=		1
											__snake_case				=		TFEfficientFormerForImageClassification(SCREAMING_SNAKE_CASE    )

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






					def 				SCREAMING_SNAKE_CASE_	(    self						:		Dict    )      ->						Optional[Any]:




											'''simple docstring'''

											__snake_case				=		self.prepare_config_and_inputs()
											__snake_case		,       __snake_case		,       __snake_case				=		config_and_inputs
											__snake_case				=		{"pixel_values": pixel_values}
											return config, inputs_dict






@require_tf
class      UpperCamelCase(	_a  ,				_a  ,				unittest.TestCase ):
					snake_case_		:					List[Any]    					=						(
					    (
					        TFEfficientFormerModel,
					        TFEfficientFormerForImageClassificationWithTeacher,
					        TFEfficientFormerForImageClassification,
					    )
					    if is_tf_available()
					    else ()
					)
					snake_case_		:					Any    					=						(
					    {
					        """feature-extraction""": TFEfficientFormerModel,
					        """image-classification""": (
					            TFEfficientFormerForImageClassification,
					            TFEfficientFormerForImageClassificationWithTeacher,
					        ),
					    }
					    if is_tf_available()
					    else {}
					)

					snake_case_		:					Dict    					=						False

					snake_case_		:					Union[str, Any]    					=						False
					snake_case_		:					Optional[int]    					=						False
					snake_case_		:					Tuple    					=						False
					snake_case_		:					str    					=						False


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Any    )      ->						str:




											'''simple docstring'''

											__snake_case				=		TFEfficientFormerModelTester(self    )
											__snake_case				=		ConfigTester(
											    self  ,     config_class=SCREAMING_SNAKE_CASE  ,     has_text_modality=SCREAMING_SNAKE_CASE  ,     hidden_size=3_7    )


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




											'''simple docstring'''

											self.config_tester.run_common_tests()


					@unittest.skip(reason="EfficientFormer does not use inputs_embeds"    )
					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[int]    )      ->						str:




											'''simple docstring'''

											pass


					@unittest.skip(reason="EfficientFormer does not support input and output embeddings"    )
					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[int]    )      ->						Dict:




											'''simple docstring'''

											pass


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Union[str, Any]    )      ->						int:




											'''simple docstring'''

											__snake_case		,       __snake_case				=		self.model_tester.prepare_config_and_inputs_for_common()

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

																	__snake_case				=		["pixel_values"]
																	self.assertListEqual(arg_names[:1]  ,     SCREAMING_SNAKE_CASE    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		str    )      ->						List[Any]:




											'''simple docstring'''

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

																	__snake_case				=		model(**self._prepare_for_class(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )  ,     training=SCREAMING_SNAKE_CASE    )
																	__snake_case				=		outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																	__snake_case				=		getattr(
																	    self.model_tester  ,     "expected_num_hidden_layers"  ,     self.model_tester.num_hidden_layers + 1    )
																	self.assertEqual(len(SCREAMING_SNAKE_CASE    )  ,     SCREAMING_SNAKE_CASE    )

																	if hasattr(self.model_tester  ,     "encoder_seq_length"    ):
																							__snake_case				=		self.model_tester.encoder_seq_length
																							if hasattr(self.model_tester  ,     "chunk_length"    ) and self.model_tester.chunk_length > 1:
																													__snake_case				=		seq_length * self.model_tester.chunk_length
																	else:
																							__snake_case				=		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:
																							__snake_case				=		outputs.decoder_hidden_states

																							self.asseretIsInstance(SCREAMING_SNAKE_CASE  ,     (list, tuple)    )
																							self.assertEqual(len(SCREAMING_SNAKE_CASE    )  ,     SCREAMING_SNAKE_CASE    )
																							__snake_case				=		getattr(self.model_tester  ,     "seq_length"  ,     SCREAMING_SNAKE_CASE    )
																							__snake_case				=		getattr(self.model_tester  ,     "decoder_seq_length"  ,     SCREAMING_SNAKE_CASE    )

