






"""simple docstring"""
import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD


torch.set_grad_enabled(False)





def 				lowercase__		(					lowerCamelCase,						lowerCamelCase=False       ):
				_SCREAMING_SNAKE_CASE   :		Tuple       		=    []
				for i in range(config.num_hidden_layers       ):
								# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
								rename_keys.append((f"""module.blocks.{i}.norm1.weight""", f"""vit.encoder.layer.{i}.layernorm_before.weight""")       )
								rename_keys.append((f"""module.blocks.{i}.norm1.bias""", f"""vit.encoder.layer.{i}.layernorm_before.bias""")       )
								rename_keys.append(
								    (f"""module.blocks.{i}.attn.proj.weight""", f"""vit.encoder.layer.{i}.attention.output.dense.weight""")       )
								rename_keys.append((f"""module.blocks.{i}.attn.proj.bias""", f"""vit.encoder.layer.{i}.attention.output.dense.bias""")       )
								rename_keys.append((f"""module.blocks.{i}.norm2.weight""", f"""vit.encoder.layer.{i}.layernorm_after.weight""")       )
								rename_keys.append((f"""module.blocks.{i}.norm2.bias""", f"""vit.encoder.layer.{i}.layernorm_after.bias""")       )
								rename_keys.append((f"""module.blocks.{i}.mlp.fc1.weight""", f"""vit.encoder.layer.{i}.intermediate.dense.weight""")       )
								rename_keys.append((f"""module.blocks.{i}.mlp.fc1.bias""", f"""vit.encoder.layer.{i}.intermediate.dense.bias""")       )
								rename_keys.append((f"""module.blocks.{i}.mlp.fc2.weight""", f"""vit.encoder.layer.{i}.output.dense.weight""")       )
								rename_keys.append((f"""module.blocks.{i}.mlp.fc2.bias""", f"""vit.encoder.layer.{i}.output.dense.bias""")       )

				# projection layer + position embeddings
				rename_keys.extend(
				    [
				        ('module.cls_token', 'vit.embeddings.cls_token'),
				        ('module.patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight'),
				        ('module.patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias'),
				        ('module.pos_embed', 'vit.embeddings.position_embeddings'),
				    ]       )

				if base_model:
								# layernorm + pooler
								rename_keys.extend(
								    [
								        ('module.norm.weight', 'layernorm.weight'),
								        ('module.norm.bias', 'layernorm.bias'),
								    ]       )

								# if just the base model, we should remove "vit" from all keys that start with "vit"
								_SCREAMING_SNAKE_CASE   :		Any       		=    [(pair[0], pair[1][4:]) if pair[1].startswith('vit'       ) else pair for pair in rename_keys]
				else:
								# layernorm + classification head
								rename_keys.extend(
								    [
								        ('norm.weight', 'vit.layernorm.weight'),
								        ('norm.bias', 'vit.layernorm.bias'),
								        ('head.weight', 'classifier.weight'),
								        ('head.bias', 'classifier.bias'),
								    ]       )

				return rename_keys





def 				lowercase__		(					lowerCamelCase,						lowerCamelCase,						lowerCamelCase=False       ):
				for i in range(config.num_hidden_layers       ):
								if base_model:
												_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    ''
								else:
												_SCREAMING_SNAKE_CASE   :		str       		=    'vit.'
								# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
								_SCREAMING_SNAKE_CASE   :		List[str]       		=    state_dict.pop(f"""module.blocks.{i}.attn.qkv.weight"""       )
								_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    state_dict.pop(f"""module.blocks.{i}.attn.qkv.bias"""       )
								# next, add query, keys and values (in that order) to the state dict
								_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    in_proj_weight[
								    : config.hidden_size, :
								]
								_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    in_proj_bias[: config.hidden_size]
								_SCREAMING_SNAKE_CASE   :		Dict       		=    in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    in_proj_bias[
								    config.hidden_size : config.hidden_size * 2
								]
								_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    in_proj_weight[
								    -config.hidden_size :, :
								]
								_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    in_proj_bias[-config.hidden_size :]





