

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


A_							:			Union[str, Any]											= "Create a default config file for Accelerate with only a few flags set."

def 	A       (     snake_case__="no"		,				snake_case__ = default_json_config_file		,				snake_case__ = False    ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      Path(snake_case__    )
			path.parent.mkdir(parents=snake_case__		,				exist_ok=snake_case__    )
			if path.exists():
						print(
						    f"""Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`."""    )
						return False
			SCREAMING_SNAKE_CASE__								=      mixed_precision.lower()
			if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
						raise ValueError(
						    f"""`mixed_precision` should be one of 'no', 'fp16', 'bf16', or 'fp8'. Received {mixed_precision}"""    )
			SCREAMING_SNAKE_CASE__								=      {
			    """compute_environment""": """LOCAL_MACHINE""",
			    """mixed_precision""": mixed_precision,
			}
			if torch.cuda.is_available():
						SCREAMING_SNAKE_CASE__								=      torch.cuda.device_count()
						SCREAMING_SNAKE_CASE__								=      num_gpus
						SCREAMING_SNAKE_CASE__								=      False
						if num_gpus > 1:
									SCREAMING_SNAKE_CASE__								=      """MULTI_GPU"""
						else:
									SCREAMING_SNAKE_CASE__								=      """NO"""
			elif is_xpu_available() and use_xpu:
						SCREAMING_SNAKE_CASE__								=      torch.xpu.device_count()
						SCREAMING_SNAKE_CASE__								=      num_xpus
						SCREAMING_SNAKE_CASE__								=      False
						if num_xpus > 1:
									SCREAMING_SNAKE_CASE__								=      """MULTI_XPU"""
						else:
									SCREAMING_SNAKE_CASE__								=      """NO"""
			elif is_npu_available():
						SCREAMING_SNAKE_CASE__								=      torch.npu.device_count()
						SCREAMING_SNAKE_CASE__								=      num_npus
						SCREAMING_SNAKE_CASE__								=      False
						if num_npus > 1:
									SCREAMING_SNAKE_CASE__								=      """MULTI_NPU"""
						else:
									SCREAMING_SNAKE_CASE__								=      """NO"""
			else:
						SCREAMING_SNAKE_CASE__								=      0
						SCREAMING_SNAKE_CASE__								=      True
						SCREAMING_SNAKE_CASE__								=      1
						SCREAMING_SNAKE_CASE__								=      """NO"""
			SCREAMING_SNAKE_CASE__								=      ClusterConfig(**snake_case__    )
			config.to_json_file(snake_case__    )
			return path

def 	A       (     snake_case__		,				snake_case__    ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      parser.add_parser("""default"""		,				parents=snake_case__		,				help=snake_case__		,				formatter_class=snake_case__    )
			parser.add_argument(
			    """--config_file"""		,				default=snake_case__		,				help=(
			        """The path to use to store the config file. Will default to a file named default_config.yaml in the cache """
			        """location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have """
			        """such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed """
			        """with 'huggingface'."""
			    )		,				dest="""save_location"""		,				)

			parser.add_argument(
			    """--mixed_precision"""		,				choices=["""no""", """fp16""", """bf16"""]		,				type=snake_case__		,				help="""Whether or not to use mixed precision training. """
			    """Choose between FP16 and BF16 (bfloat16) training. """
			    """BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later."""		,				default="""no"""		,				)
			parser.set_defaults(func=snake_case__    )
			return parser




def 	A       (     snake_case__    ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      write_basic_config(args.mixed_precision		,				args.save_location    )
			if config_file:
						print(f"""accelerate configuration saved at {config_file}"""    )





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

			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    Optional[int]:
						SCREAMING_SNAKE_CASE__								=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

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

						SCREAMING_SNAKE_CASE__								=      self.get_config()

						return config, pixel_values, labels

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

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]    ,				__UpperCAmelCase				:						List[str]    ,				__UpperCAmelCase				:						Tuple    ,				__UpperCAmelCase				:						Any       )   ->    Dict:
						SCREAMING_SNAKE_CASE__								=      self.type_sequence_label_size
						SCREAMING_SNAKE_CASE__								=      TFEfficientFormerForImageClassification(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      model(__UpperCAmelCase    ,				labels=__UpperCAmelCase    ,				training=__UpperCAmelCase       )
						self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.type_sequence_label_size)       )

						# test greyscale images
						SCREAMING_SNAKE_CASE__								=      1
						SCREAMING_SNAKE_CASE__								=      TFEfficientFormerForImageClassification(__UpperCAmelCase       )

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





			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    str:
						SCREAMING_SNAKE_CASE__								=      self.prepare_config_and_inputs()
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      config_and_inputs
						SCREAMING_SNAKE_CASE__								=      {"""pixel_values""": pixel_values}
						return config, inputs_dict