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

											__snake_case		,       __snake_case				=		self.model_tester.prepare_config_and_inputs_for_common()

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

																	# check that output_hidden_states also work using config
																	del inputs_dict["output_hidden_states"]
																	__snake_case				=		True

																	check_hidden_states_output(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[int]  ,     SCREAMING_SNAKE_CASE						:		int  ,     SCREAMING_SNAKE_CASE						:		Optional[Any]  ,     SCREAMING_SNAKE_CASE						:		Tuple=False    )      ->						Optional[Any]:




											'''simple docstring'''

											__snake_case				=		super()._prepare_for_class(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE  ,     return_labels=SCREAMING_SNAKE_CASE    )

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

											return inputs_dict


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Tuple    )      ->						List[str]:




											'''simple docstring'''

											__snake_case				=		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE    )


					@unittest.skip(reason="EfficientFormer does not implement masked image modeling yet"    )
					def 				SCREAMING_SNAKE_CASE_	(    self						:		Any    )      ->						Optional[int]:




											'''simple docstring'''

											__snake_case				=		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_image_modeling(*SCREAMING_SNAKE_CASE    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[str]    )      ->						Tuple:




											'''simple docstring'''

											__snake_case				=		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE    )


					@slow
					def 				SCREAMING_SNAKE_CASE_	(    self						:		str    )      ->						Dict:




											'''simple docstring'''

											for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	__snake_case				=		TFEfficientFormerModel.from_pretrained(SCREAMING_SNAKE_CASE    )
																	self.assertIsNotNone(SCREAMING_SNAKE_CASE    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[int]    )      ->						List[Any]:




											'''simple docstring'''

											__snake_case		,       __snake_case				=		self.model_tester.prepare_config_and_inputs_for_common()
											__snake_case				=		True

											__snake_case				=		getattr(self.model_tester  ,     "seq_length"  ,     SCREAMING_SNAKE_CASE    )
											__snake_case				=		getattr(self.model_tester  ,     "encoder_seq_length"  ,     SCREAMING_SNAKE_CASE    )
											__snake_case				=		getattr(self.model_tester  ,     "key_length"  ,     SCREAMING_SNAKE_CASE    )
											__snake_case				=		getattr(self.model_tester  ,     "chunk_length"  ,     SCREAMING_SNAKE_CASE    )

											if chunk_length is not None and hasattr(self.model_tester  ,     "num_hashes"    ):
																	__snake_case				=		encoder_seq_length * self.model_tester.num_hashes

											for model_class in self.all_model_classes:
																	__snake_case				=		True
																	__snake_case				=		False
																	__snake_case				=		True
																	__snake_case				=		model_class(SCREAMING_SNAKE_CASE    )

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

																	# check that output_attentions also work using config
																	del inputs_dict["output_attentions"]
																	__snake_case				=		True
																	__snake_case				=		model_class(SCREAMING_SNAKE_CASE    )
																	__snake_case				=		model(**self._prepare_for_class(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )  ,     training=SCREAMING_SNAKE_CASE    )

																	__snake_case				=		outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
																	self.assertEqual(len(SCREAMING_SNAKE_CASE    )  ,     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 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[int]    )      ->						Optional[int]:




											'''simple docstring'''

											__snake_case		,       __snake_case				=		self.model_tester.prepare_config_and_inputs_for_common()

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





def 						_lowerCAmelCase							(     )    ->						Dict:


						'''simple docstring'''






						__snake_case				=		Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"			)
						return image






@require_tf
@require_vision
class      UpperCamelCase(	unittest.TestCase ):


					@cached_property
					def 				SCREAMING_SNAKE_CASE_	(    self						:		int    )      ->						Tuple:




											'''simple docstring'''

											return (
											    EfficientFormerImageProcessor.from_pretrained("snap-research/efficientformer-l1-300"    )
											    if is_vision_available()
											    else None
											)


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




											'''simple docstring'''