def 				lowercase__		(					lowerCamelCase       ):
				_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    ['head.weight', 'head.bias']
				for k in ignore_keys:
								state_dict.pop(lowerCamelCase,						lowerCamelCase       )





def 				lowercase__		(					lowerCamelCase       ):
				# projection head is used in the self-supervised pre-training in MSN,
				# for downstream task it's not needed.
				_SCREAMING_SNAKE_CASE   :		List[str]       		=    [
				    'module.fc.fc1.weight',
				    'module.fc.fc1.bias',
				    'module.fc.bn1.weight',
				    'module.fc.bn1.bias',
				    'module.fc.bn1.running_mean',
				    'module.fc.bn1.running_var',
				    'module.fc.bn1.num_batches_tracked',
				    'module.fc.fc2.weight',
				    'module.fc.fc2.bias',
				    'module.fc.bn2.weight',
				    'module.fc.bn2.bias',
				    'module.fc.bn2.running_mean',
				    'module.fc.bn2.running_var',
				    'module.fc.bn2.num_batches_tracked',
				    'module.fc.fc3.weight',
				    'module.fc.fc3.bias',
				]
				for k in ignore_keys:
								state_dict.pop(lowerCamelCase,						lowerCamelCase       )





def 				lowercase__		(					lowerCamelCase,						lowerCamelCase,						lowerCamelCase       ):
				_SCREAMING_SNAKE_CASE   :		List[str]       		=    dct.pop(lowerCamelCase       )
				_SCREAMING_SNAKE_CASE   :		Tuple       		=    val





def 				lowercase__		(					lowerCamelCase,						lowerCamelCase       ):
				_SCREAMING_SNAKE_CASE   :		str       		=    ViTMSNConfig()
				_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    1_000

				_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    'datasets/huggingface/label-files'
				_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    'imagenet-1k-id2label.json'
				_SCREAMING_SNAKE_CASE   :		int       		=    json.load(open(hf_hub_download(lowerCamelCase,						lowerCamelCase       ),						'r'       )       )
				_SCREAMING_SNAKE_CASE   :		str       		=    {int(lowerCamelCase       ): v for k, v in idalabel.items()}
				_SCREAMING_SNAKE_CASE   :		Any       		=    idalabel
				_SCREAMING_SNAKE_CASE   :		Tuple       		=    {v: k for k, v in idalabel.items()}

				if "s16" in checkpoint_url:
								_SCREAMING_SNAKE_CASE   :		List[Any]       		=    384
								_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    1_536
								_SCREAMING_SNAKE_CASE   :		str       		=    6
				elif "l16" in checkpoint_url:
								_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    1_024
								_SCREAMING_SNAKE_CASE   :		Any       		=    4_096
								_SCREAMING_SNAKE_CASE   :		Tuple       		=    24
								_SCREAMING_SNAKE_CASE   :		List[str]       		=    16
								_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    0.1
				elif "b4" in checkpoint_url:
								_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    4
				elif "l7" in checkpoint_url:
								_SCREAMING_SNAKE_CASE   :		Dict       		=    7
								_SCREAMING_SNAKE_CASE   :		Dict       		=    1_024
								_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    4_096
								_SCREAMING_SNAKE_CASE   :		Any       		=    24
								_SCREAMING_SNAKE_CASE   :		List[Any]       		=    16
								_SCREAMING_SNAKE_CASE   :		List[Any]       		=    0.1

				_SCREAMING_SNAKE_CASE   :		Tuple       		=    ViTMSNModel(lowerCamelCase       )

				_SCREAMING_SNAKE_CASE   :		Any       		=    torch.hub.load_state_dict_from_url(lowerCamelCase,						map_location='cpu'       )['target_encoder']