@require_tf
class    lowerCamelCase						(A__			,A__			,unittest.TestCase			):
			lowerCamelCase__   :		Any							=							(
			    (
			        TFEfficientFormerModel,
			        TFEfficientFormerForImageClassificationWithTeacher,
			        TFEfficientFormerForImageClassification,
			    )
			    if is_tf_available()
			    else ()
			)
			lowerCamelCase__   :		List[Any]							=							(
			    {
			        'feature-extraction': TFEfficientFormerModel,
			        'image-classification': (
			            TFEfficientFormerForImageClassification,
			            TFEfficientFormerForImageClassificationWithTeacher,
			        ),
			    }
			    if is_tf_available()
			    else {}
			)

			lowerCamelCase__   :		Dict							=							False

			lowerCamelCase__   :		Dict							=							False
			lowerCamelCase__   :		Union[str, Any]							=							False
			lowerCamelCase__   :		Optional[int]							=							False
			lowerCamelCase__   :		List[Any]							=							False

			def 					SCREAMING_SNAKE_CASE				(					self				:						str       )   ->    Optional[int]:
						SCREAMING_SNAKE_CASE__								=      TFEfficientFormerModelTester(self       )
						SCREAMING_SNAKE_CASE__								=      ConfigTester(
						    self    ,				config_class=__UpperCAmelCase    ,				has_text_modality=__UpperCAmelCase    ,				hidden_size=3_7       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    Any:
						self.config_tester.run_common_tests()

			@unittest.skip(reason="""EfficientFormer does not use inputs_embeds"""       )
			def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]       )   ->    Optional[Any]:
						pass

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

			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple       )   ->    Tuple:
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs_for_common()

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

									SCREAMING_SNAKE_CASE__								=      ["""pixel_values"""]
									self.assertListEqual(arg_names[:1]    ,				__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						int       )   ->    str:
						def check_hidden_states_output(__UpperCAmelCase				:						Optional[Any]    ,				__UpperCAmelCase				:						str    ,				__UpperCAmelCase				:						int       ):
									SCREAMING_SNAKE_CASE__								=      model_class(__UpperCAmelCase       )

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

									SCREAMING_SNAKE_CASE__								=      getattr(
									    self.model_tester    ,				"""expected_num_hidden_layers"""    ,				self.model_tester.num_hidden_layers + 1       )
									self.assertEqual(len(__UpperCAmelCase       )    ,				__UpperCAmelCase       )

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

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

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

						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE__								=      True
									check_hidden_states_output(__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase       )

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

									check_hidden_states_output(__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]    ,				__UpperCAmelCase				:						Any    ,				__UpperCAmelCase				:						Optional[int]    ,				__UpperCAmelCase				:						Any=False       )   ->    List[str]:
						SCREAMING_SNAKE_CASE__								=      super()._prepare_for_class(__UpperCAmelCase    ,				__UpperCAmelCase    ,				return_labels=__UpperCAmelCase       )

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

						return inputs_dict

			def 					SCREAMING_SNAKE_CASE				(					self				:						int       )   ->    List[str]:
						SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__UpperCAmelCase       )

			@unittest.skip(reason="""EfficientFormer does not implement masked image modeling yet"""       )
			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    Dict:
						SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_image_modeling(*__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]       )   ->    Tuple:
						SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase       )

			@slow
			def 					SCREAMING_SNAKE_CASE				(					self				:						int       )   ->    Optional[int]:
						for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									SCREAMING_SNAKE_CASE__								=      TFEfficientFormerModel.from_pretrained(__UpperCAmelCase       )
									self.assertIsNotNone(__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]       )   ->    List[str]:
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs_for_common()
						SCREAMING_SNAKE_CASE__								=      True

						SCREAMING_SNAKE_CASE__								=      getattr(self.model_tester    ,				"""seq_length"""    ,				__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      getattr(self.model_tester    ,				"""encoder_seq_length"""    ,				__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      getattr(self.model_tester    ,				"""key_length"""    ,				__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      getattr(self.model_tester    ,				"""chunk_length"""    ,				__UpperCAmelCase       )

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

						for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE__								=      True
									SCREAMING_SNAKE_CASE__								=      False
									SCREAMING_SNAKE_CASE__								=      True
									SCREAMING_SNAKE_CASE__								=      model_class(__UpperCAmelCase       )

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

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

									SCREAMING_SNAKE_CASE__								=      outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
									self.assertEqual(len(__UpperCAmelCase       )    ,				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				:						str       )   ->    Tuple:
						# 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__								=      self.model_tester.prepare_config_and_inputs_for_common()

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

def 	A       (     ):




			'''simple docstring'''


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





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

			@cached_property
			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    Dict:
						return (
						    EfficientFormerImageProcessor.from_pretrained("""snap-research/efficientformer-l1-300"""       )
						    if is_vision_available()
						    else None
						)

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





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