											__snake_case				=		TFEfficientFormerForImageClassification.from_pretrained("snap-research/efficientformer-l1-300"    )
											__snake_case				=		self.default_image_processor
											__snake_case				=		prepare_img()
											__snake_case				=		image_processor(images=SCREAMING_SNAKE_CASE  ,     return_tensors="tf"    )
											# forward pass
											__snake_case				=		model(**SCREAMING_SNAKE_CASE  ,     training=SCREAMING_SNAKE_CASE    )
											# verify the logits
											__snake_case				=		tf.TensorShape((1, 1_0_0_0)    )
											self.assertEqual(outputs.logits.shape  ,     SCREAMING_SNAKE_CASE    )
											__snake_case				=		tf.constant([-0.0555, 0.4825, -0.0852]    )
											self.assertTrue(np.allclose(outputs.logits[0, :3]  ,     SCREAMING_SNAKE_CASE  ,     atol=1e-4    )    )






					@slow
					def 				SCREAMING_SNAKE_CASE_	(    self						:		str    )      ->						Dict:




											'''simple docstring'''

											__snake_case				=		TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
											    "snap-research/efficientformer-l1-300"    )
											__snake_case				=		self.default_image_processor
											__snake_case				=		prepare_img()
											__snake_case				=		image_processor(images=SCREAMING_SNAKE_CASE  ,     return_tensors="tf"    )
											# forward pass
											__snake_case				=		model(**SCREAMING_SNAKE_CASE  ,     training=SCREAMING_SNAKE_CASE    )
											# verify the logits
											__snake_case				=		tf.TensorShape((1, 1_0_0_0)    )
											self.assertEqual(outputs.logits.shape  ,     SCREAMING_SNAKE_CASE    )
											__snake_case				=		tf.constant([-0.1312, 0.4353, -1.0499]    )
											self.assertTrue(np.allclose(outputs.logits[0, :3]  ,     SCREAMING_SNAKE_CASE  ,     atol=1e-4    )    )









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

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
A				:       int      =  logging.get_logger()






@dataclass
class      UpperCamelCase:
					snake_case_		:					nn.Module
					snake_case_		:					List[nn.Module]    					=						field(default_factory=_a )
					snake_case_		:					list    					=						field(default_factory=_a )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[Any]  ,     SCREAMING_SNAKE_CASE						:		str  ,     SCREAMING_SNAKE_CASE						:		Tensor  ,     SCREAMING_SNAKE_CASE						:		Tensor    )      ->						Tuple:




											'''simple docstring'''

											__snake_case				=		len(list(m.modules()    )    ) == 1 or isinstance(SCREAMING_SNAKE_CASE  ,     nn.Convad    ) or isinstance(SCREAMING_SNAKE_CASE  ,     nn.BatchNormad    )
											if has_not_submodules:
																	self.traced.append(SCREAMING_SNAKE_CASE    )


					def __call__(    self						:		int  ,     SCREAMING_SNAKE_CASE						:		Tensor    )      ->						List[str]:




											'''simple docstring'''

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






					@property
					def 				SCREAMING_SNAKE_CASE_	(    self						:		Any    )      ->						Dict:




											'''simple docstring'''

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






@dataclass
class      UpperCamelCase:
					snake_case_		:					nn.Module
					snake_case_		:					nn.Module
					snake_case_		:					int    					=						0
					snake_case_		:					List    					=						field(default_factory=_a )
					snake_case_		:					List    					=						field(default_factory=_a )


					def __call__(    self						:		List[Any]  ,     SCREAMING_SNAKE_CASE						:		Tensor    )      ->						Dict:




											'''simple docstring'''

											__snake_case				=		Tracker(self.dest    )(SCREAMING_SNAKE_CASE    ).parametrized
											__snake_case				=		Tracker(self.src    )(SCREAMING_SNAKE_CASE    ).parametrized

