

import json
import os
import tempfile
from unittest.mock import patch

import torch
from torch.utils.data import DataLoader, TensorDataset

from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState, PartialState
from accelerate.test_utils import require_bnb, require_multi_gpu, slow
from accelerate.test_utils.testing import AccelerateTestCase, require_cuda
from accelerate.utils import patch_environment

def  lowerCamelCase__ (					):
	SCREAMING_SNAKE_CASE   :      Tuple				     =       torch.nn.Linear(2							,			4)
	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       torch.optim.AdamW(model.parameters()							,			lr=1.0)
	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       torch.optim.lr_scheduler.OneCycleLR(_a							,			max_lr=0.01							,			steps_per_epoch=2							,			epochs=1)
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       DataLoader(TensorDataset(torch.tensor([1, 2, 3])))
	SCREAMING_SNAKE_CASE   :      Dict				     =       DataLoader(TensorDataset(torch.tensor([4, 5, 6])))

	return model, optimizer, scheduler, train_dl, valid_dl

def  lowerCamelCase__ (					_a):
	return (model.weight.abs().sum() + model.bias.abs().sum()).item()

def  lowerCamelCase__ (					_a):
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       torch.nn.Linear(*tuple(model.weight.T.shape)).state_dict()
	model.load_state_dict(_a)






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







			@require_cuda
			def 							__UpperCamelCase (			self							: List[Any]	)							->       Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Any				     =       Accelerator()
				assert PartialState._shared_state["_cpu"] is False
				assert PartialState._shared_state["device"].type == "cuda"
				with self.assertRaises(a	):
					SCREAMING_SNAKE_CASE   :      Tuple				     =       Accelerator(cpu=a	)
			def 							__UpperCamelCase (			self							: Tuple	)							->       List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       Accelerator()
				SCREAMING_SNAKE_CASE   :      int				     =       GradientState()
				assert state.num_steps == 1
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       4
				assert state.num_steps == 4

				assert state.sync_gradients is True
				SCREAMING_SNAKE_CASE   :      Any				     =       False
				assert state.sync_gradients is False
				GradientState._reset_state()
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       Accelerator()
				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      str				     =       create_components()

				(
				    (
				    SCREAMING_SNAKE_CASE
				)     ,(
				    SCREAMING_SNAKE_CASE
				)     ,(
				    SCREAMING_SNAKE_CASE
				)     ,(
				    SCREAMING_SNAKE_CASE
				)     ,(
				    SCREAMING_SNAKE_CASE
				)     ,
				)   :      Union[str, Any]				     =       accelerator.prepare(a      ,      a      ,      a      ,      a      ,      a	)

				self.assertTrue(prepared_model in accelerator._models	)
				self.assertTrue(prepared_optimizer in accelerator._optimizers	)
				self.assertTrue(prepared_scheduler in accelerator._schedulers	)
				self.assertTrue(prepared_train_dl in accelerator._dataloaders	)
				self.assertTrue(prepared_valid_dl in accelerator._dataloaders	)
			def 							__UpperCamelCase (			self							: Tuple	)							->       Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Tuple				     =       Accelerator()
				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Optional[int]				     =       create_components()
				accelerator.prepare(a      ,      a      ,      a      ,      a      ,      a	)
				accelerator.free_memory()

				self.assertTrue(len(accelerator._models	) == 0	)
				self.assertTrue(len(accelerator._optimizers	) == 0	)
				self.assertTrue(len(accelerator._schedulers	) == 0	)
				self.assertTrue(len(accelerator._dataloaders	) == 0	)
			def 							__UpperCamelCase (			self							: Tuple	)							->       Dict:

				"""simple docstring"""

				PartialState._reset_state()

				# Mock torch.cuda.set_device to avoid an exception as the device doesn't exist
				def noop(*a							: Tuple      ,      **a							: Optional[Any]	):
					pass

				with patch("torch.cuda.set_device"      ,      a	), patch_environment(ACCELERATE_TORCH_DEVICE="cuda:64"	):
					SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       Accelerator()
					self.assertEqual(str(accelerator.state.device	)      ,      "cuda:64"	)
			def 							__UpperCamelCase (			self							: Optional[Any]	)							->       Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Tuple				     =       Accelerator()
				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Tuple				     =       create_components()
				accelerator.prepare(a      ,      a      ,      a      ,      a      ,      a	)