				_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    ViTImageProcessor(size=config.image_size       )

				remove_projection_head(lowerCamelCase       )
				_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    create_rename_keys(lowerCamelCase,						base_model=lowerCamelCase       )

				for src, dest in rename_keys:
								rename_key(lowerCamelCase,						lowerCamelCase,						lowerCamelCase       )
				read_in_q_k_v(lowerCamelCase,						lowerCamelCase,						base_model=lowerCamelCase       )

				model.load_state_dict(lowerCamelCase       )
				model.eval()

				_SCREAMING_SNAKE_CASE   :		int       		=    'http://images.cocodataset.org/val2017/000000039769.jpg'

				_SCREAMING_SNAKE_CASE   :		List[Any]       		=    Image.open(requests.get(lowerCamelCase,						stream=lowerCamelCase       ).raw       )
				_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    ViTImageProcessor(
				    size=config.image_size,						image_mean=lowerCamelCase,						image_std=lowerCamelCase       )
				_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    image_processor(images=lowerCamelCase,						return_tensors='pt'       )

				# forward pass
				torch.manual_seed(2       )
				_SCREAMING_SNAKE_CASE   :		str       		=    model(**lowerCamelCase       )
				_SCREAMING_SNAKE_CASE   :		int       		=    outputs.last_hidden_state

				# The following Colab Notebook was used to generate these outputs:
				# https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
				if "s16" in checkpoint_url:
								_SCREAMING_SNAKE_CASE   :		List[Any]       		=    torch.tensor([[-1.0_915, -1.4_876, -1.1_809]]       )
				elif "b16" in checkpoint_url:
								_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    torch.tensor([[14.2_889, -18.9_045, 11.7_281]]       )
				elif "l16" in checkpoint_url:
								_SCREAMING_SNAKE_CASE   :		List[str]       		=    torch.tensor([[41.5_028, -22.8_681, 45.6_475]]       )
				elif "b4" in checkpoint_url:
								_SCREAMING_SNAKE_CASE   :		List[Any]       		=    torch.tensor([[-4.3_868, 5.2_932, -0.4_137]]       )
				else:
								_SCREAMING_SNAKE_CASE   :		List[Any]       		=    torch.tensor([[-0.1_792, -0.6_465, 2.4_263]]       )

				# verify logits
				assert torch.allclose(last_hidden_state[:, 0, :3],						lowerCamelCase,						atol=1E-4       )

				print(f"""Saving model to {pytorch_dump_folder_path}"""       )
				model.save_pretrained(lowerCamelCase       )

				print(f"""Saving image processor to {pytorch_dump_folder_path}"""       )
				image_processor.save_pretrained(lowerCamelCase       )


if __name__ == "__main__":
			lowerCAmelCase__    						=			argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '--checkpoint_url',
			    default='https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar',
			    type=str,
			    help='URL of the checkpoint you\'d like to convert.',
			)
			parser.add_argument(
			    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
			)

			lowerCAmelCase__    						=			parser.parse_args()
			convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)









"""simple docstring"""
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 _lowerCAmelCase						:







						def __init__(    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_ = 1_3   ,				lowerCAmelCase_ = 6_4   ,				lowerCAmelCase_ = 2   ,				lowerCAmelCase_ = 3   ,				lowerCAmelCase_ = 3   ,				lowerCAmelCase_ = True   ,				lowerCAmelCase_ = True   ,				lowerCAmelCase_ = 1_2_8   ,				lowerCAmelCase_=[1_6, 3_2, 6_4, 1_2_8]   ,				lowerCAmelCase_ = 7   ,				lowerCAmelCase_ = 4   ,				lowerCAmelCase_ = 3_7   ,				lowerCAmelCase_ = "gelu"   ,				lowerCAmelCase_ = 0.1   ,				lowerCAmelCase_ = 0.1   ,				lowerCAmelCase_ = 1_0   ,				lowerCAmelCase_ = 0.02   ,				lowerCAmelCase_ = 2   ,				lowerCAmelCase_ = 1   ,				lowerCAmelCase_ = 1_2_8   ,				lowerCAmelCase_ = [2, 2, 2, 2]   ,				lowerCAmelCase_ = 2   ,				lowerCAmelCase_ = 2   ,				)		->							Dict:
										_SCREAMING_SNAKE_CASE   :		Dict       		=    parent
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    batch_size
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    image_size
										_SCREAMING_SNAKE_CASE   :		int       		=    patch_size
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    num_channels
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    is_training
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    use_labels
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    hidden_size
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    num_hidden_layers
										_SCREAMING_SNAKE_CASE   :		Dict       		=    num_attention_heads
										_SCREAMING_SNAKE_CASE   :		Any       		=    intermediate_size
										_SCREAMING_SNAKE_CASE   :		str       		=    hidden_act
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    hidden_dropout_prob
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    attention_probs_dropout_prob
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    type_sequence_label_size
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    initializer_range
										_SCREAMING_SNAKE_CASE   :		int       		=    encoder_stride
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    num_attention_outputs
										_SCREAMING_SNAKE_CASE   :		Any       		=    embed_dim
										_SCREAMING_SNAKE_CASE   :		str       		=    embed_dim + 1
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    resolution
										_SCREAMING_SNAKE_CASE   :		int       		=    depths
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    hidden_sizes
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    dim
										_SCREAMING_SNAKE_CASE   :		Any       		=    mlp_expansion_ratio







						def 			A      (    self							)		->							List[str]:
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    None
										if self.use_labels:
														_SCREAMING_SNAKE_CASE   :		List[Any]       		=    ids_tensor([self.batch_size]   ,				self.type_sequence_label_size							)

										_SCREAMING_SNAKE_CASE   :		Tuple       		=    self.get_config()

										return config, pixel_values, labels







						def 			A      (    self							)		->							List[str]:
										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=lowerCAmelCase_   ,				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 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							str:
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    TFEfficientFormerModel(config=lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    model(lowerCAmelCase_   ,				training=lowerCAmelCase_							)
										self.parent.assertEqual(result.last_hidden_state.shape   ,				(self.batch_size, self.seq_length, self.hidden_size)							)







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							Tuple:
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    self.type_sequence_label_size
										_SCREAMING_SNAKE_CASE   :		Any       		=    TFEfficientFormerForImageClassification(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    model(lowerCAmelCase_   ,				labels=lowerCAmelCase_   ,				training=lowerCAmelCase_							)
										self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.type_sequence_label_size)							)

										# test greyscale images
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    1
										_SCREAMING_SNAKE_CASE   :		Any       		=    TFEfficientFormerForImageClassification(lowerCAmelCase_							)

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







						def 			A      (    self							)		->							Dict:
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    self.prepare_config_and_inputs()
										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		Dict       		=    config_and_inputs
										_SCREAMING_SNAKE_CASE   :		Dict       		=    {'pixel_values': pixel_values}
										return config, inputs_dict







@require_tf
class _lowerCAmelCase						(    __UpperCAmelCase   ,  __UpperCAmelCase   ,  unittest.TestCase       ):
						SCREAMING_SNAKE_CASE_:					List[str] 					=				(
						    (
						        TFEfficientFormerModel,
						        TFEfficientFormerForImageClassificationWithTeacher,
						        TFEfficientFormerForImageClassification,
						    )
						    if is_tf_available()
						    else ()
						)
						SCREAMING_SNAKE_CASE_:					List[str] 					=				(
						    {
						        'feature-extraction': TFEfficientFormerModel,
						        'image-classification': (
						            TFEfficientFormerForImageClassification,
						            TFEfficientFormerForImageClassificationWithTeacher,
						        ),
						    }
						    if is_tf_available()
						    else {}
						)

						SCREAMING_SNAKE_CASE_:					List[Any] 					=				False

						SCREAMING_SNAKE_CASE_:					Optional[Any] 					=				False
						SCREAMING_SNAKE_CASE_:					List[str] 					=				False
						SCREAMING_SNAKE_CASE_:					Optional[Any] 					=				False
						SCREAMING_SNAKE_CASE_:					str 					=				False







						def 			A      (    self							)		->							List[str]:
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    TFEfficientFormerModelTester(self							)
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    ConfigTester(
										    self   ,				config_class=lowerCAmelCase_   ,				has_text_modality=lowerCAmelCase_   ,				hidden_size=3_7							)







						def 			A      (    self							)		->							Tuple:
										self.config_tester.run_common_tests()