											__snake_case				=		list(filter(lambda SCREAMING_SNAKE_CASE    : type(SCREAMING_SNAKE_CASE    ) not in self.src_skip  ,     SCREAMING_SNAKE_CASE    )    )
											__snake_case				=		list(filter(lambda SCREAMING_SNAKE_CASE    : type(SCREAMING_SNAKE_CASE    ) not in self.dest_skip  ,     SCREAMING_SNAKE_CASE    )    )

											if len(SCREAMING_SNAKE_CASE    ) != len(SCREAMING_SNAKE_CASE    ):
																	raise Exception(
																	    f'''Numbers of operations are different. Source module has {len(SCREAMING_SNAKE_CASE    )} operations while'''
																	    f''' destination module has {len(SCREAMING_SNAKE_CASE    )}.'''    )

											for dest_m, src_m in zip(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    ):
																	dest_m.load_state_dict(src_m.state_dict()    )
																	if self.verbose == 1:
																							print(f'''Transfered from={src_m} to={dest_m}'''    )





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase = True			)    ->						Dict:


						'''simple docstring'''






						print(F'''Converting {name}...'''			)
						with torch.no_grad():
												__snake_case				=		timm.create_model(_lowerCAmelCase   ,						pretrained=_lowerCAmelCase			).eval()
												__snake_case				=		ResNetForImageClassification(_lowerCAmelCase			).eval()
												__snake_case				=		ModuleTransfer(src=_lowerCAmelCase   ,						dest=_lowerCAmelCase			)
												__snake_case				=		torch.randn((1, 3, 224, 224)			)
												module_transfer(_lowerCAmelCase			)

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

						__snake_case				=		F'''resnet{'-'.join(name.split('resnet'			)			)}'''
						print(_lowerCAmelCase			)

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

												# we can use the convnext one
												__snake_case				=		AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"			)
												image_processor.push_to_hub(
												    repo_path_or_name=save_directory / checkpoint_name   ,						commit_message="Add image processor"   ,						use_temp_dir=_lowerCAmelCase   ,						)

												print(F'''Pushed {checkpoint_name}'''			)





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase = None   ,						_lowerCAmelCase = True			)    ->						Dict:


						'''simple docstring'''






						__snake_case				=		"imagenet-1k-id2label.json"
						__snake_case				=		1000
						__snake_case				=		(1, num_labels)

						__snake_case				=		"huggingface/label-files"
						__snake_case				=		num_labels
						__snake_case				=		json.load(open(hf_hub_download(_lowerCAmelCase   ,						_lowerCAmelCase   ,						repo_type="dataset"			)   ,						"r"			)			)
						__snake_case				=		{int(_lowerCAmelCase			): v for k, v in idalabel.items()}

						__snake_case				=		idalabel
						__snake_case				=		{v: k for k, v in idalabel.items()}

						__snake_case				=		partial(_lowerCAmelCase   ,						num_labels=_lowerCAmelCase   ,						idalabel=_lowerCAmelCase   ,						labelaid=_lowerCAmelCase			)

						__snake_case				=		{
						    "resnet18": ImageNetPreTrainedConfig(
						        depths=[2, 2, 2, 2]   ,						hidden_sizes=[64, 128, 256, 512]   ,						layer_type="basic"			),
						    "resnet26": ImageNetPreTrainedConfig(
						        depths=[2, 2, 2, 2]   ,						hidden_sizes=[256, 512, 1024, 2048]   ,						layer_type="bottleneck"			),
						    "resnet34": ImageNetPreTrainedConfig(
						        depths=[3, 4, 6, 3]   ,						hidden_sizes=[64, 128, 256, 512]   ,						layer_type="basic"			),
						    "resnet50": ImageNetPreTrainedConfig(
						        depths=[3, 4, 6, 3]   ,						hidden_sizes=[256, 512, 1024, 2048]   ,						layer_type="bottleneck"			),
						    "resnet101": ImageNetPreTrainedConfig(
						        depths=[3, 4, 23, 3]   ,						hidden_sizes=[256, 512, 1024, 2048]   ,						layer_type="bottleneck"			),
						    "resnet152": ImageNetPreTrainedConfig(
						        depths=[3, 8, 36, 3]   ,						hidden_sizes=[256, 512, 1024, 2048]   ,						layer_type="bottleneck"			),
						}

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


if __name__ == "__main__":
	A				:       Optional[Any]      =  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 resnet* architecture,'
	        ' currently: resnet18,26,34,50,101,152. 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				:       Union[str, Any]      =  parser.parse_args()
	A				:       Path      =  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)