				SCREAMING_SNAKE_CASE   :      Any				     =       get_signature(a	)

				with tempfile.TemporaryDirectory() as tmpdirname:
					accelerator.save_state(a	)

					# make sure random weights don't match
					load_random_weights(a	)
					self.assertTrue(abs(model_signature - get_signature(a	)	) > 1e-3	)

					# make sure loaded weights match
					accelerator.load_state(a	)
					self.assertTrue(abs(model_signature - get_signature(a	)	) < 1e-3	)
			def 							__UpperCamelCase (			self							: int	)							->       Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      str				     =       Accelerator()
				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      int				     =       create_components()
				accelerator.prepare(a      ,      a      ,      a      ,      a      ,      a	)

				SCREAMING_SNAKE_CASE   :      Tuple				     =       get_signature(a	)

				# saving hook
				def save_config(a							: Union[str, Any]      ,      a							: str      ,      a							: Optional[int]	):
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       {"class_name": models[0].__class__.__name__}

					with open(os.path.join(a      ,      "data.json"	)      ,      "w"	) as f:
						json.dump(a      ,      a	)

        # loading hook
				def load_config(a							: Tuple      ,      a							: Optional[int]	):
					with open(os.path.join(a      ,      "data.json"	)      ,      "r"	) as f:
						SCREAMING_SNAKE_CASE   :      Optional[int]				     =       json.load(a	)

					SCREAMING_SNAKE_CASE   :      Dict				     =       config["class_name"]

				SCREAMING_SNAKE_CASE   :      List[str]				     =       accelerator.register_save_state_pre_hook(a	)
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       accelerator.register_load_state_pre_hook(a	)

				with tempfile.TemporaryDirectory() as tmpdirname:
					accelerator.save_state(a	)

					# make sure random weights don't match with hooks
					load_random_weights(a	)
					self.assertTrue(abs(model_signature - get_signature(a	)	) > 1e-3	)

					# random class name to verify correct one is loaded
					SCREAMING_SNAKE_CASE   :      Any				     =       "random"

					# make sure loaded weights match with hooks
					accelerator.load_state(a	)
					self.assertTrue(abs(model_signature - get_signature(a	)	) < 1e-3	)

					# mode.class_name is loaded from config
					self.assertTrue(model.class_name == model.__class__.__name__	)

				# remove hooks
				save_hook.remove()
				load_hook.remove()

				with tempfile.TemporaryDirectory() as tmpdirname:
					accelerator.save_state(a	)

					# make sure random weights don't match with hooks removed
					load_random_weights(a	)
					self.assertTrue(abs(model_signature - get_signature(a	)	) > 1e-3	)

					# random class name to verify correct one is loaded
					SCREAMING_SNAKE_CASE   :      str				     =       "random"

					# make sure loaded weights match with hooks removed
					accelerator.load_state(a	)
					self.assertTrue(abs(model_signature - get_signature(a	)	) < 1e-3	)

					# mode.class_name is NOT loaded from config
					self.assertTrue(model.class_name != model.__class__.__name__	)
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       Accelerator()
				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      List[Any]				     =       create_components()
				SCREAMING_SNAKE_CASE   :      Any				     =       None

				# This should work
				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Tuple				     =       accelerator.prepare(
				    a      ,      a      ,      a      ,      a      ,      a      ,      a	)
				self.assertTrue(dummy_obj is None	)
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Any				     =       Accelerator()
				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      str				     =       create_components()
				SCREAMING_SNAKE_CASE   :      List[str]				     =       [1, 2, 3]

				# This should work
				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Tuple				     =       accelerator.prepare(
				    a      ,      a      ,      a      ,      a      ,      a      ,      a	)
				self.assertEqual(
				    getattr(a      ,      "_is_accelerate_prepared"      ,      a	)      ,      a      ,      "Dummy object should have `_is_accelerate_prepared` set to `True`"      ,      )
				self.assertEqual(
				    getattr(a      ,      "_is_accelerate_prepared"      ,      a	)      ,      a      ,      "Model is missing `_is_accelerator_prepared` or is set to `False`"      ,      )
				self.assertEqual(
				    getattr(a      ,      "_is_accelerate_prepared"      ,      a	)      ,      a      ,      "Optimizer is missing `_is_accelerator_prepared` or is set to `False`"      ,      )
				self.assertEqual(
				    getattr(a      ,      "_is_accelerate_prepared"      ,      a	)      ,      a      ,      "Scheduler is missing `_is_accelerator_prepared` or is set to `False`"      ,      )
				self.assertEqual(
				    getattr(a      ,      "_is_accelerate_prepared"      ,      a	)      ,      a      ,      "Train Dataloader is missing `_is_accelerator_prepared` or is set to `False`"      ,      )
				self.assertEqual(
				    getattr(a      ,      "_is_accelerate_prepared"      ,      a	)      ,      a      ,      "Valid Dataloader is missing `_is_accelerator_prepared` or is set to `False`"      ,      )
			@slow
			@require_bnb
			def 							__UpperCamelCase (			self							: List[Any]	)							->       Any:

				"""simple docstring"""

				from transformers import AutoModelForCausalLM

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       AutoModelForCausalLM.from_pretrained(
				    "EleutherAI/gpt-neo-125m"      ,      load_in_abit=a      ,      device_map={"": 0}      ,      )
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       Accelerator()

				# This should work
				SCREAMING_SNAKE_CASE   :      Dict				     =       accelerator.prepare(a	)
			@slow
			@require_bnb
			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       int:

				"""simple docstring"""

				from transformers import AutoModelForCausalLM

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       Accelerator()

				with init_empty_weights():
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       AutoModelForCausalLM.from_pretrained(
					    "EleutherAI/gpt-neo-125m"      ,      )
					model.tie_weights()
					SCREAMING_SNAKE_CASE   :      Tuple				     =       infer_auto_device_map(a	)
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       "cpu"

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       AutoModelForCausalLM.from_pretrained(
				    "EleutherAI/gpt-neo-125m"      ,      device_map=a      ,      load_in_abit=a      ,      llm_inta_enable_fpaa_cpu_offload=a	)

				# This should not work and get value error
				with self.assertRaises(a	):
					SCREAMING_SNAKE_CASE   :      List[str]				     =       accelerator.prepare(a	)
			@slow
			@require_bnb
			@require_multi_gpu
			def 							__UpperCamelCase (			self							: List[str]	)							->       List[str]:

				"""simple docstring"""

				from transformers import AutoModelForCausalLM

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       {"distributed_type": DistributedType.MULTI_GPU}

				with init_empty_weights():
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       AutoModelForCausalLM.from_pretrained(
					    "EleutherAI/gpt-neo-125m"      ,      )
					model.tie_weights()
					SCREAMING_SNAKE_CASE   :      List[str]				     =       infer_auto_device_map(a	)
					SCREAMING_SNAKE_CASE   :      str				     =       1

				SCREAMING_SNAKE_CASE   :      List[str]				     =       AutoModelForCausalLM.from_pretrained(
				    "EleutherAI/gpt-neo-125m"      ,      load_in_abit=a      ,      device_map=a      ,      )
				SCREAMING_SNAKE_CASE   :      int				     =       Accelerator()

				# This should not work and get value error
				with self.assertRaises(a	):
					SCREAMING_SNAKE_CASE   :      str				     =       accelerator.prepare(a	)

				PartialState._reset_state()
			@slow
			@require_bnb
			@require_multi_gpu
			def 							__UpperCamelCase (			self							: List[str]	)							->       Optional[Any]:

				"""simple docstring"""

				from transformers import AutoModelForCausalLM

				with init_empty_weights():
					SCREAMING_SNAKE_CASE   :      Any				     =       AutoModelForCausalLM.from_pretrained(
					    "EleutherAI/gpt-neo-125m"      ,      )
					SCREAMING_SNAKE_CASE   :      List[str]				     =       infer_auto_device_map(a	)
					SCREAMING_SNAKE_CASE   :      Any				     =       1

				SCREAMING_SNAKE_CASE   :      Dict				     =       AutoModelForCausalLM.from_pretrained(
				    "EleutherAI/gpt-neo-125m"      ,      load_in_abit=a      ,      device_map=a      ,      )
				SCREAMING_SNAKE_CASE   :      Dict				     =       Accelerator()

				# This should work
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       accelerator.prepare(a	)






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

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Dict				     =       torch.nn.Linear(10      ,      10	)
				SCREAMING_SNAKE_CASE   :      List[str]				     =       torch.optim.SGD(model.parameters()      ,      lr=0.01	)
				SCREAMING_SNAKE_CASE   :      List[str]				     =       Accelerator(cpu=a	)
				SCREAMING_SNAKE_CASE   :      List[str]				     =       accelerator.prepare(a	)


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	)	)