						@unittest.skip(reason='EfficientFormer does not use inputs_embeds'							)
						def 			A      (    self							)		->							Dict:
										pass







						@unittest.skip(reason='EfficientFormer does not support input and output embeddings'							)
						def 			A      (    self							)		->							Optional[Any]:
										pass







						def 			A      (    self							)		->							Any:
										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		List[str]       		=    self.model_tester.prepare_config_and_inputs_for_common()

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

														_SCREAMING_SNAKE_CASE   :		List[Any]       		=    ['pixel_values']
														self.assertListEqual(arg_names[:1]   ,				lowerCAmelCase_							)







						def 			A      (    self							)		->							Union[str, Any]:
										def check_hidden_states_output(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							):
														_SCREAMING_SNAKE_CASE   :		List[str]       		=    model_class(lowerCAmelCase_							)

														_SCREAMING_SNAKE_CASE   :		List[str]       		=    model(**self._prepare_for_class(lowerCAmelCase_   ,				lowerCAmelCase_							)   ,				training=lowerCAmelCase_							)
														_SCREAMING_SNAKE_CASE   :		Dict       		=    outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

														_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    getattr(
														    self.model_tester   ,				'expected_num_hidden_layers'   ,				self.model_tester.num_hidden_layers + 1							)
														self.assertEqual(len(lowerCAmelCase_							)   ,				lowerCAmelCase_							)

														if hasattr(self.model_tester   ,				'encoder_seq_length'							):
																		_SCREAMING_SNAKE_CASE   :		List[str]       		=    self.model_tester.encoder_seq_length
																		if hasattr(self.model_tester   ,				'chunk_length'							) and self.model_tester.chunk_length > 1:
																						_SCREAMING_SNAKE_CASE   :		Tuple       		=    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   :		List[str]       		=    outputs.decoder_hidden_states

																		self.asseretIsInstance(lowerCAmelCase_   ,				(list, tuple)							)
																		self.assertEqual(len(lowerCAmelCase_							)   ,				lowerCAmelCase_							)
																		_SCREAMING_SNAKE_CASE   :		Dict       		=    getattr(self.model_tester   ,				'seq_length'   ,				lowerCAmelCase_							)
																		_SCREAMING_SNAKE_CASE   :		Tuple       		=    getattr(self.model_tester   ,				'decoder_seq_length'   ,				lowerCAmelCase_							)

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

										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		Dict       		=    self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														_SCREAMING_SNAKE_CASE   :		str       		=    True
														check_hidden_states_output(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)

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

														check_hidden_states_output(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_=False							)		->							Any:
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    super()._prepare_for_class(lowerCAmelCase_   ,				lowerCAmelCase_   ,				return_labels=lowerCAmelCase_							)

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

										return inputs_dict







						def 			A      (    self							)		->							str:
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase_							)







						@unittest.skip(reason='EfficientFormer does not implement masked image modeling yet'							)
						def 			A      (    self							)		->							Tuple:
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_image_modeling(*lowerCAmelCase_							)







						def 			A      (    self							)		->							str:
										_SCREAMING_SNAKE_CASE   :		str       		=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase_							)







						@slow
						def 			A      (    self							)		->							List[str]:
										for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_SCREAMING_SNAKE_CASE   :		str       		=    TFEfficientFormerModel.from_pretrained(lowerCAmelCase_							)
														self.assertIsNotNone(lowerCAmelCase_							)







						def 			A      (    self							)		->							List[Any]:
										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		List[Any]       		=    self.model_tester.prepare_config_and_inputs_for_common()
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    True

										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    getattr(self.model_tester   ,				'seq_length'   ,				lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		int       		=    getattr(self.model_tester   ,				'encoder_seq_length'   ,				lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    getattr(self.model_tester   ,				'key_length'   ,				lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		int       		=    getattr(self.model_tester   ,				'chunk_length'   ,				lowerCAmelCase_							)

										if chunk_length is not None and hasattr(self.model_tester   ,				'num_hashes'							):
														_SCREAMING_SNAKE_CASE   :		List[str]       		=    encoder_seq_length * self.model_tester.num_hashes

										for model_class in self.all_model_classes:
														_SCREAMING_SNAKE_CASE   :		str       		=    True
														_SCREAMING_SNAKE_CASE   :		Tuple       		=    False
														_SCREAMING_SNAKE_CASE   :		Dict       		=    True
														_SCREAMING_SNAKE_CASE   :		List[Any]       		=    model_class(lowerCAmelCase_							)

														_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    model(**self._prepare_for_class(lowerCAmelCase_   ,				lowerCAmelCase_							)   ,				training=lowerCAmelCase_							)
														_SCREAMING_SNAKE_CASE   :		Any       		=    outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
														self.assertEqual(len(lowerCAmelCase_							)   ,				self.model_tester.num_attention_outputs							)

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

														_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
														self.assertEqual(len(lowerCAmelCase_							)   ,				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 			A      (    self							)		->							Any:
										# We use a simplified version of this test for EfficientFormer because it requires training=False
										# and Keras refuses to let us force that during functional construction
										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		Dict       		=    self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														# Prepare our model
														_SCREAMING_SNAKE_CASE   :		int       		=    model_class(lowerCAmelCase_							)
														# 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   :		Optional[Any]       		=    {
														    key: tf.keras.Input(shape=val.shape[1:]   ,				dtype=val.dtype   ,				name=lowerCAmelCase_							)
														    for key, val in model.input_signature.items()
														    if key in model.dummy_inputs
														}
														_SCREAMING_SNAKE_CASE   :		List[Any]       		=    model(lowerCAmelCase_							)
														self.assertTrue(outputs_dict is not None							)







def 				lowercase__		(					):
				_SCREAMING_SNAKE_CASE   :		List[Any]       		=    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
				return image






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







						@cached_property
						def 			A      (    self							)		->							str:
										return (
										    EfficientFormerImageProcessor.from_pretrained('snap-research/efficientformer-l1-300'							)
										    if is_vision_available()
										    else None
										)







						@slow
						def 			A      (    self							)		->							Dict:
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    TFEfficientFormerForImageClassification.from_pretrained('snap-research/efficientformer-l1-300'							)
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    self.default_image_processor
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    prepare_img()
										_SCREAMING_SNAKE_CASE   :		int       		=    image_processor(images=lowerCAmelCase_   ,				return_tensors='tf'							)
										# forward pass
										_SCREAMING_SNAKE_CASE   :		Dict       		=    model(**lowerCAmelCase_   ,				training=lowerCAmelCase_							)
										# verify the logits
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    tf.TensorShape((1, 1_0_0_0)							)
										self.assertEqual(outputs.logits.shape   ,				lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    tf.constant([-0.0_555, 0.4_825, -0.0_852]							)
										self.assertTrue(np.allclose(outputs.logits[0, :3]   ,				lowerCAmelCase_   ,				atol=1e-4							)							)







						@slow
						def 			A      (    self							)		->							Optional[Any]:
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
										    'snap-research/efficientformer-l1-300'							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    self.default_image_processor
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    prepare_img()
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    image_processor(images=lowerCAmelCase_   ,				return_tensors='tf'							)
										# forward pass
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    model(**lowerCAmelCase_   ,				training=lowerCAmelCase_							)
										# verify the logits
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    tf.TensorShape((1, 1_0_0_0)							)
										self.assertEqual(outputs.logits.shape   ,				lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Dict       		=    tf.constant([-0.1_312, 0.4_353, -1.0_499]							)
										self.assertTrue(np.allclose(outputs.logits[0, :3]   ,				lowerCAmelCase_   ,				atol=1e-4							)							)

